2. Defining churn
• Most important feature in
building models is defining your
target variable
• Target variable is straightforward
in most models
• Churn is different; why?
3. Defining churn
• Often two types of churn:
– Active vs. passive
• Examples of active churn
– Do not renew my
subscription
– Do not pay a fee for some
ongoing services
4. Defining Churn
• Examples of passive churn
– Clickstream behaviour on a website
– Spend
– Visits to a store or restaurant
4
5. Churn in a particularperiod vs. Churn over customer lifecycle
• Looking at when the customer stops doing business with the company
• The more typical and tactical type approach is defining the purchase window of time when
the customer stops doing business
5
PRE POST
PURCHASE
WINDOW
6. Defining the PURCHASE window
• A retailer has already categorized customers into segments
called decliners and defectors
– However, the organization's own internal analysis has revealed
that customer behaviour is often infrequent and cyclical
• Does a grocer have the same purchase window as someone
purchasing tires?
7. Challenge: how do we define retention across different organizations?
7
OPTION 1: EXPLORING ACTIVATIONRATES OVER DIFFERENT TIME PERIODS
DIFFERENT PURCHASE WINDOWS: WEEKS VS. YEARS
9. Evaluating accuracy of predictions
Examining a random set of records
Notice the volatility of how segments switch when changing the purchase window.
As the window of time increases, we look for that point where the segments remain the same.
In above example, this occurs at 3 months.
Month 1 Month 2 Month 3 Month 4 Month 5 Month 6
Option 1
1 month
Option 2
2 months
Option 3
3 months
Record 1 $100 $0 $0 $200 $0 $0 Inactive Defector Grower
Record 2 $300 $0 $200 $150 $0 $75 Reactivator Decliner Decliner
Record 3 $500 $400 $300 $0 $0 $0 Inactive Defector Defector
Record 4 $0 $200 $0 $0 $50 $0 Defector Reactivator Decliner
Record 5 $0 $0 $0 $50 $75 $100 Stable Grower Reactivator
Record 6 $700 $600 $500 $400 $300 $200 Stable Decliner Decliner
Record 7 $0 $0 $400 $0 $300 $0 Defector Decliner Stable
Record 8 $0 $600 $0 $400 $0 $500 Reactivator Stable Stable
Record 9 $0 $0 $200 $0 $0 $400 Reactivator Grower Stable
Record 10 $450 $300 $250 $150 $100 $0 Defector Decliner Decliner
10. What is the other challenge in building retention models?
• Defining your universe
– Build model for all customers
– Lapses/inactives, low engagers
• Who is our real target group?
10
Defining High Value Customers
MODEL FOR ALL CUSTOMERS
LAPSED/INACTIVE LOWENGAGERS OTHERS
11. Integrating value and retention risk
11
% of Customers % of All Value
High Value 20% 56%
Medium Value 20% 24%
Low Value 60% 20%
$0.00
$0.50
$1.00
$1.50
$2.00
$2.50
Low Value Low Medium Value Medium High Value High Value
MarketingSpendpercustomer
Value Segments
Marketing Spend of High Risk Group Across Value Segments
• Integration of retention risk and value
allows us to establish different
investment strategies
• Incorporating value allows for
different investment strategies for
high risk group
12. Case StudY – Wealth Management company
• Small investment company for high net worth individuals
• Key imperative: reduce attrition of high value investors
• First challenge: define attrition
– Customers who redeemed all funds in their portfolio in the last
year
– Look at the customer behaviour prior to redemption
• Second challenge: define high value
12
Value Segment # of Investors
% of All Assets in
Portfolio
1 2506 30.28%
2 2506 21.52%
3 2506 15.84%
4 2506 11.60%
5 2506 8.35%
6 2506 5.80%
7 2506 3.71%
8 2506 2.07%
9 2506 0.77%
10 2506 0.06%
BEHAVIOUR PRIOR
TOREDEMPTION
CUSTOMERS WHO REDEEMED
ALL FUNDSIN PORTFOLIO
POST
(12 months)
High
value
group
Net worth High Value: $283K
Net worth LOW Value:$59K
PRE
13. Case Study – Wealth Management company
13
Creating the analytical file
(12 MONTHS)
14. Case Study – Wealth Management company
• Key model characteristics were engagement oriented with their
portfolio being oriented more towards mutual funds
14
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
1 2 3 4 5 6 7 8 9 10
DefectionRate
Model Deciles
Decile Defection Rate Results of Model
Model was deployed to trigger high risk clients to salespeople.
