This data analysis project aims to evaluate the impact of modifications made to the gameplay of the mobile game Cookie Cats. To effectively measure and validate this potential change, the A/B testing methodology was implemented.
Cookie Cats is a hugely popular mobile puzzle game developed by Tactile Entertainment. It's a classic "connect three"-style puzzle game where the player must connect tiles of the same color to clear the board and win the level. It also features singing cats. We're not kidding! Check out this short demo:
As players progress through the levels of the game, they will occasionally encounter gates that force them to wait a non-trivial amount of time or make an in-app purchase to progress. In addition to driving in-app purchases, these gates serve the important purpose of giving players an enforced break from playing the game, hopefully resulting in that the player's enjoyment of the game being increased and prolonged.
But where should the gates be placed? Initially the first gate was placed at level 30, but in this notebook we're going to analyze an AB-test where we moved the first gate in Cookie Cats from level 30 to level 40. In particular, we will look at the impact on player retention.
Obs: This dataset is taken from DataCamp
The data we have is from 90,189 players that installed the game while the AB-test was running. The variables are:
- userid - a unique number that identifies each player.
- version - whether the player was put in the control group (gate_30 - a gate at level 30) or the group with the moved gate gate_40 - a gate at level 40).
- sum_gamerounds - the number of game rounds played by the player during the first 14 days after install.
- retention_1 - did the player come back and play 1 day after installing?
- retention_7 - did the player come back and play 7 day after installing?
When a player installed the game, he or she was randomly assigned to either gate_30 or gate_40. As a sanity check, let's see if there are roughly the same number of players in each AB group.