The Game Life Cycle & Game Analytics: What metrics matter when?

The Game Life-Cycle and Game Analytics: What metrics matter when?Casual Connect Hamburg 2012Mark Gazecki (Chairman)

Introduction HoneyTracks: Web-based game analytics solutionDeep analytical capability For all types of games Real-time / near real-timeCohort analysis, funnels, Social games, browser-games,data-filtering client games, mobile gamesCustom metrics & funnels Easy-to-use graphical For everyone in the company interface Information at everyone’s finger- Avoiding data-graveyards tips: Game design, product mgmt, (happens if people can’t use it) marketing, management, … Copyright HoneyTracks 2

Game Life-Cycle & MetricsThe 5 most important metrics The never-ending quest for the most important 5 metrics … Copyright HoneyTracks 3

Game Life-Cycle & MetricsThe 5 most important metrics The never-ending quest for the most important 5 metrics … . . . … is indeed a never-ending quest Copyright HoneyTracks 4

Game Life-Cycle & Metrics The 5 most important metrics … there is no such thing as the universal 5 most important metricsGames are unique & different Games have a life-cycleTo generate actionable insight differences in each What is important changes over the life-time of agame must be considered. This has an implication game. This must be reflected in the metrics / KPIsfor the metrics you want to monitor. Copyright HoneyTracks 5

Game Life-Cycle & Metrics Moore‘s lifecycle adoption model applied to gamesPrototypical Technology Product Lifecycle (taken from “Crossing the Chasm”) Growth Maturity & Revenues • Like any other technology-product, games have a product lifecycle (may be more or less pronounced for certain game-types and individual games) • First focus is on growth then on managing maturity and maximizing revenues Copyright HoneyTracks 6

Game Life-Cycle & MetricsVirality vs retention What would you rather have?Double the virality? Half the churn-rate? Copyright HoneyTracks 7

Game Life-Cycle & Metrics Why retention comes firstNumber of active users (conceptual) 3000 Assumptions Viral game Ret. game Viral invites / 2500 user 2.5 1.25 Viral game 2000 Churn-rate 80% 40% 1500 1000 Game with better retention 500 0 Month Month Month Month Month Month Month Month Month 1 2 3 4 5 6 7 8 9 • Game with better retention has higher number of average monthly users • No retention – no sustainable growth – no hit • … and since users tend to monetize better as they progress in the game, higher retention lays the basis for strong monetization Copyright HoneyTracks 8

Game Life-Cycle & Metrics Game life-cycle KPI framework Game Life-Cycle (time / age of game) User acquisition Retention MonetizationBring initial users Virality into the game (x-promotion,“limited launch”) Engagement Acquisition & Monetization metrics virality metrics metrics • Start out by making sure that “retention” is good enough with an initial flow of users, i.e. not all users you acquire churn out immediately • Then move onto optimizing “user acquisistion”, “virality”, and “monetization • … but of course this is an additive view!!! Copyright HoneyTracks 9

Game Life-Cycle & Metrics Retention metrics: What to start withRetention / Engagement Metrics 1-7 day retention • Optimize tutorial (to get users effectively into the Tutorial steps funnel game) • A/B-test user funnels Drop-off rates (by level) • Optimize user drop-off events (make it less difficult, more “funner”, …) Visits / DAU • Give user more / less stuff to do / more energy (-> session length. engagement) • Track feature-usages (also for mid- / end-game) Session times • A/B-test game mechanics (esp. mid- / end-game) Churn-rate (monthly) 1 / monthly churn-rate = Player lifetime (in months) Copyright HoneyTracks 10

Game Life-Cycle & Metrics Acquisition metrics: What to start withAcquisition Metrics Conversion rates (CTR) User acquisition cost (CPC, CPI / PAC) • Test different marketing channels Metrics by marketing channel / ad • A/B-test different creatives (cohort analysis) • A/B-test different targeting (demographics, geographies) Metrics by demographics (cohort analysis) • Monitor PLTV > PAC (for channel cohorts, demographies etc) Metrics by geography (cohort analysis) Metrics by user source (e.g. player life- time value) (ads, viral, x-promotion) Start tracking monetization metrics by user cohorts early on (channels, demography, …) Copyright HoneyTracks 11

