Big Data in Mobile Gaming - Eric Seufert presentation from IGExpo Feb 1 2013
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Big Data in Mobile Gaming - Eric Seufert presentation from IGExpo Feb 1 2013






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Big Data in Mobile Gaming - Eric Seufert presentation from IGExpo Feb 1 2013 Big Data in Mobile Gaming - Eric Seufert presentation from IGExpo Feb 1 2013 Document Transcript

  • Big Data in Mobile Gaming: Optimizing the UserExperience on iOS & Android Eric Seufert IGEXpo, Feb 1 2013
  • About Me• Quantitative Marketer with focus on analytics and statistical predictive methods• MA in Applied Economics, BBA in Finance / CS• Head of Marketing & User Acquisition at Grey Area Labs (Helsinki) – Previously strategy / analytics positions at Digital Chocolate (Helsinki) and Skype (Tallinn)
  • About Me• Blog frequently about F2P / “Big Data” decision science / analytics at• Feel free to reach out:
  • Presentation Agenda• Introduction to “data-driven game design”• Metrics portfolio – what’s important to track?• Feature selection and analytics strategy• Player segmentation and optimization• Free-to-play design (spreadsheet included!)• Q&A
  • What is “Big Data”?• Buzzword, but with some substance• Cheap storage = lots of data• More data = less sophisticated statistical techniques required to derive insights• More data = easier to spot true trends (vs anomalies)
  • What is “Big Data”?• Example: A/B testing• 200 data points = small sample size – Does sample size reflect population? Must perform t-test, possibly decrease confidence interval – What’s the distribution of data?• 2 million data points = large sample size – Distribution easier to infer – Less testing / normalization required
  • What is “data-driven game design”?• Iterative design• Responsive to game metrics• MVP -> behavioral feedback (metrics) -> iterate -> update -> repeat• Half intuition, half data science: fundamental game mechanics established and then tweaked based on player behavior
  • What is “data-driven game design”?• Players get more of what they want• Most enthusiastic players given latitude to enjoy the game to greatest possible extent
  • The Metrics Portfolio• Minimum set of metrics required to facilitate data-based decisions• Let players tell you what they like / don’t like through their behavior
  • Four Key Metrics Groups• Retention• Monetization• Engagement• Virality
  • Retention• D1-D7, D14, D28, D90, D365• Most important set of metrics you track• Communicates delight – extent to which players enjoy your game = extent to which they’re willing to return to it
  • Retention• Retention “profile”: D1, D7, D28• Follows general decay pattern: – D1 *.5 = D7, D7 * .5 = D28• Used to calculate player lifetime• Look for D1 >= 40% for launch• More info:
  • Monetization (F2P)• Conversion rate: % of users that ever spend money on IAPs – 3-4% is good, >=5% high• ARPU: Average Revenue per User – Average amount of money users spend in-game – Varies genre-to-genre, game-to-game• ARPPU: Average Revenue per Paying User – Greater the delta between ARPPU / ARPU, lower the conversion rate
  • Monetization (F2P)• ARPDAU: Average Revenue per Daily Active User – Average money spent per day by users• Catalog distribution: – Good to know where the bulk of revenue comes from. Small purchases? Large purchases? How do users engage with the product catalog? Best way of visualizing is graphically.
  • Engagement• Average Session Length / Median Session Length – Daily lengths per user (what’s the average session length?)• Average Session Count / Median Session Count – Daily counts per user (how frequently do they log in each day)• Medians important to track because very enthusiastic players will skew the average (not uncommon to see individuals logging in dozens of times per day or playing hour-long sessions)
  • Virality• K-Factor – Number of users, on average, a single user introduces to the game (>=1 = viral) – Hard to track on mobile – How to track k-factor when conversions are unknowable (blog post: • You know purchased users, you can estimate organic installs, back these out and lump the rest into “k- factor”) – Virality model:
  • Analytics Strategy• How do we know what to track? – Retention is easy – Monetization is easy – Engagement is medium – Virality is hard• For the very basics, tracking should be oriented toward first session and last session• For the hard stuff (behavioral predictions), tracking must be far more extensive
  • Two approaches• I’m a smaller studio with less than 500k DAU. I want to focus on the “big picture” stuff that will help me retain players and boost engagement – Backend: standard SQL-based data warehouse, nightly ETL – Frontend: Dashboards communicate high-level metrics across organization, PMs possibly use some desktop visualization software to perform basic analysis
  • Two approaches• I’m a bigger studio with >500k DAU. I want to focus on driving ARPPU and converting players better – Backend: Hadoop-based system to handle processing large volumes of data – Frontend: Dashboards + desktop analysis software for PMs – Analysis layer: team of analysts / data scientists performing in-depth research, finding patterns, and delivering insight to product teams
  • What’s the point of my analytics system?• Collect and process data that can be used to derive insight that helps increase revenue• Your analytics system should deliver $$$• Analytics is not an intellectual exercise. Analytics is the science of making more money.
  • “Analytics” is vague. Who works in analytics?• The Analytics Engineer: system architect, designs the tracking library and data transfer / transformation / staging• The Analyst: Handles reporting, dashboard design, implementation• The Data Scientist: Research and algorithmic prediction. Helps drive data product design (recommendation engines, matchmaking, gameplay optimization)
  • What do I use analytics for?• Optimizing the Player Experience:• Give players the optimal experience based on the information they’ve given you through their behavior• Improve the product catalog – What do players want to buy? Where / when do players want to buy? Are we accommodating this?• UI / UX improvements through A/B Testing
  • Player Segmentation
  • Player Segmentation
  • My players are segmented. Now what?• We have made some initial determinations about this user based on his behavior• Now we adapt the experience to fit this profile• We measure exit metrics and iterate on segmentation technique
  • F2P Design• F2P is difficult – Paradigm shift from paid downloads, not to mention console titles – Requires a different approach to design• How do F2P games make money? – LCV > CPA – Continuous monetization curve – High retention – Low likely conversion
  • The F2P Model•• Projects revenues based on comprehensive examination of user acquisition, virality, monetization and retention• Useful tool for making priority decisions on game development• Helpful when evaluating how much $ to invest in user acquisition
  • Thanks!• Q&A• More info on my blog:• Email me: