Better Together: Analytics, Data, Corporate Culture

By Marty Ellingsworth

Regardless of the type of insurance a carrier writes, analytics is often at the core of its business. Companies can make data-based decisions quicker and with more insight and accuracy using sophisticated analytics tools. When applied across the enterprise, such decisions in production, sales, and service create a virtuous cycle of continuous improvements — producing a corporate culture of analytically driven, sustainable excellence.

Virtually all carriers set the following four goals as top priorities: profitable growth, lower loss ratios, reduced expenses, and improved ease of doing business. To achieve those goals, carriers’ methods can be as varied as the carriers themselves. However, one element remains constant across the industry: Every aspect of the insurance business can benefit from an improved ability to predict the future.

When applied across the enterprise, data-based decisions in production, sales, and service create a virtuous cycle of continuous improvements — producing a corporate culture of analytically driven, sustainable excellence.

What agents, brokers, and customers (the “ABCs” of insurance revenues) require — and how an insurer can best meet those needs — is often the origination point of all business processes, with all other functions essentially falling in the support category.

Creating more value for customers does not mean just pushing products. An insurance company — like most other businesses — needs to innovate in marketing, claims, contact, and service areas while being as precise as possible in pricing and avoiding costly risk assessment mistakes.

In the insurance market, sophisticated market segmentation and risk-based pricing methodologies are proving the value of integrating large data sets with advanced analytics methodology. Loss cost prediction can be achieved by several means, but some are more expensive or erratic than others. The systematic implementation of analytics in conjunction with smart decision making helps minimize risks and exposures for insurers while providing policyholders with strong coverage and good premium pricing.

For about 250 of the 327 carriers in the personal auto marketplace in the United States (those with approximately $200 million or less in premium as of 2010 data), a highly accurate loss cost prediction model can be outsourced for less than the cost of one full-time employee a year. In equivalent terms, that one employee would need to be able to build data warehouses, create and validate predictive models, determine loss cost estimates, develop appropriate rates and incorporate them into filings, and get such filings approved in multiple states.

Similarly, a midsize carrier with $500 million in premium using a third-party analytics provider would get good outsourcing value from reapportioning the cost of just two staff equivalents. With a smart outsourcing strategy, the largest carriers could potentially reallocate half-a-dozen headcount or more to other high-priority custom projects with little or no impact on competitiveness.

Meanwhile, what about competing on factors other than price? How does a carrier allocate investment dollars across the enterprise in marketing, underwriting, claims, distribution, IT, and customer- or agent-facing perspectives? What makes the company likeable? How do advertising and branding attract prospects to one carrier or another?

The short answer: If a lack of attention to operating costs results in higher premiums, insurance customers will quickly notice and react. If resources don’t support claims service portals, social media marketing, or initiatives that promote ease of doing business, customers will find another insurer. All of those factors and more are fertile ground for analytics to help boost performance, pricing, service, brand loyalty, and growth.

While most analytic projects won’t be visible to current customers, project results may translate into:

  • underwriting appetite
  • tiering
  • rerating
  • dislocation management
  • product design
  • retention discounts

New business prospects share in those benefits, plus the analytically derived rates may be competitively better for them.

Ultimately, keeping a business grounded in sound financial reasoning — and using the science of predictive analytics to do so — is how companies will sustain their relevance to those they serve and build an enduring culture by continually improving their ABC decisions.

Marty Ellingsworth is president of ISO Innovative Analytics.