For any SaaS company, understanding why customers stay or leave is critical, and cohort analysis offers a granular view that traditional metrics often miss. In Part 1 of this series, we looked at how cohort analysis can drive customer retention by uncovering hidden patterns in user data. But that’s just one side of the coin. What if the same powerful tool could also transform your approach to customer acquisition?

Arnav Mahajan, our Product Lead, feels quite strongly about this. In fact, he urged me to work on this second part on the less-discussed application of cohort analysis. “The most common myth about cohort analysis is that it is only for retention/churn analysis,” he says. “But I think cohort analysis is absolutely necessary for the acquisition teams to understand the conversion patterns. Cohorts in my mind are the stepping stones to getting closer to an accurate multichannel attribution which most of the companies struggle with. 

“Cohorts are the basis for some of the most popular attribution models like Markov's chain, or time decay, which help you understand the engagement and conversion patterns for various channels. This data can be used to accurately attribute the value of each channel in complex buying journeys.”

So today, let’s take a look at how cohort analysis can help you optimize marketing attribution, measure CAC, refine acquisition funnels, and identify the most effective paths to conversion. 

Sharpening marketing attribution

One of the big issues with traditional marketing attribution is that it tends to be short-term. You’ll know which channels are driving immediate signups, but not how those customers behave over time. 

Say, you run campaigns on LinkedIn, paid search, and email. Looking at traditional attribution on GA, Paid might look like a winner because it brings in the most signups. But when you use cohort analysis to track those customers over six months, you might discover that LinkedIn users are more likely to stick around and upgrade their plans. Paid Search users, however, might be churning after the first month. This deeper insight means that you’re better off increasing your spends on LinkedIn and not paid search. 

Thus, cohort analysis reveals the long-term impact of your marketing efforts. It lets you see beyond just immediate conversions and helps you focus on the channels that bring in loyal, high-value customers over time. “If marketing leaders have overcommitted to a channel or a strategy and it’s bombed, cohort analysis is the way to discover what’s really happened,” Arnav underscores. “No other method, in my view, is as effective in un-fudging attribution.” 

Discovering your true Customer Acquisition Cost

CAC is one of the most important metrics for marketers—you know your overall CAC and even break it down by channel. What you might be missing is the long term view. How valuable are these customers after the initial acquisition?

Say you’re running webinars and a referral program. CAC by channel shows that webinars are offering lower CAC initially, but run a cohort analysis and a different picture may emerge. You may discover, for instance, that over a 12-month period, referral customers stay longer and are more likely to upgrade, making the true CAC for referrals much lower.

Thus, cohort analysis takes CAC a step further by tracking the behavior and retention of users from different acquisition channels over time. It reveals which channels bring in not just the most users, but the ones who stick around, engage deeply, and ultimately generate the most revenue.

Tracing acquisition funnel activity

Cohort analysis can also help you get a more nuanced understanding of your acquisition funnel. Instead of just looking at all users together, you can track how different user groups—based on when or how they entered the funnel—move through each stage and see where they’re getting stuck.

Let’s say you’re acquiring users through LinkedIn Ads and content marketing. At first, LinkedIn seems to be working well—it’s bringing in a lot of free trial sign-ups. But when you dig deeper with cohort analysis, you might notice that LinkedIn users don’t engage with the features and kind of drop off after the trial. On the other hand, users from content marketing stick around and seem to be converting to paid customers. 

With this insight, you can focus on improving post-trial engagement for LinkedIn users—maybe through targeted emails or in-app nudges—while continuing to invest in content marketing, which is already producing long-term value. 

Most effective paths to conversion

When we talk about conversions, it’s not just about getting users to sign up—it’s about guiding them along the most effective path to becoming paying customers. An "effective path to conversion" means users move smoothly from initial sign-up to trial activation, engage meaningfully with key product features, and convert quickly into paying customers. 

Cohort analysis can help us figure out which actions or behaviors are leading to the fastest, most effective paths to conversion. Imagine you’ve run a cohort analysis based on when users signed up. You group users by the week they signed up and track their engagement over time. After examining the different cohorts, you notice a trend: users who customize their profiles during their first week tend to convert to paying customers faster.

This means that if you can figure out a way to nudge profile customization early on, you could influence conversion rates! This is the beauty of cohort analysis - it unearths hidden patterns that could help you focus on behaviors that accelerate the customer journey.

A Caveat: Cohort Maturity

For cohort analysis to truly deliver value, you need to ensure that your cohorts have reached maturity. This means waiting long enough for meaningful patterns to emerge before you dive into conclusions. For example, you’re running an A/B test on a new onboarding flow. If you analyze the cohorts too early—say, after just a week—you might miss how these users behave in the long run, skewing your results. 

It’s important to set a reasonable time frame for your cohorts to ensure statistical significance. If the sample size is too small or the time period too short, your conclusions will not be reliable. You want to ensure that the behaviors you’re seeing are representative of a larger pattern, not just a fluke.

A Smarter way to Do Cohort Analysis

Why isn’t cohort analysis used more often if it’s so useful? Arnav explains, “Because it’s usually a very manual process. Analysts have to collate the data from multiple systems like CRM, billing, ad managers, etc., then run scripts to pull that into Excel sheets, clean up the missing pieces or contradicting entries, and so on. Only then can they use any of it to do meaningful analysis.” Another common practice is taking snapshots of the funnels at regular time intervals, say weekly or monthly, to see how the cohorts move through it. This is also fairly manual. 

Recognizing these pain points, we’ve built a cohort analysis supertool within DataviCloud that simplifies this in multiple ways.

• Automated Data Snapshots. DataviCloud automatically keeps monthly snapshots of your data, ensuring that your cohort analysis is always based on the most accurate and up-to-date information.

• Segmentation and Backtesting: Defining your user segments correctly (not too broad, not too narrow) is a crucial part of cohort analysis. DataviCloud simplifies this for you with a pre-populated list of segments based on your vertical. This way, you don’t start from scratch and you can define/refine these without writing lines of code. No more blank page syndrome!

If you’re not already leveraging cohort analysis, now is the time to start. And with DataviCloud, you can make the process not only easier but also more powerful. 

Sign up for an outcomes-focused demo on www.datavicloud.com.