Adding a look at metrics in design and marketing projects is certainly a differential. In addition to more information available for decision-making and task priority setting, using this data dimension also fosters a performance culture: you can only optimize if you keep your eyes on the gauges!
See the definition, what it is for and how to apply the cohort analysis technique.
One of the metrics whose adoption by our industry is growing more is cohort analysis. It is a more complex technique because it is dynamic – instead of a given number for a period (eg, total sessions, time on page), cohort analysis signaled usage behavior of specific user groups or customers over different periods .
Cohort is a group of people who share a common trait in a certain period of time.
In apps, for example, we can define cohorts according to the application’s application date:
Another example of how much it is used is in and commerces; here the cohorts can be defined according to the reference channels:
The first step in the analysis is to define relevant cohorts for your case. Next, indicators are defined that can answer a question or hypothesis.
In the first example given, from an application, we could look at cohorts for conversions (or actions) completed in the application. In this case, a decrease in conversions in each successive cohort over a period is a dangerous signal: average user quality is declining over time, changes have been made within the app that have affected engagement and retention. Here’s how to use cohort analysis to measure behavior .
In the second example of the virtual store, the cohorts defined by the acquisition channel may show that reference types are more valuable to the business, not only based on the revenue of a single purchase from each user in a cohort, but also from purchases that can should be repeated in the period. In this case, cohort analysis can help measure the value of the customer’s life , or LTV (Lifetime Value).
It is a good choice for those who have little time and need a report that summarizes important points such as retention and value per customer, applications, systems, stores and sites. Tools like Google Analytics already offer this kind of analysis, which can also be done manually with simple data sheets.