life insurance: predictive approach for reducing policy surrenders
DESCRIPTION
Predictive Analytics Approach for reducing policy surrendersTRANSCRIPT
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Analytics
Big Data
Insights Big Data Analytics Company
Improving Customer Retention
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Objective : Improving Customer Retention
Case Study: Customer Retention
To identify policy holders who are likely to lapse and move out of the program
Take proactive measures to keep them in the program.
Quantitative Analysis of Lapsation
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What are the reasons for attrition?
What are patterns in customer attrition across different tenure of policy?
How does the attrition rates change by changing factors?
What is the probability of a customer to attrite?
What channel or combination of channels which will deliver the most conversion?
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Income banding
Education categorized into Under Graduate/Graduate/Post Graduate
Age at entry
Marital Status
Customer segmentation
APE
Sum Assured
Premium payment term
Policy term
Product Classification
Region based classification
Zone based classification
Urban/Rural
Demographics Financial & Product Behavior Geography
Multiple derived variables were created for testing the improvement in the performance of the model
Agent vintage
Agency region
Agent status
Branch office vintage
Channel code
Sub broker
Policy vintage
Total received premium
Time paid percentage
Premium paid percentage
Premium paid to sum assured ratio
Call Centre Agency level info Length of relationship
Number of inbound/outbound calls
Number of complaint/queries/request calls
Time taken to resolve complaints/ queries/ requests
Resolving department wise(CS, New business) time taken
Sample Deliverable: Data Points Considered
Illustrative
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Analysis window:
Annual Sep-11-Sep-12
Semi-Annual Mar-12-Sep-12
Quarterly Policies Jun-12-Sep-12
Monthly Policies Aug-12-Sep-12
Base Considered: All policies since inception with renewal in the analysis window.
Target Definition (Moving window): All policies that have come for renewal and lapsed in the analysis window
Variables considered: All product details, demographics details
Exclusions: Terminated Cases, Single Payment
Modeling Base Details
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The construct for Annual mode customers*
Approach - Target Variable Definition
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13
M14
M14
M14
A moving window construct used to tag the lapsers 1 month post the month of renewal Target Definition :- Customers who have not renewed the policy even after the completion of the
grace period are tagged as lapsers.
• Similar construct would be used for Monthly, Quarterly and Half-yearly modes
Acquisition /Last premium month
Month of renewal
Month of lapse
Legend
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Policies for renewal between Analysis Window
Characteristics
CharacteristicsScoring model
Likelihood to Lapse
Scoring Algorithm for Calculation
Propensity to lapse
Application on policies coming for
renewals in following month
RetentionCampaigning
Non Lapsed
Lapsed
Lapsed and Reinstated
Policies lapsed between Analysis window are bad
Lapsation Model: Customer Scoring
Policies lapsed between Analysis window are good
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Sample Deliverable: Customer Risk Profiling
APE BAND
Risk Group <18KBetween 18K and
25K>25K Total
High 18% 8% 14% 40%
Medium 15% 8% 7% 30%
Low 10% 7% 13% 30%
Total 43% 23% 34% 100%
Risk_Group Probability of LapsationH >0.18M 0.03-0.18L <=0.03
Customers were segmented on basis the probability to lapse and APE band
Customers were segmented in High, Medium and Low risk profiles on basis of Annual Premium and their probability to lapse.
Cut off probability band for High, Medium & Low group was identified from customer deciles. i.e. For High band probability cut off was based on top 30 percent of lapsers.
Proactive campaigning to customers with higher likelihood to lapse
High Risk – Priority 1
Medium Risk – Priority 2
Low Risk – Priority 3
Legend
Illustrative
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Sample Deliverable: Trends & Factors contributing to Lapsation
• Agency region in particular region showed higher rates of Lapsation.
• Agent vintage up to 14 months had higher rates of Lapsation.
• Policy vintage up to 12 months had higher rates of Lapsation.
• Customer contactable by mobile had higher probability to lapse.
• Payment frequency semi annual has higher rate of Lapsation.
• Product class protection had lower propensity to lapse.
• Channel partner XXXXX had higher probability of Lapsation.
