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If you’ve been living under a rock the last 18 months than you might have missed that tens of thousands of employees have lost their jobs as a result of AI.

I think these mass layoffs are only the beginning and a lot of people smarter than me seem to agree.

I’m going to argue in this post that the fundamental shift in how tech companies operate, as a result of AI tech, will result in the majority of tech workers being forced out of the industry.

I truly believe that most tech workers are f*cked and have less than 24 months to make significant changes to how they earn a living.

I know this sounds very “doomer” but before you’re half way down the page my arguments will be made clear and you’ll be nodding along with me. If you disagree my my thesis, please let me know in the comment section at the end of the post. I’d love to read your counter points.

A fundamental shift in how businesses address tasks

The most basic building block in business is the “task”, an action that needs to be performed that is supposed to help the business make more money.

An easy example to understand is responding to a customer’s support ticket. This is a task that is performed by millions of employees on a daily basis all around the world.

Before the introduction of agentic AI agents, the approach companies took to complete more tasks was clear; they either hired more people, trained their people to do a better job, or a combination of the two.

Now that we live in a world where more and more mundane intellectual-based tasks can be completed by AI, companies are asking a new question, “can this task be handled by AI, or do we need to hire someone?”.

So how does this look in practice?

Introducing the Age of the Implementor

Let’s continue using the example of customer support. A customer support organization in a medium-sized tech company might look something like this:

You have a VP who reports to the CEO or COO and he or she manages 3 support team leads (2 tier 1 and 1 tier 2), an ops manager responsible for tooling, documentation and training, and an analyst who helps pull numbers and prepares reports.

In the age of the implementor, the org chart looks very different:

In the new org chart we’ve dropped 15 humans and replaced them with 3 AI bots, a 79% reduction in headcount.

The first person that is dropped is the analyst.

AI is good enough now that the VP can simply ask an agentic agent to pull numbers from the data warehouse and prepare reports. Once the KPIs, goals and report types are finalized, AI can handle the vast majority of the work.

For any ad-hock, complex data requests, the VP can escalate it to the data team who will have senior analysts on staff.

In the world of agentic AI, there simply isn’t enough day-to-day work to justify having department-based analysts.

The next role that drops off is the junior support rep. A tool like Intercom already has native AI support for drafting replies and responding to chats with a high resolution rate of 67%.

Even if you don’t use a tool like Intercom, setting up an agentic agent that can draft replies to emails is something you can set up in an afternoon.

Since the AI agents can work at scale and solve complex actions almost instantly, the difference in output between AI and humans is difficult to comprehend. Think of it like digging a ditch with a spoon compared to digging one with a mechanical digger.

The reason we still need human senior support reps in our support organization is for reviewing replies drafted by the AI, handling inquiries the AI can’t, or didn’t handle successfully, and for helping to improve the overal level of support provided.

These senior reps will need to handle a lot more ticket volume and we may need to hire a few more if we want to provide strict SLAs, but overall we’ll need far fewer and they can be paid better.

The ops manager has an even more important role to play in the organization since he or she is the “implementor”, the individual responsible for setting up, managing, scaling and optimizing the AI tooling and infrastructure.

This individual is a major force multiplier since without their help and skills, the organization couldn’t implement AI and maintain standards while cutting the workforce by 79%.

In my example the ops manager is the implementor but this doesn’t have to be the case. It could be a senior support rep who has the necessary skills to take on the job.

What can be said for sure is that everyone in the new organization needs to be at the bare minimum “AI aware”. This means that each employee understands what AI is, how it works, how to use it, and how it helps the business accomplish it’s goals.

In the new world of AI driven task completion, you are either an implementor (“force multiplier and extremely high value to the business”), or you are “AI aware”, and still play a role. Everyone else is redundant, or soon will be.

Being “AI aware” will only protect you as long as you perform tasks AI can’t, or hasn’t learnt how to just yet.

So why are the majority of tech workers screwed?

There are four main reasons I believe most tech workers are going to be forced to make a significant change to how they earn a living in the next 24 months:

1. Most tech workers are performing tasks which AI either can already, or soon will be able to perform far better than them at a fraction of the cost. This will force these individuals to either pivot into different roles, switch industries, or become entrepreneurs.
   

2. Following on from the above point, most employees go to work to perform their tasks to collect a paycheck. From my experience most people don’t have a growth mindset and push back against having to learn new skills.
   

3. You can’t simply teach someone to become an “implementor”. The kinds of people that become “implementors” have personality traits, attitudes and a culture which you can’t refine in people. “Implementors” are curious, intelligent, like to learn new things, natural problem solvers and care about efficiency.
   

4. AI is simply too efficient. The number of humans organizations will need will continue to shrink. You only need so many “implementors”. Some businesses will outsource AI implementation and adoption to specialized agencies that can help many companies in parallel thus reducing the need to have your own “implementors”. Some companies will form an internal implementation team that is responsible for AI across the entire organization.

If you take into account these 4 factors, I think it’s clear that the vast majority of tech employees are f*cked.

So what’s the solution if there is one?

How to protect yourself in the world of agentic AI?

If you’re working in tech and concerned that AI is coming to take your job then you’re already a head of many who still have their heads in the sand. AI is coming for most jobs and we’re already seeing more and more startups reaching large valuations with smaller and smaller teams.

With all that being said, there are a few things you can do to improve your chances of riding out the disruption and keeping your paycheck.

Become an implementor

If you’re a curious person who likes to learn new things and enjoy problem solving then you’re probably a good fit to become an AI implementor.

If you’re working for a company which hasn’t yet started implementing AI then this is your chance to show management that you’re the right person to help.

To become an implementor of AI, you need to start learning AI, the tooling, how everything connects together, and to start experimenting and building a plan for how your department can use AI to scale.

My post below is a good place to learn some of the basics.

By becoming the person in your organization that helps the company leverage AI to improve performance, you will have leverage and a lot harder to replace.

Once AI is good enough to implement itself throughout a business, even implementors will be out of a job. For now we need humans to implement, scale and manage AI systems.

Become an artist, entrepreneur, or change industries

If you’re not willing to learn new skills and “become one with AI” then you have 3 other choices.

You can either start your own business (not easy to do and requires learning a lot of new skills), change industries, or become an artist.

Changing industries is the easiest option in my opinion. There are industries which have not yet been heavily affected by AI and many tech workers will want to continue earning a paycheck with minimal disruption.

A junior analyst who just lost their job at Amazon could be hired by a shipping company which is still using legacy systems.

The problem is this is a losing battle since AI is spreading across all industries and it’s just a matter of time until certain jobs are wiped out completely.

It might take a few years but the analyst at the shipping company will end up in the same place as before, and have even fewer options available to him or her.

Many will choose to start their own thing and/or become artists (I include YouTuber’s, streamers, writers, etc in this category).

It’s never been easier to create things online and the number of individual creators making a living online is growing all the time.

I also predict “vibe-coding” will become more and more attractive, especially to junior and mid-level programmers who are replaced similarly to the junior support reps shown in my example above.

Ironically, these “vibe-coders” will use AI to create massive leverage themselves resulting in an explosion in single-founder / small team tech companies operating at very high margins.

In Summary

We’re all living through the AI revolution and it’s fundamentally changing how businesses operate. One of the main “victims” of this revolution is the average tech employee.

I’ve argued in this post that the vast majority of tech employees are f*cked and have less than 24 months to make significant changes to how they earn a living.

Employees that embrace AI, learn how it works and can help a business scale or efficiently have a chance of becoming “implementors”. These individuals are a force multiplier and become very valuable to their employer.

Those that don’t become implementors will need to change their role, industry, become entrepreneurs or earn a living as an artist.

Mass layoffs will continue and I’m pessimistic that most of those that lose their jobs will end up gaining new skills, finding a spot in a new industry, or succeed in making a living as an entrepreneur or artist.

I think universal basic income will become a requirement in some form or another, especially in the west.

How do you see things playing out? Let me know in the comments below.

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Thanks for reading and I’ll see you in the next one.

Justin

Hey friends,

I’m excited to announce the launch of The Vault, a curated benefits program for SaaS Decoded subscribers.

My vision for SaaS Decoded is to help as many SaaS founders and operators as possible.

The Vault is another way I can do that, by offering you discounts on some of the best software products I’ve used personally.

Each month I will be adding new tools and other exclusive perks to The Vault and sharing them with you in the weekly newsletter.

Below is a list of the perks included in the launch version of The Vault:

• Slite (30% off for 3 months)

• Tally (50% off their subscription for 3 months)

• Digital Ocean ($200 in credit over 60 days)

• Google Workspace (10% off your first year) [Use the promo code X7MUCCJ3WPNQYQU]

• Mercury ($250, after deposit 10k USD within 90 days)

To take advantage of the perks, simply click on the links above, sign up to the services and you’ll get the benefit listed.

I vouch for each of the services above and have used each of them for years across multiple businesses. I’ve recommended them to friends and fellow entrepreneurs many times.

If you have any questions on The Vault, check out the FAQ on it’s dedicated page at saasdecoded.com.

Thanks for your ongoing support of SaaS Decoded. I’m excited to continue building out the business and supporting more SaaS operators.

Enjoy the perks and I’ll see you in the next one.

Justin

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**I suggest that you don’t delete this email so you can come back and claim the perks at a later stage. I plan to remove the links above from the published post at saasdecoded.com so I can keep the perks exclusive to subscribers.

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Hey friends,

In this week’s post I’m going to teach you how to build an AI-friendly “brain” for your business that AI agents can use it to make you more money.

If you’re motivated to use your company’s unique knowledge and AI in meaningful ways, this guide is just what you need.

By the end of the post, you’ll have learnt the following:

1. Why you need a “brain” for your startup in the AI era.

2. What is an MCP server and why should I care?

3. What to look for in an internal knowledge base solution.

4. How Slite, Notion, Confluence, and Guru stack up against each other.

5. How to connect a knowledge base to AI tools, specifically Viktor and n8n.

6. How to set up 6 AI workflows that use your company’s unique knowledge.

If you make it to the end of this post, you’ll also know the exact steps, prompts and data you need to set up these 6 AI workflows:

1. Send a list every Monday of software that’s up for renewal that week (save 💵)

2. Speed up new employee onboarding (save 🕝 & make 💵)

3. Remind team members of non-work days in the coming week (nice to have for employees 🎉)

4. Use AI to pull answers to frequently asked questions and draft replies (save 🕝 & 💵)

5. Once a month share a summary of last month’s bank statement (save 💵)

6. Once a week get a content digest of relevant industry news shared with the team in Slack (make & save 💵)

This is long post (over 4,500 words) written as a step-by-step guide for SaaS businesses that don’t have a knowledge base and want to go from zero to AI-driven workflows using their unique knowledge.

If you already use any of the 4 knowledge base solutions I cover in this post (or an AI-friendly alternative), feel free to skip straight to step #3 (about a 3rd of the way down).

If today you don’t have an internal knowledge base in your business, let’s start off by answering the most obvious question.

Why does my startup need a “brain” and what the hell is an MCP server?

We are now living in the AI era but most SaaS startups are either not using AI at all, or using it to create new things.

Claude, Cursor, Lovable and many other agentic AI tools are exploding in popularity for a good reason; they are incredibly powerful tools for creating new things.

Where businesses are falling short is in leveraging their unique knowledge and AI in meaningful ways.

AI needs data to perform its “magic”. The one source of data the AI tools you use don’t have is your own. Therefore, until the AI tools you use have access to a central knowledge base that houses your unique data, you will be missing value.

Once you have your company’s unique knowledge in an AI-friendly knowledge base, we can connect it to AI tools and run workflows that leverage that data.

Getting to this end point is a 4 step process:

• Step #1 - Adopt an AI-friendly knowledge base

• Step #2 - Add your unique knowledge to your knowledge base

• Step #3 - Connect your knowledge base’s MCP server to your AI tools

• Step #4 - Set up AI-driven workflows

The way AI agents communicate with 3rd party tools, such as knowledge base tools like Notion, Slite and Confluence, is done via APIs connected to an MCP server.

MCP, or Model Context Protocol, is often called the “USB-C for AI”. Just as USB-C replaced dozens of different charging cables with one standard, MCP allows AI agents to connect to any data source without developers having to write custom code for every single integration.

If you’d like a deeper breakdown of what an MCP server is and how AI works with it, the video below does a good job.

So if we are going to implement a “brain” in our startup, we need a knowledge base solution which has MCP server support, thus making it “AI-friendly”.

Step #1: Adopt an AI-friendly knowledge base

For this post I researched 4 popular knowledge base tools, Notion, Slite, Confluence and Guru.

I’ve worked with all 4 throughout my career and been using Slite on a weekly basis for the past few years.

All 4 solutions have MCP server support which was a key consideration. Other considerations are price, the positioning of the solution (SMB vs. Enterprise), and if we want a dedicated knowledge base tool, or something more robust.

Notion

Notion is a multi-billion dollar company with a cult following that is a very popular knowledge base tool for early stage businesses.

I’ve personally never used Notion as a company knowledge base but have worked with businesses that have and it’s a great option.

It’s affordable, highly flexible, supports teams of all sizes, and has become a lot more AI-focused over the last 12 months.

Where I feel Notion falls a bit short is on focus.

Notion isn’t a pure knowledge base solution but rather a collection of tools that can work together. It’s more like an online “operating system” for your business where project management, task to-dos, documentation, calendars and databases live.

Notion is now leaning heavily into AI automation, bots and agentic workflows which may give it an edge over some of the other tools on the list; but then again it could also waterdown its strengths as a low-friction knowledge base.

Slite

Slite is an internal knowledge base tool I’ve used for my online businesses the last few years.

At first glance you’ll notice a lot of similarities between Slite and Notion but you quickly see Slite’s value as being solely focused on helping online businesses store and retrieve their unique knowledge.

What makes Slite special is its intuitive approach and extremely low friction user experience.

Commenting, audit logs, extremely useful embed options, a smart folder structure, and public sharing of documents are just some of the great benefits the solution provides.

Confluence

Confluence by Atlassian has been around for over 20 years. It launched back in 2004 as an internal wiki and team collaboration platform.

I used Confluence at my previous job about 10 years ago and back then it was a great solution, especially for creating product requirement documents (PRDs).

Confluence is a more enterprise focused (notice the team size option on their pricing page) knowledge base solution compared to Notion and Slite.

Jira is another product of Atlassian which is very popular among R&D teams; so if you’re already using Jira, you might want to take a closer look at Confluence and see if it is a good fit for your business.

Guru

Out of 4 solutions I looked at for this post, Guru is the least known.

I remember being very impressed with Guru when I used it in my previous company since it had a very powerful chrome extension which made it easy to retrieve info from the knowledge base.

Since then Guru has moved up market becoming enterprise focused and positioning itself as a multi-product company. The company has leaned fully into AI.

At $30 per month per seat, Guru is also the most expensive of the 4 solutions on my list.

If you’re running a larger organization and doing an evaluation of the knowledge base market, then I’d suggest reaching out to Guru to request a demo.

The winner: Slite

Out of the 4 solutions I covered, Slite would be my knowledge base of choice for most online startups.

It’s affordability, ease of use, MCP server support, and focused approach check the boxes which I consider the most important when choosing a knowledge base.

For the remainder of this post I’m going to show you how to leverage Slite to achieve our end goal.

With that being said, the steps I’ll walk you through would also apply to any of the other knowledge base solutions I’ve covered.

What’s important is understanding the key concepts and use cases and then to apply them in whichever knowledge base you choose.

Step #2: Add your unique knowledge to your knowledge base

Every online business has different information that it should store in it’s “brain”.

A helpful rule that I’ve used for my own businesses is to save in the knowledge base any piece of information that you believe will need to be retrieved at least once in the next 12 months. When in doubt, add it.

This includes standard operating procedures, brand kits, tax returns, company specific info like tax ids, useful analyses, onboarding and offboarding checklists, user research, and the list goes on.

Organize everything by department and don’t be shy of using sub-folders.

Below are 15 examples of information that could go into your knowledge base.

Click on any of the items in the list below to find a useful template you can quickly add to your Slite account.

1. New Employee Onboarding Checklist [by role]

2. Employee Handbook

3. Employee Registry

4. Standard Operating Procedures (SOPs) [By department / role]

5. Investor Update

6. Project Budget Template

7. OKRs

8. Brand Kit / Style Guides

9. Customer Avatar Breakdown Document

10. Customer / User Research

11. Job Description Template

12. Product Requirement Documents

13. Product Roadmap Planning

14. Product Release Notes

15. Marketing Campaign Planning Documents

Tips for optimizing, scaling and managing your knowledge base

Assign a dedicated owner

In the early days this should be the CEO, but at scale it should fall under the COO. An ops manager should manage the day-to-day responsibilities.

Invite everyone to the knowledge base

The whole idea of a knowledge base is to centralize, organize and reduce the friction of retrieval of company-specific knowledge. Everyone in the company should have access to the knowledge base.

Slite, and the rest of the knowledge base tools I’ve covered, have adequate controls for limiting who can see what.

A knowledge base only works if it is maintained

Managers in the company should be responsible for maintaining their teams SOPs, checklists and other documents in the knowledge base.

Introduce company wide policies for conducting quarterly reviews of the documentation and make it clear to management that maintaining the knowledge base is expected and a requirement of their roles in the company.

Teach management exactly how to use the knowledge base

Whoever in the company is responsible for the knowledge base should conduct training sessions to teach management the “ins and outs” of the knowledge base. The deeper the training the better.

Use examples throughout the training and record the sessions. Save the records in the knowledge base.

Training videos can be shared to new employees as part of their onboarding.

Take advantage of the full capabilities of the knowledge base

Solutions like Slite have hundreds of minor features which provide incredible flexibility and power to users. Encourage team members to get to know the solution thoroughly and include useful examples in the training sessions.

Have a “rule book” for the knowledge base

The owner of the knowledge base should be responsible for maintaining a set of rules for how to use the knowledge base. This includes formatting, folder structure, what should and shouldn’t be added, how change requests should work, etc. The idea is to maintain consistency as much as possible and encourage order.

Step #3: Connect your knowledge base’s MCP server to your AI tools

Now that you’ve got your unique knowlege in one place, it’s time to connect your knowledge base to your AI tools so we can start building some workflows.

