The document outlines a data science meetup held on June 5, 2018, in Amsterdam, focusing on churn prediction strategies at VodafoneZiggo, featuring presentations by Florian Maas and Tom de Ruijter. Key topics include the current approaches to churn prediction, the importance of advanced analytics in customer retention, and hands-on modeling techniques. The meetup aimed to provide insights into preventing churn and optimizing customer interaction through data-driven methods.

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Churn Prediction in Practice
Hands-on data science meetup
Amsterdam 5 June 2018

2. PRACTICALITIES
18:00 – Food & Drinks
18:25 – Welcome
18:30 – Churn Prediction in Practice - by Florian Maas
19:00 – The Churn Prediction Toolbox - by Tom de Ruijter
19:30 – Hands-on
20:30 – Drinks, gezelligheid & networking
21:00 – Everybody out

3. Streaming
microservices
Data science in a data pipeline

6. 7
CHURN MODELLING IN PRACTICE
VODAFONEZIGGO
FLORIAN MAAS – 05-06-2018

7. 8
• This presentation:
- The current approach to churn prediction within VZ
- Lessons learned & challenges faced while building this model
• Second presentation:
- More complex modelling approaches
TODAY

8. 9
• Florian Maas
• Econometrics & Management Science, specialization in
Operations Research and Quantitative Logistics
• Supply Chain Improvement Specialist at Interface
• Co-founder of Xaperi, started joint-venture with Cadran.
• Data Scientist at VodafoneZiggo since jan 1st.
• I like running, hiking, guitar, concerts, field hockey, cycling,
boxing(?), Calvin & Hobbes and capybara’s.
WHO AM I?

9. 10
VodafoneZiggo at a glance
#1 in Cable
8,000 employees
#2 in Mobile
4m TV connections
2.5m fixed phone connections
3m broadband internet connections
5m mobile voice/internet connections
€4b revenue
Cable Network
Coverage
Mobile
Network
Coverage
7m homes passed
VodafoneZiggo Kick off

10. 11
A Dutch company with a global scale
• €55.9bn Revenue
• 108,000 employees
• 523m mobile customers
• €20bn Revenue
• 45,000 employees
• 50.1m homes passed
VodafoneZiggo Kick off

11. 12
What we do really matters
For our customersFor our people For society
VodafoneZiggo Introduction

12. 13
NIELSEN’S LAW OF INTERNET BANDWIDTH
Summary: Users' bandwidth grows by 50% per
year (10% pts. less than Moore's Law for
computer speed).

13. 14
WHO ARE WE | ADVANCED ANALYTICS
Our mission is to unlock the value hidden in our huge pile of
data and translate it into valuable insights and data driven
products for our customers, business and stakeholders using
techniques from the field of Machine Learning and Artificial
Intelligence

14. 15
WHAT IS CHURN?

15. 16
WHAT IS CHURN?
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.

16. 17
HOW CAN ADVANCED ANALYTICS HELP PREVENT CHURN?
Provide a better service to our customers.
• Network optimization
• Where to place new broadcasting
equipment?
• Proactive monitoring of the network.
Provide special offers to customers.
• Who is likely to churn?
• Which offer should we give to which
customer?
Understanding the churn drivers
• What drives a customer to churn?

17. 18
• Team:
• Platform:
• Data:
• Goal: Get a working model up and running quickly and pave the way for more complex modeling
approaches.
INITIAL APPROACH

18. 19
1. Data
exploration
2.
Feature
engineering
3.
Model
optimization
4.
Segmentati
on &
Targeting

19. 20
B2C FIXED CHURN PROPENSITY MODEL
All Ziggo customers Machine Learning model
0.92
0.85
0.63
0.62
0.59
0.46
0.32
0.02
Ziggo contacts customers with top
churn predictions on weekly basis
with service call
Monthly output of
customer predicted
propensity to churn
…

20. 21

21. 22
CURRENT MODELING APPROACH
Customer id Feature 1 Feature 2 … Churned?
1 x 0.2 … 0
2 x 1 … 1
… … … … …
Binary classification

22. 23

23. 24

24. 25
DATA USAGE BOARD (DUB) & GDPR
Data of customers that have left
us some time ago
Network traffic
Certain TV package
subscriptions, such as erotic or
Arabic.
Customer data (gender, age,
customer lifetime, …)
Active products
Household information bought
from external parties
Limited data on interactions
with the help desk

25. 26

26. 27
PRODUCT DUMMIES – INFORMATION LEAK IN MODEL?
• This variable will be considered very important by the model and the model will draw incorrect conclusions based
on this variable.
Insert presentation title via header & footer
Product
introduction
Jan 1st April 1st
May 1st
Product unavailable; dummy 0. Product available; dummy 0 or 1