Illustrative
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Agency Region
Agency Region in North Kerala, South Kerala, AP have a higher propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Agency Region in North Kerala, South Kerala, AP is coded as 1
Illustrative
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Agent Details
Agent Vintage up to 14 months have a higher propensity to Lapse
Bi-Variate Analysis
Model Variable
Note: Agent Vintage up to 14 months is coded as 1
Illustrative
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Product Details
Policy Vintage up to 12 months have a higher propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Policy Vintage up to 12 months is coded as 1
Illustrative
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Product Details
Payment Frequency Semi Annual has a higher propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Payment Frequency Semi Annual is coded as 1
Illustrative
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Product Details
Product Class Protection has a lower propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Product Class Protection is coded as 1
Illustrative
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Customer Details
Customer Segment Blue has a higher propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Customer Segment Blue is coded as 1
Illustrative
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Customer Details
Contactable by only mobile has a higher propensity to lapse
Bi-Variate Analysis
Model Variable
Note: Contactable by only mobile is coded as 1
Illustrative
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Channel Details
Channel Code Business Alliances is coded as 1
Bi-Variate Analysis
Model Variable
Note: Channel Code Business Alliances is coded as 1
Illustrative
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Channel Partner RFL REFERRER has a higher propensity to lapse
Channel Details
Note: Channel Partner RFL REFERRER is coded as 1
Illustrative
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Model Performance Table
Development
Decile minp maxp Total NNumber Of Lapse
Cumulative Number of Lapse Dev Lift
10.99398 0.9983 7311 7304
7,304 26%
20.98922 0.99398 7311 7257
14,561 53%
30.2989 0.98906 7311 5030
19,591 71%
40.23807 0.2989 7311 1965
21,556 78%
50.19213 0.23807 7311 1465
23,021 83%
60.16605 0.19213 7311 1240
24,261 87%
70.13796 0.16605 7311 1146
25,407 92%
80.10858 0.13796 7311 991
26,398 95%
90.10858 0.10858 7311 933
27,331 99%
100.09012 0.10858 7312 399
27,730 100%
Validation
Decile minp maxp Total NNumber Of
Lapse
Cumulative Number of
Lapse Val Lift
10.99399 0.99789 1348 1025
1,025 32%
20.98832 0.99399 1349 901
1,926 60%
30.97653 0.98832 1348 414
2,340 73%
40.24507 0.52942 1349 302
2,642 82%
50.19218 0.24507 1348 289
2,931 91%
60.16275 0.19218 1349 42
2,973 92%
70.13799 0.16275 1349 73
3,046 95%
80.10861 0.13799 1348 63
3,109 97%
90.10861 0.10861 1349 78
3,187 99%
100.09013 0.10861 1349 31
3,218 100%
Top 3 deciles are being able to capture over 70% of the Lapse casesIllustrative
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Lift
The model is being able to consistently capture over 70% of the lapse population in top 3 deciles. Policies having scores up to 3rd deciles should be targeted under High Risk Group in strategy implementation.
Validation is done for one month (Oct ‘11) Out of Time data and the model is holding good for top 3 deciles
Illustrative
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Lapsation Scorecard
Custome
r Segmentation into
Risk
Bands
Devise /Recom
mend Retention
Strategies
Campaign
Execution and Performance Measurement• Poor customer service
• Insurance mis –sell• Complex policy underwriting• Better policy available elsewhere.
Assign Low campaign Priority.
Assign High campaign Priority.
Request Product switch orLower premium etc
Disposition Code
Customer service, customer engagement, X-Sell campaigns
Types of campaignsCampaign Response tracking
Feed
back
Loo
p
• HNI• MNI• LNI
Retention campaigns –pre lapse
Measuring the performance of the campaigns
Overall Execution Strategy
1 2 3 4
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ROI of Modeling Exercise
Lapse Model led to Superior Customer Retention thus
improved the Bottom Line
APE Persistency improved by 20% over a period of six months.
Policy Persistency improved by 18% over six months period.
Model resulted in saving of 16 Crores over a period of 6 Months.
Improved effectiveness of Retention strategies.
Enhanced opportunity for cross sell thus decreasing the cost of customer acquisitions.
Lowered cost of retention campaigns.
Increased organization awareness of factors affecting retention for different customer segments.
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