Mobile Game Application Analysis

High Level Design


Mobile game applications generate a large amount of player monetary activity and game-event data. This data has the potential to provide insights into player behavior and their engagement with the game.


Provide a high-level approach to analyze player’s sessions and in-game purchases data as long with profiling the most profitable players behavior to increase purchases conversion and retention rates.


  • Discover the Monetary KPIs that explains about the company’s general revenue, conversion rate, retention rate, time to first purchase and average user purchase.
  • List the top 3 mobiles games with their performance KPI – Monthly average users, Daily Average users and Average revenue per user.
  • Identify the user’s session pick time based on session time and revenue.
  • Discover the current user’s purchases activities value.
  • Find the most and less worthwhile user’s countries and mark it on a map.
  • Identify trends and “peak” times of users first time purchase trends in month over month and hour over hour analysis.

KPI Architecture

Objectives KPIs Measures Data Source
Understanding current state of the company through high level KPIs. $ Total Revenue sum([Session Payment USD]) Fact Game Sessions
% Revenue Growth

Compared to last year

GrowthPastYear(sum([Session Payment USD])) Fact Game Sessions
% Conversion Rate (count([User ID]),[SessionPaymentUSD])/[UniqueUsers] Fact Game Sessions
%Conversion Growth

Compared to last year

GrowthPastYear(count([User ID]),[SessionPaymentUSD])/[UniqueUsers]) Fact Game Sessions
% Retention Rate 100*DiffPastYEAR([UniqueUsers])/[PastYearUniqueUsers] Fact Game Sessions
Time to first Purchase (Days) First Session Date – First Purchase date DIM Users
$Average User’s Purchase (AVG([SessionPaymentUSD]),ALL([UserID])) Fact Game Sessions, DIM Date
Track the performance of the top 3 games #Monthly Average Users

(top 3 games)

Count(Users) based on Month Fact Game Sessions, DIM Date, Dim Game
#Daily Average Users

(top 3 games)

Count(Users) based on Day Fact Game Sessions, DIM Date, Dim Game
$Average Revenue Per User

(top 3 games)

((AVG([SessionPaymentUSD]),ALL([UserID]))*[UniqueUsers],[Game1]) Fact Game Sessions, DIM Date, Dim Game
Performance Analysis #Average Session Time


avg([Session Time Seconds])/60 Fact Game Sessions
#Average User’s Age avg([Age]) DIM Users
#Users Sessions COUNT(SessionID) Fact Game Sessions

Entities Relationship Diagram

Plugins & Scripts

1. Blox
2. Tabber
3. Distribute Widgets equally in a row


The following resources will enable you to design your dashboard and data model with sample data and then apply it to your own data. Note that you will need to have a previously installed version of Sisense (you can use the free trial version if you’re not a customer).

Sample data and dashboard examples (direct download)