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Data Scientists Need not apply

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GameAnalytics
GameAnalytics

Use Game Analytics to understand your players and your monetizers. Anyone can do it! Alison Bilas, VP of Product in GameAnalytics, explains how to best integrate your data and optimize your game accordingly.

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Data scientists need not apply:? How anyone can do game analysis Casual Connect, November 2014 @allisonbilas | allison@gameanalytics.com
Using data to understand your players and monetizers.
Data Scientists Need not apply
Analyst Jr. Producer Producer Monetization Mgr. Developer Senior PM
Descriptive Exploratory Inferential Predictive Causal Quantitatively describing data Looking for previously unknown relationships in data Testing theories with a sample of data Analyzing current events to predict future events Measuring what happens to one variable when you change another Distribution; ? 5-number summary; ? Before/after Directionality; Visualizations Regression models; ? Chi squared Modeling, machine learning, data mining A/B testing OBSERVE EXPERIMENT
Descriptive Exploratory Inferential Predictive Causal Quantitatively describing data Looking for previously unknown relationships in data Testing theories with a sample of data Analyzing current events to predict future events Measuring what happens to one variable when you change another Distribution; ? 5-number summary; ? Before/after Directionality; Visualizations Regression models; ? Chi squared Modeling, machine learning, data mining A/B testing “EASY”
? Quantitatively describing data ? Distribution of data; 5-number summary; Outliers Score Percentiles by Boost Used 0 28 56 84 112 140 10. 25. Median 75. 90. 99. Valentine Winter Prism Ruby Blaze Burst Stone No Boost Descriptive
? Looking for previously unknown relationships in data ? Visualizations are key component Exploratory Monetizers by Amount Spent per Customer 0 1,000 2,000 3,000 4,000 $0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975 Total Bookings by Amount Spent per Customer $0 $25,000 $50,000 $75,000 $100,000 $0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975 4% 6% 16% 22% 35% 17% 34% 18% 24% 15% 9% 1%
? Measuring uncertainty in analysis ? Linear regression; Chi squared Inferential
Three Big Questions INSTALLS How many people have installed my game? DAU How many people are playing my game? REVENUE How much money is my game generating? Acquisition Engagement Monetization
Really good analysis…
Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45% Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis
? Continue to use push noti?cations to re-engage users and support DAU ? Prioritize dev work for daily prizes to increase return rates ? Consider creating a high priced gem package to increase ARPPU Recommendations
Data Scientists Need not apply
You do see, you just don’t observe. Sherlock H.
Allison Bilas, VP of Product @allisonbilas allison@gameanalytics.com Additional Reading: ? https://bitly.com/bundles/sparklingallison/ We’re hiring!
UNESCO Game Design Challenge $100K Prize Incubate a game on peace, sustainable development and global citizenship. http://mgiep.unesco.org/
BEST CHART EVER!

More Related Content

Data Scientists Need not apply

  • 1. Data scientists need not apply:? How anyone can do game analysis Casual Connect, November 2014 @allisonbilas | allison@gameanalytics.com
  • 2. Using data to understand your players and monetizers.
  • 4. Analyst Jr. Producer Producer Monetization Mgr. Developer Senior PM
  • 5. Descriptive Exploratory Inferential Predictive Causal Quantitatively describing data Looking for previously unknown relationships in data Testing theories with a sample of data Analyzing current events to predict future events Measuring what happens to one variable when you change another Distribution; ? 5-number summary; ? Before/after Directionality; Visualizations Regression models; ? Chi squared Modeling, machine learning, data mining A/B testing OBSERVE EXPERIMENT
  • 6. Descriptive Exploratory Inferential Predictive Causal Quantitatively describing data Looking for previously unknown relationships in data Testing theories with a sample of data Analyzing current events to predict future events Measuring what happens to one variable when you change another Distribution; ? 5-number summary; ? Before/after Directionality; Visualizations Regression models; ? Chi squared Modeling, machine learning, data mining A/B testing “EASY”
  • 7. ? Quantitatively describing data ? Distribution of data; 5-number summary; Outliers Score Percentiles by Boost Used 0 28 56 84 112 140 10. 25. Median 75. 90. 99. Valentine Winter Prism Ruby Blaze Burst Stone No Boost Descriptive
  • 8. ? Looking for previously unknown relationships in data ? Visualizations are key component Exploratory Monetizers by Amount Spent per Customer 0 1,000 2,000 3,000 4,000 $0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975 Total Bookings by Amount Spent per Customer $0 $25,000 $50,000 $75,000 $100,000 $0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975 4% 6% 16% 22% 35% 17% 34% 18% 24% 15% 9% 1%
  • 9. ? Measuring uncertainty in analysis ? Linear regression; Chi squared Inferential
  • 10. Three Big Questions INSTALLS How many people have installed my game? DAU How many people are playing my game? REVENUE How much money is my game generating? Acquisition Engagement Monetization
  • 12. Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
  • 13. Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
  • 14. Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
  • 15. Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45%
  • 16. DAU = 1.25M Revenue = $41.4K ARPPU = $7.29 Conversion = 0.45% Revenue declined by $2K (-5%) week-over-week after the re- engagement campaign ended. This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%. Those that did spend continued to spend at the same ARPPU of $7.29. Reporting Analysis
  • 17. ? Continue to use push noti?cations to re-engage users and support DAU ? Prioritize dev work for daily prizes to increase return rates ? Consider creating a high priced gem package to increase ARPPU Recommendations
  • 19. You do see, you just don’t observe. Sherlock H.
  • 20. Allison Bilas, VP of Product @allisonbilas allison@gameanalytics.com Additional Reading: ? https://bitly.com/bundles/sparklingallison/ We’re hiring!
  • 21. UNESCO Game Design Challenge $100K Prize Incubate a game on peace, sustainable development and global citizenship. http://mgiep.unesco.org/