How to connect Slite’s MCP server to Viktor

The first connection we are going to set up is Slite’s MCP server to Viktor, a Slack-based agentic AI agent.

If you aren’t familiar with Viktor, check out my previous post below on how to add an AI agent to Slack. I go into detail on how Viktor works and how it compares to Runbear, another popular Slack-based AI agent.

If you don’t use Slack in your business, don’t worry, the use cases I’ll share later could still be set up using other AI tools that aren’t native to Slack.

To connect Slite’s MCP server to Viktor, you first need to log into Viktor, click on “Integrations” in the left hand menu and then on “Add Custom MCP” in the top right corner.

In the popup that appears you’ll need to enter “https://api.slite.com/mcp” in the Server URL field.

After clicking on “Connect”, you’ll need to log into Slite, pick your workspace and authenticate the connection. This takes a few clicks and less than 1 minute.

Once connected, you’ll see the custom MCP server listed in Viktor as a connected integration.

Congrats, you’ve now connected your knowledge base to an agentic AI agent.

One thing that can be a bit confusing is that it’s possible to connect Viktor directly to Slite using a standard API integration via Viktor’s Pipedream partnership. I’ve tested this integration and I’d skip it. The MCP connection is far more powerful.

To confirm that the integration is working, you can ask Viktor to retrieve a document and share it in Slack.

How to connect Slite’s MCP server to n8n

If you’re not yet a fan of adding an agentic AI agent to Slack then you might want to consider n8n.

To connect Slite’s MCP server to n8n you’ll first need to add n8n’s MCP Client node to your workflow.

Once you’ve added the node to your workflow, you’ll need to add Slite’s MCP server details and authenticate, just like we did when setting up the connection with Viktor.

You’ll want to enter “https://api.slite.com/mcp” in the MCP Endpoint URL field and use MCP OAuth2 as the authentication method.

Next you’ll want to add “credentials” which is an easy process once you’ve followed the steps above. Click on “Create Credentials” and follow the steps.

Once you’ve successfully connected n8n to your Slite’s MCP server, you can go ahead and use the “search-notes” tool, run a search and confirm that n8n can access the MCP server.

Take note in the screenshot above that I ran a search for “Non-work days 2026” which is the title of a document in my Slite. The result of the search appears on the right.

I can now use the id of the document that was returned in the search, and fetch the contents of that document using the “get-note” tool.

n8n is a powerful automation tool but it does require understanding how APIs work, data structures, logical progression and other technical concepts to really master.

If you’re new to n8n then I suggest watching the useful beginners guide below.

Step #4: Set up AI-driven workflows

So far we’ve laid the foundation, it’s now time to start using that foundation to help improve the business’s bottom line. It’s time to build some AI-driven workflows.

The following 6 workflows were all created using Slite and Viktor.

For each, I’ve included the exact prompts I used to trigger the workflows, and in some cases, turn them into recurring tasks for Viktor.

AI workflow #1: Software renewal weekly reminder

How often do you lose track of the software tools you pay for annually? It’s frustrating when a tool you stopped using gets renewed automatically and you have to chase down the company for a refund.

The first automation I want to teach you solves this issue by getting a weekly reminder of upcoming renewals shared directly with your CFO in Slack.

The first step in the process is to create a software expense registry in Slite.

Create a dedicated document in Slite titled, “Software Expense Registry”, create a table and start filling in the details.

In my example, I used the following columns:

• Software - The name of the software (text field type)

• Billing Frequency - Either Monthly or Annual (single select field type)

• Renewal Date (Annual) - The renewal date of the software. Only relevant for software which you pay for annually (date field type)

• Is License Based? - Either “Yes” or “No” (single select field type)

• Notes - Helpful additional info on the software (text field type)

This document should be maintained by your head of IT along with your bookkeeper / administrative assistant.

Once the document was in place, I sent Viktor the following message in Slack:

“@viktor Look in the table titled, Software Expense Registry, in the Slite MCP server and return a list of software with an annual billing frequency which have a renewal date anytime between today and ten days from now. List out these software in a bulleted list, ordered by renewal data in ascending order, provide their renewal dates and any notes.”

Below is Viktor’s reply.

To turn this into a weekly task for Viktor, I sent the following:

“@Viktor Please turn the above request into a weekly recurring task. I want you to fetch me the information I requested every Monday morning at 9am UTC time.”

That was enough to set up the automation on Viktor’s backend.

AI workflow #2: New employee onboarding to-do list

When a new employee joins the business there are a lot of tasks that need to be checked off before the employee is ready to start working.

It can be helpful to share that list directly in Slack with the employee to help speed up the process.

The first step is to create a dedicated document in Slite with the onboarding to-do list for new employees.

Below is the prompt I sent to Viktor in Slack:

“@Viktor Please share the new employee to-do list as a new message in this channel. Share it as a bulleted list in the same order it appears in the document in Slite. Use the Slite MCP server to find the document.”

And below is the outcome of the message to Viktor.

AI workflow #3: Non-work days reminder

As more and more companies employ fully remote, global teams, not everyone in the company is always aware of all the non-work days scattered throughout the year.

By creating a list of the non-work days in Slite, you can create a simple automation in Slack to let everyone know whenever there is an upcoming non-work day.

Below is the prompt I sent Viktor in Slack:

“@Viktor Look in the table in the document titled, “Non-work days 2026” in Slite using the Slite MCP server and share any holidays which are happening this week. List the occassion, the date, and the day of the week the holiday falls on. Share as a new message in the channel, not as a reply to this message.”

And below is what Viktor returned:

To turn this into a recurring task, I wrote the following to Viktor:

“@Viktor Turn the above request into a recurring weekly task. Run the request on Monday at 4:15AM UTC time. If there are no holidays listed in the table for the date range you checked, don’t send a message.”

Once again, Viktor took my message in Slack and added a recurring task to the list of “scheduled tasks” in my account.

*I purposefully changed the date of President’s day in my table to show you how the task would be completed by Viktor.

AI workflow #4: Retrieve support FAQ responses

I own a number of SaaS products and get the occassional support ticket. I usually respond to each from scratch but this is far from optimal. I’ve decided to start using AI to help me provide better support, and to save me time.

To take advantage of this workflow you first need to create a FAQ document in Slite which addresses your most common support questions.

Once the document is in place, you can use Viktor to pull relevant info from the knowledge base and draft responses.

“@Viktor Below is an email I got from a user of Meet Slack:

"Hi, I need help authenticating my Google account"

Check the support FAQ document for Meet Slack in Slite's MCP server, find the relevant answer to the question above and draft a response I can send the user. The user's name is John.”

And Viktor’s reply:

Viktor did a fantastic job and I can now just copy the response and send it.

In the future I want to take this flow even further by simply adding a label to the email and have Viktor create a response directly in Gmail as a draft.

AI workflow #5: Share summary of bank statements to better understand financials

One of the most powerful things AI agents can do is view pdfs and other file types via the MCP server.

In this workflow I’m going to show you a powerful use case where we can use Viktor to analyze financial documents straight from our knowledge base.

The first step is to have a place in Slite where exported pdfs from your bank are saved.

Slite allows you to add files to documents and you can use headers and the useful “table of contents” block to keep things organized.

In the example above, I’ve saved two exported pdfs from my online bank, Mercury, for one of my checking accounts for Hawkeye Venture, my software holding business.

Believe it or not, Viktor, our Slack agent can access these pdfs directly via Slite’s MCP server.

This is what I wrote to Viktor to see if it could analyze the pdfs:

Below is Viktor’s response.

Just incredible! I went over all the numbers and Viktor was spot on!

To turn this into a recurring task for Viktor, I sent it the following:

“@Viktor Great job. Please create a recurring task which should take place on the 2nd Monday of every month. I want you to look for the previous month’s PDF in the “Mercury Monthly Bank Statements - Hawkeye Ventures” document in Slite using the Slite MCP server and share a summary of the month’s transactions. I want to see balances and a breakdown of revenue and expenses by description, type and amount.”

AI workflow #6: Build a content digest and stay on top of industry news

Most industries have a handful of websites where the most relevant news is made public.

You can use Viktor and your knowledge base to build a repository of relevant articles and then turn it into a content digest to help stay on top of industry news.

The first step to build this workflow is to create a document in Slite which can act as your “database”.

Next I asked Viktor to visit the RSS feed of one of the websites which are relevant to my industry, Techcrunch, and add the contents of the RSS feed to the table.

I did this to see if Viktor could visit the RSS feed, pull the relevant contents and add them to the table. If it can do all that with a single prompt, the rest of the flow will be easy.

I could have improved the prompt above by mentioning that the document was in Slite but Viktor was smart enough to work that part out itself.

Viktor got the details and was ready to add them to the table but it first needed approval since the request was to update a Slite document which is a riskier request.

You can change which actions require approval for each integration in your Viktor account.

After providing Viktor with approval and waiting a few seconds, it let me know the task was complete.

I now knew that Viktor could achieve what I wanted so I could now optimize the prompt.

“@Viktor Good job. I want you to visit https://techcrunch.com/feed/ once a day at midnight UTC time and record the contents in the “Publications Worth Reading” document in Slite.”

You could include multiple RSS feeds in the prompt above to expand your data collection.

Since Techcrunch publishes more than 20 articles a day (their RSS feed is limited to the most recent 20 posts), this flow wouldn’t capture every publication so you might want Viktor to run the check more frequently (will be more expensive), or to add more filters so Viktor isn’t saving every post in the table.

At this point we are automatically seeding our database but we aren’t done yet. We need Viktor to summarize the most recent publications in our database.

“@Viktor Visit the document, "Publications Worth Reading", in Slite and share a summary of the most recent publications listed in the table in the document. Most recent means posts which were published in the last 7 days. Limit the summary of each publication to 5 sentences or less.”

Viktor didn’t disappoint.

Make sure you are very clear in your prompt so Viktor doesn’t end up summarizing the entire database which would cost you a lot of credits.

The last step in the process is to turn the request into a recurring task.

“@Viktor Once a week on Monday at 8AM I want you to visit the document, “Publications Worth Reading”, in Slite and share a summary of the most recent publications listed in the table in the document. Most recent means posts which were published in the previous week. Limit the summary of each publication to 5 sentences or less. Limit the number of posts you summarize to no more than 15. If there are more than 15 posts which have been published in the previous week in the table, prioritize posts which include “fund raising”, “SaaS”, “Cursor”, “Anthropic”, “Claude” or “VC” in their titles.”

Final words

If you got this far, pat yourself on the back.

You’ve built a “brain” for your company and learnt how to use your unique knowledge to run powerful AI workflows.

You now have the skills to continue to scale your knowledge base and AI tools to add more and more value to your company.

I’ve done my job and it’s now your turn.

I want you to share with me below in the comments section one example of an AI workflow you’ve built that leverages your knowledge base.

Thanks for reading and if you liked this post, please go ahead and hit the share button below so others can find my content.

Justin

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*This post includes affiliate links for both Viktor and Slite. I’m a fan of both of these solutions and use them regularly to support SaaS Decoded. I pick the tools I partner with very seriously. If you decide to signup and pay for either Viktor or Slite, I’ll get a small cut of the money you spend in your first 12 months.

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Hey friends,

Last week I published part 1 of my series on product-led growth for B2B SaaS.

In part 1 I introduced the concept of PLG and shared 8 examples of companies which grew to “unicorn” status using PLG tactics.

In part 2 I’m going to go deeper on the core concepts behind a PLG strategy.

I’ve also decided to cover the topic of PLG metrics in next week’s post which will be part 3 of this series.

The PLG concepts I’m covering in this post include:

1. The Bowling Alley Framework

2. Time-to-value (TTV)

3. The Flywheel Effect

4. Product-Qualified Leads (PQLs)

The Bowling Alley Framework

The Bowling Alley Framework is a powerful user onboarding strategy developed by Wes Bush, the founder and CEO of ProductLed.

A business which adopts a PLG strategy needs users to drive user acquisition.

Therefore, it’s critical that users experience the desired outcome of the product so they can tell their friends, coworkers and extended network.

As the image above shows beautifully, the Bowling Alley Framework is designed to help guide users to their desired outcome.

To implement the Bowling Alley Framework, a business needs to do 3 things:

• Define the straight line to the desired outcome

• Create one or more product bumpers

• Create one or more conversational bumpers

Define the straight line to the desired outcome

Each SaaS product needs to have a clearly defined straight line to their desired outcome.

This would be the path that you’d expect the majority of users to take to experience the core value the app provides.

Let’s look at Canva’s “straight line” and see what we can learn.

Step #1 - Visiting Canva’s homepage and clicking on “Sign up” or “Start designing for free”.

Step #2 - Choosing your desired signup method and complete the signup process.

Step #3 - Answering the question, “what will you be using Canva for?”.

Step #4 - Either start a trial of Canva Pro / Canva Business, or skip.

Since I chose “Small business”, Canva gave me the option to immediately start a trial of their premium offering.

This is smart because a certain percentage of businesses will convert immediately to the paid version which benefits Canva, while adding very little friction to the onboarding flow.

Step #5 - Either opt into receiving content from Canva, opt out, or skip.

Step #6 - Choose what to design.

I was now done with the onboarding (notice the confetti to congratulate me) and ready to get designing.

Canva does an amazing job of showing the wide range of design options and emphasizing “templates”.

Templates are key for helping non-designers (Canva’s target market) create designs without having to create designs from scratch.

Step #7 - Pick design use case.

For this example I want to create an image I can use in a LinkedIn post to promote this Substack post.

I clicked on “Social media” and then “LinkedIn”.

Step #8 - Write out my desired outcome, pick a free template and make changes.

In 8 short steps I went from a non-user to my desired outcome, designing an image for a LinkedIn post.

The time-to-value, a topic I’ll be covering later in this post, was less than 5 minutes.

Create one or more product bumpers

Product bumpers are user-interface-based elements and messaging set up in the product to help users get to their desired outcome.

Product bumpers can take many forms from product tours, to progress bars, checklists and welcome messages.

In the case of Canva, their use of the search bar, icons and cards are product bumpers since they help guide the user to the design builder where value is created.

A nother great example of a business that uses product bumpers effectively is Canny.

After signing up to Canny you are taken to a “Get Started” page (screenshow below).

On this page Canny has 3 distinct sections.

On the left you have listed the main product that is chosen during the onboarding, in my case it was the Feedback Portal. Canny indicates to me that there are 4 steps to finish setting up this feature. Those 4 steps are shown directly to the right of the left hand section.

Canny emphasizes that there are other products I should explore and three ways I can get more value from their platform (by setting up integrations, deploying their widget, and by inviting my team to my Canny account).

Canny has effectively communicated multiple things to me with clear emphasis on the additional steps they suggest I take to complete the onboarding.

Create one or more conversational bumpers

Conversational bumpers are messages that are set up outside of the product with the purpose of educating, re-engaging and helping guide users to their desired outcome.

The most common example of a conversational bumper is the onboarding email series which most SaaS have in place. These are the emails you receive for the first few weeks after signing up to a SaaS.

Below is the welcome email I received after signing up to Canny.

I’ve highlighted in green the elements of the email I want you to pay attention to.

Notice the clear intention of the email, to help you start generating feedback. Canny’s whole focus is on helping its users generate and manage product feedback.

Once a single piece of feedback exists in Canny the user experiences the “Aha Moment” which is why Canny aggressively pushes the user to reach this point.

Canny’s welcome email has multiple calls-to-action which is typically not best practice, but notice how well the different options are laid out. Since the email has one main goal, providing multiple options to the user, each with its own CTA isn’t a big deal.

Other mediums such as SMS, explainer videos, and push notifications are also used for conversational bumpers.

A tool like Intercom is very effective for creating smart welcome series. Intercom allows you to leverage your user and product usage data to build communication flows with smart branching.

Time-to-value (TTV)

Time-to-value or TTV for short, is the amount of time it takes for a user to realize the value they were promised.

In PLG, we specifically focus on the "Aha! Moment"; the point where the user’s mental model of your product shifts from "Is this what I think it is?" to "I get it."

If the Bowling Alley Framework is designed to help get users to their desired outcome, then TTV is the time it takes the user to reach that point.

As a general rule TTV needs to be as short as possible without deminishing the value the user receives when he reaches the “Aha! Moment”.

The main reason TTV needs to be short is users need to quickly see the value the service provides or they will churn (leave and usually never return).

Key variations of TTV

TTV can be measured in a few different ways.

• Time to Desktop/Login: The literal time to access the app.

• Time to “Aha!”: The moment of emotional/mental realization of value.

• Time to ROI: When the customer sees a measurable return (e.g., their first sale, first automated report, first lead captured, etc).

• Time to Expansion: When the user realizes enough value to upgrade or add seats.

The most important is “time to ‘Aha!’” since once the user “gets it”, users will be more likely to give the app a chance and continue using it. This gives the SaaS more time to show ROI and set up the conditions for account expansion.

Strategies to Reduce TTV

To win at PLG, you want to drive TTV as close to zero as possible. Here is how the best SaaS companies do it:

1. Progressive Onboarding

Don’t demand a 10-step setup immediately. Use functional deconstruction: give the user a small win within 30 seconds, then ask for more actions later.

We see this in action in Canny’s onboarding.

After a short signup and onboarding flow, you get to see your feedback board (the small win), but Canny does require the user to take a few more steps to complete the setup.

2. “Empty State” Optimization

Nothing kills TTV like a blank dashboard.

• Templates: Provide pre-filled boards or docs (e.g., Notion, Canva).

• Dummy Data: Show them what a “success state” looks like.

After completing the onboard, Canva gives you dozens of options for easily getting started. Imagine how much tougher it would be for users if they didn’t have these options listed out for them?

3. The “Frictionless” Path

The more friction there is to getting to the “Aha! Moment”, the worst off users will be. PLG businesses are obsessed with removing friction wherever possible.

• Login using Google/Facebook: Google/Facebook login removes the “check your email for a link” or remembering password friction.

• No Credit Card Required: Removing the paywall at the start allows the user to reach the “Aha!” moment without a financial barrier.

4. In-App Guidance

Use interactive walkthroughs (like Pendo, WalkMe or Intercom) that guide users toward the specific feature that triggers the “Aha!” moment.

