3 Ways to Use Cohort Analysis (Beyond Improving Retention)

Archana Madhavan

Instructional Designer

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5 minute Read,

Posted on August 7, 2020

Cohort analysis is a great method for measuring retention, but it can also do more than that.

Google “cohort analysis” and you’ll find plenty of resources telling you how you can use it to improve customer retention. And they’re right: a cohort analysis is a great method for understanding how your users engage with your product. But using cohort analysis to only improve your retention is like using your smartphone only to make phone calls—you’re wasting its potential.

Cohort analysis can show you additional opportunities across your company, including product development, marketing, and sales.

Here are three ways to use cohort analysis to evaluate more than just retention.

But First, What Is Cohort Analysis?

A cohort is a group of users who share a common characteristic. There are different types of cohorts, but this piece is centered on a behavioral cohort, which is a group of users who interact with your product in a specific way. One cohort might be users who watch a help video during their first session or engage with three other members within a week of joining your platform.

Cohort analysis is where you compare a specific cohort to another group of users. For example, let’s say you had a cohort of people who enabled push notifications during their first session. By comparing that cohort to another cohort, such as all active users, you can see whether that action affects how the notification-enabled users engage with the platform compared with everyone else.

Now for three ways to use your cohort analysis beyond customer retention.

Use #1: Understanding New (and Underused) Feature Adoption

When you launch a product, it’s hard to know which features users will find valuable, which ones they will ignore, and which ones they will misconstrue completely. Behavioral cohort analysis can help you identify little-used features that lead to more engaged users and can also help you understand how new functionality can be better communicated.

Let’s say you’ve just instituted a new feature with which users can create their own avatars, and you want to see whether those users engage differently with the platform than users in general. To create a cohort analysis to understand the feature adoption, first define your cohort based on the feature you want to track–in this case, those who created an avatar. You can limit the cohort to those who used the feature within a certain time frame, such as after an update.

Once you’ve created your cohort of people who have created an avatar, compare that cohort with those who haven’t created an avatar. You might find that those who create an avatar are more likely to use additional features, for example, or that your cohort doesn’t upgrade their subscription at the same level as the rest of your users. Your results can help you fine-tune the communication around the feature, encourage other users to take advantage of the updated tool, or get rid of the feature altogether.

Let’s take a look at some real examples:

Meditation app Calm wanted to test their reminder feature. They noticed that a small set of highly engaged users actively used the feature, but the feature was buried in the settings menu. Calm wanted to know if the reminder feature was helping increase engagement or if the users who were dedicated enough to wade into the settings were just already highly engaged, regardless of the reminders. The meditation company ran a test in which select users got a prompt to set a reminder after their first meditation session. Using behavioral cohort analysis to compare those who set a reminder with all active users, Calm was able to see that using the reminder feature increased engagement across the board and not just for those users who explored the web of menus.

Even more problematic than a buried feature is a feature you think is clear but is causing frustration among your users. By using behavioral cohort analysis, you can see whether your users understand the value of a feature and then make adjustments based on actual user behavior.

In looking at why more users weren’t converting to paid subscriptions, online language academy ABA English noticed users were stagnating after their initial course. Through behavioral cohort analysis, ABA English found that what they had thought was a short and efficient onboarding process was actually causing many users to choose the wrong course level and ultimately frustrating them. Applying what they’d learned from the cohort analysis, ABA English was able to revamp their onboarding flow into something a little longer but much more helpful for their users.

Use #2: Improving Advertising Performance

Behavioral cohort analysis can save time and increase the effectiveness of marketing and advertising efforts by helping you understand where your most engaged users come from and what else they like so you can personalize your marketing to get more users like those.

To use cohort analysis to inform your marketing and advertising, identify the channel or inbound tactic you want to measure. For example, your Twitch stream is starting to drive new subscribers. Are users that find you from Twitch more engaged than those who come through organic search?

To help you answer that question, first define your cohort based on that channel—in this case, Twitch. Refine the parameters you want to focus on, such as sign-ups for a particular Twitch event. You can compare that cohort with users who came in from all other channels, or you can compare it with a different cohort, such as those who found you through a Google search. Based on the results, you can make data-informed decisions whether you want to put more marketing spend toward Twitch or focus on another channel completely.

Behavioral cohort analysis can be particularly useful in identifying how power users found you, as well as finding and encouraging other users to behave like power users. Budgeting and credit-building platform Dave was using behavioral cohorts to track successful members when the company realized they had the information they needed to target users who were similar to those power users, which the company, in turn, used to drive their marketing decisions to reach potential users with the same profile as their power users.

Retail technology company Flipp started using behavioral cohorts to better understand the brands their best users were also loyal to. Using that information, Flipp then created targeted marketing campaigns for users who frequented the same retailers as the company’s best users. That personalized approach drove a 2x CTR increase in those campaigns.

Use #3: Understanding Seasonal Differences in Product Usage

A product may meet the needs of different audiences, depending on the time of year or on shifts in market trends. A behavioral cohort analysis helps spot those changes in your audiences so you can adjust your messaging, tools, and UI to accommodate the potential new market.

When building a cohort analysis to identify changes in audience, you are looking for product and website usage outliers. Let’s say you notice unusually high traffic on your help videos. To better understand what that means, create a cohort of people who viewed your help videos at least twice in their first session. When you compare that cohort with your active users, you can look for differences such as demographics, job title, or industry. By focusing on a specific time frame, such as gift-giving holidays or national events such as Tax Day, you can create some parameters to refine your cohort and recognize differences that might get lost in a broader data analysis.

These changes in audience can be due to a number of factors. If your ecommerce site has a high-end product aimed at men, you might find more people within a certain age group making purchases around Father’s Day. You might find that your product for professionals in a certain industry sees an unusually high number of sign-ups from people who want to use it for a short-term project, such as tax planning. Understanding this audience shift is the first step to knowing what to lean into.

More Than Retention

Retention and driving product loyalty deserves a spot on your monthly data reports. But if that is all you are using behavioral cohort analysis for, you are missing key insights into your products, marketing, and audience. From using data to find the best way to launch a new feature to identifying the best users to target with a personalized marketing campaign, behavioral cohort analysis helps you create a better product for the users you have as well as the users you want to have.

Archana Madhavan

Archana is an Instructional Designer on the Customer Education team at Amplitude. She develops educational content and courses to help Amplitude users better analyze their customer data to build better products.

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