Maximise the value of app users with predictive analytics
•
0 likes•818 views
How to maximise the value of mobile game players using predictive analytics. Predict user churn/conversion/spend and use these predictions to properly manage the lifecycle of your players. Slides from talk by Ryan Topping (Cubitic) 25th Feb 2015.
Recommended
More Related Content
Similar to Maximise the value of app users with predictive analytics
Similar to Maximise the value of app users with predictive analytics (20)
Maximise the value of app users with predictive analytics
- 2. Cubitic’s suite of behavioural prediction algorithms tell you what your users will do, before they do it …allowing you to proactively drive engagement and monetisation.
- 3. COST OF ACQUISITION IS RISING CPI up by 84% since January 2012 Harder to rely on acquisition to drive game profit Rising importance of maximizing value of existing users Source: Venture Beat & Fiksu.com, 31 December 2014. $- $0.50 $1.00 $1.50 $2.00 $2.50 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Cost Per Loyal User Index iOS
- 4. THE TRADITIONAL APPROACH Segment users by historic behaviour Define desired outcome Targeted intervention Strategy used for CRM, paid re-targeting, tailored game experience This is a reactive approach
- 5. Cubitic’s suite of predictive analytics include… PROACTIVE APPROACH Use Predictive Analytics to anticipate user behaviour Act ahead-of-time to influence behaviour Increase effectiveness & ROI of marketing spend Which users will stop playing? Churn prediction Which users will convert? Conversion prediction How much will users spend? Spend prediction
- 6. EXAMPLE – BRIAN’S GAME! Brian has a small iOS game with around 1,000 daily installs and 50,000 DAU The game is doing well but he thinks retention could be higher 0% 20% 40% 60% 80% 100% 0 5 10 15 20 25 30 Day After Install RETENTION £0.00 £0.10 £0.20 £0.30 £0.40 0 5 10 15 20 25 30 Day After Install REVENUE PER INSTALL How can we use predictive analytics to improve retention?
- 7. BRIAN’S CURRENT APPROACH With no intervention, on a given day 20% of DAU will not return to the game Brian is sending a push message to users inactive for 3 days (40% of DAU) Reduced churn to 15% of DAU Cost Per Impression Users targeted Total Cost Churn Prevented Traditional Approach $0.001 20,000 (users inactive 3 days) $20 25%
- 8. BRIAN’S CURRENT APPROACH 20,000 Inactive for 3 days 30,000 Active 7,500 Churned 42,500 Active now + 3 days future All Users 50,000 push msg 15% of DAU
- 9. BRIAN’S CURRENT APPROACH Problems with this approach? 1. Not the right time 2. Not the right users 3. Not the right message
- 10. PROACTIVE PREDICTIVE APPROACH SEGMENTATION BASED ON PREDICTED BEHAVIOUR Transient Abandoning Recruit Veteran Early Player Established Player Predicted Churners Predicted Returners This can be incorporated into existing segmentations or be used as basis for new segmentations
- 11. TARGETED INTERVENTION For example: CHURN PREVENTION push message in-game offer Transient AbandoningPredicted Churners Target each segment with relevant message! Early Player Established Player
- 12. PROACTIVE PREDICTIVE APPROACH 5,000 Churned 45,000 Active now future All Users 50,000 push message in-game offer transient abandoning 10% of DAU
- 13. AB TESTING 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 5 10 15 20 25 30 Days After Install Old Reactive Approach (A) New Predictive Approach (B)
- 14. SUMMARY 1. Churning users are targeted early (right time!) 2. Only users likely to leave are targeted (right users!) 3. Users receive a relevant message (right message!) Cost Per Impression Users targeted Total Cost Churn Prevented Traditional Approach $0.001 20,000 (users inactive 3 days) $20 25% Predictive Approach $0.001 10,000 (churning users) $10 50%
- 15. QUESTIONS Thank you for your time! Get in touch www.cubitic.io ryan@cubitic.io Follow us! @ryan_does_data @cubitic