Measuring the TTV Funnel

To improve TTV, you have to track it.

The easiest way to do that would be in a spreadsheet where you can record your targets and cohort-based analyses.

It’s important to use cohort-based analyses to track the TTV metrics since it’s reasonable to assume your product and marketing are evolving and not every user is the same, or has the same experience.

Remember that you need a sufficient number of users and event tracking in place in order to conduct the kind of analyses needed to build the table above.

*The results in the below table would be either the average across the entire cohort, or the bottom 80% percentile of users (rank users by quickest to “Aha Moment” and measure users up to the 80% percentile).

The Flywheel Effect

When it comes to exponential, viral growth (what PLG businesses hope to achieve) flywheels are everything.

First let’s try and understand a flywheel.

Below is an example of a PLG flywheel that Notion has built.

The first step in the flywheel is for a user to sign up to Notion. To get this flywheel started, Notion had to run marketing campaigns and drive traffic to their website.

Most flywheels require an initial “push” by the business to get it started but after enough “energy” is put into the “system”, the flywheel continues on its own.

The second step in the flywheel is for a user to create a template in Notion and share it publicly on social media.

The result is that some non-users see the template, get their first taste of Notion and then decide to signup and try out Notion for themselves.

Note how step 1 was started by Notion but the rest of the steps are performed by actors other than Notion.

By making part of their product modular and sharable, Notion was able to construct a very successful PLG flywheel. But it doesn’t end there.

By allowing search engines to crawl the templates, and by launching their own template marketplace, Notion was able to leverage more traffic engines (SEO and marketplace) to “speed up” and enhance the flywheel.

The most successful PLG businesses leverage multiple flywheels and have taken steps to enhance them.

Let’s take a look at another example.

DropBox is one of the legends of PLG. I covered them in detail in part 1 of this series.

As the image above shows, DropBox leverages multiple PLG flywheels.

The first is a classic “collaborative” loop where users of DropBox can use the product to share files with others, driving non-users to DropBox without DropBox needing to do anything.

The second flywheel is another “collaborative” loop where one DropBox user exposes the service to their colleagues, driving brand awareness and creating DropBox advocates who will introduce the tool to future employers.

The third flywheel is DropBox’s most powerful.

It leverages a two-sided incentive referral program to empower users to drive new signups.

What makes this referral program special is that both parties have a powerful incentive while the cost to DropBox is extremely minimal (the cost of storage is very low).

Product-Qualified Leads (PQLs)

Most PLG businesses drive the majority of their revenue through a freemium, self-service model.

The idea is to allow users to experience the product for free and once they are ready they can upgrade to a paid subscription to experience more value (or continue to receive value).

Once a freemium SaaS has enough paying users certain patterns emerge which can be used to predict the likelihood of a user becoming a paying user.

A product-qualified lead is a user, or account in the case of B2B, which has taken the necessary actions, or received enough value that they have a high chance of upgrading to a paid subscription.

Reaching the “magic number”

There is a fascinating story from the early days of Slack which PLG businesses should know about.

The founders of Slack noticed by looking through their data that 93% of businesses that wrote 2,000 messages ended up becoming paying users.

Since Slack had it’s “magic number” it could define users as being “product-qualified” once they hit that number.

Once a PLG business identifies it’s “magic number”, the entire focus of user activation should switch to removing friction and enabling users to reach that number.

The “magic number” doesn’t always exist in a vacuum.

Often the qualifications for a user to become “product-qualified” include a combination of product usage, depth of usage (number of features, number of integrations setup, etc), time of use (used the product for more than X months), and characteristics of the business itself.

Everything from the number of employees, age of the company, the culture of the employees, geography, and industry play a part.

Once a user, or account, gets “flagged” as a product-qualified lead, it’s common for SaaS business to start sending emails and other forms of communication (popups for example) to introduce the user to the benefits of the paid plans.

This little “push” helps improve conversion to paid and has no down side.

That’s it for part two, make sure you subscribe to receive part three in your inbox next week.

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Have a fulfilling and productive week ahead.

Justin

This legendary growth tactic is one of the best examples of how product-led helped a B2B SaaS business reach “unicorn” status.

In this two part series I’m going to be covering the topic of product-led growth (PLG) in all its glory.

In part one I’m going to introduce the concept of PLG and share 8 examples of companies that have grown to a billion dollar valuation using PLG tactics.

I’m going to share the main tactics each of these businesses deployed and why they were so affective.

In part two (coming next week), I’ll go into detail on core PLG concepts and the growth metrics PLG companies need to care about.

If you’re new to SaaS Decoded, welcome, I’m Justin. Each week I publish an in-depth, actionable guide to help SaaS operators grow their businesses. If you enjoy this content, consider subscribing below.

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What is product-led growth (PLG)?

The term Product-Led Growth (PLG) was coined in 2016 by Blake Bartlett, a partner at the venture capital firm OpenView Venture Partners.

Product-led growth is a strategy where the product itself acts as the primary driver of user acquisition. This is done typically through word of mouth, viral loops or a combination of both.

Software businesses were using product-led growth strategies much earlier than 2016, but until PLG was coined we didn’t have a term for the overarching strategy of using the product to drive the majority of user growth.

8 B2B SaaS “unicorns” that adopted a PLG strategy

Here are 8 standout examples of B2B SaaS businesses that scaled to over $1 billion dollar evaluation using a product-led growth strategy.

1. Slack

Slack is the “poster child” for PLG. It famously grew to a $1.1B valuation in just over a year without a traditional sales team or marketing budget in its early days.

Slack grew by users inviting teammates to collaborate within their workspace. This created a viral internal loop.

Slack’s freemium model allowed businesses to get a taste of the platform at no cost. Once businesses hit their 10,000 message limit they were hooked and converting to a paid subscription became a “no-brainer”.

The combination of a freemium model, network affects, and the internal viral loop turned Slack into one of the fastest growing software businesses in history.

Another powerful PLG tactic Slack used was the launch of Slack Connect, Slack’s cross-workspace collaborative feature.

Slack Connect allows businesses to connect with each other directly in Slack.

This allows B2B service providers, like agencies and freelancers, communicate with their clients directly within Slack. This reduces email load and helps keep communication all in one place.

Slack Connect helps promote word of mouth because it fundamentally changes how businesses that know each other communicate with each other.

2. Zoom

Zoom disrupted a market filled with legacy giants (Webex, Skype) by focusing on a frictionless user experience and a “1-to-many” viral loop.

Every time a host sent a meeting link to a non-user, they effectively “demoed” the product.

When your product, by it’s very nature, is organically shared with other potential users, you have the opportunity for viral growth.

Zoom experienced hyper growth during Covid because so many businesses were forced to move to virtual meetings.

3. Calendly

Calendly reached the $100M ARR mark with fewer than 250 employees, a testament to the efficiency of its viral “scheduling loop.”

Similarly to Zoom, when users share a Calendly booking link they introduce the service to potential users. Recipients of the Calendly link are introduced to the seamless experience and see the value the service provides.

4. Notion

Notion used a community-led and product-led hybrid strategy to grow the business into over a $10 billion valuation.

Notion allows users to create wikis, roadmaps and other templates which can be shared publicly. This allows users to expose the Notion brand and the modular nature of its product to potential users.

This has been such a successful growth strategy that for many years Notion has supported an entire marketplace of templates where creators earn revenue per sale.

Notion is built with both individuals and teams in mind allowing it to support a wide range of avatars, and online businesses of all sizes.

5. Dropbox

Dropbox pioneered the “referral loop” in the early days of SaaS, rewarding users with extra storage for inviting friends.

The genius behind Dropbox’s referrel loop was that both parties benefitted from it (both the referrer and recipient received extra storage).

In addition to the referral program, Dropbox used a second PLG tactic, the collaborative loop, to drive more growth.

Users of Dropbox can share folders to others, forcing them to sign up to the service to access the contents of the shared folders.

6. Figma

Figma took over the design world by being browser-based and inherently collaborative.

Before Figma, design tools were used exclusively by designers.

Figma changed the game by allowing everyone involved in the design process, from product developers and marketers to engineers, to collaborate in one place.

Each designer that signed up to Figma effectively became a promoter of Figma purely by the nature of how the product was used.

Since Figma has seat-based pricing, the more people that sign up to collaborate in Figma, the more the company makes.

For more on Figma’s PLG strategy, check out this insightful post by Aakash Gupta, The Figma Product Growth Strategy.

7. Zapier

Zapier, and similar companies like n8n, use an SEO-based “integration loop” growth strategy that ties back to the product.

It starts with potential users searching for “how to connect app X to app Y” and then finding a dedicated landing page that covers that specific combination.

Users then sign up to Zapier to set up automations. As the userbase grows so does the demand for more integrations.

As the number of integrations grows, so does the number of potential combinations that are covered through search engine searches.

8. Cursor

Cursor is one of the darlings of the agentic AI wave and has quickly become one of the most popular software products among developers in the world.

Cursor is a revolutionary product which is changing how developers are building software.

Cursor is also one of the main contributors to “vibe-coding” which is reshaping the entire SaaS market.

Cursor has deployed some smart community-led growth tactics but most of its growth has been through word of mouth.

Every now and then a product comes around that is such a game changer that people can’t help but talk about it. Social media is flooded with references to Cursor.

In cases where the product is so “mind blowing”, a company doesn’t need to invest in traditional marketing channels since word of mouth is viral in nature and enough to result in exponential growth.

How PLG companies grow

Let’s summarize what we know about the 8 PLG companies we covered above.

The most common PLG tactic used is a collaborative loop, where the product is introduced to team members, clients, or the general public through sharing or invitations.

In some cases, like Slack and Notion, there are network affects (each individual that is invited to the workspace benefits the rest of the users in the workspace).

Referral and integration loops are much rarer since they are hard to implement or very niche.

Many of the companies in the list have referral programs but they are structured, partner-based and not self-service. I don’t categorize these as PLG-based.

Word of mouth is the gold standard and the hardest to pull off. It requires a truly outstanding, revolutionary product.

A common tactic among PLG companies is to have freemium pricing (users can signup and use the service for free). Notice that every company in our list allows users to sign up and use the service for free.

The more loops a PLG company has, the higher the possibility of extreme exponential growth.

Combining freemium, multiple PLG loops, and a revolutionary product is the recipe for record-breaking growth.

For more examples of PLG companies, check out the product led growth market map by OpenView Venture Partners below.

That’s it for part one, make sure you subscribe to receive part two in your inbox next week.

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Until next week.

Justin

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Hey friends,

This weeks edition of SaaS Decoded is going to be different.

I want to experiment with a new format called “Justin’s Field Notes”, where I share some of the best social media posts, news items, tools and other tidbits I’ve come across in the last few weeks.

Since I spend a lot of time online and often come across very insightful and useful content, I think it would be a good idea to share some of my favorites with you.

If this post does well and the feedback is positive I’ll aim to share a post like this once a month.

Simple, affordable pricing + clear need + large TAM + good execution + time = massive success

Judge.me is a SaaS app operating in the direct-to-consumer eCommerce market that makes it easy for brands to generate customer reviews.

These guys have been in the market for a long time and have slowly grown to $15M ARR by being focused on solving a clear need for a large market at a great price.

What makes Judge.me stand out is their simple pricing. They have a free plan and a paid plan. The paid plan is just $15 a month.

Another great example that there are many ways to play the SaaS game.

Meet Viktor, my virtual employee

Last week I published a post teaching you how to add an AI agentic agent to Slack. In the post I covered a service called Viktor which was the best agent out of the two services I tested.

I’ve since spent some more time playing with Viktor and I’ve become a big fan.

Since I’ve been focusing on simplifying my day-to-day work I don’t yet have enough recurring tasks for Viktor to move from the free plan to the paid plan but I’m now convinced that using a service like Viktor on top of Slack is a no brainer.

Below are some ways I plan to use Viktor to improve my productivity:

• Create weekly recurring tasks in Asana and assign them to relevant projects.

• Let me know if any Tableau reports belonging to my clients fail to refresh (I have one remaining projectBI client).

• Help me draft SOPs in Slite.

I’m purposefully cutting tasks and drastically simplifying how I work throughout the week so the number of tasks I can automate through Viktor is minimal but I would still recommend giving the service a try.

I’ll report back once Viktor is doing a few more things for the business.

Wallet share, smart user interviews, and other insightful lessons from social media

Below are some of the best posts I’ve come across recently on social media.

1. That gap has a name: wallet share By Jeff Ignacio [LinkedIn]
   

2. They buried their most valuable feature in the Enterprise plan By Mayengbam Tommy [LinkedIn]
   

3. BOOOOOM! $1,000,000 ARR By Elston Baretto [X]
   

4. This is why i never worry about competition By Robleh [X]
   

5. We only had 50 users two years after founding Stripe By Startup Archive [X]

HubSpot Acquires Starter Story After Eight-Year Bootstrapped Rise

Yesterday the news broke that HubSpot has acquired Starter Story.

Pat Walls, the founder of Starter Story built the business from the ground up into a 7-figure a year media business with a massive audience.

Pat did a fantastic job telling the full story behind Starter Story in a recent threaded post on X which you should definitely check out.

Here are the highlights:

• Started Starter Story from a Starbucks while still working a job. Pat was 27 years old when he started the business.

• It slowly grew to $3k - $4k per month which is when Pat quit his job.

• He didn’t believe in Starter Story and tried many other ideas.

• At 29 he went all-in on Starter Story and grew it to $1M a year in revenue after 2 years.

• 5 years in he started publishing on YouTube.

• Today Starter Story has 4k+ founder stories, 1.6M+ followers, 250k+ newsletter subscribers, 10k+ paid members and millions in annual revenue.

Did you enjoy this format of the newsletter or do you miss the long-form guide style that I usually go with?

Please let me know in a comment below or by sending me a short email to justin@projectbi.net. Any feedback you have for me is helpful.

Until next week.

Justin

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Hey friends,

Last week I published a beginners guide to AI for non-developers.

To continue the theme I decided to go down the agentic agent rabbit hole, specifically when it comes to leveraging an agent directly in Slack.

In this week’s edition of SaaS Decoded, I’m going to show you how to add a virtual AI coworking to your Slack workspace in just a few minutes.

I’m then going to share with you 12 practical ways to leverage that virtual coworker to get real value for your business.

Before we jump in, a quick update on SaaS Decoded.

As you can see from the graph above SaaS Decoded is growing from strength to strength and I just wanted to thank you for subscribing and supporting the newsletter.

A special thanks to Luke, the newsletter’s first paying subscriber.

If you have a friend or colleague who you know would benefit from the newsletter, send them the link —> saasdecoded.com, or hit the share button below.

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What’s the best agentic agent for Slack?

The number of high quality services that allow you to set up agentic agents (bots that can take action on your behalf) is growing every week.

I was looking for a “plug-and-play” service that allowed me to communicate to a smart agent directly in Slack.

I want that agent to be able to perform tasks across a number of services, complete recurring tasks, and if possible, be proactive in a practical way.

During my research I came across 4 services which seemed to met my requirements.

• Runbear

• Viktor

• Lindy

• Arahi AI

*I’m in no way associated with the services listed above. They just stood out to me among the companies I researched for this post.

**Edit on 16th Mar 26: A few weeks after publishing this post I decided to join Viktor’s affiliate program after being impressed with the product. If you click on any links in this post that direct you to Viktor’s homepage and sign up, you’ll become an affiliate of mine. I’ll earn a small slice of the money you spend with Viktor over your first 12 months as a subscriber.

Runbear

The first service I looked at was Runbear, a California-based AI company that boasts over 300k users.

What caught my eye about Runbear is that they are focused on two main areas, adding an agent to Slack, and adding an agent to your Gmail inbox.

I like companies which are focused and since I was specifically interested in a Slack-based agentic agent Runbear seemed to be a great fit.

Signing up to Runbear and adding the agent to Slack went relatively smoothly.

Runbear has a free 7-day trial and allows signup via Google oauth which I use whenever I can.

After signing up I was able to connect Asana (to-do list and project management tool) and Slite (internal knowledgebase) to Runbear which meant my Slack bot could now perform certain actions in these services, for example, creating a task.

The next step in the process was to add the Slack bot to relevant channels.

This can be done by visiting the settings of the channel, and then in the integrations tab you add the bot.

For both Runbear and Viktor, which I’ll cover shortly, I only used the agents in my sandbox channel.

I didn’t feel comfortable using them in Slack Connect channels with external contacts, but I feel that will change after spending more time with these tools.

Once added to my sandbox channel, I was ready to tag the agent in a message and send it requests.

The agent is smart enough to ask questions to help guide it.

This was a great start and overall I was impressed with Runbear the more I used it.

The company does a great job of providing many customization options, enhancements, and scalable functionality in an easy-to-navigate UI.

Runbear provides sections for providing clear instructions, context, rules, triggers, and hundreds of possible integrations.

This is great for non-developers who want to leverage AI but have no interest in building their own.

The triggers section is especially powerful because it allows you to set up clear use cases for triggering action by the agent.

This trigger-action logic makes the agent operate similarly to workflows you’d build in Zapier or n8n.

If you’re like me and want Slack to be the central hub for communication, alerts and updates, then this functionality is a game changer.

Overall Runbear is an awesome service and I’m excited to play with it more but I do need to mention some negatives I experienced while playing with the service.

• Runbear allows you to connect custom MCP servers which is great but it kept on failing when I tried connecting Slite’s MCP server. I didn’t have this problem when using Viktor which has similar functionality.
  

• Lack of granular controls. Runbear doesn’t give you the ability to force the bot to ask permission before performing certain actions. I’m able to turn off certain abilities which is useful but I’d like to have more control.

• Lack of usage reporting. I performed just a handful of tasks during my trial of Runbear and used up 4/5ths of the credits allocated to me. The worst part is I had no idea which tasks used how many credits. This makes it tough to estimate the monthly costs for using Runbear.

Viktor

Viktor is a very new service that recently announced their public launch on X and went quite viral. This caught my eye and I decided to give them a try.

Viktor stands out to me among the services I’m covering in this post since it’s by far is the most “Slack first”.

Viktor initially launched with a free and a $5,000 a month plan which was a bit of a turn off since the free plan seemed quite limited, and I could never afford the 5k a month plan.

