Presented by Karla Vega

1. Churn Prediction: Understanding
your customers and taking action.
@datoinc
#churnPredictionDato

2. Hi! My name is …
Antoine Atallah
Principal Data Scientist
Dato toolkits team, novice powerlifter, Hawks fan.
2
Hi! My name is …
#churnPredictionDato

3. Hi! My name is …
Karla Vega
Customer Success Manager
Aerospace engineer, dog trainer, running fan
@vegakp
3
Hi! My name is …
#churnPredictionDato

4. About Us!
#churnPredictionDato

5. + =
Questions?
• (Now) we love questions. Feel free to interrupt for questions!
• (Later) Email us antoine@dato.com, vega@dato.com
Webinar!
#churnPredictionDato

6. Extracting Insights from Data

7. Data Science Workflow
Ingest Transform Model Insight
#churnPredictionDato

8. Log Journey
Lots of data
Insights Profits
#churnPredictionDato

9. Mining Log Data

10. Logs are everywhere!
#churnPredictionDato

11. Different kinds of logs
• Raw logs
• Each row containing an individual event for a user, at a given
time
• Aggregated logs
• Each row contains the interactions for a user over a period of
time
• For instance, user activity over one-month rollups
• This is the traditional data output of Business Intelligence
infrastructures
• User side-data
• Information about each user (demographics, etc…)
#churnPredictionDato

12. Logs contain usage patterns
Small Purchase
Large Purchase
#churnPredictionDato

13. Different kinds usage patterns
Kinds of Patterns
Visits, Purchases, Events Frequency
Visits, Purchase Quantity
Changes in value over time
Change in time between visits, purchases, events
Time since last action or visit
Demographic information (age, gender, …)
Types of items purchased (seasonality, quality)
…
#churnPredictionDato

14. Retaining customers/visitors is important
• Cost to acquire a new customer is high vs retaining a customer
• Gives a pulse on the health of the business
• Can help take preventive actions and act before it’s too late
• Can help create more effective marketing campaigns
#churnPredictionDato

15. What is Churn Prediction

16. What is Churn
• Churn Prediction is predicting user’s probability to stop coming
back (churn)
• Works by observing past user behavior
#churnPredictionDato

17. Churn Prediction
#churnPredictionDato
(Apr 2016)
Daily activity logs for Jan 2015 – April
2016

18. More Precisely
• Churn Prediction is predicting user’s probability to stop coming
back (churn)
• Works by observing past user behavior
• We define a time boundary at which we want to predict churn
• Anyone not present N days (default is 30) after the boundary is
considered to have churned
• The M days (default 60) before the boundary are used to
generate features
• Multiple boundaries can be specified to extract more patterns
#churnPredictionDato

19. Feature and Label Generation
#churnPredictionDato
(Apr 2016)
Daily activity logs for Jan 2015 – April 2016

20. How to use Churn Prediction

21. Choosing Time Boundaries
• Time Boundaries are moments in the past that are used to
observe user behavior and generate labels
• The time before the boundary is used to observe patterns
• The time after the boundary is used to generate labels
Boundaries Meaning
January 1st 2016 Will use the patterns from before January 1st 2016 to
predict User Churn after January 1st 2016
January 1st 2016,
December 1st 2015
Will use the patterns from before January 1st 2016 to
predict User Churn after January 1st 2016;
Will use the patterns from before December 1st 2015 to
predict User Churn after December 1st 2015
This will analyze more patterns and build a richer model
#churnPredictionDato

22. Choosing a Churn Period
• The Churn Period corresponds to how far in the future we want to
predict.
• It also means that for training purposes, users who have not been
active for this amount of time will be considered to have churned
Churn Period Predicts
7 Days Probability for each user to be leaving next week
30 Days Probability for each user to be leaving next month
3 Months Probability for each user to be leaving next quarter
#churnPredictionDato

23. Choosing Lookback Periods
• Lookback Periods is how far in the past we look to extract user
behavior patterns (features)
• Multiple lookback periods can be provided to generate richer
features
Lookback Periods Features
3 Days Will use the 3 days before each Time Boundary
to extract usage patterns
30 Days Will use the 30 days before each Time
Boundary to extract usage patterns
7 Days, 1 Month Will use the week and the month before each
Time Boundary to extract usage patterns
#churnPredictionDato

24. Choosing appropriate parameters
• If we want to predict Churn for this quarter, we might want to set:
• Churn Period to be 3 Months (how far in the future we predict)
• Lookback Periods to be 2, 4, 8, 16 weeks (how far in the past
to extract patterns from)
• Time Boundaries to be January 1st 2016, January 1st 2015,
January 1st 2014
• Notice that we chose the same quarter each year for Time
Boundary
• Choosing past data with the same underlying behavior will
provide more accurate predictions
#churnPredictionDato

25. Choosing appropriate parameters
• If we want to predict Churn for this month, we might want to set:
• Churn Period to be 1 Month (how far in the future we predict)
• Lookback Periods to be 7, 14, 30, 60 days (how far in the past
to extract patterns from)
• Time Boundaries to be January 1st 2016, October 1st 2015,
September 1st 2015, August 1st 2015
• In this case, we intentionally skipped over November and
December 2015 since it is the holiday season, and may exhibit
very different behavior
#churnPredictionDato

26. Key Takeaways
• Label generation is extremely simplified (choose a Churn Period)
• Feature generation is extremely simplified (choose Lookback
Periods and Time Boundaries)
• Choose representative time frames to predict churn in the desired
time frame
#churnPredictionDato

27. Interpreting the Results

28. Output of the model
• The Churn Prediction model returns a probability of churn for
each provided user
#churnPredictionDato

29. Using the Probabilities
Churn Probability
NumberofUsers
High Probability of
Churn:
Might be hard to
rescue these users
Mid-Probability of
Churn:
We should try to
rescue these users
Low-Probability of
Churn:
Send a thank-you
note!
#churnPredictionDato

30. Using the Probabilities
• We can target different users, using their probability of Churn as
a guideline
• Different marketing messages can be created based on the
probability of Churn
• The highest-probability users are not always the best to target,
depending on the cost of the action to take to retain them
• Gives a new dimension on the user base
• Can be used to monitor the health of the user population over
time
#churnPredictionDato

31. Demo

32. Summary
Log Data Mining
≠
Rocket Science
• Define time parameters to identify patterns and generate labels.
• Extract predictions to gain insights about your user population.
• Take action and help grow your healthy business.
Churn Prediction
#churnPredictionDato

33. SELECT questions FROM audience
WHERE difficulty == “Easy”
Thanks!