# Activation Analysis

The future depends on what we do in the present - Mahatma Gandhi

***"what my users should do in the pre period to retain/uninstall/ activate to specific event in the post period". - Apxor Insights***

### Objective:

To figure out what are the actions that are taken by the user inside the app are leading to certain metrics like Retention, Churn or Doing a aha moment and many more.

### How to Do:

![](https://300211688-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQuYbJ9bg7CFtrBaVp9pB%2Fuploads%2FLg33e8BtnLjep3OeEat8%2FAA_HowToDo.png?alt=media\&token=2a60133d-339b-4fab-8b24-ee0a7f4d4819)

#### Pre Period:

* Select the user journey days in the app in which you believe the opportunity window to nudge the user that will lead for the conversion event in the post period
* Include a event or list of events, on which you are sure that they will have the impact on the conversion event
* Exclude a event or list of events, which you don’t want to include this analysis, might be some SYSTEM EVENTS etc

#### Post Period:

* Select the user journey days in the app in which the conversion event should happen
* Select Metrics such as “Retention” or “Activation”
  * Retention: Opening the app in the post period
  * Activation: Select the event that you want to label as the conversion event

#### Users:

* Select whether this analysis should be performed on
  * All users: who ever opened the app
  * Segment: A pre defined segment based on certain specific user behavior
  * Cohort: A fixed set of users, that you obtained it from some other platform

Once you fill these details, click on “Generate Results”

**Note:**

The report generation might take 2 to 3 minutes based upon the data that is being analyzed

### Results:

![](https://300211688-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FQuYbJ9bg7CFtrBaVp9pB%2Fuploads%2FDcMgUXMGjhQHp4SGFTdA%2FAA_Results.png?alt=media\&token=939c587e-8cdd-44ec-9123-526d0a750de2)

* By default, the analysis will be run on the last 30 days data, and once can change to last 15Days or last 7 Days.

The table will provide the following:

* Event: the event name that is done in the pre period
* Impact: indicates whether it has the positive impact on the metric selected during the post period
* Score: It is the uplift in the conversion when the user has done this event in the pre-period , compared to just opening in the app
* Confidence: It is the statistical significance that doing this event has significantly effect on the conversion during the post period. Usually the events that bear ≥0.95 can be considered as significant events that have impact on the conversions
* % Users: It is vital to observe the percentage of users that have done the specific event in the pre period. One can considers such events that are done by at lease 1% of the users in the pre period

From the above table, one can select the events for further analysis based on the frequency that the specific action is performed:

* Pick Automatically: By checking the box “Pick Automatically”, we will select the events automatically and dig further on the **Frequency Chart**
* Using the corresponding values, pick the events you want to analyze further using the **Frequency Chart**

### Frequency Chart:

This chart lets you know how many times an event should be done ideally in the pre-period so that the user converts in the post period.

* The X-axis will denote the number of times an event is done in the pre period
* The Y-axis will denote the score, uplift from the baseline conversion that is opened the app in the pre-period
* On hovering , we can observe,
  * Event: Name of the event
  * Score: Uplift from the baseline
  * Count: the number of times the pre-event is done
  * Confidence: Statistical significance
* Using this one can observe the following patterns:
  * The score increases as the frequency increases - indicates that user must habituate to this action in the pre period
  * The score increases till N number of times and starts descending from there- indicates that the user will get vexed once he does the action N number of times
  * The score will keep steady after N number of times - indicates that there would not be much difference , once he does the action N number of times and there after
  * The score decreases as the frequency increases- indicates that the action negatively impacts the conversion