I was glad to see that they changed that and now offer usage based pricing.

Signing up to the service is even more straightforward compared to Runbear and after connecting Viktor to my Slack and connecting one integration (I chose Asana) I was ready to start talking with “Viktor”, my new virtual employee.

After less than an hour playing with Viktor I could already see that the service was far more refined than Runbear, and a hell of a lot more proactive.

Check out this message I got from Viktor 6 minutes after adding it to my Slack.

Not only does the message clearly tell me how to use Viktor but it provided a clear use case for a recurring task with Asana (which I connected during the onboarding).

What really impressed me is the second suggestion, E-commerce BI industry research. Clearly there was a behind the scenes prompt where the AI did some research on my agent, projectBI, and worked out that the agency works with eCom businesses and provides BI services.

When it comes to a product like Viktor, the small details really matter. Look at the example below where I asked Viktor to return me data from HubSpot.

Notice how at the bottom Viktor mentioned that there are “no contacts currently set to New, Contacted, Connected, or Introduction Call Scheduled”. It didn’t need to include that but it did.

Similar to Runbear, Viktor supports recurring tasks but I didn’t see anything related to trigger-based workflows which is definitely a gap.

I asked Viktor about this and got a great answer back.

Viktor does have usage reporting but its quite simplistic at this stage.

Apparently Viktor can even create web applications on your behalf. My initial response to this feature is that it’s overkill but perhaps once I see some clear use cases I’ll change my mind.

Viktor provides granular controls for each of the integrations you connect to it which is so important.

Since I’m new to AI I’m very worried about using agents that can cause damage so granular controls like this are crucial for building trust.

Overall I was very impressed with Viktor and at least for now it’s my top choice for agentic agent for Slack.

A quick word on Lindy and Arahi AI

Both Lindy and Arahi AI look like impressive services, especially Lindy which I first heard about over a year ago.

Both Lindy and Arahi are more general agentic agent workflow builders. Both services can work with Slack and you could use them to set up specific workflows that can be triggered directly in Slack.

Since they aren’t “Slack first”, I decided to skip the deep dive since Runbear and Viktor are “plug-in-play” and meet all my requirements.

I can see myself giving Lindy more of a deep dive since I may want to start running certain automations which have nothing to do with Slack.

For now I’m focusing on trying to improve my day-to-day by using AI and since so much of that takes place in Slack, that’s where I’m focusing my attention.

12 practical ways SaaS businesses can utilize an agentic agent in Slack

Below are 12 practical ways I would use an agentic agent in Slack if I was running a B2B SaaS company.

Which use case below is your favorite? Let me know in a comment below.

1. Send a daily summary covering the number of your sales funnel (new leads, MQLs, SQLs, opps, deals, new ACV, etc)
   

2. Share a list of leads which haven’t been contacted in X days. This list could be shared daily with the VP of Sales who can then follow up with the owners of those leads.
   

3. Share closed won deals in Slack so everyone can celebrate the win and praise the closer.
   

4. Create weekly reminders both in Slack and in Asana / Monday / Clickup for specific employees that have their own recurring weekly tasks.
   

5. Look up high risk accounts in HubSpot / SalesForce and share a list every Monday morning with the retention team on Slack. Create tasks for each high-risk account in Asana / Monday / Clickup.
   

6. Share a project progress report once a week until the project is completed. Good for long-term projects which are organized in a proper project management solution such as Asana.
   

7. Update the VP of client success when a support ticket goes unanswered for more than 3 days. Share the ticket in a specific channel on Slack.
   

8. Share a summary of SalesForce marketing campaigns 30 days after launch in Slack.
   

9. Help an employee share a specific SOP to another directly in Slack.
   

10. Every Wednesday share a digest in the #general channel of the most important news stories in the business’s niche for that week.
    

11. Monitor new opportunities created in HubSpot and share opportunities with the VP of Sales which are missing key data (the sales rep didn’t follow instructions and something is missing).
    

12. Send a message to the #vp-success-alerts channel when an enterprise client has opened a support ticket, look up the owner of the account in HubSpot and ping them in the message.

For more use cases, visit https://runbear.io/use-cases.

That’s it for this week friends.

If you enjoyed this post, please consider hitting the share button below.

Thanks for reading and enjoy the rest of your week.

Justin

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Hey friends,

In this weeks edition of SaaS Decoded I’m tackling the topic of AI.

I’ll be honest, I’m very late to the AI game.

Even though I consider myself quite technical, I’ve avoided going down the AI rabbit hole the last 2 years simply because I saw it mostly as a distraction.

My focus was on growing my businesses and I didn’t have the bandwidth or the desire to learn a whole new set of skills.

As AI adoption continues to grow and the use cases become clearer to me, I’ve come to realize I can no longer “sit this one out”.

I decided to do a ton of research to help me understand what’s going on and I’ve crystalize my thoughts in this post.

This guide is the by-product of over 20 hours of research on a wide range of topics related to the ongoing AI boom.

I’m sure there are many people out there like me who are late to the party and need a helping hand getting up to speed. I hope this guide can be that helping hand.

By the time you are done reading this post you’ll have a much better understanding of the following:

• What exactly is AI and can machines really think?

• What exactly is this AI wave and how did it start?

• How is AI disrupting the world and why should I care?

• Who exactly are the “big players” in the current AI ecosystem?

• What the hell is ChatGPT, Claude, Gemini and all these other AI tools I’m hearing about?

• The art of prompting and how to communicate with AI like an expert.

• What is “vibe coding” and should I start building my own apps?

• Should I install Clawbot and set up my own AI assistant?

• Is AI really going to kill my SaaS business?

• Is my job safe from AI and if not, what should I do about it?

This is going to be a long read so get a coffee, strap yourself in and enjoy.

What exactly is AI and can machines really think?

AI stands for artificial intelligence and is a field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence.

The field of AI has a long history going back to the 1950s but for much of the last 70 years the field has gone through an “AI winter” where the lack of large datasets and adequate hardware held back major progress.

We now live in a world where those constraints have been removed, allowing for AI to become available for use at scale by the general population.

To learn more about the interesting history of AI, check out the two videos below.

What exactly is this AI wave and how did it start?

The “big bang” moment for AI was the public launch of ChatGPT’s new model, GPT 3.5 on 30th November 2022.

Many consider this the start of the Generative AI (GenAI) Revolution where AI moved from being “discriminative” (used to classify data, like identifying spam or suggesting a movie) to “generative” (it creates new content like text, code, videos and images).

This revolution has quickly created an entire ecosystem of generative AI solutions which are disrupting basically every industry on earth.

As we move into 2026 the AI space is seeing another major shift.

We are transitioning from the “Chatbot Era” to the Agentic Era where instead of speaking with AI and getting information back, we are using AI to complete tasks on our behalf.

How is AI disrupting the world and why should I care?

The level of disruption as a result of the generative and agentic AI waves is difficult to put into words.

We are now living in a world where machines can accomplish a wide range of tasks which just 24 months ago would have been impossible.

Below are just some of the areas where AI’s capabilities now supersede that of human-beings.

1. High-speed pattern recognition and anomaly detection

2. Mass personalization and dynamic optimization

3. Technical translation

4. Synthesisand content transformation

5. Structured mathematical reasoning and simulation

6. Real-time supply chain

7. Legal tech and “multi-document” discovery

8. Environmental monitoring

9. Space operations and “edge” computation

10. Hyper-efficient software “modernization”

Even though it’s hard to quantify, AI is undoubtably resulting in waves of layoffs especially in tech where AI is being adopted much quicker than other industries.

Earlier this year Amazon fired 16,000 employees in its second round of layoffs in just two months. What is interesting is that this round of layoffs included many mid-level managers and other white collar workers.

According to Amazon these rounds of layoffs were to help the company “stay nimble”.

According to consulting firm Challenger, Gray & Christmas, over 55,000 people in the US lost their jobs in 2025 as a result of AI.

Almost every week new AI models are being released which can be used to create images, entire short films, and even mimic live video in real-time.

Who exactly are the “big players” in the current AI ecosystem?

To understand the “big players” in the current AI ecosystem it’s useful to first understand the different categories these companies fall into.

The main categories are:

• Infrastructure

• The Ecosystem

• The Brains

• The Specialists

Infrastructure

Without infrastructure there wouldn’t be any AI.

Nvidia is the most well-known AI infrastructure company since its the main supplier of the crucial chips that the large language models (LLMs) and cloud companies need to provide their AI services.

Databricks, Amazon AWS, and Oracle are some other well-known AI infrastructure companies.

The Ecosystem

In the current AI landscape, Microsoft, Google, Salesforce, and IBM are examples of “ecosystem” companies because they don’t just sell a single AI tool but rather provide the fundamental “soil” in which other technologies, developers, and businesses can grow.

An ecosystem company operates as a platform orchestrator, creating a self-reinforcing network where the value of the system increases as more participants join.

The Brains

OpenAI, Google Deepmind, Anthropic, xAI and DeepSeek are examples of companies whcih would fall into “the brains” category in the current AI landscape.

In the AI landscape, these companies sit at the "Model Layer." They do the heavy lifting of training massive neural networks on vast datasets, creating the core intelligence that other companies then plug into their apps.

When someone sends a prompt to ChatGPT, the “brains” behind the response belongs to OpenAI.

The Specialists

The final category is “the specialists”, AI companies which focus on a specific medium, vertical or function.

Harvey is a great example of a specialist AI company since its focus is the legal domain.

Below is a list of 50 AI “specialists”.

What the hell is ChatGPT, Claude, Gemini and all these other AI tools I’m hearing about?

As the adoption of AI continues to gain momentum and moves into the mainstream, the market starts to position certain services as “best-in-class”.

ChatGPT

ChatGPT is special among the dominant AI services since the launch of it’s underlying model, GPT 3.5 back in 2022 is credited for kicking off the GenAI wave.

This new version of ChatGPT was such a significant improvement to previous models that word of mouth helped accelerate adoption to over 1 million people in just 5 days after launch. Hundreds of millions of people tried the service in the weeks that followed.

ChatGPT has evolved a lot since 2022 but back then it was fundamentally a chatbot that you can send questions and get back answers.

Today ChatGPT is not limited to just providing answers to questions. It can complete complex tasks on your behalf such as planning an entire trip overseas, draft emails, conduct deep research, and even review legal documents.

Claude

The first thing to know about Claude is that it means different things depending on the context.

There is Claude, the AI chatbot, Claude Code, the coding assistant for developers, and Claude’s models, Opus, Sonnet and Haiku.

Claude is a direct competitor of ChatGPT.

Where Claude stands out is in the foundational difference in its underlying models.

Unlike other models that are trained primarily on human feedback (which can sometimes teach the AI to be "people-pleasing" even if it's wrong), Claude is built using Constitutional AI. Claude is considered the most “honest” among the chatbots.

Claude Code is a popular AI assistant for developers who use the service to write code using prompts.

This new way of writing software is revolutionizing the SaaS industry. I’ll be covering this topic in more detail later in the post.

Claude Code is also popular among “vibe coders”.

To learn more aboute Claude Code, check out the introduction video below.

Gemini

Gemini is another ChatGPT competitor, this time built and run by Google.

Some would argue that Google has a competitive advantage of OpenAI and the other “chatbot companies” because Google has more data than the rest that it can feed to its models.

I’ve started to use Gemini for deep research, help with brainstorming, and to create some basic images and it’s very impressive.

If you’re not sure which AI chatbot you should be using, the graphic below might come in handy.

The art of prompting and how to communicate with AI like an expert

If there is one skill which AI is pushing to the forefront it would be “prompting”.

Prompting is the skill of writing commands, or “prompts”, to AI bots (now commonly called “agents”).

An example of a prompt would be:

“Write a press release announcing that my startup, MobileX, has raised $1M dollars in funding as part of a seed round. Make sure you mention our main investor, John Smith, and our plans to use that money to grow a team and work towards profitability in an estimated 24 months from now.”

AI agents know how to take a prompt and perform the desired action but the way you write the prompt, the context you provide, the constraints you include, etc are important for helping the AI achieve the best possible outcome.

Below are some tips for writing prompts like an expert.

Assign a Persona (The "Who")

AI models are trained on everything from academic papers to Reddit threads. If you don’t tell the agent in your prompt who to be, it gives you a generic average of all of them.

For example, the prompt, “write a marketing plan”, is too generic and doesn’t have a “who”.

A better version would be, “Write a marketing plan. Act as a Senior Growth Marketer with 15 years of experience in SaaS. Your tone should be data-driven, concise, and focused on ROI.”

Use "Chain of Thought"

AI often fails at complex tasks because it tries to jump to the answer too quickly. Forcing it to slow down and “think” improves accuracy significantly.

The magic phrase, “think through this step-by-step before providing the final answer” will force the model to process logic in a linear sequence which will improve accuracy.

Use "Negative Constraints"

Agents are prone to “looping” or over-complicating simple tasks. To prevent this, define what they must not do.

• “Do not perform more than 3 web searches per sub-task.”

• “Only provide the top 5 use cases.”

• “If a search returns no results, do not guess. Stop and report the failure.”

For more tips on writing better prompts and how to get move value from ChatGPT and similar tools, check out the video below.

What is “vibe coding” and should I start building my own apps?

"Vibe coding" is a term that exploded in late 2024 and 2025 to describe a new way of building software where the human provides the vision, requirements and logic, and AI handles all the development and implementation.

“Vibe coding” is one of the most consequential outcomes of the GenAI wave because for the first time in history, non-technical people with an internet connection and credit card can build their own software applications.

Tools like Claude Code, Cursor, Lovable and Replit are empowering millions of “vibe coders” who are collectively disrupting the entire software industry. Building software is now very much a commiditized service.

So I guess the most obvious question is, “should I become a vibe coder and start building software”?

This is a difficult question to answer without having the full context.

For some, vibe coding will be nothing more than a distraction.

Should, for example, the CEO of a marketing agency spend 10 hours “vibe coding” a new to-do list app for his business to replace Asana or Monday.com? Probably not.

On the other hand you have a seasoned software engineer who can really maximize his output through AI coding tools who definitely should explore vibe coding.

One of the dangers of vibe coding is that on the surface it makes building applications extremely easy.

This gives non-technical vibe coders the false belief that as the underlying logic, interfaces and functionality of the app grow in complexity, that they will be able to continue building just like before.

Writing code is only one piece of building complex software. Vibe coders don’t know application design, database schema best practices, or how to set up the app to handle massive scale. The assumption is that the AI service will handle that perfectly.

These are just some of the areas which will stop vibe coders dead in their tracks, often wasting many hours.

The vibe coder is like someone who can’t read a map relying on a GPS to navigate a forest. Once the GPS fails he is stuck. A senior developer knows how to read the map but using the GPS is the smart decision.

Should I install Clawbot and set up my own AI assistant?

The newest sensation in the ongoing AI revolution is Clawbot (recently rebranded to OpenClaw).

OpenClaw is an open-source “agentic” AI project which has exploded in popularity.

OpenClaw is essentially an AI assistant that has “hands.” While ChatGPT and Claude live inside a browser tab and wait for you to talk to them, Clawbot is designed to live on your computer or a private server and act as a proactive digital employee.

The video below does a great job of showing you how to set up OpenClaw and how it works.

I’ve yet to try OpenClaw myself but the number of insane use cases is growing every day and I’m very tempted.

You should keep in mind that OpenClaw is still quite new and there are serious concerns about it from a security standpoint.

It’s quite a leap to provide an AI with complete access to your local computer especially, when it has the power to connect to the Internet, run applications, send emails, etc.

You might want to wait until OpenClaw has matured a bit and becomes a bit more mainstream before giving it a try.

Is AI really going to kill my SaaS business?

This is a big topic which deserves it’s own post on SaaS Decoded but I’ll do my best to answer it here.

If you spend anytime on X these days you’ll come across dozens of posts predicting the collapse of the SaaS market.

Of course you have people that believe the opposite.

There’s no doubt in my mind that the current AI wave is disrupting the SaaS market but is the SaaS market dead as a result?

Here is what I believe.

AI will push SaaS prices down and change pricing models

As I previously wrote, GenAI is empowering millions of people to build apps in minutes for very little money.

Some businesses will end up vibe coding replacements for certain SaaS with cheaper self-built solutions.

When there is more competition in a market, prices go down.

If I can vibe code an app in an afternoon which costs my business tens of thousands of dollars a year, I might seriously consider doing so.

I don’t think this will happen across all verticals and will be much rarer in the enterprise market compared to SMB and midmarket.

Certain SaaS will need to revisit their pricing model. Those that use license-based, long-term contracts, and other “exploitative” practices will be under pressure to adapt to a market with a lot more competition.

SaaS companies will need to shift focus from product to distribution and business strategy

In a world where product development is commoditized, the way you differentiate yourself and win in the market is with better distribution and a stronger moat.

SaaS businesses will need to shrink their R&D teams and move budget to marketing, business development, finance and business strategy.

These teams will be under a lot of pressure to help the company stand out in a far busier environment and establish far stronger moats.

Most will fail but those that succeed will see massive growth.

CRMs, ERPs and other multi-departmental SaaS with high switching costs aren’t going anywhere

There are mission critical SaaS which are major data centers that support multiple departments. CRMs, data warehouses and ERPs are the best examples.

Often a large percentage of the company are using these tools and even though they are expensive, the ROI they provide is high and well understood by the business.

These SaaS are by far the safest from “vibe coding” and the AI wave in general since switching out these systems is extremely expensive.

Tools like SalesForce, HubSpot and Netsuite have hundreds of thousands of man-hours worth of development, bug fixing, QA and UX invested in them.

Even though some functionality can be easily replicated using a tool like Replit or Cursor, large businesses aren’t going to create massive disruption to their operations to drop software costs which are typically less than 10% of their overall expenses.

There are entire industries which are not technical that won’t be rushing to hire “AI consultants” or “vibe coders” to start replacing their established software systems which are working well for them.

Is my job safe from AI and if not, what should I do about it?

AI is coming for many jobs.

It’s difficult to predict how things will unfold but I’m not very optimistic for low skilled workers, especially in certain industries like banking, legal, recruiting, etc.

I think the best thing you can do to protect yourself is to upskill and make yourself as valuable as you can to your employer.

