Welcome folks! 👋

This edition of The Product-Led Geek will take 8 minutes to read and you’ll learn:

• Why traditional single-metric cohorts are limited.

• 10 nuanced applications of cohort analysis to drive PLG decisions and impact.

Let’s dig in!

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How Nylas Drove 80% Increase In Free-To-Paid Conversions

Nylas discovered a secret to boost their free-to-paid conversions by an impressive 80%.

Their secret? A shift from time-based drip onboarding campaign to product activity based onboarding sequence.

Nylas made a strategic shift from a static drip campaign in Marketo to a dynamic, event-based onboarding approach with Inflection.io. In conjunction with better targeting and streamlined processes, this change helped drive an 80% increase in free-to-paid conversions.

Learn how Inflection.io helped Nylas to drive that growth in detail in this case study.

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Cohort Analysis in B2B PLG: 10 Actionable Applications

I used to be surprised about how frequently I’d come across B2B product and growth teams that use cohort analysis in a surprisingly limited way.

These days? Not so much.

With a few exceptions, in the companies I’ve joined, or worked with as an advisor, I've found that teams rarely go beyond tracking basic retention curves and growth metrics.

The trouble is, they're missing the more nuanced insights that could genuinely inform many of the decisions they make.

The real value of cohort analysis is in looking at data from diverse perspectives.

Slicing and dicing along different vectors.

Understanding precisely how different user and team behaviours translate into business outcomes.

When done properly, it should inform everything from product development to pricing decisions.

This guide outlines 10 practical applications I've found particularly useful in B2B PLG.

What is Cohort Analysis?

At its core, cohort analysis is a method of breaking down your user base into groups that share common characteristics or experiences.

Rather than looking at all users as one amorphous mass, you examine how specific groups behave over time.

The most common approach is time-based cohorts - grouping users by when they first signed up.

And theres value in doing that - primarily to understand how you're improving certain aspects of your product and experience over time.

But the real power comes from looking at the data from different angles.

Perhaps most powerfully - behavioural cohorts: groups defined by specific actions they've taken, features they've used, or ways they've implemented your product.

This gives you a significantly more nuanced perspective.

For example, instead of just knowing that 20% of users churn after 30 days, cohort analysis might reveal that:

• Users who connect 3+ data sources in their first week have 80% week 4 retention

• Teams that add 5+ members in month one are 3x more likely to upgrade

• Customers who start with API integration retain differently from those who start with the UI

This granularity transforms vague trends into actionable patterns.

So with that said, here are 10 ways to put cohort analysis to work in your B2B PLG strategy.

1. Decode Onboarding Patterns That Predict Success

Application: Move beyond simple funnel analysis to identify which early actions truly predict long-term success.

How: Create two parallel cohort analyses:

1. Track standard onboarding step completion

2. Compare against a separate cohort analysis of users grouped by their first-week behaviour patterns

Often, the quick wins in onboarding (like completing a profile) matter less than behavioural signals around the core product use cases (like connecting real data sources). Use cohort analysis to identify which early actions correlate most strongly with long-term retention, then (traffic permitting) run experiments to isolate causation, and redesign your onboarding to prioritise these actions.

2. Build a Multi-Signal Churn Early Warning System

Application: Move beyond simple engagement metrics to build a more nuanced understanding of churn signals

How: Create three types of cohort analyses:

1. Feature usage patterns (Which features do successful customers use vs. churned ones?)

2. Team engagement patterns (How many seats are active? How are they interacting?)

3. Time-based patterns (what times/days/frequency/duration do successful customers use the product?)

Different customer segments show different warning signs. Enterprise customers might show churn risk through declining team adoption, while SMBs might show it through irregular usage patterns. Build segment-specific warning systems.

Check out the post below for a deep dive on PQARC (Product Qualified Accounts at Risk of Churn):

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