Smarter Product Pricing: It’s Now a NecessityThanks to a decade of technological advancements, computers now have the power to process massive amounts of data.
For property/casualty insurance companies, the struggle to price products competitively is a never-ending set of challenges, loaded with tough questions:
- What should our overall rate level be, and how should it be spread to classification?
- What’s the competition doing?
- What are market conditions?
- How will customers respond to a price change?
- What will be the impact on earnings?
Traditionally, companies have relied on cost-based pricing strategies to set prices. And certainly cost remains a critical consideration. But an understanding of cost alone doesn't take into account the competitive landscape or customer behavior.
Moreover, despite advances in information technology, many insurers continue to assess their competitive position using inadequate tools. They're not leveraging information — which can be obtained — to make critical decisions about pricing strategy and structure.
The result is that good products can get mispriced. At best, an opportunity to increase profitability isn't fully realized.
Predictive Modeling Comes of Age
To be sure, innovative pricing techniques, which differentiate among customers based on geography or personal characteristics, have been part of the property/casualty business for many years. Auto insurers use hundreds of rating classifications, and similar refinements have spread to homeowners and some commercial lines. But that still doesn't mean your organization is getting the best information possible to set the prices of your products.
Predictive modeling uses advanced statistical modeling techniques, along with critical data related to claim patterns, market conditions and customer behavior, to determine optimum pricing levels. Some companies in Europe, the U.K. and the U.S. are already using these techniques — and they’re setting standards for pricing products. Predictive modeling has become the new path to price optimization.
Three types of predictive models for price optimization are currently available:
- claim propensity models, which collect information on customer attributes to develop new rating plans or customer scoring systems for underwriting
- market situation models, which capture how the company's competitive position and the market's competitive intensity vary by segment or niche within the industry
- customer behavior models, which can be used to predict response rates or lapse rates.
These models can be run separately. Or they can be integrated to support a comprehensive strategy for price optimization.
With predictive modeling, your company can more confidently meet particular profit objectives or change prices to achieve market share targets. At the same time, you can be confident that you’ll understand the profit implications of what you’re doing — and say goodbye to outdated pricing techniques.