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:

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:

• 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