The document discusses using logistic regression to identify customers of a major life insurance company in Asia who have a propensity to purchase additional insurance policies or riders. Logistic regression was used to analyze customer data and compute probabilities of customers purchasing different products. A cross-sell campaign was launched targeting customers identified as having a high probability of purchasing a specific additional product. The test group that was targeted based on the model outperformed a randomly selected control group in response rates and value of policies purchased.

1. 1
Next best Product to Sell
Increase the “Per Customer Range” of Products in an Insurance Arena
New Developments in Measurement and Analytics

2. 2
Background
• Client : Major Life Insurance Company inAsia
• 12 years in Operations
• Product Range Included:
– Term Policies
– Unit Linked
– Endowment
– Pensions
• All polices had the option of Riders

3. 3
Task at Hand
• After 12 years of operations there were 17 million
customer on the books
• Over 95% of customers had only one policy
• Identify Customers who had the propensity to buy an
additional policy and /or Rider

4. 4
Technique
• Logistic Regression was used to discriminate between
those customers who had bought two policies and those
who bought just one
– Gender, Age Group, Nominee Relationship, Sum Assured,
Time since Last Purchase were variables that discriminated
between the two groups
• Every Customer had 4 probabilities computed for each of
the other products and for one additional rider

5. 5
Technique
Pictorial Representation
Customer Id:
1001943 TERM
POLICIES
UNIT LINKED ENDOWMENT PENSIONS RIDERS
0.9 0.7 0.8 0.4
Target this product based
on high propensity to buy
Next best
Product
Propensity
of Buying
Customer
Already Owns

6. 6
Implementation
• ACross Sell Campaign was created targeting every
customer with the product which had the highest
probability of being bought
• Consisted of an Email with communication that spoke of
the benefit of the targeted product
• Two audiences were tested with the campaign
– those who were identified by the model for a specified
product
– Arandom selection of customers across all probabilities

7. 7
Results
• The test group (those with high probabilities) out pulled
the control group (the randomly selected) by a ratio of
1.9 : 1.0 on response rate
• They also bought higher value polices with an average
difference of $ 53 for the same type of policy
• The tenure of policies was not significantly different
between the two groups

8. 8
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