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Optimizing monetization KPIs (such as conversion rates or daily average revenue per user) has always been a major challenge to drive the success of mobile apps. In this project, I used a mobile app database to extract a few of these monetization KPIs with SQLite.

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Mobile-Analytics-KPIs-with-SQLite

Optimizing monetization KPIs (such as conversion rates or daily average revenue per user) has always been a major challenge to drive the success of mobile apps. In this project, I used a mobile app database to extract a few of these monetization KPIs with SQLite.

The csv files used to create the database can be found in the course "Customer Analytics & A/B Testing in Python" on DataCamp. The metrics were calculated using Python on DataCamp.

Table of Content

I) Description of the Mobile App Database

II) Conversion Rates

A) Overall Conversion Rate

B) Conversion Rate at D+28

C) Cohort Conversion Rates

  1. Conversion Rates at D+28 by Gender and Device
  2. Conversion Rates at D+28 by Country

III) Average Purchase Prices

A) Overall Average Purchase Price

B) Average Purchase Price at D+28

C) Cohort Average Purchase Prices

  1. Average Purchase Prices at D+28 by Gender and Device
  2. Average Purchase Prices at D+28 by Country

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Optimizing monetization KPIs (such as conversion rates or daily average revenue per user) has always been a major challenge to drive the success of mobile apps. In this project, I used a mobile app database to extract a few of these monetization KPIs with SQLite.

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