Optimizing Policy Pricing
Company Size
1,000+
Region
- America
Country
- United States
Product
- APT’s Test & Learn Management System
Tech Stack
- Test & Learn software
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Services
- Software Design & Engineering Services
- System Integration
About The Customer
The customer is a leading North American insurance company specializing in property and casualty (P&C) insurance. With a significant market presence, the company serves a diverse range of clients, offering various insurance products tailored to meet the needs of both individuals and businesses. The company is known for its commitment to innovation and customer-centric approaches, constantly seeking ways to improve its services and maintain a competitive edge in the insurance industry. By leveraging advanced technologies and data analytics, the company aims to optimize its operations, enhance customer satisfaction, and drive sustainable growth.
The Challenge
A leading P&C provider analyzed its product line and discovered that pricing was out of kilter with customer risk levels. Lower risk customers were paying more than their losses warranted, and higher risk customers were paying less. The company believed that better aligning customer pricing with customer risk would create a more resilient product line. The company’s best customers would pay less and therefore be less exposed to potential competitive pricing pressures, whereas more risky customers would pay more to cover the cost of their potential losses. The company was concerned, however, that a wholesale change in its pricing structure might lead to unexpected reactions from the customer base. Rather than simply rolling out the aggressive pricing and rating changes that strictly aligned pricing with risk, the company tested two versions – Option A, an aggressive price change and Option B, a moderate price change.
The Solution
Using APT’s Test & Learn software, sales and losses for customers on each of the new pricing strategies were compared to a scientifically matched control group of customers drawn from markets outside of the tests. The results of the test were clear and compelling. In Option A, revenues fell sharply as customers who received significant price increases left the company for less expensive alternatives, and customers who were offered price decreases happily enjoyed them. While losses improved somewhat, these improvements were overwhelmed by the revenue loss in the customer base. In Option B, the story was very different. Customers who were priced up less aggressively generally stayed with the company, and overall revenues increased significantly. Losses were unchanged, and, in aggregate, the program dramatically improved company profits.
Operational Impact
Quantitative Benefit
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