Maximise the value of app users with predictive analytics
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Transcript of Maximise the value of app users with predictive analytics
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.
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.
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Cost Per Loyal User Index iOS
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
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
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?
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%
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
BRIAN’S CURRENT APPROACH Problems with this approach?
1. Not the right time
2. Not the right users
3. Not the right message
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
TARGETED INTERVENTION For example: CHURN PREVENTION
push message
in-game offer
Transient Abandoning Predicted Churners
Target each segment with relevant message!
Early Player
Established Player
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
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)
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%
QUESTIONS
Thank you for your time!
Get in touch
www.cubitic.io
Follow us! @ryan_does_data
@cubitic