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!