Jakub Chour, Mobile Marketing Manager at AppAgent, presented at App Promotion Summit 2017 in London how he decreased churn of Liftago (an Uber competitor in Central Europe) users by 20% using a logistic regression-backed campaign. Learn how you can apply churn models to your mobile application and save the most valuable users.

1. CASE STUDY: MAKING YOUR BEST
CUSTOMERS CHURN LESS
Jakub Chour | jakub@appagent.co

2. WHAT ARE TRYING TO ACHIEVE?
SAVING CUSTOMERS BY USING CLASSIC
STATISTICAL METHODS AND MARKETING
AUTOMATION TOOL INTERCOM

3. Best-rated taxi app
in Europe, operating
in Czech a Slovak
Republic
Full-stack mobile
marketing agency.
Analytics, acquisition,
retention, positioning,
ASO, segmentation

4. The only statistics you will need
38%
users rarely or never allow
push notifications. 7% higher
than in 2015*
*Mobile App Report by ComScore, 2016
49%
of mobile users download 0
apps per month, 13% download
a single app only *.

5. Got churn? Let’s roll!
Import churn in Intercom
Define a large enough audience
Test within 30 days
Evaluate

6. Does it work? Not really
Just because it uses math, it doesn’t make it valid. #mathwashing
21% users reached the goal (ride in that 30 days). What does it mean?
Is it reliable? We didn’t know at the time of the set-up

7. First insight: Know how to evaluate
the results. Before you do anything.

8. The definition of churn (rate)
Churn-rate
“Percentage of subscribers to a service who discontinue their subscriptions to
that service within a given time period”
Churned passenger at Liftago
„A passenger with at least one ride who hasn’t ridden for at least 90 days.“
Day of the ride 90th day

9. Don’t stop everyone from churning
Important factors of a good Liftago passenger
▪ no. of rides
▪ % of rides with discount coupons
▪ rides per period
▪ time of the ride
▪ geolocation - cities
▪ customer type: business, personal
Why not just TOP 10%?

10. Creating the model - it’s not magic
Put all the data you have into one pile
In our case:
• app usage data
• pay-gates data
• geolocation data
• calculated data from ETL
And now: Let the bright guys from IT/Stats do their job

11. Important variables explaining churn for Liftago passengers
▪ total number of rides
▪ coupons
▪ avg. revenue
▪ avg. number of rides in given time frames
Day of the ride 90th day

12. Model reliability and validity
The Liftago anti-churn model is 75% reliable. What does this mean? And why
isn’t it 100%?
The first test ended up with 79% users churning despite an anti-churn message.
Does it mean something is broken?

13. Our 2nd test design
Bigger sample of thousands
Users randomly split into groups
Knowing statistical model
Knowing how to evaluate

14. Why is Intercom great but not for testing churn

15. Second insight: Churn model is
process. Maybe your time-frames are
wrong. Maybe you are missing some
important variable. Maybe groups
were set-up badly.

16. What did we discover?
What did our analysts say?
1. No push message group has 75% churn rate
2. The push message group has 71% churn rate
(71% of the users did not make a ride)
=> 4% difference, wow! But wait, it’s only 4%. Why?

17. How did we get to 20%

18. Improved working with the model
1) Added another variables to model: rides-in-periods, passenger type
2) Repeated every experiment 2-3 times, to be sure
3) Iterated every 30 days for 9 months
4) Evaluating right in Intercom

19. How did we divide groups in Intercom
1)Group I. - no push
2)Group II. - push but no incentive
3)Group III. - push with incentive
4)Group IV. - random crap
5)Group V. - Facebook look-a-like anti-churn campaign

21. How did we divide groups in Intercom
1)Group I. - no push
2)Group II. - push but no incentive
3)Group III. - push with incentive
4)Group IV. - random crap
5)Group V. - Facebook look-a-like anti-churn campaign

22. Last insight: Be patient and
ABT - Always be testing

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