Game Life-Cycle & Metrics Example: Marketing channelsScreenshot: Channel profitability ... shows that Channel 1 has 50% of Channel 32 revenues despite having 2.5x in DAU Segmenting users by marketing channel ... 1 32 Marketing Channel 1 2 3 4 5 6 7 8 9 10 11 32 33 Marketing Channel Comparing payouts to „revenues“ shows that Channel 1 has more „lost revenue“, i.e. issues in the We could improve the game: payment process • Focus on user aquisition from ch32 • Double check payment type (SMS) and charge backs in ch1 • Switch off certain payment methods at special times 1 Marketing Channel 32 Copyright HoneyTracks 12

Game Life-Cycle & Metrics Virality metrics: What to start withVirality Metrics k-factor Number of sent invites / DAU • A/B-test content for viral message (how should buttons look, images, etc) Acceptance rate (by type of invite) • A/B-test different viral triggers (in the game) • A/B-test different acceptance mechanisms % of virally acquired users (last 30 days cohort) Number of viral users by viral source Copyright HoneyTracks 13

Game Life-Cycle & Metrics Monetization metrics: What to start withMonetization Metrics ARPU ARPPU • A/B-test alternatives to improve first-time buyer conversion (e.g. specials, variants of that particular virtual good) Payment conversion rate • Optimize user-flow towards first purchase trigger (-> get more users there) Avg. transaction value • A/B-test different virtual goods & packages • Optimize payment process (conversion steps) First purchase trigger • A/B-test pricing Paying user cohort (by marketing channel, by geography Player life-time value (PLTV) Copyright HoneyTracks 14

Custom MetricsGame life-cycle KPI framework: Introducing custom-metrics User acquisition Retention Monetization Virality Standard metrics Custom metrics • Standard metrics are great for detecting issues on a high level • To derive actionable insight need to drill deeper and look at custom metrics Copyright HoneyTracks 15

Custom Metrics Drill-down capability & custom metrics to derive actionable insight Observe slight decrease “Peeling in aggregate ARPU the onion” in month of February Payment conversion rate ARPPU is decreasing remains constant Payment conversion Payment conversionfor existing users stays for the user cohort acquired constant in January is very lowUsers acquired in January The pricing for a virtual Mix of users in Januaryfrom marketing channel good, which typically was shifted towards countries“SuperDuperAds” have a the first virtual good with generally lower significantly lower purchased by users, was conversion rates conversion rate changed Copyright HoneyTracks 16

Game Life-Cycle & Metrics Example: ARPU cohort analysisScreenshot: ARPU cohort analysis Aggregate ARPU is 2 Euro Monthly cohorts show ... and we see that that ARPU actually ARPU improved from becomes 4 Euro! April to May cohort • Aggregate numbers don‘t tell the truth • As a next step we would dig deeper into the May-cohort to understand why it generated better ARPU Copyright HoneyTracks 17

Game Analytics Examples „Peel the onion“: Payment conversion (1)Screenshot: Revenue analysis by level Pretty effective at Majority of revenues monetizing advanced achieved in levels 20-30 users ... 0 10 20 30 40 50 0 10 20 30 40 ... but what about users in earlier levels? Can we push users into making purchases earlier? What are virtual goods that are useful at earlier levels? 0 10 20 30 40 50 Copyright HoneyTracks 18

Game Analytics Examples „Peel the onion“: Payment conversion (2) At lower levels „food“ is being purchased relatively higher ... so this may be the virtual good, which converts users into „first time buyers“ ... even though „food“ doesn‘t play a major role in revenuesWe could improve the game• Offering „food“ specials to users at lower levels• Try lower prices for food to generate more first time buyers Copyright HoneyTracks 19

Game Analytics Examples „Peel the onion“: Whales Analysis What is her profile Who are my whales? What, when and how much of each item works best for her? What paymentWe could improve the game does she use• See what works best for whales and offer higher variety of same type• Increase prices step-wise for new items and monitor closely• Try out special offers for items that work Different colors indicate different feature/ for other whales item types - “mouse over “shows details• Optimize payment options Copyright HoneyTracks 20

Game Analytics & Game Life-CycleHow to approach it rightStart with retention metrics.Then move to user acquisition-, virality-, and monetization metrics.Start with standard metrics.Then move to custom metrics to generate actionable insight„Peel the onion“ to derive actionable insight(cohort analysis etc)Understand it is an ongoing effort, which involves multiplefunctions / departments in your company (not all which are tech-people)Make sure you have the right game analytics system(it should support all of the above) Copyright HoneyTracks 21

Contact information Want to see HoneyTracks in action? Check out: www.honeytracks.com @HoneyTracks Mark Gazecki mg@honeytracks.com Copyright HoneyTracks 22

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The Game Life Cycle & Game Analytics: What metrics matter when?

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