27. 28
PRODUCT DUMMIES
Insert presentation title via header & footer
Current solution:
• Only include a dummy if a product was available in the entire period.
today – one year today – ½ year today
Included in dataset
Included in dataset
Excluded from dataset
Excluded from dataset

28. 29
CUSTOMER INTERACTIONS
• Helpdesk interaction data
• Now included in the model as follows:

29. 30
CUSTOMER INTERACTIONS: FUTURE
Sentiment analysis on interactions:
• Sounds nice, but this has not passed the DUB yet.
• "Calls may be recorded for training and quality purposes“

30. 31

31. 32
DATASET
Customer id Feature 1 Feature 2 … Churned?
1 x 0.2 0
2 x 1 0
3 y 0.5 0
4 y 0.3 1
5 z 0.2 1
6 z 0.1 0
Customer id Feature 1 Feature 2 … Churned?
1 x 0.2 0
2 x 1 0
Customer id Feature 1 Feature 2 … Churned?
3 y 0.5 0
4 y 0.3 1
Customer id Feature 1 Feature 2 … Churned?
5 z 0.2 1
6 z 0.1 0
Customer id Feature 1 Feature 2 … Churned?
1 x 0.2 0
2 x 1 0
3 y 0.5 0
4 y 0.3 1
Customer id Feature 1 Feature 2 … Churned?
5 z 0.2 1
6 z 0.1 0
Customer id Prediction
5 0.05
6 0.25

32. 33
Customer id Feature 1 Lifetime Months in
future
6 b 10 0
6 b 11 1
6 b 12 2
Customer id Feature 1 Lifetime
6 b 10
Customer id Feature 1 Lifetime Months in
future
Prediction
6 b 10 0 0.0
6 b 11 1 0.0
6 b 12 2 0.7
Customer id Prediction
6 0.7

33. 34
PERFORMANCE

34. 35
MORE IMPROVEMENTS FOR THE FUTURE
• Better predictors: CSQ, 2020 errors, …
Predict which retention offer to give to which customer.
Make better use of the temporal component in the modelling of this problem.
1
3
4
Predict which customers to target, rather than predicting which customers are most likely to
churn.
2

35. 37

36. !"($%, ℎ%)
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Machine learning toolbox
for churn prediction
by
Tom de Ruijter
Amsterdam 5 June 2018

37. Tom de Ruijter

38. Churn models don’t add
value
by themselves

39. Agenda
1. Churn: what, why and how?
2. Machine learning for churn prediction
3. Use case: churn classification at VodafoneZiggo

40. Agenda
1. Churn: what, why and how?
2. Machine learning for churn prediction
3. Use case: churn classification at VodafoneZiggo

41. What is churn?

42. Why does it happen?
%
PULL
PUSH

43. What happens?
NON-
CONTRACTUAL
CONTRACTUAL CANCELLATION

44. CHURN PREDICTION
Churn prevention
customer
Prevention
strategy
Churn probability
Value estimation
!(#)
%(#) Take action
Customer
contact
ORGANISATION
ALIGNMENT
ANALYTICS
EXPERIMENTAL
MINDSET

45. Agenda
1. Churn: what, why and how?
2. Machine learning for churn prediction
3. Use case: churn classification at VodafoneZiggo

46. Capturing customer life cycle into features
time
CHURN
Prediction window
Calls for time-series prediction
PROBLEM: we know very little about customers

47. Capturing customer life cycle into features
PULL PUSH
Competing products
Campaign history
Market presence
Spending behavior
Customer loyalty
Client interaction
Product usage
Device logs
Event logs
Customer loyalty

48. System design
Churn prediction algorithmcustomer data
!(#)
output
static time-series Churn in x days? Probability on time

49. Churn prediction algorithm
!(#)
Business rules simple, explainable > baseline
Classification model
churn in the next N days? Y/N
pre-defined window size
Probability estimation
can use censored data
flexible predictions

50. Client data Model Output
∑ > 3?
Baseline: business rules
=activity
=inactivity
Take action

51. A simple linear model
one-hot
encoded
categorical
features
…
output
!"#
$#%numerical
features
&#%

52. Sequence representations
timeline
Campaign
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call center contacted? days since last product use
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*1 *2 *3
representation
vector

53. other inputs
…
Recurrent neural network architecture
sequence representation
sequence
inputs

54. Agenda
1. Churn: what, why and how?
2. Machine learning for churn prediction
3. Hands-on: churn classification at VodafoneZiggo

55. HANDS-ON WITH GOOGLE COLAB
1. Find a partner and go to: https://goo.gl/8hHbMt
2. Open with Colaboratory 3. File > Save a copy in Drive…

56. THANK YOU
Keep an eye on the Meetup page for the next events.