In-app offers monetization from basics to advanced technics based on Crazy Panda experience
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The document discusses in-app offer monetization strategies, from basic concepts to advanced techniques, emphasizing the importance of user segmentation and A/B testing. It presents case studies demonstrating the effects of different offer sizes and bonuses on user conversion rates, as well as the application of machine learning for predicting user needs. The overall message highlights that personalized offers can significantly boost revenue, with a need for continuous testing and adaptation.
In-app offers monetization from basics to advanced technics based on Crazy Panda experience
- 1. IN-APP OFFER MONETIZATION FROM BASICS TO ADVANCED TECHNIQUES IVAN KOZYEV
- 2. IVAN KOZYEV HEAD OF ANALYTICS CRAZY PANDA
- 3. OUR PRODUCTS World Poker Club Casual Poker 72M registrations Stellar Age MMO Strategy 2.1M registrations The Household Social Farm 30M registrations Pirate Tales Party Battler 1.4M registrations
- 4. Theory, p.1 Basic definitions ⊳ Bank ⊳ Bank item ⊳ Offer ⊳ Personalization
- 5. Theory, p.2 Offers ⊳ Price ⊳ Bonus/discount ⊳ Lifetime ⊳ Cooldown ⊳ Content ⊳ Pop-up
- 7. Personalization Basics Bank case p-value = 0.28 no significant difference
- 8. Personalization Basics Bank case p-value < 0.01 for both pairs significant difference
- 9. Personalization Basics User segmentation Segmentation through user`s payment behavior: 1 2 3 4 ⊳ Converting non-payers into payers ⊳ Establishing a regular payment relationship ⊳ Regular payers group ⊳ Returning churning payers
- 10. Theory, p.3 AB testing ⊳ Not starting with a hypothesis ⊳ Stopping the test too early ⊳ Stopping the test too late ⊳ Measuring the wrong metric ⊳ Not taking into account multiple testing ⊳ etc. Audience Control Test change Some mistakes in AB testing:
- 11. Advanced Techniques Case 1: Small vs big offers ⊳ Conversion: 5.4% ⊳ Conversion: 3.8% VS * - in the first 30 days ⊳ ARPPU*: $36 ⊳ ARPPU*: $59
- 12. Advanced Techniques Case 1: Small vs big offers Hypothesis: ⊳ There will be more high-value payers for the bigger starting offer than for the smaller one Conversion into payer that spends at least $30*: * - in the first 30 days ⊳ Conversion: 1.9% ⊳ Conversion: 2.8%
- 13. Advanced Techniques Case 1: Small vs big offers * - in the first 30 days ⊳ Conversion: 5.5% ⊳ $30 payer conversion: 2.9% ⊳ ARPPU*: $60 +
- 14. Advanced Techniques Case 2: Bad offers ⊳ Share of offers bought: 0.5% ⊳ Offer bonus 30% less Payers group lambda Solution: ⊳ Increasing the offer bonus by 30% AB test summary: ⊳ Share of offers bought: 22% ⊳ Project revenue expected uplift: 3% Starting conditions:
- 15. Advanced Techniques Case 2: Bad offers Investigating the case: ⊳ Next offer was 1.8 times bigger ⊳ Payments shifted towards smaller offers ⊳ Total revenue loss was about 44% for those users Tests that followed: ⊳ Increasing lambda offer prices ⊳ Decreasing next group offer prices ⊳ Increasing next group offer bonuses ⊳ Changing content of lambda offers ⊳ etc. Payers group lambda Next group
- 16. Advanced Techniques Case 2: Bad offers Bad offer upsides: ⊳ Making other offers more desirable ⊳ Can lead to more or bigger payments ⊳ Skipping the loop of constantly increasing offer value ⊳ Can be used as part of a double offer
- 17. Advanced Techniques Case 3: Machine learning ⊳ Set up a goal ⊳ Check historical data ⊳ Prepare dataset ⊳ Some magic happens ⊳ And the model is done ⊳ Now, implement it ⊳ And, finally, test it
- 18. Advanced Techniques Case 3: Machine learning Predict which currency user needs the most: ⊳ Setting the correct target (there were no soft currency offers) or Hard currency exchange CatBoost and special data handling ⊳ Making the model very fast (less than 5s to collect data and make a prediction) Main issues:
- 19. Closing Words Summary ⊳ Personalization is awesome ⊳ Up to 80% of revenue from personalized offers ⊳ Testing is a must ⊳ 150+ AB tests per year just for World Poker Club ⊳ Tests will fail ⊳ 42% successful test rate ⊳ 14% tests yield 77% of effect ⊳ ML is challenging ⊳ Only the 3rd model proved to be profitable
- 20. THANK YOU FOR YOUR ATTENTION! ANY QUESTIONS? IVAN KOZYEV Head of Analytics at Crazy Panda i.kozyev@crazypanda.ru telegram: @IvanKozyev https://www.linkedin.com/in/ivan-kozyev/