During this session, Vera Karpova from devtodev will discuss how product analytics changes during the development process: what tasks and issues the team faces on different stages and which appropriate analytical tools should be used to solve them.

1. PRODUCT ANALYTICS EVOLUTION
Analytics approaches on different stages of game development
Vera Karpova Product Owner at devtodev

2. 2
WHAT IS THIS PRESENTATION ABOUT?
● How and why the company’s use of analytics changes
over time
● Analytical questions and tasks that come up as the
product develops
● When you should use certain analytical tools and why

3. 3
EARLY RELEASES
Analytics Tasks:
● Determine the market demand of your product
(number of downloads, top countries for downloads,
revenue, and retention rate)
● Quality of presentation
(app store conversion rate)
Analytics Tools:
● App store data
(iTunes Analytics, Google Play Developer Console)

4. 4
AUDIENCE GROWTH
Analytics Tasks:
● Assess paid traffic performance
Analytics Tools:
● A traffic analysis tool
● An analytics platform with custom events and
an evaluation tool for traffic information

5. 5
AUDIENCE GROWTH
Analytics Tasks:
● Identity the drop-off points in the customer journey
Analytics Tools:
● An analytics platform with custom events

6. 6
PRODUCT IMPROVEMENT
Analytics Tasks:
● Identify growth areas, ways to increase
conversion and retention rates
● Why a metric has dropped
Analytics Tools:
● An analytics platform with custom events

7. 7
ANALYTICAL METHODS
● Segmentation
● Funnel comparison
● Studying user flow and user profiles
● Custom action analysis
● etc.

8. 8
PRODUCT IMPROVEMENT
Analytics Tasks:
● Hypotheses testing
Analytics Tools:
● A/B testing tool
A B
VS
12%
Conversion
36%
Conversion
Winner
A/B testing

9. 9
BENEFITS OF A/B TESTING
● Check your subjective perceptions and hypotheses
● Mitigate negative impact of a failed test
● Get to know more about your users
● Avoid implementation of pointless changes

10. 10
TEAM GROWTH
Analytics Tasks:
● Automate some reports
● Ensure fast and convenient access to
metrics for the team
Analytics Tools:
● An analytics platform that allows for
building custom reports and dashboards
● or a DB and a tool for SQL query
visualisation

11. 11
WHAT TO DISPLAY ON DASHBOARDS
● Fundamental product metrics
● Teams’ KPIs
● A release or promotion report
● Statistics on functionality usage and product stability
● Data on traffic sources
● Real-time metrics
● Financial metrics for managers
● etc.

12. 12
PERSONALIZATION
Analytics Tasks:
● Segment and personalize to improve metrics
Analytics Tools:
● A tool for sending push notifications, in-game
messages, personalized offers

13. 13
HOW TO SEGMENT
● By basic information (OS, country, device, ...)
● By total number of days spent playing
● By visit frequency
● By payments and their total amount
● By level
● By player ratings
● By percentage of wins
● By available features
● By available currency and its amount
● By days without making payments
● ….
1 DAY IN
GAME
2-3 DAYS
IN GAME
4-7 DAYS
IN GAME
...
BOUGHT
ITEM X
750 1020 4750
BOUGHT
ITEM Y
320 420 650
BOUGHT
ITEM Z
74 105 343
BOUGHT
NOTHING
3400 5700 7690

14. 14
HOW TO USE SEGMENTS
● ‘Intensify’ engagement: give personalized offers /
bonuses … to your users
● Do not waste your effort to “ineffective” segments
with few or inactive users
Hello ! This is a push notification.
Click on it !

15. 15
PREDICTION
Analytics Tasks:
● Compare traffic sources by LTV
● Estimate the probability of payment
Analytics Tools:
● Excel/Google Sheets
● Python/R
ML
MODELS
SIMPLE
FORECASTING
MODELS
HISTORICAL
DATA
ANALYSIS

16. 16
SIMPLE MODELS
Function approximation
● Lifetime * ARPDAU
● y = a*(x)^b
● y = c*ln(x)+b
● y = a/(ln(x)^b+c+d)
● Cumulative ARPU
CUMULATIVE ARPU
LTV

17. 17
BUILDING MODELS USING ML
● Define a problem
● Choose the model type
(linear, decision tree, etc.)
● Train the model
● Evaluate the result (MAE, RMSE)
● Improve the model (add parameters, change model
type)
TRAIN
TEST

18. 18
DISTINCTIVE FEATURES
OF THE METHOD
● Allows for early prediction
● Ensures high accuracy
● Allows for LTV prediction for each user
Data-demanding if you want to build an accurate model

19. 19
● Select tools that are suited to the task at hand
● and think about what you will need at the next stage
CONCLUSIONS

20. 20
karpova@devtodev.com
www.devtodev.com
THANK YOU!