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How to boost your ASO with data analytics?

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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.

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How to boost your ASO with data analytics?

  1. 1. How to boost your ASO with data analytics
  2. 2. Few words about me 2 years experience in ASO Working at Gamesture Optimizing mobile games: ● Questland ● Fablehood Combining data with ASO
  3. 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. 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. 5. Correlation between UA & Similar Apps Post-analytics (III)
  6. 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. 7. Exporting Similar Apps data
  8. 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. 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. 10. Removing outliers
  11. 11. Presenting results Correlation coefficient (CC): 0.81 High positive correlation
  12. 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. 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. 14. Using your own resources to boost conversion and revenue Real-time analytics (II)
  15. 15. In-game mechanism Social media Using “Secret codes”
  16. 16. Adapting to the app listing 1st screenshot
  17. 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. 18. +29% more installs Analyzing GP store performance +36% higher CR
  19. 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. 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. 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. 22. Analyzing keyword KPIs for ASO purposes Pre-analytics (I)
  23. 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. 24. No of words: =IF(LEN(TRIM(B2))=0,0,LEN(TRIM(B2))-LEN(SUBSTITUTE(B2," ",""))+1) Language: =DETECTLANGUAGE(B2:B20) Importing data
  25. 25. Creating pivot tables
  26. 26. Visualizing data
  27. 27. Visualizing data
  28. 28. Checking language performance
  29. 29. Checking keyword length performance
  30. 30. Other examples of data sheets ● Installs & Conversion Rate ● Installs & ARPU ● Buyers & Conversion to Buyers ● Buyers & ARPPU ● Keywords & Revenue etc.
  31. 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. 32. Thank you!
  • jonolewis

    May. 25, 2021

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.

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