1. Improve Success Rate for Telemarking with Decision Tree
KU, Taekyun (Student ID: )
MAFS6010U Spring 2019
May 24th 2019

2. Business Model and Challenges
Target Business model
Telemarketing channel in Life Insurance company
Target customers are existing customers for cross-selling
The number of the existing customers can be growing from Telemarketing business as well as other channels (Agency, Brokerage)
Business Challenges
Numerous attempts to call the customers for cross-selling to sell more products.
Generally, low success rate because of cold manner on telemarking business
If we can make calls to the rights customers who actually have additional needs for insurance products, then it could help
To improve the success rate
To protect the reputation not to bother customers who don’t have interest

3. The Current Practice Logical thinking or targeting particular groups
Based on the products or campaign that the company is targeting to promote, the selection criteria is determined.
Example 1. If a health insurance product is launched, then targeting to customers who has only life insurance or saving products.
Example 2. The promotion can be ‘Birthday’, then the targeting customers are the ones whose birthday is within the defined range.
Managing the expectation from customers
To set up an internal guideline (i.e. 6 months) to manage the expectation of customers. The company is to make a new call to customers after the internal guideline in order not to try to bother customers.

4. Decision Tree Decision Tree can help the business
Decision Tree can be designed to classify the customers who purchased additional products from our Telemarking channel.
Once the company has the profile of a new customer, then the decision tree can help to figure out whether this new customer will likely to purchase new products or not.
Method to build the tree
Iterative Dichotomiser 3 (ID 3) uses entropy function to build the tree

5. Model Challenges and Suggestion
There are too many success cases, then to find the characteristics, there could be a lot of noise factors.
The answer of the tree for a new customer is binary which is yes or no. It is preferred to show the probability for better decision making.
Suggestion to improve from the challenges
Try with some samples to build the trees instead of just one tree. Then it could give probability of success for a new customer.
Suggestion for different usages
Instead of success rates, the company can think of the decision tree for lapse cases. With the tree, if certain customers are classified to be likely to lapse the policy, then the company/agent can call/contract the customer to retain.