You want to be in a role which involves multiple overlapping skills.

An example would be an event planner who has to be a good communicator, negotiator, project manager, people person etc.

The next best thing you can do is become your own boss.

Even highly sort after jobs like lawyer, radiologist and software engineer are under risk of being replaced by AI.

If you enjoyed this post and want to get next week’s post in your inbox (I publish a new post every Tuesday at 10AM EST), enter your best email below.

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Thanks for reading and have a great week.

Justin

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Hey friends,

In this week’s edition of SaaS Decoded I’m sharing the final part of my 2 part series on B2B SaaS growth tactics.

In part 1 I covered the following tactics:

1. Real-world meetups

2. Unique data-backed content

3. Programmatic “connector” SEO

4. Launch a startup program

5. Build an army of influencers by launching a marketplace

6. Product-Led SEO

7. Launch a “university”

8. Use extreme transparency to maximize word of mouth

9. Launch a “sidecar” product

10. Start as open-source, then build a hosted solution

In part 2 I will be covering the following tactics:

1. Creating a new role as a “Trojan Horse”

2. Strategic merch giveaway

3. Product demo on homepage

4. The “unhinged” TikTok persona

5. “Dogfooding in Public”

6. Build in public

7. Grow TAM using a stand-alone service

8. Strategic partnerships

9. Create a new category and own it

10. Launch apps on marketplaces

Let me know your favorite tactic from the series in a comment below the post.

Let’s jump in.

Growth Tactic #11 - Creating a new role as a “Trojan Horse”

One of the most well known companies operating in the “go-to-market” (GTM) space is Clay.

Back in 2023 Clay coined a new role, the “GTM engineer”.

According to Clay, a GTM engineer “can solve problems across any revenue-critical function”, and “build revenue engines using AI and automation”.

Clay’s history is interesting and covered in great detail in this post by West Operators.

According to the post, Clay struggled for years to optimize their messaging and only after shifting their focus to AI and the GTM market were they able to accelerate growth and become a category leader.

Another great resource on Clay’s history and their struggles nailing their ICP is this in depth post, Clay’s Path to Product-Market Fit — A 7-Year ‘Overnight Success’.

Why does this tactic work?

It’s very tough for a B2B SaaS to gain significant traction without having a very strong understanding of their ideal ICP.

By creating a new role and popularizing it, Clay was able to position themselves as a category leader. This tactic helps position a product as a “must have” which will accelerate growth and increase word of mouth.

Growth Tactic #12 - Strategic merch giveaway

In 2024 Yotpo launched their “walk away” campaign that created millions of organic impressions for their brand.

The campaign involved sending custom-made shoes to select influencers, Yotpo customers and others in their market.

The purpose of the campaign was to entice businesses to “walk away” from their existing email service provider (Klaviyo in most cases), and move over to Yotpo’s competing service.

Since only 100 pairs were made and distributed, those that received them were quick to share the campaign to social media.

The campaign was so effective that even people that didn’t receive a pair talked about it online.

According to Yotpo’s VP of Advocacy, Eli Weiss, the campaign achieved over 1 million organic impressions within the first 48 hours after launch.

Why does this tactic work?

The reason Yotpo’s “walk away” campaign worked is because it’s a “purple cow”.

The campaign was very different to the standard marketing campaigns run by B2B SaaS in the direct-to-consumer eCommerce space.

The campaign actually provided value to the recipients who were made to feel special since they were handpicked (only 100 pairs were made and distributed).

Yotpo picked the right people who had influence in their market. This meant that even if a fraction of the people that received a pair talked about it, a lot of potential businesses for their email product would hear about it.

Driving brand awareness is expensive.

The typical cost per mille (cost per thousand impressions) on a platform like Meta is in the range of $10 - $60+ depending on who is being targeted. Assuming a middle point of $30 per thousand. That would mean driving 1 million impressions would cost a business about $30,000. This would be the cost to just get the impressions on an advertisement, not to drive a million people to the website. That would cost far more.

By targeting the right people using a limited physical giveaway that made the recipients feel special, Yotpo was able to drive hundreds of thousands of dollars in media to their brand at a fraction of that cost.

Growth Tactic #13 - Product demo on homepage

The quicker you can demonstrate the value your product provides, the quicker people will “get it” and know if its what they need.

Senja is a testimonial collection and publication SaaS that does a great job of showing off what they do on their homepage.

After scrolling down on their homepage you will start seeing a ton of testimonials displayed in a grid. Scroll down some more and you will see the Senja badge, proudly showing that Senja use their own product on their homepage.

The use of a homepage-based demo of the product with a ton of social proof will do wonders for Senja’s visitor-to-signup conversion rate. This is a powerful combination that all SaaS should try and leverage if possible.

Why does this tactic work?

The average person’s attention span is getting shorter every year. Businesses need to make it clear what they do and how they help within a few seconds or risk losing potential users.

If a SaaS business can demonstrate their product directly on their homepage, either by showing off the actual product, like in the case of Senja, or through a short video, they will have an advantage over businesses that don’t.

The homepage is a SaaS business’s most important online real-estate, and should be optimized to help visitors make a decision within a few seconds of landing on the page.

Growth Tactic #14 - The “unhinged” TikTok persona

Duolingo is a SaaS business that has absolutely crushed it on TikTok.

Today the company has over 17 million followers and 473 million likes on their videos.

After watching a few of Duolingo’s TikTok videos you start to see a pattern.

The videos are "unhinged” and designed perfectly for the younger TikTok audience. Duolingo understands that to stand out on TikTok, videos need to raise eyebrows.

The consistent use of branding (green color, maskot, etc), music, sharp visuals and a fun, humorous style makes Duolingo’s videos stand out on a very busy social network like TikTok.

Why does this tactic work?

If the name of the game is to get attention then there are few places today where more attention is spent than TikTok.

By mastering TikTok, Duolingo was able to leverage the platform’s viral nature to reach millions of potential users for their platform.

TikTok is a platform that is better suited for B2C SaaS but shouldn’t be dismissed entirely by B2B SaaS.

The right content on TikTok can go viral and reach a very large number of people resulting in a high ROI.

Shopify, Canva, CapCut and Adobe are just some B2B SaaS businesses with large audiences on TikTok.

Growth Tactic #15 - “Dogfooding in Public”

“Dogfooding”, and “eating your own dog food” are terms used to describe a business that uses it’s own product. “Dogfooding in public” is when a business uses their own product publicly.

A great example of a business that uses this tactic is Plausible.

Plausible is a Google Analytics alternative that uses their own product to track their web analytics.

When visiting Plausible’s homepage, you’ll notice a “View live demo” call-to-action.

After clicking this button you’ll be taken to a live demo of the Plausible product showing Plausible’s actual web analytics data.

This not only allows you to see the product in action, but creates trust because you can clearly see that Plausible is using their own product. It creates the question in the visitor’s mind, “if it’s good enough for them, perhaps it’s good enough for me?”.

Why does this tactic work?

The use of a live demo is a great way to quickly show the value a product provides.

Add to that transparency and “dogfooding” and you have a powerful combination that immediately creates trust between the viewer of the demo and the company behind it.

This trust will result in a higher conversion free-to-signup conversion rate, more product demos and higher conviction behind the product.

Growth Tactic #16 - Build in public

The tactic of building in public isn’t new. Last week, in part 1, I mentioned Buffer which I consider the most public company in the world.

Buffer is an extreme case of using transparency to create word of mouth and interest in your business, but there are many SaaS businesses which choose to use transparency to stand out by “building in public”.

Peter Levels is a well-known “indie developer” who embraces the “build in public” philosophy.

The idea behind building in public is to share frequent updates and the “story” behind building the product, or software business.

This not only helps market the product, but is also a way to create an audience, gather feedback, and create some buzz around your personal brand.

X is a great platform for those that want to “build in public” but isn’t the only platform that can be leveraged. I personally lean into this philosophy through Coffee + Revelation, my personal substack, where I publish updates on my entrepreneurial journey.

Coffee + Revelation

Follow my journey navigating the world of entrepreneurship, personal finance, and what it means to live a good life.

By Justin Butlion

Some other examples of SaaS founders that “build in public”:

1. Marc Lou (@marclou)

2. Tony Dinh (@tdinh_me)

3. Simon Høiberg (@SimonHoiberg)

4. Guillaume Moubeche (@GuillaumeMbh)

5. Danny Postma (@dannypostma)

Why does this tactic work?

Transparency is a virtue that people are attracted to, therefore those that use transparency as part of their communication style standout in a very noisy world.

By regularly sharing updates on their entrepreneurial journey, changes they’ve made to their products, and other personal insights, the individuals I’ve listed above have created huge, loyal online followings.

Having an audience of tens of thousands of loyal fans is a very valuable asset that these entrepreneurs can leverage at anytime to test new ideas, gather useful feedback, and help market new products they launch.

Growth Tactic #17 - Grow TAM using a stand-alone service

Stripe is a popular fintech company used by developers for subscription management and payment collection. Unfortunately because of regulatory reasons Stripe doesn’t operate in every country.

I’ve personally dealt with this problem being a tax resident in Israel. Other countries where Stripe isn’t available include India, Argentina, Indonesia, Russia, and South Korea. Stripe is available to businesses in 46 countries.

If someone in one of these restricted countries wants to use Stripe, they will need a business entity in a country which is supported, such as the USA.

Back in 2016 Stripe launched a stand-alone service called Stripe Atlas.

Stripe Atlas helps non-US individuals form a US-based limited liability company (LLC), or corporation (c-corp).

For just $500, the Stripe Atlas team will submit the necessary paperwork to the US government and help you create the entity.

Since launching, Stripe Atlas has helped over 100,000 founders incorporate their businesses in the USA.

I’ve personally used the service twice and I’m a big fan.

As a direct result of Stripe Atlas, I now have 4 micro SaaS running on Stripe, each paying fees to Stripe every month.

By offering a stand-alone service, Stripe has managed to grow it’s total addressable market (TAM) while making over $50 million in setup fees along the way.

Why does this tactic work?

By offering a stand-alone service that solves a real pain for a potential customer, you not only build trust, but enable someone to start using your core service.

Stripe has managed to do this at scale (100k+ businesses) but as long as the economics work, any B2B SaaS could create a stand-alone service that removes a potential roadblock for individuals that want to use their core service.

This tactic would work best in industries which have a lot of regulation, geographical constraints, and legal considerations.

Growth Tactic #18 - Strategic partnerships

Mercury is a fintech company that I’ve growth to love over the last 3 years.

Mercury provides a fantastic service to entrepreneurs that need US-based online banking services.

On top of providing an incredible online banking experience, Mercury uses strategic partnerships to provide financing options to users.

By partnering with other fintech companies that offer different financing options, Mercury is able to provide value throughout most of the lifecycle of their users. Startups get online banking services, and more mature businesses that need to raise capital can leverage Mercury’s partnerships.

This allows Mercury to focus on their strengths while using others to provide additional value.

Stripe Atlas has also used strategic partnerships in a very affective way.

Stripe Atlas helps entrepreneurs set up US-based business entities like LLCs and C-corps but setting up the entity is only one step towards running a successful business.

Stripe Atlas has partnered with a long list of service providers from bookkeeping to virtual mailbox providers. This allows Stripe Atlas to provide huge additional value to their customers while also getting new business from their partner network.

Why does this tactic work?

Building partnerships with companies that provide value to your users in an adjacent area is a great way to amaze your users without needing to shift too much focus away from your core business.

Building a network of partners can be a great, high leverage way to drive new referral business.

Growth Tactic #19 - Create a new category and own it

Back in 2005, Brian Halligan, the CEO and co-founder of Hubspot famously coined the term, “inbound marketing”.

Brian had seen how consumers were getting better at blocking out ads and outbound marketing and used the term to describe this shift.

For most of the last 21 years since then HubSpot has positioned itself as the “all-in-one” software solution for inbound marketers.

HubSpot’s emphasis on using content, lead magnets, and SEO to drive top-of-funnel awareness of their brand is directly out of their inbound marketing playbook.

Their focus on educating the market through the HubSpot Academy has helped to turn tens of thousands of marketers into “inbound marketing” evangelists.

Why does this tactic work?

HubSpot introduced a brand new way of marketing to the world just as social media was gaining traction.

This contrarian approach helped create massive brand awareness for HubSpot among the digital marketers of the world who connected strongly with this new way of thinking.

The concept of inbound marketing spread online like a wildfire which helped HubSpot’s revenue grow to over $50M within less than 7 years.

By owning the category and helping to shape it into a real set of beliefs that millions of marketers around the world could connect with, HubSpot become both a “thought leader” and “must-have” solution for a new generation of digital marketer.

Growth Tactic #20 - Launch apps on marketplaces

Asana is one of my favorite productivity apps.

As a stand-alone app, Asana is fantastic, but the company has proactively launched integrations on a number of platforms which makes using it seemless across so many platforms.

A great example is Asana’s Slack app.

I often use their Slack app to create tasks directly from conversations in Slack.

Asana has integrations with Microsoft Teams, Zapier, n8n, Harvest and dozens of other workflow-related applications.

In some cases these businesses have built these integrations because of Asana’s popularity, but in many cases the apps were built and managed by Asana directly.

Why does this tactic work?

By being listed in so many popular app marketplaces, businesses that adopt this tactic can benefit from a lot of “free” exposure.

As long as your app is of high value to the marketplace it should get good reviews which will lead to higher rankings and more direct exposure.

Existing users benefit from this tactic since they are able to connect more and more of their apps together, leading to higher usage and better retention.

Which of the 20 tactics I covered in this series did you like the most? Let me know in a comment below.

If you enjoyed this series and want to get next week’s post in your inbox (I publish a new post every Tuesday at 10AM EST), enter your best email below.

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Thanks for reading and have a great week.

Justin

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SEO, content marketing, and paid ads are standard marketing channels we are all familiar with.

But what about some of the lessor known growth tactics used by B2B SaaS companies?

This week’s edition of SaaS Decoded is part 1 of a 2 part series where I share interesting growth tactics being used by successful B2B SaaS companies.

In part 1 I will be covering the following tactics:

1. Real-world meetups

2. Unique data-backed content

3. Programmatic “connector” SEO

4. Launch a startup program

5. Build an army of influencers by launching a marketplace

6. Product-Led SEO

7. Launch a “university”

8. Use extreme transparency to maximize word of mouth

9. Launch a “sidecar” product

10. Start as open-source, then build a hosted solution

Some of the companies mentioned in this post include:

• Cursor

• Gong

• Zapier

• Shopify

• Intercom

• n8n

• HubSpot

• Canva

• Notion

• Buffer

• Airbyte

Let’s get started.

Growth Tactic #1 - Real-world meetups

How can an AI company that sells a highly technical product rise above all the online noice to get in front of new potential users?

One way is to rent out coffee shops and set up meetups all over the world.

Cursor is one of the darlings of the AI wave and have leaned hard into community-related marketing tactics to help accelerate growth.

One smart move the company made was hiring Ben Lang, the former head of community of Notion. Ben is a seasoned marketer who specializes in building communities.

According to Ben’s X account, Cursor has been “taking over” cafes in dozens of cities from Caira, Madrid, Chiang Mai to Berlin, Prague and Taipai.

Why does this tactic work?

Cursor’s target market are developers that spend a lot of their waking hours sitting in front of computer screens.

Visiting one of Cursor’s cafe takeovers is a great excuse to break the routine, meet other developers, and get closer to a brand that is loved by its users.

Fans of Cursor who support the product will spread the word and invite their friends to join them at the cafe. This helps Cursor increase awareness and in return more marketshare.

Cursor has also smartly collaborated with other groups to help spread the word about their cafe “takeovers”.

Below is a great example of how Cursor partnered with the Digital Nomads Thailand Facebook group to promote their Chiang Mai Cafe Cursor meetup.

We can see on Cursor’s website that Cafe Cursor isn’t a one-off marketing initiative but rather a key piece of their community-driven marketing strategy.

Growth Tactic #2 - Unique data-backed content

If your SaaS product is collecting millions of unique data points, then you are well positioned to leverage this next growth tactic.

Gong is an intelligence platform for revenue and sales teams. It uses AI to analyze sales calls and source useful insights to help improve closure rates (among other important sales metrics).

Gong has analyzed millions of calls which has resulted in a goldmine of interesting insights.

Gong leans into this advantage through “Gong Labs”, a section on their public blog where these data-driven insights are shared.

A great example of such a post is “How one simple word can derail your sales deal”.

Gong found out that by mentioning “list price”, “typical price”, or “standard price” on sales calls, the sales process was 19% longer.

Why does this tactic work?

By publishing unique content backed by data only they have, Gong is positioning itself as an authority in their space. This leads to trust, more word of mouth and greater overall attention.

Their content is highly actionable and valuable to their target audience which will result in shares, references by other publications, and in turn more eye-balls on Gong.

Growth Tactic #3 - Programmatic “connector” SEO

There are a number of popular automation tools like Zapier, Make and n8n that have smartly leveraged programmatic SEO to capture high-intent traffic.

If you run a Google search for “how to connect Hubspot to Trello” and scroll through the first page results you’ll notice something interesting.

Somewhere in the top 10 you’re almost guaranteed to find a result by one or more of these automation businesses.

The app that dominates this tactic is Zapier.

Below are screenshots showing Zapier’s landing page that appears in the result page for the search phrase, “how to connect Hubspot to Trello”.

You’ll notice the SEO optimized h1 tag, “Connect Hubspot and Trello to power AI-driven automation”.

As you scroll down you’ll then notice a list of popular templates for how users have actually connected HubSpot to Trello using Zapier.

These templates not only provide value to the person viewing the page but will also help capture long-tail searches such as “create Trello cards for new Hubspot form submissions automatically”.

Zapier supports over 8,000 apps.

Assuming they create a page for every possible combination of these apps, the number of SEO optimized landing pages to cover the “connect A to B” usecase would be almost 32 million.

Why does this tactic work?

Google.com is still the number one place people go to find answers to their questions.

By capturing high-intent traffic, Zapier, n8n and other apps that leverage programmic SEO exposure their brands to new potential users with extremely high leverage. Once the pages are live and rank, the traffic comes in on auto-pilot.

