This document provides tips and guidance on using data analysis to better understand users and make improvements to apps. It recommends analyzing metrics like conversion rates, retention, daily/monthly active users, average revenue per user, and lifetime value to understand user behavior and the impact of changes. The document also discusses tools for analysis, A/B testing techniques, common mistakes to avoid, and focusing on conversion, retention, and the user experience.

2. How To Get More From Your App Using
Data Analysis

3. Warning signs that tell
you should focus on analytics
1. Your team has no plan of developing your product for the next months.
2. You don't understand why a feature has been added to your product.
3. You plan to add a feature to your product only because you think it's cool.
4. You are not sure about the success of your latest changes in product.
5. You don't know the weak and strong points of your product.

4. Why do you need to manage changes
Users demand more and more + Market is constantly changes
= Changing of your product is only way to survive
Why we can't add all features we want? Or why we can't add features in random order?
1. Limited resources
2. You may discover that your product does not fit to your audience
3. Users just can't find the feature they really need
You should research a few things:
1. The present and past value of your product metrics
2. How many users will be able to use the new feature?
3. How the new feature should change your product indicators?
4. Is it working like you’ve been expecting?

5. What tools you should use and how the
analytics work
1. You you should use more than one tool
2. Sooner or later you will need to develop your own analytics system

6. Definitions of typical metrics:
Users conversion rates
User retention
DAU/MAU
ARPU
ARPPU
Percentage of users who pay
LTV

7. First steps: conversion rates
something wrong%
levels or
checkpoints
Conversion rates
1. into active users
2. into engaged users
3. into paid users

8. User Retention
"Direct/Simple" retention
Day X
Install
VS "Rolling" retention
Day X
Install

9. Monetization
1. How much currency they have.
2. How much currency they earn, and where they do it.
3. How much they spend and what they buy?
ARPU = ARPPU * Paying Conversion
LTV = ARPUMAU * average number of months a user remains active
GAME PRODUCT

10. How A/B testing works and how to use it
Why A/B testing
1. Traffic depends on day of the week or public holidays
2. More than one test at a time
Tips
1. Don’t reject the results because of your your gut feeling
2. Make changes that can greatly modify user behavior
A
B

11. Common mistakes in data analysis
1. You forgot to сheck the data
2. You forgot to сheck it again
3. Not enough users in a group
4. More than one element at a time
5. Different quality of traffic for testing two variations
6. You forgot to write down all changes in your game in one place
7. Don’t be afraid

12. What's next in game analytics?
1. Managing the difficulty of a game by managing random events in your game
2. Different ways of working with different users who already pay in your game
3. Managing predictability of users drop off

13. Beyond the spreadsheet: user experience
and your team
Tips :
1. Don’t listen to players, listen to data
2. It’s really easy to get information overload
3. You should work on conversion & retention of your game before fixing monetization
4. You should play your game many times before starting to work on conversion & retention
5. You must have confidence in development & analytics team before spend your time for
playing your game.

14. That's all folks!
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