AY1516 T2 Team Hew - Overview

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Background

Tokio Marine Insurance Group was established in 1879 as the first insurance company in Japan. Growing over the years through a mix of acquisitions, joint ventures and organic growth, the company’s network now spans 486 cities in 37 countries. The company started with a single sales channel which is a traditional agency force but has now diversified distribution by establishing a broad distribution network to include:

• Independent Financial Advisors (IFA)
• Bancassurance
• Brokers (Local and International) Group business
• Agency

Some of the company’s sought-after insurance product highlights include:

• Marine Cargo Insurance
• Engineering
• Personal Lines
• Life Insurance

In 1945, Tokio Marine re-entered the Asian market, providing a comprehensive range of Life and General Insurance solutions in multiple Asian countries. Tokio Marine Asia was set up in Singapore as the regional headquarters to manage the Group Companies (GCs) in Hong Kong, Singapore, Indonesia, India, Australia, Philippines, Malaysia, Thailand, Taiwan, China and Vietnam.

Motivation

Tokio Marine’s Group Companies (GCs) collect a lot of data required for underwriting products only at the time of sale. Over time, many data points have been captured with little insights derived other than for underwriting purposes. This data is stored on multiple platforms. While some customers have multiple products, it currently is limited in the utilization of data captured to really understand the profile of the customers, what they bought, channel preference, etc

Data

The dataset will be provided by one of the Asian GCs - Tokio Marine Life Insurance Singapore (TMLIS). It contains about 100,000 transaction records gathered over several years from TMLIS’s customer policy purchases.

Sample Dataset headers:

• Customer NRIC
• Customer Name
• Customer Age upon purchase
• Customer Age now
• Policy plan type purchased
• Channel (purchased from)
• Adviser name
• Adviser firm
• Premium size
• Any subsequent policy purchases made
• Email
• HP no
• PDPA consent (Y/N)
• DNC consent (Y/N)

Objectives

There are a few objectives which were formulated together with Tokio Marine at the initial meeting. Only some of the objectives listed down will be chosen as the focus as final paper as it is dependent on the availability and feasibility of data.

1. Develop a database analysis to formulate a demographic and psychographic profile of customers
2. Analyse average product holdings per customer
3. Determine which customer segments and products are more profitable
4. Which channels are more profitable, direct online or through agents
5. Propensity to buy or assess next best offer for customers to enhance effectiveness of marketing campaigns.

The first objective is to demographically segment customers for more targeted marketing efforts. A psychographic profile here refers to the tendencies/behaviours of certain demographic groups. Tokio Marine will provide external datasets and research to supplement our findings. The second objective would be to analyse the average product holdings per customer (to see when and how often a certain demographic terminate/renew their policies). The third objective, linked to the first, is the determine which customer segments are more profitable. The fourth objective is to determine which sales channels are more profitable and thus where efforts should be concentrated on. The last objective is to determine the customer’s propensity to buy and also the next best offer.

Scope of Work
While Tokio Marine has several objectives, we will be directing our efforts towards Objective 1 due to time constraints. This is also the main objective which Tokio Marine hopes to achieve.

Research & Methodology

Review of Similar Work

Typically, Affinity Analysis has been applied to consumer products like groceries and thus there exists little literature on this. However recently, Affinity Analysis has been used to analyze different segments of customers and this has helped in their decision support in targeting groups of customers.

Kamakura’s work on Market Basket Analysis and Path Analysis is valuable. He demonstrates that Path Analysis can be more useful than Market Basket Analysis in some cases as the sequence of purchase of products is more insightful than the static final basket of goods. Path Analysis can clearly illuminate whether goods are substitutes of each other or complements which provided decision support for cross-selling and bundling of grocery products.

Survival Analysis models factors or variables that affects the time to an event. Two common methods are used to investigate the survival analysis of a model. First, Nonparametric methods provide simple and quick looks at the survival experience. Second, the Cox Proportional Hazards Regression Model relate the time that passes before some event occurs to one or more covariates that may be associated with that quantity of time. It remains the dominant analysis method for Survival Analysis.

Methodology

Many methodologies are discussed as this group is still in the initial stage of scoping the project. It is estimated that only 1 or 2 objectives will be taken for the project eventually.

Objective 1 (Develop a database analysis to formulate a demographic and psychographic profile of customers):

For this objective, we will investigate how the demographics of the customers impact the psychographic/behavioral profile of the customers and investigate the correlation using explorative data analysis and clustering techniques.

Some of the demographics we will investigate are:	Some of the psychographic/behavioral profile we will investigate are:
Age Gender Nationality Profession Pay Marital Status Channel purchased	Policy purchased Subsequent Purchase Profitability of customer Policy payment default rate Likelihood of recommending policies to others

An example of this will be the investigation of demographic segmentation of different nationality. We will investigate if nationality plays an important role in the behavior of customers. An example is to investigate if Japanese customers are more likely to make subsequent purchase of policies in Tokio Marine than local customers.

The investigation of demographic and psychographic profile of the customers will translate into more effective marketing strategies. The company can concentrate its efforts of marketing on demographics of customers that are more profitable and give greater incentive to loyal customers to stay with the firm. Alternatively, the company can also incorporate upselling strategies for customers that are demographically more likely to purchase more expensive policies.

We will also conduct a time series analysis to examine how demographic/psychographic profiles change with time.

Objective 2 (Analyse average product holdings per customer): Default rate

This objective investigates the length of time a customer continues to hold a product. This investigates the likelihood and the rate at which customers default of periodic policy payments and the rate at which they cancel policies. A high rate of policy cancellation might indicate a flaw with the product that the product management team might need to investigate. The primary techniques to be used for this is Survival Analysis, where Nonparametric methods and Cox Proportional Hazards Regression Model will be used, using the SAS software.

The Nonparametric method used will be a descriptive technique used to provide the rate at which the consumer drops a product. In SAS, a graph of the Kaplan Meier estimate can be used to allows users to see the survival function of the policy changes over time:
[PICTURE GOES HERE]
The Cox Proportional Hazards Regression Model investigates how different variables affect the policy cancellation rate (survival rate) of the policyholders using SAS. The graph below shows the survival function segmented to different age groups:
[PICTURE GOES HERE]
Some potential problems we might face is that most customers hold the policies until maturity. This means that there will be little variance among different groups. We might need to transform the data in order to obtain a more meaningful analysis.

References