By being able to show visitors actual templates of how past users have used their services to connect the searched apps, they are able to provide real value.

Growth Tactic #4 - Launch a startup program

Intercom has an early stage program designed specifically for early-stage startups.

Startups which get accepted into the program get a 90% discount on Intercom for the first year plus other benefits.

The program includes 6 advanced licenses which is enough for a sizable support team for an early-stage SaaS business.

Intercom gives up very little by offering these major discounts. Even if a small percentage of startups that are accepted into the program scale beyond the program, Intercom wins.

Notion, HubSpot and Zendesk are other examples of companies that offer major discounts to early-stage startups.

Why does this tactic work?

The reason this tactic works is because everyone likes discounts, especially early-stage startups which are trying to limit their burn rate.

The program is a powerful asset for sales which can use it to help convert relevant leads which which might be on the fence.

Since software businesses like Intercom, Notion and HubSpot typically run at a high gross margin, offering significant discounts to increase sales is financially viable.

Growth Tactic #5 - Build an army of influencers by launching a marketplace

Notion, Shopify, Canva and Webflow are just some of the companies which have leveraged a marketplace to build an army of influencers marketing their companies around the clock.

Thousands of creators have built templates which are listed on Notion’s marketplace.

Many of these creators create social media posts, YouTube videos, newsletters and other forms of content to promote there templates to increase sales. All of this marketing effort helps raise awareness for Notion.

Webflow is another great example of a company that has leveraged a marketplace to provide more value to their community.

After signing up to Webflow you are encouraged to pick a template for your new website. Instead of building these templates themselves, Webflow encourages front-end developers to build templates and host them on the Webflow platform.

Templates can be either free or paid. Webflow only takes 5% in commission for sales of premium templates.

Why does this tactic work?

This tactic works incredibly well because it’s not only scalable and provides incredible value to the userbase, but it acts as a flywheel.

As more creators join and publish templates and other resources, the more people learn about the service, which drives more users and creators.

By allowing creators to earn money from sales, the marketplaces become very attractive as “side hustles”. Creators in turn become marketers that help drive eye-balls back to the service they are supporting.

Growth Tactic #6 - Product-Led SEO

Canva has implemented a very powerful SEO tactic which has helped me standout many other SaaS apps in the creative space.

Canva targets long-tail keyword phrases that relate to specific “jobs to be done” that are possible with the Canva product.

Below is a great example of this tactic in action.

Run a Google search for any specific design-related usecase and the likelihood of Canva appearing in the top ten results is very high.

Why does this tactic work?

By targeting hundreds, if not thousands, of high intent keywords through dedicated landing pages, Canva is able to capture large volumes of quality traffic which will result in more signups.

SEO is a winner-takes-all game so it will be difficult for other SaaS businesses to compete against Canva using this tactic, but I think the “jobs to be done” aspect is interesting and possibly replicable in certain cases.

Growth Tactic #7 - Launch a “university”

HubSpot, Tableau, Intercom, and Salesforce are just some of the well known SaaS companies that offer certifications through their own learning portals (mini-sites dedicated to education).

HubSpot implements this tactic better than most through their HubSpot Academy.

This learning portal is jam packed with content to help people become experts in their products and in the broader topic of digital marketing.

Through HubSpot Academy, interested individuals can try their hand at 12 different certifications. There are certifications in Inbound Marketing, Social Media Marketing and even certifications dedicated to the HubSpot software itself.

Why does this tactic work?

HubSpot has always positioned itself as an authority on inbound marketing (they popularized the concept) and their HubSpot Academy ties into that positioning perfectly. They want to help educate more people on digital marketing to help increase brand awareness and expand their total addressible market (TAM).

HubSpot Academy and similar education portals are a great way to increase word of mouth, especially when the majority of the content on offer is accessible for free.

Lastly, individuals that get certified are encouraged to share the news on social media and to show off their certification “badges” on LinkedIn. This taps into human psychology (our desire to stand out and show off our achievements) and helps increase brand awareness.

Growth Tactic #8 - Use extreme transparency to maximize word of mouth

Imagine you’re running a company and decide to make the salaries you pay your staff publicly available.

Most would call you crazy but this is just one of the many ways the founders of Buffer have decided to run their business.

I think it’s fair to call Buffer the world’s most transparent company.

Everything from their performance metrics, shareholder updates, formulas for calculating compensation, product roadmap, company goals and even the books the team are reading is made public.

Why does this tactic work?

Whenever a company takes an extreme position it creates buzz, a ton of word of mouth, and loads of discussions.

Buffer is an extreme case of “build in public” and have decided that the pros of being ultra transparent outway the cons.

Joel Gascoigne, the founder of Buffer frequently shares the company’s numbers on social media which leads to a lot of shares, discussions and more exposure for Buffer.

Growth Tactic #9 - Launch a “sidecar” product

A “sidecar” product is a standalone product built to provide value to the same avatar as the primary product.

This tactic is used to solve a simple need of the avatar and in doing so, indirectly promote the primary product.

PandaDoc, HubSpot and Shopify are SaaS businesses which use this tactic very affectively.

CreateMySignature is an example of a “sidecar” product. On the surface it looks like it’s own thing but it’s actually owned by PandaDoc who smartly integrated this simple product into PandaDoc’s primary signup funnel.

By using CreateMySignature to solve a simple problem (creating an eSignature), visitors are introduced to PandaDoc and may decide to signup to use their service. Throughout CreateMySignature there are calls-to-action to learn more about PandaDoc.

This is a high leverage play since once CreateMySignature was built, the amount of work needed to maintain it is low, while it continues to get high-intent visitors every day looking to get an eSignature.

Another classic “sidecar” product is HubSpot’s Website Grader.

Everyone who runs a website would love to have their website graded. HubSpot has built a free product which does just that.

Notice in the screenshot above how HubSpot requests an email address to use the grader. This makes the Website Grader a powerful lead generation tool for HubSpot.

Need help naming your new company? Shopify has you covered with their AI business name generator.

Why does this tactic work?

This tactic works because a company can build multiple “sidebar” products and it’s high leverage. Once the product is live and ranking on Google, it will get eye-balls and drive exposure for the parent company.

“Sidebar” products can also help drive traffic to businesses which are in a new category with limited direct search traffic. They can do a great job of educating people who are not yet aware of the solution offered by the parent company.

Growth Tactic #10 - Start as open-source, then build a hosted solution

This tactic is definitely the most controversial in this post because its risky and carries complications.

n8n and Airbyte are examples of companies which started as open-source but later built hosted versions of their software as a way to monetize.

Airbyte started out as an open-source solution and today has over 20,000 stars on Github.

In April 2022 Airbyte launched Airbyte Cloud, a hosted solution of their solution.

Businesses that have no interest in self hosting Airbyte can now sign up to Airbyte cloud and use the service without having to deal with any of the underlying infrastructure and maintenance. Businesses that use Airbyte Cloud pay the company based on usage.

n8n launched the hosted version of their service in 2021, almost 2 years after the self-hosted version was available.

Now for as little as 24 euros a month businesses can leverage the powerful of n8n without having to spin up a server and deal with any infrastructure.

Why does this tactic work?

This tactic works because by starting as an open-source solution, you can take your time to reach a high level of product-market fit before starting to monetize.

You also have the advantage of leveraging a community of smart engineers that believe in the project and can help contribute towards building a better product.

By being open-source, your service is free to use by the world’s developers which make it easier to get initial exposure, feedback and some traction.

The reason this tactic is controversial is because there are risks involved with being open-source, the most obvious being that your code is publicly available and can be forked. Most tech businesses don’t want to start as open-source and prefer to build the product themselves.

That’s it for part 1. To get part 2 directly to your inbox next week, make sure to subscribe by entering your best email below.

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Thanks for reading and have a great week.

Justin

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I still remember the moment like it was yesterday.

I was sitting in one of the last lectures of my business administration degree half listening, half in deep thought.

I was running through a problem I had stumbled across a few weeks earlier. Then it hit me… I had worked out the solution.

I almost jumped out of my seat in excitement and immediately knew that the next phase of my life would involve building a tech startup.

I spent the next two and a half years working on Bmark, a “smart” bookmarking and sharing solution.

To say Bmark went no where would be an understatement.

I made so many mistakes, went into debt and burnt a lot of time.

I learnt a lot but no where near what I could have if I spent that time working for the right company. Opportunity costs are real and usually higher than we like to admit.

The idea of building a large, world-changing tech startup is very romanticized.

We all know the names behind the great success stories but you almost never hear the stories of the tens of thousands of entrepreneurs that took a shot and failed.

When I think back on this period I wish I was a bit more grounded and understood what it really takes to build a “unicorn” (a business with a billion dollar valuation).

I also wish I understood back then that there are many ways to play the game of business, and perhaps trying to big a large tech business backed with VC money wasn’t the best route for me.

This post is the guide I wish I had back when I first sat down to start building Bmark.

My hope is that this guide will help you think through the best path for your business and save some of you years of regret.

This post will cover the following topics in detail:

• Do you really want to play the “VC-backed tech startup” game?

• Is my startup what VCs are looking for?

• Should I raise VC money for my tech startup?

• The three most important lessons I learnt failing to raise VC money

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It should start with what you want

Do you want to be financial independent, change the world, live a comfortable life while working a minimal number of hours, or be filthy rich as quickly as possible?

There are many different games you can play when it comes to business but before you choose “your game”, it’s important that the game aligns with how you want to play it.

There are a few key variables that you have to take into account.

“Impact” would be the first one on my list.

This would be the impact your business will have on the world.

There is a reason Jeff Bezoz and Mark Zuckerberg are super wealthy. Their businesses changed the world and have impacted billions of people.

That chrome extension you’re building that will help accountants save ten minutes a month updating their Google sheets is on the other end of the spectrum.

For the most part the richest people in the world have provided a disproportionate amount of value to the world.

The next variable to consider is “effort”.

If what you really want is to lead a balanced life with plenty of time for hobbies, relaxation, socializing and adventure then certain ways of playing the business game isn’t for you.

If you really want to change the world by tackling a really difficult problem, or set of problems, then it might just take everything you have in order to succeed.

Time is the next variable on the list.

Let’s assume for a second your goal is to build a billion dollar software business.

How long, in years, do you expect you’ll need to work to reach that goal? Well luckily we have some data we can use to answer this question.

Above is a table of 50 well-known, publicly traded software businesses.

Here are some interesting insights from this list:

• The median time to a $1 billion dollar valuation from founding is 6 years. The 80th percentile is 9 years. The max is 18 years!

• The median time to IPO from founding is 9 years. The 80th percentile is 12 years. The max is 20 years!

Atlassian, the makers of Jira, one of the most well-known software products in the world took 12 years to reach a billion dollar valuation. Even the almight Google took 5 years to hit the milestone.

Most people lack patience and aren’t willing to dedicate years towards a single mission.

Once you’ve understood these key variables and decided where on the spectrums you land, you can decide which game you want to play.

Just remember, most people lose because they aren’t willing to pay the price it takes to win.

Should I raise VC money for my tech startup?

As a software founder you have 3 choices when it comes to choosing how to scale your business.

• Option #1 - Bootstrap

• Option #2 - Raise capital from venture capitalists

• Option #3 - Raise capital from other sources

Option #1 - Bootstrap

To bootstrap the business means to grow the business using revenue generated by the business.

The founder or founders will cover the operating costs of the business until the business breaks even and then use the revenues generated to invest back into the business.

The initial money will usually come from savings, or by the founders working at some capacity until they can go full-time on the startup.

The benefits of this approach are:

• Keep complete control of the business.

• No pressure to grow the business at all costs.

• No need to raise additional rounds of funding every 12 - 36 months.

• No long term valuation-based goals makes the business suitable as a “lifestyle” business.

• Can optimize for higher profits since growth isn’t necessarily the main focus.

The downsides of this approach are:

• Slower growth compared to the option of raising outside capital.

• More risk since competitors could raise funding and outpace your product development and start capturing your marketshare.

• Having VC money could help you survive financial downturns or other unforseen influences. Without having the protection of cash in the bank, there is a higher risk that the business goes under.

Option #2 - Raise capital from venture capitalists

The second option is to raise capital from venture capitalists (VCs for short).

To go this route requires giving up equity in the business (ownership) in exchange for cash.

This cash is then used to hire employees and grow the business at a significantly faster pace than would be possible if the business decided to bootstrap.

This opproach is the only opproach for founders that are playing a “winner takes all” game.

Good examples of such businesses would be Facebook, Uber and Airbnb. These three businesses are essentially monopolies but needed significant capital to get to that point.

The benefits of this approach are:

• If you’re playing a “winner takes all” game, raising significant capital from VCs will give you a fighting chance.

• Allow you to hire a team and accelerate growth far quicker than if you bootstrapped the business. It allows the business to grow while being unprofitable.

• Raising VC money gives you the opportunity to get some press which can help with growth and hiring.

• The right VCs can become long-term partners and mentors that will increase your chances of long-term success.

• It allows the option of a “secondary” round for the founders. This means the founders can take some money “off the table” in exchange for selling their equity.

The downsides of this approach are:

• You will have to give up equity in your company and in most cases set up a board of directors (most VCs will want one or more board seats in addition to equity). This adds complexity and overhead.

• Once you go the VC route, it usually means needing to raise multiple rounds of funding until the company goes public. This means more dilution of ownership of the founders, an increasingly more complex cap table, and more overhead for the CEO and CFO.

• There is no guarantee you will be able to raise a round of funding when needed which will lead to major layoffs, or even the business having to shut down. Being bootstrapped forces more financial discipline.

Option #3 - Raise capital from other sources

VCs aren’t the only source of capital for startups.

Friends, family and angel investors are commonly used to raise the first round of capital (the “seed” round).

Banks and even the government can also provide capital to startups, however this route is a lot less common.

Some accelerator programs provide some seed funding to participants in their programs.

Y Combinator is the most well-known accelerator in the world. Y Combinator invests $500,000 in startups accepted into their program. In return the accelerator gets 7% of equity.

Is my startup suitable for VCs?

A question a lot of inexperienced founders forget to ask themselves is if their startup is suitable for VCs.

VCs are playing their own game and its critical that founders understand it before trying to raise money from them.

VCs raise money from principles, form a fund and invest in companies. The expectation is that within 8 - 12 years the VC returns the money (and some profit) to their investors.

For every 10 bets that VCs make, most will go to zero, 1 or 2 will have a small “exit” (1 - 5X return), and 1 will have a large exit (50X+ return).

That single large exit could return the entire fund and is the holy grail for VCs. For more on VC math, check out this useful article.

Since the math is so scewed towards these large exits, VCs won’t make bets that don’t have the potential to have huge exits.

Let’s say your startup is a CRM system designed specifically for pet store operators. You decide to target the US market and based on preliminary market research you can charge the average customer $50 a month for your solution.

A quick Google search will tell you that there are roughly 18,000 pet stores in the USA (in 2025). Assuming you can get 100% market share, you can expect your US business to generate $10,800,000 in revenue a year. This assumes zero churn.

A VC would never take a bet on such a business because the potential upside is simply too small.

A business like the one I described above would be great for a solo operator who could run it on the side while working a full-time job, or simply operate it as a high-margin lifestyle business.

Now that we’ve covered the three main ways a tech startup can grow, and what kind of businesses VCs are interested in, let’s answer the question, “Should I raise VC money for my tech startup?”

The decision tree below should help you get to your decision.

The three most important lessons I learnt from failing to raise VC money

During the 2.5 years working on Bmark I approached over 70 angel investors and VCs and failed to raise any funding.

Anyone who has tried to raise funding for a startup will tell you how tough it is.

Jeff Bezoz famously said that raising the seed round for Amazon was “the hardest thing I’ve ever done”.

From all the rejection I experienced trying to raise a seed round for Bmark, I managed to learn three important lessons.

Lesson #1 - Risk is an important factor for VCs and shouldn’t be ignored by founders

VCs see thousands of opportunities every year but can only make a few bets per fund.

One of the most important factors that go into VCs calculus is risk.

When I was trying to raise funding for Bmark I was a solo founder with no prior startup experience. I had no traction, a half backed product and no revenue.

To say Bmark was a risky bet would be the understatement of the year.

As a startup founder, it’s your job to acknowledge the risk factors in your business and work hard to mitigate them.

Showing potential VCs that you are capable of hitting milestones, building a product loved by users around the world, and generating revenue helps to lower risk.

Lesson #2 - VCs prefer startups with multiple founders

Let’s look again at the list of 50 publicly traded tech companies.

If you look closely you’ll notice only 6 of the 50 (12%) have a solo founder, the most notible being Amazon.

As I previously mentioned, it takes many years to get a tech company to a $1B valuation and even longer to an exit. There are a lot of ups and downs during this time and it can be very tough for a solo founder.

To help lower the risk, VCs prefer to invest in startups with multiple founders.

VCs aren’t the only ones who prefer to invest in multi-founder startups.

The table below shows the top 50 Y Combinator startups ranked by exit amount or estimated valuation. Only 2 in the list have a solo founder.

So what does this mean for you as a solo startup founder? The data is clear but I think it is a very personal decision and it comes down to what you want and how you play with others.

Lesson #3 - Raise a pre-seed from angels, then go after VCs

If you have decided to raise capital for your startup, you’ll find it far easier to raise your initial investment from angel investors instead of VCs.

This is especially true if you decide to raise a relatively small seed round (<$1M). You could even call it a “pre-seed” round.

You could then use that capital to launch your product, gain some traction, and maybe even generate some revenue.

This will help lower perceived risk in the business and better position you to raise a significant round of funding in your next round from VCs.

I’m a fan of this approach since it allows the founder/s to move the business forward, experience having capital to work with, and operate with some financial constraints (since it’s only a small pre-seed round).

The business gets a taste for the “outside capital hamster wheel” without having to spend 6 - 12 months chasing VC money (raising VC money is typically a 3 - 9 month process).

A lot has changed in the VC space in the last ten years and there are far more funds interested in investing in seed rounds of far riskier startups. I would target these VCs along with any relevant angels for your “pre-seed” round.

Thanks for reading and have a great week.

Justin

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Hey friends,

In this week’s guide I’m going to be sharing my business intelligence blueprint for early-stage SaaS founders.

