Managed customer retention for life insurance co

www.valiancesolutions.com © 2013 Valiance Solutions

Analytics

Big Data

Insights

Big Data Analytics Company

Improving Customer Retention

http://www.linkedin.com/company/valiance-solutions?trk=cp_followed_name_valiance-solutions

http://valiancesolutions.com/


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


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?


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


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


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


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


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


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.


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


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


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


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


Product Details

Product Class Protection has a lower propensity to lapse

Bi-Variate Analysis

Model Variable

Note: Product Class Protection is coded as 1


Customer Details

Customer Segment Blue has a higher propensity to lapse

Bi-Variate Analysis

Model Variable

Note: Customer Segment Blue is coded as 1


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


Channel Details

Channel Code Business Alliances is coded as 1

Bi-Variate Analysis

Model Variable

Note: Channel Code Business Alliances is coded as 1


Channel Partner RFL REFERRER has a higher propensity to lapse

Channel Details

Note: Channel Partner RFL REFERRER is coded as 1


Model Performance Table

Development

Decile minp maxp Total NNumber Of Lapse

Cumulative Number of Lapse Dev Lift

1 0.99398 0.9983 7311 7304 7,304 26%

2 0.98922 0.99398 7311 7257 14,561 53%

3 0.2989 0.98906 7311 5030 19,591 71%

4 0.23807 0.2989 7311 1965 21,556 78%

5 0.19213 0.23807 7311 1465 23,021 83%

6 0.16605 0.19213 7311 1240 24,261 87%

7 0.13796 0.16605 7311 1146 25,407 92%

8 0.10858 0.13796 7311 991 26,398 95%

9 0.10858 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

1 0.99399 0.99789 1348 1025 1,025 32%

2 0.98832 0.99399 1349 901 1,926 60%

3 0.97653 0.98832 1348 414 2,340 73%

4 0.24507 0.52942 1349 302 2,642 82%

5 0.19218 0.24507 1348 289 2,931 91%

6 0.16275 0.19218 1349 42 2,973 92%

7 0.13799 0.16275 1349 73 3,046 95%

8 0.10861 0.13799 1348 63 3,109 97%

9 0.10861 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 cases


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 strategyimplementation.

Validation is done for one month (Oct ‘11) Out of Time data and the model is holding good for top 3 deciles


Lapse Policy Profile

HIGH

Policy Vintage Average is 15 months

Semi Annual is higher than portfolio average (14% to 9%)

Savings and Health products are higher than portfolio average(65% to 59% and 9% to 3% resp.)

Customer Segment Blue is much higher than portfolio average (81% to 43%)

Agent Vintage Average is 19 months

MEDIUM


Annual is lower than portfolio average (73% to 80%)

Child Plans are higher than portfolio average(14% to 10%)

All Customer Segments are close to the portfolio averages


LOW


Annual is higher than portfolio average (87% to 80%)

Protection Product are higher than portfolio average (33% to 17%)

Customer Segment Gold and Platinum are much higher than portfolio averages (46% to 30% and 53% to 25% resp.)


Top 30% Next 40% Bottom 30%

20 of 35


LapsationScorecard

Customer Segmentation

into Risk Bands

Devise /Recommend

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

bac

k L

oo

p

• HNI• MNI• LNI

Retention campaigns –pre lapse

Measuring the performance of the campaigns

Overall Execution Strategy

1 2 3 4


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.


Valiance Solutions Private LimitedA-146, Opposite TCS building,Sector 63, Noida, U.P - 201306India.

Contact Person: Vikas KamraOffice No: +91 120 4119409Contact No: +91 8750068961

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Managed customer retention for life insurance co