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Feb 10, 2022

User Retention Analysis: Meaning, Models & Reporting On Customer Cohort Retention

Alberto Incisa

Let’s talk about a practice new startup founders often underestimate: retention analysis. While Monthly Recurring Revenue (MRR) is all the rage in Twitter threads and other celebratory posts, few mention retention numbers. But if there is one metric you absolutely need to focus on right now, it’s customer retention.

Customer retention is the life and blood of your company, no matter its size or business model. Focusing solely on acquiring new customers is tempting, but keeping them is ultimately what makes you profitable:  users who keep coming back to your app present a higher probability to become customers, and already-existing customers are 60% more likely to buy something new from you.

Think about it. You might spend hundreds, if not thousands of dollars each month to find and interact with users. Acquisition is estimated to be 5 times more expensive than retention: getting all these people on board is expensive, so it would be unsustainable to not nurture them to stick around.
At June, we made it our mission to simplify how people build great online businesses using product analytics. In this article, we tell you everything we know about measuring retention.

Why User Retention Analysis Matters

Retention analysis tells you how, when and why customers opt out of your product or service. It’s a data-driven process examining a variety of metrics, including how long users stay active on your product, who they are, or which feature they use most.

  1. To control your user retention rate: retention analysis gives us the means to measure user retention and control it, to some extent. Measuring customer retention allows you to predict future profitability and adapt accordingly. For example, having a retention rate lower than 20% is a red flag for software-as-a-service businesses.
  2. To track your marketing efforts’ return-on-investment. Being able to perform A/B tests, identifying changes, activities, or features that keep customers happy over time, and to see how well your efforts fare, you need insights from the retention analysis. Without it, you will aim in the dark, wasting your marketing budget. Without customer retention, your payback period—how long it takes to recover your acquisition costs—only keeps increasing. To transform users into power users. Being able to understand what makes your users love your product allows product managers to devise growth hypotheses, find your best audience segments, and develop data-driven strategies to do more with less.

User Retention Tracking: How To Measure And Monitor Your Retention

Calculating your User retention rate

Retention tracking requires three things:

1) a way to know when your user subscribed to your service,

2) a way to know whether your user is still using your service or not, and

3) a meaningful time scale for tracking and analyzing.

Depending on the metric you use for retention, the first two pieces of data are either provided by your user authentication system, your payment gateway, or some sort of analytics platform like Google Analytics.

For example, a B2B subscription service using Stripe as a payment system can track when a user purchases or cancels a subscription plan. A user database could also tell you when someone registered and when was the last time this person logged in. In both situations, you’d already have all you need to track your retention rate.

The last parameter depends on the kind of business you’re running: daily, weekly, monthly… and so on. If your user needs to use your product everyday to be considered active, then you’d take a day as the meaningful time period. To figure out your ideal time-frequency, check out our free user retention playbook.
Once you have obtained all this information, your retention rate is the percentage of customers who still actively use your product after starting to interact with it for a given time period. Say 10 customers bought your software service but only 5 didn’t cancel their subscription after a week, your retention rate is 50% for this week. Here’s a bare-bones retention graph showing this:

The higher the rate, the better.

Automating Retention Tracking

Doing this calculation every week is a tad repetitive, and your time would probably be better spent somewhere else anyway. All these steps can be automated by connecting your Segment account to June, it only takes a few minutes.

Segment is a platform made by Twilio to centralize all your customer data coming from different sources, such as Google Analytics, Stripe, or any other business tools using Segment’s application programming interface. Using Segment, you can automatically collect user metrics from hundreds of software tools commonly used to build SaaS businesses. By connecting it with June, you instantly receive beautiful retention reports and insights for free:

Once you’re done setting up your retention tracking process, you can move on to the interesting part: retention analysis.

How To Do Cohort Retention Analysis

Retention Models

To make comparisons possible, retention analysis uses the concept of cohort: a group of customers sharing the same characteristic. A cohort retention analysis for a given characteristic is called a retention model.

3 types of user retention model are particularly interesting to start analyzing a SaaS product:

  1. Acquisition cohorts: group users based on when they signed up for a product
  2. Behavioral cohort: group users based on the activities that they undertake in the product, like using a specific feature or clicking on a certain button
  3. Demographic cohort: group user based on distinctive trait they have, like the plan they’re in, or the average revenue they bring

The more models you use, the more insights you will gain. Acquisition is often the standard model to get started, but you can also use demographics, used features, or traffic sources as standalone retention models as your retention analysis process gets more detailed.
June proposes two types of retention graphs for cohort retention analysis:

A retention curve is computed by calculating the retention rate for each time period and displaying them on a line chart.
This retention report offers a global overview to find changes that impact your retention rate. Customer retention models provide a great way to follow the evolution of your product strategy, and see where you need to pay attention to your retention in your marketing funnel:

Cohort Retention Chart

A cohort retention chart breaks down the retention rates by cohort. It’s basically a way to display the retention curves of each cohort for a given retention model (acquisition, in the case of the graph showcased above) in a single view.
It gives us precious insights about different user segments you can use to your advantage:

How To Use Retention Reports And Graphs To Improve Retention

Now that you know how to obtain all the data you need, it’s time to have a deeper look at how to make it actionable.

1.  Find when customers drop out

Retention graphs show you when exactly customers churn throughout the user journey: during the onboarding process, after you released a product update, or even when you forgot to address a support ticket.

2.  Find why customers leave

When you know where to look into your product, you can add more relevant metrics to dig deeper, or ask your users for specific feedback at this stage of the product lifetime to figure out why these people leave.
For example, if you notice users churning after downgrading their pricing plan, you could formulate the hypothesis that they can’t find a feature that was relevant to them anymore. Running a feature audit could help you figure out if changing your pricing structure, product flow, or improving onboarding (e.g., through tooltips and tutorials) could increase retention:

Depending on the retention model you look at, you can also obtain precious information about your audience: who they are, where did they learn about your product, or which features do they use, for example.

3. Establish a plan to improve retention

At this point you can attribute behaviors to customer segments to predict churn and act to increase retention. Combined with other metrics like engagement or activation, you can come up with hypotheses to prove or refute them.

Some of the most common reasons for churn include:

  • Bad onboarding experience: if people don’t understand how to use your product to achieve their goals, you need to offer support systems like a FAQ section or an email contact they can use to reach out.
  • Bad pricing: your pricing plans are too complicated, or your competitors offer higher value at a lower price.
  • Wrong audience: look at your buyer persona, what your current cohorts are, which marketing channels referred them to you, and act accordingly. Bugs: if you cannot deliver what you promise, customers have no reason to stick around. You need to reassure them, or tell them when issues are fixed.

Get Started With Retention Analysis Fast, For Free

Retention is the most important metric you must carefully monitor to reach product/market fit. After all, acquisition costs 5x more than retention. And it doesn’t have to be hard: June offers free analytics reports in just a few clicks, including retention analytics. All you have to do is to sign up here.

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June 1.0 - Instant analytics reports built on top of Segment | Product Hunt