How to unlock strategic growth using customer cohort analysis

SaaS businesses really love their metrics. It would be hard to find one that doesn't routinely monitor a least half a dozen SaaS metrics, and many track a lot more than that.

SaaS companies rely heavily on metrics to measure their business performance and financial health and to help them understand what is happening in their business. However, to understand the why—what exactly is driving the results for a given metric, they need to employ a customer cohort analysis.

Customer cohort analysis is a powerful yet often underused method that helps finance teams drill down into the SaaS metrics they’re already tracking to identify behavioral patterns, discover growth opportunities, and generate data-backed insights for smarter business decisions.

This article discusses what cohort analysis is, how it compares to churn analysis, and the key types of cohorts to track. It also provides a step-by-step guide for doing your own cohort analysis and how to apply cohort insights to optimize pricing, improve onboarding, and forecast revenue more accurately.

 What is customer cohort analysis?

Customer cohort analysis is a way to group customers by shared traits—such as, signing date, acquisition channel or process, subscription plan—and track how those groups behave over time. Instead of looking at performance in the aggregate, this approach analyzes the cohort-specific data for defined timelines to uncover patterns in customer behavior and engagement.

When done right, cohort analysis helps you explain not just what’s happening, but why. When you understand what’s driving customer behavior, you can leverage that information to drive growth.    

The best thing is, you’re probably already tracking most of the information you need for a cohort analysis, such as churn rate, monthly recurring revenue (MRR), net revenue retention (NRR), customer acquisition cost (CAC), and customer lifetime value (LTV). 

You can use cohort data to analyze the same metrics in greater detail. For example, you can do a cohort analysis with:

• Use LTV data to determine which cohorts generate the most long-term value.
• Use NRR data to assess whether certain segments are more likely to expand or churn.
• Use your churn rate to understand when customers drop off.

With this level of clarity, you can make more insightful and data-backed decisions—whether it’s refining GTM strategies, doubling down on “sticky” segments, or adjusting pricing to improve product-market fit.

Types of customer cohort analysis

One of the most common uses for cohort analysis in SaaS is to uncover what drives customer retention and expansion. The different types of cohort analysis are based on how the cohort is defined, which typically uses one of the three following approaches.  

Time-based cohorts 

To create time-based cohorts, customers are grouped according to when they signed up for products and services. This is usually defined as a timeframe, such as a particular month or quarter, or maybe a specific campaign, to get enough customers for a meaningful analysis. This format is especially useful for tracking how product updates, pricing changes, or seasonal promotions impact your customer behavior and engagement over time. 

For example, if 75% of the customers acquired during your Q1 promo are active in Q3, but only 50% of your Q2 signups are active in Q4, it could indicate mismatched messaging, unclear expectation setting, or a weaker onboarding in Q2.

SaaS companies often use time-based cohorts to analyze churn and monitor retention curves. It’s common to see churn spike early in the customer lifecycle, especially in the first 30 or 60 days, then stabilize over time. Cohorts defined by when they signed up are useful to better understand how each cohort behaves in its first few months, isolate the churn points, and take corrective action. It also gives you a real-time view into whether your retention efforts are improving and whether recent signups are more or less valuable than older cohorts.

Segment-based cohorts

Here, customers are grouped on the basis of their subscription plan/pricing tier, product type, or acquisition channel, offering a clear view into how different offerings or marketing tactics influence customer behavior and expectations from the product, ultimately impacting revenue.

For instance, if basic plan users stick around longer than premium users, it may imply that the basic plan is comprehensive enough to accommodate user requirements, while the premium plan isn’t delivering enough value. These insights help the sales and project teams redirect their efforts on the segments worth investing in, correct misaligned pricing and/or double down on the most effective channels. 

Size-based cohorts

With size-based cohorts, customers can be grouped in a couple of different ways. The most common way is to group customers based on how big they are. Note that size could be defined in any way that’s relevant to your business, such as number of employees, ARR, etc. For example, depending on the market sectors you serve, you could define cohorts as small businesses (1-50 employees), mid-market companies (51-1000 employees), and enterprises (1,000+ employees). 

This approach not only reveals which customer segments truly drive sustainable growth for your business, but can also uncover issues with your  product. 

Let's say your data reveals that the small-and-medium businesses are generating high churn, while mid-market accounts are driving MRR growth with lower support costs. This may signal the need to refocus sales targets to the mid-market segment. This approach would align well with the common trend where smaller businesses and startups churn at a higher rate than mid-market and enterprise customers.

Size-based cohorts can also refer to the size of the customer’s initial or average investment in your SaaS product—commonly referred to as spend-based grouping. This is useful for identifying pricing tiers or deal sizes that generate the most long-term value, as well as revealing segments where churn or retention is concentrated. 

The insights derived from this cohort type enables SaaS business leaders to make smarter decisions around redirecting sales resources, structuring pricing tiers, and prioritizing product features.  

How to do a customer cohort analysis in 10 steps

Here is a step-by-step guide to run a cohort analysis to understand customer behavior and retention/churn trends in your SaaS business.

Step 1: Define the cohort criteria 

The first step is to determine how you wish to group your customer cohorts for analysis. Common criteria include signup date, acquisition channel (e.g., organic or paid) and subscription type (e.g., pricing tier or type of plan).

