INSURANCE
Customer Acquisition of an Insurance Product
Background
A leading Canadian utility company offers home protection plans for plumbing, heating and air conditioning systems. They built risk-based pricing models to tailor insurance plans for different risk groups. For example, customers with the highest risk score might be better off replacing their equipment. Customers with the lowest risk scores for both heating and cooling might be upgraded to special protection plans. The standard insurance plan will be offered to those with moderate risk scores.
In addition to pricing, the risk score was used to identify the best prospects for promoting the proper insurance plans.
Analytic Objectives
The objective of analytics was to build risk scoring models that can predict the likelihood of calls for servicing HVAC equipment and potential costs associated with them. Each current customer will be scored with risks. The risk score of the current customer base serves as the basis for developing pricing models in order to refine pricing and product offering at renewals; for winning back lapsed customers; and up-selling additional products, e.g., premium over risk, new equipment, special protection plans.
Manifold’s Data Products Used in Risk Modeling
The following variables in Manifold’s data products played a significant role in the client’s risk models:
- Homeownership
- Rural/Urban status
- Occupation: white and blue collar
- Dwelling age
- Presence of senior care
- Education levels
- Income levels
- Consumer lifestyle clusters (CanaCode).
Manifold’s Data Products Used in Custom Profiling
In addition to risk models:
- Manifold’s data products on consumer psychographics, lifestyles, and product usage patterns helped the client gain insight into risk groups and tailor messages in communications.
- In particular, the data product on Consumer Media Usage Patterns helped the client identify media channels for more efficient engagement with different risk groups of customers.
Benefits of Using Manifold’s Data
Manifold’s data has increased the predictive power of risk models significantly:
- Number of service calls reduced 18% in the high season thanks to proactive and targeted engagement with the high-risk group of customers. The cost of services (Contractors) was reduced by 25% while revenue increased by 5% in the first year of implementation.
- Win back of lapsed customers increased 46% compared with random sampling.