Presenation of Marek Pasieczny from Gamesture at ASO GameCamp webinar. No matter if you optimize your paid or organic traffic, even simple data analysis is a must in today's world of App Store Optimization. The best conclusions come from the most creative approaches, i.e. those that allows you to save some time and make the right decisions. I'll give you 3 examples of ASO data analytics in practice.

1. How to boost your ASO
with data analytics

2. Few words about me
2 years experience in ASO
Working at Gamesture
Optimizing mobile games:
● Questland
● Fablehood
Combining data with ASO

3. Agenda
Pre-analytics (I) Post-analytics (III)
Real-time
analytics (II)
(I) Analyzing keyword KPIs for ASO purposes
(II) Using your own resources to boost conversion and revenue
(III) Correlation between UA & Similar Apps

4. ASO & data analytics help you with:
● making the right decisions
● predicting some app store behavior
● targeting the right audience
● improving KPIs
● investing budget the right way
● minimizing the risk of bad attempts
● reinforcing opinions & negating
false assumptions

5. Correlation between UA & Similar Apps
Post-analytics (III)

6. Scope & goal
- Similar Apps traffic data from AppTweak
- UA traffic (Android) data from Questland’s BI tool
- Using of ‘Pearson correlation coefficient’
Goal: Measure linear correlation between Similar Apps and UA traffic

7. Exporting Similar Apps data

8. Pearson correlation coefficient (CC)
Size of correlation Interpretation
0.9-1.0 (-0.9 to -1.0)
Very high positive
(negative)
correlation
0.7-0.9 (-0.7 to -0.9)
Highly positive
(negative)
correlation
0.5-0.7 (-0.5 to -0.7)
Moderate positive
(negative)
correlation
0.3-0.5 (-0.3 to -0.5)
Low positive
(negative)
correlation
0.0-0.3 (0.0 to -0.3)
Negligible
correlation
Scatter chart [-1:1]

9. Calculating correlation coefficient (CC)
Column A: =COUNTUNIQUE(A2:A351)
Columns R-V: =SUM(R2:R351)
Correlation: =((A352*T352)-(R352*S352))/SQRT(((A352*U352)-(R352^2))*((A352*V352)-(S352^2)))

10. Removing outliers

11. Presenting results
Correlation coefficient (CC):
0.81
High positive
correlation

12. Calculating CC for individual markets
Market CC Correlation
DE -0,18 negligible
FR -0,04 negligible
RU 0,09 negligible
CA 0,55 moderate positive
IT 0,41 low positive
NL 0,33 low positive
UK 0,63 moderate positive
US 0,71 high positive
US UK
CC: 0.71 CC: 0.63
DE
CC: -0.18
RU
CC: 0.09

13. What’s next?
● Correlation coefficient can be used for checking the linear influence of
two metrics on each other
● Use it only if you know/think that there are no other variables
● If you see high or very high correlation between metrics, take this into
account when planning your marketing budget
● Boost your organic traffic (organic uplift) up by investing in the most
correlated markets and UA channels

14. Using your own resources to boost conversion and revenue
Real-time
analytics (II)

15. In-game mechanism Social media
Using “Secret codes”

16. Adapting to the app listing
1st screenshot

17. Variants: 2
Audience share: 50/50
Estimated installs: 1767
Overall installs: 3014
Expected MDE: 15%
Running for: 7 days
Statistical power: 95%
Confidence level: 90%
Market: 󾓦 (en-US)
A/B testing

18. +29% more installs
Analyzing GP store performance
+36% higher CR

19. Analyzing in-game performance
*The data applies only to users who have created an account and used THUNDER20 as a secret code between 14-27/07/2020.

20. Analyzing in-game performance
88 payers (11,5%)
677 non-payers (88,5%)
Revenue:
$3394,77
*The data applies only to users who have created an account and used THUNDER20 as a secret code between 14-27/07/2020.

21. What we've learned from this approach?
● it’s worth the risk even when your approach may bend the rules
● it’s good to review your own resources from time to time
● it’s necessary to check major changes performance in real-time
● it’s profitable when creativity is on in the long term
● it’s nice to have great graphic designers and analysts aboard

22. Analyzing keyword KPIs for ASO purposes
Pre-analytics (I)

23. Scope & goal
- 38 most popular keywords on Google Play store
- analyzing listing and financial KPIs
- importing data acquisition reports from the classic GP Console
Goal: Discover the most engaging and profitable keywords

24. No of words: =IF(LEN(TRIM(B2))=0,0,LEN(TRIM(B2))-LEN(SUBSTITUTE(B2," ",""))+1)
Language: =DETECTLANGUAGE(B2:B20)
Importing data

25. Creating pivot tables

26. Visualizing data

27. Visualizing data

28. Checking language performance

29. Checking keyword length performance

30. Other examples of data sheets
● Installs & Conversion Rate
● Installs & ARPU
● Buyers & Conversion to Buyers
● Buyers & ARPPU
● Keywords & Revenue
etc.

31. What to use this data for?
● Optimizing text assets on the listing (title, description)
● Enriching screenshots with features connected with keywords
● Looking for profitable languages/markets for localization
● Using keywords in UA campaigns (eg. Google Ads)
● Increasing revenue and user engagement

32. Thank you!