In today’s competitive landscape, understanding user behavior and engagement is crucial for businesses to drive growth, retain customers, and optimize their offerings. User lifecycle analysis provides a powerful framework for categorizing users into distinct stages based on their actions, behaviors, and engagement patterns. In this blog, we’ll explore the key stages of user lifecycle analysis and how businesses can leverage this approach to make informed decisions and enhance their overall success.

Breaking users into groups tells us how our user base is evolving.

Let's say Money Transfer (Product) has a monthly active base of 1M in January, and in Feb the base has increased to 1.2M. Now we can see what group has contributed to the overall growth. By digging into the data, we found out that in Feb, fewer users churned out and there were almost 800K users who started using Money Transfer for the first time in Feb. So, the most important factor in the growth was new users addition.

Here comes the difficult part: now we have to retain these users (if not then there will be a major increase in churned users next month) for the next month.

This can be done by doing a campaign or sending some personalized notifications.

The main reason for creating the analysis at the product level (rather than the user lever) is because at multi-product companies (such as my company) or at other large companies, different teams have different designated products and they are solely responsible for their acquisition, growth, and retention.

So each team can use this analysis to see what user groups are contributing to the growth (or shrinkage) of their product. They might not be interested in seeing the overall product churn or any other user group.

However, this analysis will not only help you explore the user lifecycle of each product. With a small query, we can also get the user lifecycle of the whole app.

Small query : Select user_id, max(DSLA) from user_lifecycle group by user_id

The main reason for targeting the churned bill pay users who were active on other products is because these users are low-hanging fruit for us.

They are already active on our app, so there is a chance that if we give some kind of a discount or cashback on bill payment, they might use that product too.

Ahsen Waheed

This document will let you create a user lifecycle analysis for your company. It will give an understanding of the stages and behaviors of users throughout their journey and help businesses make data-driven decisions to enhance user experiences, optimize conversion rates, and boost customer retention.

User lifecycle analysis to explore user behavior (with SQL)

, it helps to reveal what exactly is contributing to the growth (or shrinkage!) of your user base, and helps teams build more sustainable user growth.



. And you want to do a marketing campaign for users who are active on Product A and Product B but churned on Product C. By using user lifecycle, it is very easier to extract such user bases and perform post-campaign analysis to gauge the success.

Let’s jump on what exactly is User Lifecycle analysis. While creating user lifecycle, we group users according to their 

. The state describes the users’ current and past behavior.

Every state in the above table is calculated by 

 will have DSLA=0, but on the very next day (if he didn’t perform any activity) his DSLA will become 1 and his state will be 

. From now onwards, his state will be old (DSLA between 0 and 29) until his 

. And if he doesn’t perform any activity, his DSLA will increase and his state will be the same.


And when the user performs any activity from a churned state, his DSLA will become 0 and his state will be ‘

‘. A rejoiner can keep this state for only 1 day and from the next he will again become ‘

‘.

A bit confused 😕 about this. Let me explain this in a diagram.

I hope it’s clear now.


You can create your own definition of the states depending upon the needs and nature of your product. In the above example, we defined the activity period for different states as 



How to create user lifecycle for a multi product app?

Being a data analyst in my previous jobs, I am very comfortable in writing SQL queries. So instead of working on mixpanel etc tools to create a user lifecycle, I started working on SQL. This also gives more autonomy on how to transform raw data according to the requirement.

At first, you have to create a table where you will insert the user activities data. Below is the query that can be used for insertion. And it should be scheduled on a daily basis. (let’s assume the activity is doing a transaction)


The output will be user_id ,transaction_date and product. Let’s say you are working on fintech app, Product could be money transfer, bill payment, savings etc.

Example of how transaction table looked like:

Next step is to use the above table in another query for creating the user lifecycle. The below should also be scheduled on a daily basis.

So after executing the above two queries, output of user_lifecycle will be as follows:

Now let’s recall the example shared earlier: If you want to do a marketing campaign for users who are active on Product A(Money transfer) and Product B(savings) but churned on Product C(Bill payment). Now with the above output, you can simply extract by using this query.

This will help you extract users who are active on Product A(Money transfer) and Product B(savings) but churned on Product C(Bill payment). And as per the above output table, we will get 543u (user).


Now we can do targeted campaigns, send push notifications/emails or SMS to hook them back at our app. And at easypaisa, we have been successfully targeting our users to increase usage on our app. 

We recently did a campaign for the churned bill payment users who were active on other products

. And by doing this targeted campaign, we effectively managed to get 15% incremental user on our bill payment product.


You can tailor this above query or churn user cycle as per your requirement and product specifications. But exploring the lifecycle journey will not help you do campaigns, but it will also help you discover how users are using your product and at what stage they are coming back to your product.


User Lifecycle Analysis at Easypaisa

User lifecycle analysis to explore user behavior (with SQL)

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