Step 2: Choose the relevant metrics 

It is important to focus on metrics that are relevant for your specific cohort analysis and aligned with your business priorities. Here are some examples:

• Struggling with churn? Track retention and feature adoption rates.
• Seeking growth levers? Monitor expansion revenue and upgrade paths.
• Worried about profitability? Measure support costs and margins by cohort.

Step 3: Select the right time periods 

If you’re an early-stage SaaS business with lower volume, analyzing your defined cohorts on a weekly, monthly, or quarterly basis (as needed) gives you quicker insights and feedback on what’s working. The key is to find a timeframe that’s long enough to spot meaningful trends, but not so drawn out that you miss opportunities to course-correct.

Step 4: Gather clean, complete data

If your data’s messy, your cohort trends will be too. When consolidating data from different sources and systems, it’s important to spend some time reviewing it (removing duplicates, checking for missing values, etc.) to ensure a more accurate analysis.

Step 5: Visualize results to check for patterns faster

Use charts and graphs to ensure that your cohort trends and variances are immediately obvious to all your stakeholders. For example, line graphs can be used to track retention over time or heat maps can help plot cohort performance by segment. 

Step 6: Track retention for each cohort over time

Let's say, if 100 users signed up in January and 60 are still active by the end of March, that’s 60% retention for Q1 for your customer cohort by sign-up date. Then go deeper on their spending, upgrading, or churning patterns? Analyzing both retention and revenue help distinguish between cohorts that are just hanging on and those which drive real value. 

Step 7: Compare cohorts to check for recurring patterns

Perhaps users from organic channels stick around longer than those from paid ads. Or, maybe Q2 signups always spend less. A comparative analysis of the different cohorts helps reveal what’s working and what’s not, enabling you to refine and optimize your and refine your sales and marketing efforts.

Step 8: Dig deeper into the cohorts that stand out (positive or negative) 

Breaking cohorts down even further by attributes like company size, industry, or time-to-activation often reveal patterns hiding just below the surface. That’s how you find your best-fit customers or uncover what’s causing others to churn. 

Step 9: Map events to outcomes 

If you changed onboarding in June, check if July and August cohorts stuck around longer. By layering product or marketing updates over cohort data, you turn analysis into a real feedback loop so you’re not just making changes, you’re proving what works. 

Step 10: Turn insights into action

Finally, use the insights to inform your strategy. If your premium plan consistently underperforms, revisit its value proposition. If early churn spikes, dig into onboarding flows. The goal isn’t just to report, but to use the cohort data to shape pricing, GTM, and product decisions that move key metrics forward.

Why should you do a customer cohort analysis for your SaaS business?

Customer cohort analysis helps SaaS businesses move from reactive reporting to proactive strategy. Let's look at some of the ways you can use a cohort analysis in your business.  

Identifying churn patterns

By tracking retention rates across cohorts, you can see where they typically churn in the customer lifecycle. 

This helps in identifying critical drop-off points while introducing solutions that address them.

Optimizing renewal strategy

If a certain group of customers keeps renewing month after month, that’s your blueprint for retention. 

Analyzing what those groups have in common can help you shape upsell paths and inform customer success strategy.

Evaluating pricing strategy

Cohort analysis helps review if your high-tier plans are actually delivering value or just pushing people to churn. 

Perhaps your lower-tier users are sticking around and expanding steadily. 

These insights are useful for refining your pricing to match what users are actually willing to pay for, instead of assuming what they want.

Reprioritizing or refining ICPs 

Over time, cohort analysis helps uncover which customer profiles, industries, or geographies are best served by your product. 

This allows you to prioritize segments that consistently generate revenue.

Designing targeted lifecycle engagement incentives

Map behavior trends to build stage-based incentives, like reactivation campaigns at risk points or expansion nudges during product engagement peaks.

Focusing on forecasting and product validation

Use historical cohort behavior to forecast revenue more accurately. 

Monitor how product changes impact cohort performance to validate whether a release actually improves retention or spend.

How Drivetrain helps finance teams operationalize cohort analysis 

Cohort analysis is only as effective as the systems you use to track and act on the insights it can provide. The problem is, cohort analysis can get pretty complex, pretty fast. 

This is because tracking a given metric for a single cohort is pretty straightforward, tracking it across several cohorts over time can be pretty tough, especially when you’re using spreadsheets to do it. (To get a sense of how complex that can be, check out our article on  using cohort analysis to track month-over-month net revenue retention (NRR).) 

 While it can be done, using spreadsheets for anything more than the most basic cohort analysis will make the entire process complex, time consuming, and highly prone to error. 

That’s why more and more SaaS finance teams are turning to strategic finance software, like Drivetrain, to track and analyze retention, revenue, and engagement across customer groups and streamline their overall financial processes.

With over 800 integrations, Drivetrain automates data consolidation from sources like CRMs, billing systems, and spreadsheets, into one platform. This provides real-time visibility into key SaaS metrics and customer patterns across timelines, channels, and lifetime stages and eliminates any need to manually compile the data you need for your analysis.  

The platform’s user-friendly interface with built-in formulas and commands also allows users to easily develop and share specific cohort analysis reports, enabling SaaS sales and finance leaders to make more strategic business decisions, backed by data. Simply put, Drivetrain makes cohort analysis fast and easy. 

Explore how Drivetrain can help you scale your business faster with robust customer cohort analysis and reporting.