This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.

1. How to data model Churn
Real life examples

2. Quick quizz
• How many of you are familiar with Churn issue?
• with Machine Learning?
Logistic Regression, Random Forest, Gradient Boosting trees?
(Not the subject here)
• With SQL?
(we may see some code later)
• What database tech do you use?
What about EMC Greenplum or Vertica?

3. Who I am
• Senior Data Scientist at Dataiku
(worked on churn prediction, fraud detection, bot detection, recommender systems,
graph analytics, smart cities, … )
• Occasional Kaggle competitor
• Mostly code with python and SQL
• Twitter @prrgutierrez

4. Churn definition
• Wikipedia:
“Churn rate (sometimes called attrition rate), in its broadest sense, is a measure of the
number of individuals or items moving out of a collective group over a specific period of
time”
= Customer leaving

5. Two types of Churn
• Subscription models:
• Telco
• E-gamming (Wow)
• Ex : Coyote -> 1 year subscription
-> you know when someone leave
• Non subscription models:
• E-Business (Amazon, Price Minister, Vente Privée)
• E-gamming (Candy Crush, free MMORPG)
-> you approximate someone leaving
Candy Crush: days / weeks
MMORPG: 2 months (holidays)
Price Minister: months

6. Two types of Churn
• Blurred Separation:
• Ex: T-mobile: 1 month subscription -> paying each call
• Ex: Wow: 1 month to 6 month subscription
• Banking?
• Focus : no subscription:
• Can be seen as a generalization where you have to approximate the target
• Bonus : Seller churn
• Market places
• Clients that participate product life
• Forums (Reddit)
• E-gamming (Korean competitions, guilds etc.)

7. Dealing with churn
• Motivations :
• Saturated market
-> cost get new client >>> cost keep client
• Ex : http://www.bain.com/publications/articles/breaking-the-back-of-customer-churn.aspx
• Wireline company : 2% to 2.5 % churn rate per month.
• If 5 M customers -> 1.32 M churn per year
• When reducing from 2.5% to 2% lowest estimation : 240 M $ in 18 month

8. Dealing with churn
• Predict churn :
• One model for performance <- our focus, short term, more ML
• One model for understanding <- long term, more Analytics
• Act on it (short term) :
• Special offer (telco call, free in game money, discount coupon … )
• Does it work? Feedback loop needed!
• Model probabilities of leaving because of offer. A/B tests. Multi arms Bandit?
• Significant LTV for activation?
• Act on it (long term) :
• Is there a problem in my purchasing funnel?
• Is the game too hard at some point?

9. Dealing with churn
• Candy Crush Rumor :
• Change the distribution of
probabilities of candies / bombs
• Change the difficulty of the
game
• Loosing a lot makes the game
easier

10. Modelling Churn
• Machine learning model (classification) -> target:
• Known in subscription
• Unknown in general
• Step 1 : Maintain customer status
• Do you care only about your best?
• Anyway churn action won’t be the same
• Has a client churned?
-> target = churner = don’t buy / visit since time X
-> best = buy / visit more than y since time Y
• Can be refined (“new customer”, several class of best or inactive, reactivated…)
• Storage : maintain only the difference!

11. Modelling Churn
• Machine learning model -> features:
• Explicative factors to use as input for the model
• Step 2 : Maintain customer features
• Social (woman, age, etc.)
• Behavioral!
• Utilization / buying rate
• Trend in utilization / buying rate
• Ad hoc features :
• WoW / Social game churn: take into account friend network churn
• Telco: call to call centers
• Beware of time dependence!

12. Data Model

13. Computation Dependency diagram

14. Ex : Train and predict scheme
Time
T
:
present
,me
T
–
4
month
Data
is
used
for
target
crea,on
:
ac,vity
during
the
last
4
months
Data
is
used
for
feature
genera,on.
Use
model
to
predict
future
churn
Train
model
using
features
and
target

15. Ex : Train Evaluation and Predict Scheme
Time
T
:
present
,me
T
–
4
month
Data
is
used
for
target
crea,on
:
ac,vity
during
the
last
4
months
Data
is
used
for
feature
genera,on
Valida&on
set
Use
model
to
predict
future
churn
Training
Evaluate
on
the
target
of
the
valida,on
set
T
–
8
month
Data
is
used
for
features
genera,on.
Data
is
used
for
target
crea,on
:
ac,vity
during
the
last
4
months

16. Thank you for your attention !