How to identify key opportunities to grow using product metrics pyramid: f2p guide / Oleg Lapin (AppQuantum)
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The document discusses the 'metrics pyramid' framework for identifying growth opportunities in mobile games, emphasizing the importance of various metrics such as ROI, LTV, and ARPU. It outlines specific analytical approaches and tools to track game performance, explore user behavior, and make data-driven decisions to enhance revenue. The presentation stresses the need for an analytical culture within companies and the use of diverse methods to prioritize product features based on expected outcomes.
How to identify key opportunities to grow using product metrics pyramid: f2p guide / Oleg Lapin (AppQuantum)
- 1. How to identify key opportunities to grow using product metrics pyramid: f2p guide Oleg Lapin Head of Analytics, AppQuantum
- 2. SPEAKER Oleg Lapin analyst, team lead, father PROF EXP - more than 10 years in analytics - building analytical department in AppQuantum - built Product Analytics in Nexters - before it, participated in Avito’s success - before it worked in FMCG, science and consulting
- 3. team AppQuantum WHO WE ARE A free-to-play mobile game publisher based in Moscow, Russia WHAT WE DO We assist developers with many aspects of their apps, helping them stay focused on making games and delivering high quality content to users
- 5. What is it “metrics pyramid” ● it is framework of using metrics ● my favourite author is Elena Seregina / DataLatte “Иерархия метрик vs пирамида метрик” ● an older article from Google “How to choose the right UX metrics for your product” ● or you could just Google)) * Cool viz from the Elena’s article mentioned
- 6. Simple metrics hierarchy * ROI - return of investment, LTV - lifetime value, ARPU - average revenue per user, ARPPU - average revenue per paying users, AOV - average order value, OPP - orders per payer, rv_per_user - rewarded videos per user ROI = LTV / CPI LTV = ARPU_inapp + ARPU_AdsMon ARPU_inapp = ARPPU * %payers ARPPU = AOV * OPP ARPU_AdsMon = rv_per_user * ecpm LTV ROI CPI CPM CTR ARPU_inapp ARPU_AdsMon ARPPU %payers rv_per_user ecpm
- 7. ROI = LTV / CPI LTV = ARPU_inapp + ARPU_AdsMon ARPU_inapp = ARPPU * %payers ARPPU = AOV * OPP ARPU_AdsMon = rv_per_user * ecpm Simple metrics hierarchy * ROI - return of investment, LTV - lifetime value, ARPU - average revenue per user, ARPPU - average revenue per paying users, AOV - average order value, OPP - orders per payer, rv_per_user - rewarded videos per user LTV ROI CPI CPM CTR ARPU_inapp ARPU_adsmon ARPPU %payers %payers ecpm SO WHAT???
- 8. We need to learn how to use this tool!
- 9. First Story
- 10. Version N1 - classic Event analysis / liveops: - launch event - LVL_1 uplift Revenue, ARPDAU - LVL_2 - ARPDAU by cohorts - LVL_3 - ARPDAU, currency balance, AOV, %PU, %new payers, bundles, completion funnels - LVL_4 - LVL_3 + planning what metric should we uplift, estimate were we true - LVL_5 - your variant)) * You could have an operational dashboard for this purpose with all metrics calculated
- 11. We already have some metrics pyramide! Your KPI Revenue, ARPU, ARPPU, … Spending Structure, баланс харды, payers_ret Bundle conversion
- 12. What for is metrics pyramid ● searching for right reasons ● analyzing small changes ● differentiating effects
- 13. Do you need smth more interesting?
- 14. OK, let’s do it for the whole project!)
- 15. Second Story
- 16. ARPU curve. Is it ok? here we see that lifetime is more than 30 days (users are still paying) and LTV is more than $1,4 and in-app share is around 60% - is this LTV is OK for this market / optimization / genre? - is this lifetime is OK for this genre? - is this ARPU_inapp is OK for this type of game? - do we have a problem in early/mid/end game in ARPU dynamics? * just for an example ** some project, some cohort, some geography *** lifetime 0, 1, 3, 14, 30,...
- 17. Retention by LT here we see that Retention at first day is 28% and retention_30 is 6%. - is it OK? - do we have any gaps at tutor / early game? - why relative retention (2nd graph) stop increasing after 2 weeks? do we have any lack of gameplay after 2 weeks? * just for an example
- 18. ARPDAU by LT. Are they still paying in midgame? here we see that ARPDAU is around $0.4 and falling over time! (both in-app and ads, very bad). - why it’s falling? - let’s decompose, is it falling because of %payers or payers_churn or AOV? - do we have any economic restrictions? probably we don’t have enough content or enough offers * just for an example ** some project, some cohort, some geography ** lifetime 0, 1, 3, 14, 30,...
- 19. Daily payers by LT. Are they stop spending in game? here we see that daily payers is around 5% (seems it is purchase optimization traffic) but falling over time! - is there any problem with a first payment? - do we have enough content in game to spend much? - do we have enough offers? - do payers get an advantage in game, do we have any VIP_lvl system?? - yeap, all questions are the same as for ARPDAU in this game * just for an example ** some project, some cohort, some geography ** lifetime 0, 1, 3, 14, 30,...
- 20. ARPPDAU by LT. Do they pay a lot? here we see that is around $4 - is this ARPPDAU OK for this country / optimization? - why it’s not increases over time but instead starting to decrease at second week? - do we have enough whales content? * just for an example ** some project, some cohort, some geography ** lifetime 0, 1, 3, 14, 30,...
- 21. Example of summary table ● We could summary all metrics in one table and use weekly lifetime instead of daily ● it’s more useful when you do overall conclusions * just for an example ** some project, some cohort, some geography ** lifetime 1-2-3-4 weeks after registration
- 22. And then we go deeper and deeper ● Also we analyze time spending ● then we go deeper to economics, offers, social interactions and so on ● At the end we’re trying to sum up all problems we found and identify key opportunities to grow (is it about mechanics or economics or offers or smth else) * the example of our report, the main part is conclusions for sure))
- 23. FINAL Story
- 24. The main idea of this presentation is not about metrics itself
- 25. Expectations ● each feature should has its own expectations of outcomes in terms of metrics and user segment ● yeap, we need data to calculate all metrics ● yes, we need some BI system to have useful dashboard ● for sure we need some work space to store all our expectations ● and finally we need some kind of analytical culture in company
- 26. Prioritization we have a few instruments: - our game design thinking or user voice ("it's good to have this feature/content here") - previous experience (smth like "this type of content is always increase this metric) - analysis of user behaviour / metrics that I described in presentation. So here we could understand that based on metrics we see a growth opportunity in the certain user segment - the best thing when your team could use all these methods at once!
- 27. linkedin: https://www.linkedin.com/in/oleg-lapin-521891b5 tg me: Oleg_the_analyst ping us at: hi@appquantum.com Thanks!