How to do Mobile App Cohort Analysis: a short guide
For today’s businesses, understanding customer groups and their actions are key to improving the product or service you are offering. Grouping customers together into ‘cohorts’ allows for a clearer understanding of overall user behaviour than if you were to conduct user research on a one-by-one level.
Your cohorts are your valued customers and users — grouped, based on having shared similar experiences when using your product or service. They provide larger data sample sizes which allow you to observe results collected in terms of common trends, instead of individual decisions.
Interested to know why cohort analysis is a crucial part of building a product roadmap for your app? Read our guide here.
What is app cohort analysis?
Customer cohort analysis, therefore, is a form of analytics that records grouped user engagement over a set period. The resulting data provides insights you can use to better understand your product, marketing, and business performance.
By segmenting your sample customer base into groups with shared attributes or experiences like this, you can collect user feedback and track similar actions made by a collective sample group, then begin to assimilate behavioural patterns. Businesses such as app development agencies will use these patterns to make informed decisions on how to improve revenue and user retention rate post-download.
Improving customer retention rate is one of the main reasons today’s companies conduct cohort analysis.
Example of a cohort analysis chart
The different types of customer cohort analysis
There are generally two kinds of cohort analysis — each providing benefits to businesses:
Acquisition-based cohort analysis measures customer retention rate from the time they begin using a product or service. For example, you may wish to track user activity within your newly-released app from when they first download it. You might divide customers based on which day of the week, or month they use the app, for example. Thus creating daily, weekly or monthly cohorts to track user engagement over time.
You can then determine how long people continue their interaction with the product from the start point.
Behavior-based cohort analysis measures the actions either taken or not taken by users within your specified time frame. Let’s take our app example again. You could include any number of predetermined possible scenarios — for example, installing an update to your application, or responding to an application push notification. Cohorts are made up of customers who made decisions consistent with one another, and within a certain time.
You’re then able to track user engagement after taking actions at different times.
How to use cohort analysis
So, how do you use all the information you’ve acquired from your cohorts to positive effect? Well, you can look at the correlations between the number of customers who are choosing to stop using your service, and the points at which they are doing so.
For example, analyze the rate of customer churn* over the first three months that your specific cohort signs up to use your service.
* the number of people that cease interacting with your business (i.e. customer loss).
How to increase customer retention rate using cohort analysis
Increasing retention with cohort analysis is a data driven game. If your cohort indicates the rate of customer churn is high, then for the reputation (and potentially the survival) of your business, it’s paramount that you work out what the issue is and seek to resolve it. Customer churn is usually caused by:
- The onboarding experience and user journey post-purchase needing improvement
- Customer expectations are not being met by the service you are offering
- Your process of acquiring new users containing fundamental flaws
For example, Skype is a popular app for video calling. But had they initially marketed the same product like a video conferencing app for businesses, the reality of using Skype would not have met people’s expectations.
Yes, it would have fulfilled its role as a basic video calling app, but it would have struggled to deliver the desired capabilities that purpose-built business solutions offer. Customers would likely churn in favor of a more advanced product. Luckily for them, Skype for Business circumnavigates this problem, but you get the idea! This is where cohort analysis could be used to work out if customer expectation is aligned with reality, and what is making your users drop your product.
There is a saying: ‘numbers don’t lie.’ The data you are collecting will indicate where along the user journey requires the most of your attention.
Although, acquiring and managing your user feedback from customers is also vital to truly understanding the issues you need to resolve. Remember, your cohorts are people! If their experience with your business hasn’t been a positive one, then they will be forthcoming in telling you why that is. Fixing key pain points for your newly acquired customers will not only reduce churn, but increase retention rates and, ultimately, your revenue.
Some strategies for increasing retention from cohort analysis results include:
- Targeted promotions and loyalty rewards
- Coordinated reactivation prompts
- Small changes to the overall user journey
Many businesses have already reaped the benefits of using cohort analysis to study their customer base, and there are likely many things you can learn about your business from conducting this kind of research.
Improving lifetime value (LTV)
Conducting cohort analysis can help you observe churn patterns over months and years as well. After all, your aim should be maximizing your customer lifetime value for as long a time as possible! Comparing your cohorts to your average churn time and looking at the reasons for cancellation over specific time frames is a good way of highlighting areas in need of improvement.
If you’re seeing consistent churn over longer periods (across several cohorts), you can take these steps to improve rates of longer-term retention:
- Find the drop-off point
- Find out the cancellation reasons
- Reach out to customers before reaching the drop-off point
- Reach out to customers beyond the drop-off point
- Send customers a survey form, or utilize online review platforms
- Use aggregate review feedback data to incrementally improve retention rate
Cohort analysis vs vanity metrics
With cohort analysis, you avoid so-called vanity metrics, whereby your business data appears positive yet actual user pain points and general areas for improvement remain concealed. Where vanity metrics promote casual ignorance and often inform poor business decisions, cohort analysis works as a method of identifying and tackling problems head-on.
Dividing users into cohorts allows you to record observable trends, and take actionable steps towards rectifying them. Repeat the process regularly, make changes, and measure the results — improvements will eventually come.