Game Economy Prediction | Guy Bar Sade
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Delivered at Casual Connect Europe 2018. Game economy design for a new game is complicated. Changes in game economy hold risks to the game performance. Months ago, Gamepoint took a decision to integrate level progression in their Bingo game to maximize performance. During this session, we’ll review the process of new economy goals definition, risk management and how we used a game economy prediction system, Simpool, in order to maximize results in quick iterations.
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Game Economy Prediction | Guy Bar Sade
- 1. Game Economy Made Simple
- 2. About me GUY BAR SADE CO-FOUNDER & CEO Over 13 years of experience in in-depth data cycles (BI, analysis, big data) in leading companies (Playtika, bwin)
- 3. Game Economy management is A challenge Engagement RetentionMonetization Currency INCurrency OUT $$$
- 4. How does it look like? Level XP Level Up Daily Bonus Hourly 1 100 20 10 2 2 150 30 15 4 3 200 40 20 6 4 250 50 25 8 5 300 60 30 10 6 350 70 35 12 7 400 80 40 15 8 450 90 45 18 9 500 100 50 21 …. … … … … n ∞ ∞ ∞ ∞ X X X
- 5. Different users play the game differently Payer Vs. non payers Whales, Dolphins, Minnows Social Vs Anonymous Bonus seekers
- 6. Different users pay differently • Minnows - onetimers, MARPPU<$1 • Dolphins – MARPPU<$5 • Whales – MARPPU>$20, 15% of payers and responsible to 50% of revenues • Freeloaders – not paying
- 7. Game economy management is not easy Cost = time + UA Risk of wrong monitoring Lack of knowledge
- 8. What if • We could have simulate millions of users ? • We could have generate the data ? • We could have test X100 more hypothesis ?
- 9. GamePoint’s use case • Active for 18 years • 350,000 DAU • 750,000 MAU • 16 games • 5 Platforms
- 10. GamePoint’s current state analysis • High ARPDAU • High PU/DAU • The game contained level, but it wasn’t beneficiary for users • Shop has static price points across levels
- 11. 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 1-9 10-25 26-45 46-75 76-99 >=100 Users distribution across levels 100% 98% 96% 89% 72% 53% 0% 0% 1% 5% 18% 37% 0% 1% 3% 6% 10% 10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1-9 10-25 26-45 46-75 76-99 >=100 User segment across levels not_member SVIP VIP
- 12. Users purchase trend, across levels
- 13. • Higher short/mid term retention • More progression in the game • More engagement • Higher rooms • More cards • More rounds GamePoint’s requirements
- 14. GamePoint’s concerns • Protect current whales and gods • Community/FB/Chat feedback • Adjust the shop to game progression
- 15. Main questions were raised and simulated • Introduce progression to a live game • What is the projected OOC point? • How will the users’ progression looks like? • How will monetization and sale cycles performs
- 16. Economy file Generate game flow Define segments Simulate Insight Optimize Simulation and learning loop
- 17. Simulated room opening rate
- 18. Actual room opening rate New ProgressionOld Progression
- 19. Simulated churn & room opening
- 20. Results • Retention: +13% to +54% improvement • Play time: +69% increase in session length • Rounds: +28% increase in number of rounds
Editor's Notes
- Good morning everyone, during the next session I’m going to talk about game economy, simulation and prediction and our use case with GamePoint
- Few words about me
- AS you probably all know, manage and maintain a game is a challenge, constant battle between the main forces in the game : Retention which in many case is the source for currency in the game. Engagement , which is the tool to drain coins out of the game. And the delicate balance between the 2 which is the key to create a monetization point
- Simplifying a game economy file will make it look like this, commonly, we levels, xp andall other currency sources, In reality it I closer to this, and still, only few dozens rows dsiplay
- However , users are different, they have different game habbits, they consume coins differently Payers have different pattern, whales, mostly play longer Social users, in many cases use the social gifts Bonus seekers play occasionally after funding their activity by harvesting bonuses in the game
- And
- These debates and concerns means long experiments (if you want to improve 30 day retention or ROI, you need to wait 30 days)and require many users many users means lots of money Not all companies have game economist and above all, in the F2P world one of the most important missions is to constantly and efficiently monitor the game, its balance and the users’ wealth
- By using 3 pillars: Economy, game flow, and profiles we ran simulation, using MCMC approach, monte carlo Mrkove chain to get better understanding of the flow Our first phase was focusing on user churn and retantion