This blueprint is designed to help SaaS founders go from zero analytics and “gut-feel” decision making, to being able to answer key business questions quickly and accurately using their data.

In this post I’m going to be covering the following topics in detail:

• The correct mindset early-stage founders need to have when it comes to BI.

• The 6-step game plan to go from zero analytics to data-driven decision making.

• The key business questions you need to answer using your data.

• My recommended data stack for early stage SaaS businesses.

• The importance of the “core funnel”.

• Why you don’t need fancy, live dashboards and the report you DO need.

• Who should own the analytics before and after you raise a Series-A.

• And more.

Let’s jump in.

The first thing to get right is the mindset

In the early days of a SaaS business its all about survival while trying to reach product-market fit.

A SaaS business’s overall approach to business intelligence, or BI for short, needs to fit this reality.

The correct mindset for early stage founders to have when it comes to analytics, tracking, data collection and all the other areas of focus we could umbrella under BI is “lean, cheap and focused”.

Lean = A small number of tools, no dedicated human resources.

Cheap = Low cost.

Focused = The analytics serves the primary goals of the company. Only build what is needed. Focus on questions that need answering rather than metrics.

The game plan

Now that we’ve got the mindset down, it’s time to map out a game plan for using data to help us achieve our goals.

When it comes to setting up analytics for an early stage SaaS, the game plan can be summarized as follows:

“We need to take the steps needed so that we can quickly and accurately answer the most critical questions on our path towards the next step in our journey, improving our product-market fit and raising a Series-A round of funding.”

It all comes down to being able to answer key business questions.

In order to be in a position to answer these key questions, we need to work backwards from this end goal.

The game plan has the following steps:

1. Mapping out the key business questions

2. Implementing a modern data stack

3. Filling in the data gaps

4. Preparing the data warehouse

5. Build the core funnel report

6. Answer the key business questions

Game plan step #1: Mapping out the key business questions

There is a long list of questions that SaaS founders would love to be able to answer using their data but in the early days it’s critical to be focused.

Below is a list of the most important questions that SaaS founders need to be able to answer using their data.

These 4 questions will take up the majority of the business’s entire focus as it scales towards Series-A and beyond.

Key question #1: Do we have a healthy top-of-funnel?

In order to answer questions using data, we first need data. This is why this is the first question on our list.

Without a healthy top-of-funnel AKA enough weekly signups to our app, we won’t collect enough data and be able to answer the rest of our key questions.

So what would be considered a healthy top-of-funnel?

There’s no clear answer to this question but we can say the following with confidence:

• A dozen signups a week isn’t enough.

• A thousand signups a week is more than we need.

The correct answer for your business is somewhere in the middle of these two.

Ideally you’re signing up a few dozen to a few hundred signups every week.

That would give you enough “through-put in the system” to conduct cohort-based analysis with reasonably high statistical significance.

Key question #2: Do we have product-market fit?

Product-market fit is a complex topic that most founders don’t truly understand.

This topic was tackled in a very interesting way by Rand Fishkin in his post titled, Product-Market Fit is a Broken Concept. There’s a Better Way.

Rand argues that instead of looking at PMF as a binary yes, or no, it should be looked at on a spectrum.

So instead of asking, “do we have product-market fit”, instead a founder should ask, “how much product-market fit do we have among our user-base?”.

Another great resource on this topic is the video below by Rob Walling.

Key question #3: Where are the leaks in our core funnel?

To simplify things dramatically, I recommend SaaS founders focus on mapping and measuring their “core funnel”.

In the video below I introduce this topic and cover the steps in the core funnel.

SaaS founders need to know their “core funnel” numbers like the back of their hand and obsess over optimizing it, especially the initial steps of the funnel.

The core funnel will help you identify where you have “leaks” and where to focus your attention.

Key question #4: How are users actually getting value from our product?

Did you know Instagram didn’t start as a photo sharing app?

Instagram began as a location-based check-in app called Burbn. Only after analyzing what users were doing with the app did the founders, Kevin Systrom and Mike Krieger pivot and go all-in on photo sharing.

Only after analyzing usage and talking with users will you get the complete picture of how users see, and get value from your app.

This question is critical to answer in order to scale the business. You may have a number of assumptions on how the market is using and getting value from your app, and you might be completely wrong.

This will lead to bad decisions that could end up killing your company.

So now that you have mapped out your key questions, lets move onto the next step in the game plan, implementing a modern data stack.

Game plan step #2: Implement a modern data stack

My recommended data stack for early-stage SaaS is made up of the following tools:

1. ETL - Fivetran

2. Data warehouse - Google BigQuery

3. Data modeling - dbt

4. Code repository - Github

5. Data visualization - Mixpanel and Google Data Studio (now named Looker Studio)

6. Event tracking - Segment

This stack aligns with your BI mindset, it’s lean, cheap and focused.

It’s also made up of best-in-class solutions which will scale well with your business as your data-needs grow.

This data stack allows you to leverage Mixpanel’s useful out-of-the-box reporting, while at the same time giving you the flexibility to build your own custom analyses and reports.

Let’s go through each piece of the stack.

The ETL solution - Fivetran

The first major piece of the data stack is our ETL solution, Fivetran.

ETL stands for extract, transform, load. The purpose of the ETL solution is to move data from silos, such as your application database, to a centralized data warehouse.

You want to put all you important data in the data warehouse so you can more easily blend data sources, and work efficiently with the data.

The two main data sources you want to centralize are the application database (information saved on the users that sign up to the service) and the subscription management solution.

Sources such as advertising platforms, email marketing solution etc aren’t a focus at this stage and can be analyzed manually in excel if needed.

Fivetran is a best-in-class ETL solution that connects with hundreds of data sources, including all the popular databases used to build SaaS applications.

The main issue with Fivetran is its pricing. At scale Fivetran is expensive.

The reason I still recommend it is because of its reliability, large number of supported sources and destinations, and world-class support. It also has a generous free plan which includes moving 500k rows of data per month at no cost.

Using an out-of-the-box ETL solution is far quicker than the alternative approach, building data pipelines yourself.

To keep costs down, only connect relevant tables / collections which you know will be used for analysis.

The data warehouse - Google BigQuery

I’m a big fan of Google BigQuery and have been using it for many years to support my consulting clients.

It takes just a few minutes to set up Google Cloud Platform where Google BigQuery is one of dozens of services.

I recommend Google BigQuery for the following reasons:

• It’s very affordable.

• Auto-scaling (no need for ongoing maintenance, dev ops).

• Very well supported in the market.

• Easy to set up.

• Useful UI for users to work with the warehouse.

• Multiple individuals can work with it in parallel without performance issues.

Data modeling - dbt

After Fivetran has loaded all the relevant business data into the data warehouse, you will want to turn that raw data into useful data models.

dbt is the most popular data modeling solution on the market.

It allows engineers and analysts that know SQL to quickly and easily turn raw data in the data warehouse into production-ready tables for reporting and analysis.

There is a bit of a learning curvey for dbt but a junior engineer or mid-level analyst with SQL experience should be able to master the initial data models with 15 - 30 hours of focused experience using dbt.

There are some great online courses and free YouTube tutorials that can be used to learn the basics of dbt.

Code repository - Github

Since dbt is a code-driven data modeling solution, it requires a place to save the code.

Github is the most popular code repository and version control solution on the market. It’s very affordable and your R&D team are probably already using it.

Each individual responsible for writing code in dbt will need access to the dbt Github repository.

Data visualization - Mixpanel & Google Data Studio (Looker Studio)

Mixpanel and Looker Studio are all you need in the early days for data visualization.

Mixpanel provides out-of-the-box reports on product usage, user engagement, and even retention by leveraging the event data you will send to Segment.

Mixpanel’s generous free plan makes it a very popular choice for early stage SaaS and there’s no reason not to include it in the stack.

Looker Studio can be connected to BigQuery and used to build reports. This will save a lot of time and make data and insights more accessible to more people on your team.

Both Mixpanel and Looker Studio are relatively easy to work with. If you need additional help, you can find a data visualization expert who knows these tools well on Upwork.

Event Tracking - Segment

The single biggest data gap that 90% of early-stage B2B SaaS have is user events.

Segment is the tool you will use to close this gap.

Segment is a robust product that serves multiple purposes. It’s considered a CDP (customer data platform).

To keep things simple and focused in this early stage, Segment should be seen solely as an event tracking solution.

Your developers will write code that will send data to Segment’s API and Segment will know how to take that data and send it through to 3rd party applications such as Mixpanel and your BigQuery data warehouse.

You will start by implementing 5 - 10 core events and scale this number over time.

How much will this data stack cost to run?

Below is a rough estimate of the costs to run my recommended data stack for a B2B SaaS that has a 500 - 1k monthly active users. Costs will be higher for B2C SaaS which typically have far more daily signups than B2B SaaS.

• Fivetran - $0 to $500 a month

• BigQuery - $0 to $50 a month

• dbt - $100 a month (1 developer license)

• Github - $8 a month (2 user licenses on the team plan)

• Mixpanel - $0

• Data Studio - $0

• Segment - $0 (after 1k monthly active, will need to move to the business plan which is $120 a month)

So in total, the estimated price to run my recommended data stack is between $108 a month and $658 a month.

The variables which dictate how the total costs of the data stack scale are:

• The average number of daily signups. More users create more data.

• Which tables / collections you choose to sync to the data warehouse.

• The frequency you decide to sync the data in Fivetran (I recommend no more than once a day in the early days).

• The number of reports, and the frequency they are viewed in Google Data Studio.

• The number of individuals, and the nature of their work in BigQuery. Two analysts running heavy queries will cost the business more money than one senior analyst running optimized queries.

• The number of custom data models you’ve developed in dbt.

• The frequency that dbt runs to build the custom models.

• The overall volume of data stored in the data warehouse.

There are a lot of factors that go into setting up, optimizing and scaling a modern data stack.

The truth is that much of it isn’t important in the early days as the company works towards improving product-market fit.

Implementing the data stack - The check-list

There is a long list of tasks that need to be completed to implement the data stack shared above.

I’ve created a check-list which you can view here.

Game plan step #3: Filling in the data gaps

Now that your data stack is in place it’s time for R&D to get to work filling in the data gaps.

From my experience working with many SaaS businesses, the most common data gap is the lack of user-based event data.

To fill in this gap, one or more developers will need to write code that will send events to Segment so you can start tracking specific user actions.

This data can then be used to understand a) what users are doing, and b) if they are receiving value from the application.

To do this correctly you need to build a tracking plan that covers your core events.

Your tracking plan should list the following:

• Event name - The name of the event.

• Event properties - The properties you want to send with each event.

• Trigger criteria - When exactly the event should be triggered.

Start with no more than 5 - 10 events and get them live in production ASAP so you can start collecting this important data.

Below is a list of the events you want to include in your initial batch. These events will become critical for building your core funnel report:

• Signed Up —> When the user signed up to the application. We can track this “event” in the database but its still best practice to include it in the initial list of events.
  

• Onboarding Completed —> If you have a complex onboarding process it’s a good idea to include an event which indicates that the user got to the end of that process. We can use this event to determine the percentage of users that completed the onboarding. If you don’t have an onboarding phase, you can skip this event.
  

• Wow moment reached → This one is tricky and will be different for every SaaS business. It’s an event which indicates that the user experienced value from the app. It’s the moment when the user would say, “wow, this is cool”. This event could also be the moment a key feature is used.
  

• Subscription Created —> The moment a user upgrades from a free user to a paid user. If you opt free users into a paid plan without requiring a credit card, then I recommend adding an additional event, Payment Method Added, or something along those lines. There needs to be some friction in this step. This event will be used to see the percentage of users that are ready to pay for your software.
  

• Charge Processed Successfully → The moment the paid subscriber is actually charged. It’s not enough for us to just track when users create subscriptions. What we really care about are paying users. This event also allows us to track recurring payments which we can use for analyzing revenue retention. This event is also important if you have a free trial. We know the trial ended successfully when we receive a charge.
  

• Subscription Canceled —> The moment a user cancels their subscription. This is the end of the user’s lifecycle. It is important to track this event for measuring and analyzing churn.

Once R&D have finished programming these events and they are being sent to Segment, we can move onto the next step, preparing the data warehouse.

Game plan step #4: Preparing the data warehouse

At this point you should be sending a number of useful events to Segment. These events should be “forwarded” to 2 destinations, Mixpanel and your data warehouse, BigQuery.

The next step in the process is to take the raw data you have in your data warehouse and develop useful data models which can be used for answering your key business questions. You will use dbt to create these models.

To be clear, at this point your goal isn’t to develop a fully functioning data warehouse which can support multiple departments and stakeholders. You are focusing on building the underlying infrastructure to answer the key business questions you mapped out in step #1 in the game plan process.

The good news is that you only need 1 custom-built, production-ready data model to answer 3 out of 4 of the key business questions. You can call this model, the “core funnel” model.

The core funnel data model should have the following fields:

• user_id: The internal id of the user in your database.

• created_at: The timestamp when the user was created. This field will be used for segmenting users into cohorts.

• onboarding_completed_at: The timestamp of the onboarding completed at event.

• first_wow_moment_reached_at: The timestamp of the first time the user experiences your wow moment.

• first_subscription_created_at: The timestamp of the first subscription created by the user.

• first_successful_charge_processed_at: The timestamp of the first successful transaction belonging to the user.

• first_subscription_canceled_at: The timestamp of the first time the user canceled their subscription.

You can also add any useful descriptive fields you want to the model to allow you to filter / segment the data. An example might be company size (assuming you collected that field during the onboarding phase).

In order to build this table you’ll need to start with the users table and left join certain events from the Segment events table, and from your subscription management solution tables.

Notice your core funnel data model is made up of data from 3 sources, your application database (I’ve used a MongoDB in my example), Segment, and your subscription management solution (I’ve used Stripe in my example).

Game plan step #5: Build your core funnel report

Once you’ve developed the “core funnel” data model in your data warehouse, you will be ready to build out a core funnel report.

In the video below I show the report in a Tableau dashboard but you could build the same report in Google Data Studio or even with some manual work in Excel.

When building your core funnel report, you will want to do a distinct count of users in each step in the funnel.

To achieve this, you may need to use IF/ELSE logic wherever you are building the report.

For example, to count the users that completed your onboarding flow, you might need to us “count distinct (IF onboarding_completed_at IS NOT NULL THEN user_id ELSE NULL)”.

This logic would return the user_id of the user if the user has a value for when he completed onboarding. You’d then do a distinct count of these user_ids to know how many users got to that step in the funnel.

The reason I’m a fan of the core funnel report is that it can be used to answer 3 out of the 4 key business questions.

• Do we have a healthy top-of-funnel? ✔️

• Do we have product-market fit? ✔️

• Where are the leaks in our core funnel? ✔️

• How are users actually getting value from our product? ❌

Let’s see how.

Game plan step #6: Answer the key business questions

The final step in the game plan is to answer the key business questions.

Let’s go through each question.

Key question #1: Do we have a healthy top-of-funnel?

You can answer this question by looking at the number of signups in each weekly cohort in your core funnel report.

The smaller the number, the harder its going to be to judge the performance of the cohort, and if the product is succeeding in moving users down the funnel.

Ideally you have a few hundred signups each week consistently so you can consider this question answered, and you can move onto the next question.

Key question #2: Do we have product-market fit?

Remember, this question doesn’t have a binary answer.

What you hope to see is a percentage of your users becoming paid subscribers, even if its a very low percentage (1%).

This is a positive sign that you have some level of product-market fit (“some percentage of people that experience our product are willing to pay for it”.)

You can then isolate those paying users and try and see what makes them different. You may find that the 1% that pay you are very different than the other 99% that have signed up.

Key question #3: Where are the leaks in our core funnel?

As long as you are showing the conversion rate between each step in the funnel, you’ll be able to quickly see where there are “leaks” in your core funnel.

There are certain “leaks” which can to be expected.

Most SaaS have a relatively low conversion to paid so expect a large percentage of users to never become paid subscribers.

You can also expect 10 - 30% of users to never complete the onboarding phase, depending on the nature of your app, the quality of your onboarding experience, and the market you are targeting.

You will also most likely have a high churn rate (% of subscribers that cancel) in the beginning of your journey as you work on improving your product and provide more value to users.

Key question #4: How are users actually getting value from our product?

Answering this question will take some work since it can’t be answered using the core funnel report.

The way you answer this question is by analyzing how users use your product, and by talking with them.

You should aim to talk with as many users you can to find out how users perceive, use, and describe your product.

Look for patterns in the answers users provide and come with no expectations. Have an open mind.

You can use Mixpanel to answer basic usage, retention and activation questions and to see which events users are triggering and when. This can lead to some interesting questions you can ask specific users when you talk with them.

Tools such as Intercom, and Customer.io can be used to automate data collection and setting up video calls with targeted users. Intercom + Calendly is a powerful combination.

Final thoughts

Congratulations, you’ve implemented a modern data stack that has helped you answer some key business questions.

You can now scale the infrastructure to answer more questions and use your data to guide your roadmaps. You’ll also be much better prepared to answer questions sent your way from VCs when you start your next funding round.

Before I end this guide I want to answer some lingering questions you might have at this point.

Who exactly in the organization should implement the game plan you shared aboved? Who should be responsible after we raise our Series-A?

I believe the CEO or COO should be responsible for business intelligence in a SaaS business, both before and after the business raises a Series-A round.

Before the Series-A, when the team is small, the CEO or COO may need to do the implementation (setting up the tools, working with R&D to send events etc) him or herself since there isn’t anyone else that can do it.

After the Series-A round has been raised, its a good time to either hire a head of data, or delegate the responsibilities to someone competent on the team.

What if we are pre-revenue? How can we use our data to understand if we have product-market fit?

If your product is completely free (not something I’d recommend by the way) and you want to know if you have some form of product-market fit then you need to look more closely at retention.

The core funnel report should still be built so you can see “leaks” in your core funnel, but what you want to look for in addition to a healthy funnel, is high, repeat usage.

The best proxy for product-market fit if you don’t have paid memberships, is high usage. The assumption is that if you see high usage among a segment of your users, at least some of those users would pay you to be able to continue using the product.

My advice would be to launch with a paid plan on day 1.