15. CASE STUDY – Gaming Company
15
Decile # Members % of All Bets
Average Annual Bet
Per Customer
High Value 3182 99% $50K
Low Value 2121 1% $397
Defining high value:The data flow:
One challenge: defining the target variable
(too sparse to develop a model)
16. CASe STUDY – Gaming Company
• In model evaluation, we validated both on defectors and decliners
• The results indicate that the model is robust as the validation results
are similar across the three groups
16
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
1 2 3 4 5 6 7 8 9 10
IntervalResponseRate(%)
Rank (Decile)
Interval Response Curve
-4.00%
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
1 2 3 4 5 6 7 8 9 10
IntervalResponseRate(%)
Rank (Decile)
Interval Response Curve
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 2 3 4 5 6 7 8 9 10
IntervalResponseRate(%)
Rank (Decile)
Interval Response Curve
DefectorsDefectors and Decliners Decliners
17. CASE STUDY – Loyalty PROGRAM
• Organization had developed one retention model across its member base
– Members were divided into value tiers: (Gold, Silver, and Reward)
• Recommendation was that separate models should be developed for each value
tier
• Both retention and migration models were built for each segment
17
INTEGRATING MIGRATION AND RETENTION MODELS
18. 18
Models are very effective at predicting value tiers as demonstrated by variance:
Actual
New Segment (Current)
Est #
Customers
Gold Silver Reward Lapsed
Old Segment
Pre Period
Gold 50,000 50% 30% 15% 5%
Silver 150,000 20% 30% 30% 20%
Reward 300,000 5% 10% 50% 35%
Total 500,000
Predicted
New Segment (Current)
Est #
Customers
Gold Silver Reward Lapsed
Old Segment
Pre Period
Gold 50,000 60% 20% 15% 5%
Silver 150,000 15% 25% 35% 25%
Reward 300,000 10% 15% 50% 25%
Total 500,000
Variance
New Segment (Current)
Est #
Customers
Gold Silver Reward Lapsed
Old Segment
Pre Period
Gold 50,000 -10% 10% 0% 0%
Silver 150,000 5% 5% -5% -5%
Reward 300,000 -5% -5% 0% 10%
Total 500,000
CASE STUDY – Loyalty PROGRAM
INTEGRATING MIGRATION AND RETENTION MODELS
12 months was determined to be the post period
19. OBJECTIVE:
Can we identify and increase customer retention for a given restaurant chain?
19
CHALLENGE
No customer level
information
SOLUTION
Use mobile device to identify
customer (MACID) and develop
retention model based on MACID
THE NEW CHALLENGE
Handling sensor data
USING MOBILE/SENSOR DATA
20. Created a structured file (1mm+ records over four months)
20
event_date mac_id venue_id first_seen last_seen max_rssi frames
2/2/2015 64852236 4235665 2/2/2015 13:37 2/2/2015 13:48 -66 6
2/2/2015 64852237 4235665 2/2/2015 13:37 2/2/2015 13:48 -65 6
2/2/2015 64852238 4235665 2/2/2015 13:37 2/2/2015 13:48 -68 6
2/2/2015 64852239 4235665 2/2/2015 13:37 2/2/2015 13:46 -42 4
1
The real work begins now!2
USING MOBILE/SENSOR DATA
21. Structuring the analytical file
21
3
Creating the RIGHT analytical file4
USING MOBILE/SENSOR DATA
100+ derived variables created based on:
• Store information
• Strength of signal
• Time
• Changes
• Creating the target variable of defection
• All customers had some activity in first three months
• Defectors were defined as having no activity in fourth
month
• Comprised visitors that came into
restaurant within the pre-period
THREE MONTHS ONE MONTH
327,000
VISITORS- Store info
- Strength of signal
- Time changes
22. Model validation:
• Results indicate that model is performing very well in identifying repeat visitors
• Best Visitor Program for top 20% of customers
• Stimulation/reactivation for bottom 40%
USING MOBILE/SENSOR DATA
23. KEY TAKEAWAYS
Analytics and collaboration with business stakeholders
is required upfront in defining defection.
Caution and care is important in defining pre and post
windows in analytical file.
Restrict your modeling universe to those customers
that have significant impact on business and are high
value.
23
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