User Lifecycle Analytics Framework

The “User Lifecycle Analytics Framework” is a structured way to look at a metrics of a business to help understand its current status, measure evolution, find opportunities, etc…

Different business might need somewhat different frameworks, but an important realization is that having a framework is useful, gives structure, reveals measures to look at and insights that ultimately help the business.

A business will naturally go through different stages and will have different needs depending on the stage that is in. For example, a business might not have enough users, and thus needs to invest on acquisition, or instead it has enough users but the revenue is not realized yet.

So in general the Business might need over time to shift focus and zoom in on a specific topic at a time, while never loosing attention on the business as a whole.

This framework is about the user lifecycle, it considers that within a business the user has a specific lifetime and different phases of maturity, from first discovering the product, to becoming an active user, to using it consistently, to potentially influencing others to join in, etc… all the way to stop using it.

Each business has natural specific user lifecycle strengths and might even want to target on specific strengths.



Retention and Stickiness



Net Promoter Score - tool that can be used to gauge the loyalty of a firm’s customer relationships. It serves as an alternative to traditional customer satisfaction research


Churn is a measure of when customers leave, of when users stop using the product or service. A business wants to keep its customers, so it wants to minimize churn. Churns is all about “customer retention”.

A look into a business churn

Similar structure can be used for other topics, like acquisition, retention, etc…

Avoiding Churn

Understanding why users churn can be useful, it can open opportunities for saving users from churning. A Churn prediction model would tell us userA has 95% probability of churning next week. We could then reach out to the user with an incentive (email, an offer, a survey) to try avoid the churn.

Acceptable Churn

We often want to have the churn as small as possible, but do take into account of what is the common churn for this type of application, from competitors for example, because it might be the effort of reducing churn from a certain point onwards requires an effort that could bring better returns if applied else where.

In an extreme example: the money invested in reducing churn from 3% to 1%, if applied in a new feature of the product, might return more users than the gain of extra users saved from churn.


Quality & Performance

Business Scorecard

Once the frameworks and its definitions and metrics are agreed upon, we can build a scorecard that includes all of the framework’s metrics so we can have a view of the business end-to-end.

A funnel or an approximation of it is commonly used approach.

This can be generated as a recurring report for: Week on Week, Month on Month, Year on year, etc…

Besides the framework metrics, we need include also the defined top level business goals metrics, they might have been defined outside of the user life-cycle model context.

Measuring activity impact

This scorecard can also be used for another application, which is measuring impact of a product change:

The scorecard applied in this way, should be answering 2 questions:

Metrics: Active Users

A fairly standard metric (industry standard) is the user activity in the application (in-app activity), for example:

*Activity is defined by user explicit interactions with the application. For example, open the application, browsing application content, using application features, etc… similar definition to a web user session. Note that notifications that reach the user but are not interacted with, should not trigger an AU signal.

The AU metric in the User life cycle:

Other metrics:


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