What about Google analytics and tracking website traffic?

I think its a good idea to keep an eye on website traffic since this does connect with one of the key business questions, “do we have a healthy top-of-funnel?”.

Without traffic you won’t have signups.

Unfortunately Google Analytics 4 is a disaster and quite difficult to use effectively. A good alternative I can recommend is Plausible. It’s a simple web traffic reporting and analysis tool that “just works”.

I run an enterprise B2B SaaS, is this guide still relevant for my business?

There are parts of this guide which are definitely relevant for enterprise B2B SaaS, for example the sections covering my recommended data stack.

The main challenge early-stage enterprise B2B SaaS will have when it comes to BI is the lack of data.

Since the number of accounts signing up will be very small, there’s no way to get statistical significance on key metric.

A lot more “gut-feel” and qualitative data is leveraged to gauge if the business is on the right track.

The game is more about getting high usage and adoption among the businesses that have engaged with the SaaS, rather than incremental gains across a large sample size of accounts.

I have money in the bank and would prefer to pay an expert to help us implement a modern data stack and answer our key business questions instead of doing it ourselves. Do you have someone you can recommend to help us?

Yes I do. I’m close with a number of SaaS-focused BI service providers that could help you out.

Reach out to me in a DM either on LinkedIn, X or on Substack and I’ll see how I can help.

Thanks for reading this weeks post.

If you’re finding this newsletter valuable, share it with a friend, and consider subscribing if you haven’t already.

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Best,

Justin

Churn rate is the single most important metric for determining the growth trajectory of a SaaS business.

A business that has a high churn rate will struggle to grow, no matter how good the company is at marketing it’s products or services.

Imagine trying to fill a bucket with holes. A SaaS business with a high churn rate is fighting the same type of battle.

How to conduct a churn analysis?

Before you jump into your churn analysis you need to do some initial planning.

Step #1 - Defining churn

The first step in conducting a churn analysis is to define churn within your organization.

Churn can mean different things to different people so it’s critical that you come up with specific definitions and then add these to your data dictionary.

My suggestion would be to start with the following terms:

Subscriber / Logo Churn

Defined as the count or percentage of individuals or businesses that stop paying you within a given cohort.

B2C companies should use the term subscriber churn, B2B companies should use the term logo churn (logos as in company logos).

Revenue Churn

Defined as the absolute value, or percentage of the total revenue that is lost as a result of subscribers or logos cancelling or downgrading their subscriptions.

Step #2 - Planning the churn analysis

Now that you’ve defined churn you can start planning your analysis.

The first decision you need to make is to decide which question or questions you want to try to answer in your analysis.

Below are some examples of the types of questions you may want to tackle in your churn analysis:

• What is my subscriber / logo churn rate?

• How many of my subscribers are still paying me after 6 months?

• Do users that signed up to our quarterly plan churn at a higher, or lower rate, than users that signed up to our monthly plan?

• Do subscribers from non-English speaking countries churn at a higher rate than subscribers from English speaking countries?

• Which of my account managers are doing the best job of lowering logo churn?

For my example we are going to start with the basics.

I’m going to show you how to create a visualization that will help us analyze our subscriber / logo and revenue churn. Once we have the high level picture we can start slicing and dicing our data to answer more granular questions.

To simplify things a bit I’m going to make the following assumptions:

• We are a B2B SaaS business that sells monthly subscriptions only.

• There is only one plan which costs $50 a month.

• The company only works with one currency, US dollars.

• The company started offering subscriptions from the 1st January 2019.

Don’t worry, I’m still going to show you how to tackle quarterly or annual subscriptions. You’ll also soon see that the methodology I share below can be used to calculate churn for a B2C business.

Step #3 - Prepare your subscription data

This step in the process is by far the most tricky. If you don’t have any experience with SQL or at the very least with Excel, you are going to struggle.

The data set you need to build in order to conduct a churn analysis needs to have the following structure:

Each row in our data set needs to represent a successful transaction.

You want to have the following columns in your data set:

• Id of the transaction (typically transaction or charge Id)

• The timestamp of the transaction

• The value of the transaction (if you work with multiple currencies you will need to pick one and normalize your values to that one currency)

• The internal id of the individual / business who paid you (typically user Id or account Id)

• The date when the individual or business first became a subscriber

• The number of months between 1st subscription creation date, and the transaction (”datediff”).

Your data set needs to look like the dummy data set shown below:

Step #4 - Build your cohort tables and retention curve

Once you’ve collected all the relevant data and structured it as I’ve shown above, you can easily pivot the data it into the following cohort table.

In the screenshot above you have a cohort table showing the number “logos” that become subscribers in a given month (cohort), and how many paid you over a 6 month period. Column zero is your starting point. It contains all the subscribers that started in that month.

This view is helpful but a better way to view this data is by looking at each cell in each row as a percentage of total. When you do that you get the following table.

By adding conditional formatting to the table, we can now clearly see where we stand in terms of logo retention and churn.

We can see that we are getting worse at retaining our accounts since there is more red in the bottom right hand corner of the table. We used to retain 75%+ in month 6 (column 5) but now are under 70%.

In my example I only showed 6 month retention but ideally you want to expand that out as far as your data allows so you can see as full of a picture as possible.

The next step in the process is to calculate your average retention rate and create a retention curve graph.

Based on my dummy data, my 6-month retention curve would look like the graphic below.

The main take away from this graph is that after 6 months we’ve lost on average a quarter of our subscribers.

If it is easier for you to see the churn curve, which is the inverse of retention, then simply calculate 1 minus the retention rate for each period.

In the case of a business which offers quarterly or annual subscription options, the approach is exactly the same. The only differences are:

• Longer lag time - More time needs to pass in order to have enough data points. A subscriber who pays quarterly only has 4 data points in a 12 month period.

• When preparing your data you need to add an extra field which stipulates that a user is on a monthly, quarterly or annual subscription. Build tables and line graphs for each subscription type, or add a filter which makes it easy to view one category at a time. The tables will look different for each category unless you add additional logic so that the count of each payment is the same for all categories.

Step #5 - Start answering your churn-related questions

The visualizations you’ve created at this point will give you a high-level understanding of your retention and churn rates.

You might be in shock at these numbers but at least you now know where you stand.

Having a benchmark for your churn rate is the first step in improving your churn rate.

In the example above I calculated logo churn. You can repeat the process for revenue by summing up the total amount generated from transactions by subscribers in each cohort. Since everyone paid in column zero, this will be your starting amount.

As more subscribers cancel, you collect less money from each cohort over time.

If you want to calculate the churn rate of specific subscriber segments, like US companies vs. rest of world, or the performance of certain account managers, then you’ll need to enrich your initial data to include these additional dimensions.

Now instead of pivoting with subscription creation month as your grouping, you can group by whichever dimension you care about. You could even group by 2 or more dimensions to see if churn rates have improved for certain segments over time.

Your business needs a churn dashboard

I highly recommend that you invest in an automated reporting solution such as Tableau, PowerBI, or event Google Data Studio so you can quickly view your retention and churn metrics.

Churn is such an important metric for the business that I believe every decision maker in the company should have access. The best way to do this is by implementing a cloud-based reporting solution.

There are of course a number of considerations involved in adopting a sophisticated reporting solution but thankfully the costs, learning curves and access to skilled human resources in data visualization is improving all the time.

The process of building a churn dashboard is very similar to the process I covered in this blog post but you’ll need to either invest in learning a reporting tool, or hire someone that can set up the dashboard for you.

How to reduce churn

Congrats, you now know your churn rate and have a basic dashboard in place that can help you keep tabs on this critical metric.

You’ve rounded up everyone in the company and set a goal of reducing churn by 20% over the next 6 months. You’re excited and everyone is with you but you don’t have a clue where to start.

Don’t worry, the rest of this blog post is dedicated to helping you build out a comprehensive action plan for reducing churn.

Assume you know nothing

This might be tough to accept but trust me, you’ll be better off assuming you know nothing about your subscribers, especially why they cancel.

In order to start understanding your subscribers you need to start collecting qualitative information from them. You can do this in a number of ways.

Below are just some ideas:

• Set up a cancellation survey.

• Send out surveys to users when they reach certain milestones in their user journey (they will be more likely to respond at this point).

• As the founder, you should commit to chatting with 5 customers a week over Skype / Zoom. Use Calendly and add a widget within your app to make it easy for subscribers to schedule time to chat with you.

• Meet customers face-to-face when visiting a new city on business.

• Add a step in your onboarding process in which your account managers ask strategic questions to your subscribers.

• Add more questions to your “did support do a good job” survey.

• Launch a beta program so high engaging customers can opt in to try new features. When new features are released get your product managers to get on the phone and survey these engaged customers. Expand the questioning beyond the scope of the product.

Collect all this information in Google Sheets and block off time to go through it. The more data you collect the more trends you’ll identify.

Focus on getting to the “wow” moment

Most great products have a “wow” moment, that moment when the user sees the value you offer with their own eyes. It’s a moment of enjoyment and one to be remembered for the user.

You have to understand what that wow factor is for your product or service and help users get to that point quickly.

If your business is self service then this has to happen seamlessly within the product. In the case of a more complex platform the responsibility shifts to your customer success team.

Your account managers (also often called success managers), need to understand why the business signed a contract with you and what value they expect from your product or service. If they don’t you probably have a poor process and a disconnect between sales and customer success.

If the account manager knows what value the subscriber expects then it is the main responsibility of the AM to help the subscriber receive that value as quickly as possible. This typically takes the shape of an onboarding call, and subsequent follow ups.

There are some amazing platforms out there like Gainsight and Totango which are designed to help account managers manage these responsibilities. If you are an operations-focused company and have the budget I’d highly recommend adopting one of these solutions.

Invest in a retention team

Many companies have a team dedicated to reducing churn. Members of these teams are somewhere between success managers and sales reps.

There primary responsibility is to actively reduce churn and ideally even help the company achieve negative net churn (make more from existing users than you lose).

The retention team can also help you collect useful information on why subscribers are cancelling and feed this information back to other departments.

Understand the competitive landscape and adapt as needed

One of the biggest drivers of churn are cheaper or better alternatives.

Think about this in your own life. You might go every week to the same hamburger joint until you find that a better one has opened up just around the corner.

If you’re doing a good job in your market others will pay attention and competition will grow. If these competitors start offering a cheaper and/or better solution then your customers will jump ship.

In order to counter this you need to constantly be improving your solution and keep an eye on the market landscape.

Some businesses hire a VP of Strategy who is responsible for surveying and analyzing the market landscape in order to protect the company against changes in the market. If you aren’t at that point yet then focus on building out a great offering and outwork your competitors.

There are many ways to create a competitive advantage and they don’t all involve massive capital or other rare resources.

Invest in business operations and become proactive instead of reactive

One of the biggest impacts you can make to your business is a shift in mindset from a company that is reactive, to one that is proactive.

To reach that point a lot of variables have to be molded into place so you have a well-oiled machine that operates efficiently.

One of the first steps to get to that point is to invest in business operations, in particular business intelligence. My BI competency status framework is a good place to start.

Once you’re organization is collecting operational data which covers the entire customer life cycle, and can make that data accessible to your customer success teams, you are ready to make the shift.

I previously mentioned Gainsight as a great solution to help you become more proactive. Tableau can also be used to provide self-service business intelligence capabilities to your account managers and support staff.

Once the infrastructure is in place the next step is to adapt your internal processes to leverage the new infrastructure.

Account managers should be trained to use the data available to them to proactively help users receive more value and have an overall better experience.

Each AM should have time blocked off each week to go through their accounts and identify ways that these accounts can get more value.

Below are some examples:

• The AM notices that one of his accounts opened a support ticket a week ago and no one has responded. The AM can forward the ticket to a senior support rep so it gets taken care of immediately.

• The AM notices that one of his accounts stopped using a key feature. The AM can reach out and ask why the account stopped using the feature and forward the feedback to a relevant product manager.

• The AM notices that one of his accounts has invited a new team member to their account. The AM can reach out and introduce himself to the new contact and offer to conduct a demo to the new contact.

• The AM notices that one of his accounts has never used a certain feature. After some investigation it looks like the subscriber would really benefit from that feature. The AM can now write a detailed email to the main point of contact explaining how the feature would benefit the subscriber’s business.

If you made it this far, thanks for reading and if you have any questions on churn then please comment below and I’ll happily help you out.

In this post I’ve going to share with you a product analytics framework I’ve developed called the feature adoption funnel. The main purpose of this framework is to help the company as a whole understand feature adoption.

What is the feature adoption funnel?

The feature adoption funnel is a 4 step funnel which can be used to measure every feature in a product. The feature adoption funnel is a high-level way to understand the most basic usage metrics of your features.

Below is a basic representation of the feature adoption funnel.

Step #1 - Exposed

The first step in the feature adoption funnel is “exposed”. In this step we want to measure the percentage of our users which were exposed to the feature. The idea is that we can’t necessarily blame the feature itself for poor adoption. We need to know how many people could have activated the feature and started using it.

The easiest way to measure exposure is to measure the percentage of users which viewed the page where the feature is presented. You want a yes / no indication.

So lets say for example you have 1,000 users that signed up in September. Out of those 1,000, 450 of them landed on feature X’s page. This would mean an exposure rate of 45%.

Step #2 - Activated

The second step in the funnel is “activated”. In this step we want to measure the percentage of users which actually activated the feature.

Some features don’t require activation, like for example importing a list of emails into Mailchimp. If the feature you’re measuring doesn’t require the user to perform any action to activate the feature then you can skip this step entirely.

The best way to measure activation rate of your features is by using an event-based tracking system like Segment. Most startups would simply look in their database if the feature is enabled but this an incorrect approach since features can be disabled. You need to create a historical record when it comes to enabling and disabling features and Segment can help you do this.

If you are saving a historical record of feature activation then you’ll want to look for the first instance where a user enabled the feature. Just like in step 1 you want a yes / no indication per user.

So lets say that out of the 450 users which saw the feature, 300 of them activated the feature. We can now say that your exposure to activated rate is 67% (300 / 450) and your activation rate for the September cohort is 30% (300 / 1000).

Step #3 - Used

In the third step of the funnel we want to measure the percentage of users which actually used the feature. Just so it’s clear we’re talking about used the feature at least once. None of the steps in this funnel have anything to do with time. We are simply measuring the percentage of users which did each of the steps ever.

How a feature is used is very different from feature to feature. Some features like adding a bio to a profile for example are very straight forward while others like setting up an integration are much more complex. Like step 1 and 2, we simply want a yes / no indication per user. Yes if the user used the feature at least once, and no if the user never used the feature.

With some features simply activating them results in usage and the user isn’t required to do anything else. If this is the case with your feature then skip this step.

Out of the 300 users which activated your funnel, only 50 used it. We can now say that your activated to used rate is 16% and your overall used rate is only 5%.

Step #4 - Used again

The last step in the funnel is “used again”. In this step you are going to measure repeat usage and turn that into a yes / no indicator per user. The idea behind this step is to see if once someone uses a feature if they are likely to use it again. This is a very high-level indicator and you’ll want to dive much deeper into repeat usage as a separate analysis.

We see that out of the 50 users which used the feature at least once, 45 of them have used the feature more than once. This means our “used again” rate is 90% (45 / 50). Our overall “used again” rate is 4.5% (45 / 1000)

What can we learn from the feature adoption funnel?

The feature adoption funnel behaves just like any funnel when it comes to analyzing performance. We can analyze the funnel both from one step to the next, or from the start of the funnel.

If we analyze our example we can see the following:

From start of the funnel

• Exposed Rate - 45%

• Activated Rate - 30%

• Used Rate - 5%

• Used Again - 4.5%

From previous step of the funnel

• Exposed Rate - 45%

• Activated Rate - 67%

• Used Rate - 16%

• Used Again - 90%

We can clearly see that overall the adoption rate is very low (only 5%) for our hypothetical feature. What also stands out is that there is a big drop between activation and used, and a very high rate between used and used again.

These findings tell us that for some reason few users that activate the feature are using it but out of those that do use it most use it again. This indicates to me that there is either a bug or technical issue preventing users from using the feature, or that the process to use the feature is too complicated for most users.

Can you see how powerful the feature adoption funnel is for helping product managers understand where they should focus their attention to improve usage?

What else can I learn from the results of the feature adoption funnel?

Low exposure rates means that product marketing need to up their game

If you notice that you have very low exposure rates to your features it could indicate that either you have a bad product market fit (AKA low retention) or that product marketing needs to do more. Be more aggressive with your marketing automation and email tools to communicate the value your features provide users.

Low activation rates means poor messaging, bugs or unattractive features

If your exposure rates are fine but users simply aren’t turning on features it could mean a few different things. The most common will be poor messaging and unattractive features. Most products suffer from feature bloat and many features just don’t appeal to the majority of users.

Another culprit could be bugs which are preventing users from activating features. Make sure you periodically run through your product and make sure that everything is working as expected. QA should have automation tests set up and your analysts should also help catch significant bugs by keeping an eye on your numbers.

Low used again rates indicate that users did not get value from the feature

This one is quite obvious. If a user used a feature and didn’t get value from it the chances of him using it again are low. If you have poor used again rates then it might be time to scrap some features.

The feature adoption funnel is an optimization framework

One of the most powerful aspects of the feature adoption funnel is that it can be used to optimize your feature usage over time.

The screen shot below shows this perfectly.

All diagrams in this post were created using Lucidchart.

The feature adoption funnel should always be used in relation to cohorts. Product managers can use different cohorts to see if they are optimizing key usage KPIs over time. The framework keeps the product team focused on nailing the basic usage metrics first before getting caught up in the weeds.

Final thoughts on the feature adoption funnel

There are a ton of nuances that you will have to take into account when using the framework. For example, there are certain features which aren’t necessary relevant for all users. In this case you’ll want your cohorts to reflect that.

You might have a feature which is aimed at a specific geography. In this case it would be useful to see adoption by different segments of users by geography. Other common use cases include company size, number of users using the service, and lifetime value.

In the end of the day you should set realistic expectations when it comes to the results of your feature adoption funnels. It’s probably a good idea to filter out all the users which never successfully finish onboarding, and any other relevant filtering that is needed to reduce the noise. This will be especially helpful if you run a B2C company.