Insurance is a tough business today and carriers face myriad competitive pressures. Customers are better informed and have more channels to shop for insurance than ever before. And these customers expect instant and special service—online access to their information, instant ability to file claims on mobile devices, quick settlement of claims—all at the lowest possible cost to them.

Meanwhile, investment returns have been at historically low levels for the past several years, which spotlights the importance of underwriting results, adds pressure on expenses, and increases the prioritity of new initiatives. And as consolidation within the industry continues, insurers look to decrease expenses, invest in new technologies and grow their market share in the broad market. For those insurers that are not one of the industry behemoths, survival means establishing strategies that focus on portions of the market that require unique products, specialized insights, or customized service. The bar of competition in personal lines insurance has been raised, and there's little prospect for it to get easier in the future.

What is the secret to competing in the increasingly challenging personal lines markets? Is it enough just to work harder than the other guy? No. Don't work harder, work smarter. It's more important than ever in today's environment to make decisions—or those great innovative leaps—only after quantifying, understanding and evaluating the inherent risks. Working smarter means making decisions based on solid data and information derived from a robust data and analytics capability.

Analytics investments that find knowledge will give a competitive edge, which is what Verisk Analytics refers to as n+1, and should be a top priority for insurers. The good news is that many carriers in the marketplace have invested in analytics to some degree, especially in the area of rating refinement. The first-movers in the rating analytics arena a decade or so ago were able turn their unique insights into strong competitive advantages. They "right-priced" the risks that the market may have "overpriced" and left the potentially "underpriced" risks to their competitors. But with the increased use of rating analytics throughout the industry, the first-mover advantage has decreased over the years. Rating sophistication is now table stakes to play the insurance game successfully. Rating isn't the n+1 that will provide a competitive advantage. Yet most of the analytics effort in personal lines continues to be in making incrementally better loss cost models.

For those insurers that have a good handle on their loss costs, the n+1 will come from applying that same discipline of data-based decision making to other areas of the business. Ratemaking may have been the most obvious place to start, but all parts of the insurance company value chain are open to comparable improvements through the use of similar advanced analytics techniques. Customer lifetime value (CLV) analysis, for example, is an analysis that has proven extremely useful in other industrie,s but is largely underused in property/casualty insurance. At a high level, the purpose is to help an insurer understand which segments of a book of business actually add economic value to the insurer and which customer segments drain value away. CLV does that by looking at the total value of the customer relationship with a company over the total length of that relationship, rather than the policy-centric way insurers have traditionally evaluated profitability—that is, how profitable a particular policy was over the policy term.

CLV done right is a major analytic exercise, but it can also have wide-ranging strategic implications for an insurer. That analysis can often uncover that a company's best customers are actually destroying its value, what the company believed was its target market does not constitute most of its book, or customer segments that have been ignored are more valuable than originally considered.

But CLV analysis is only one example. Perhaps your company's n+1 will be found in claims models. Fraud is a pervasive issue for the industry. If you could identify more suspicious claims while sending fewer honest claims for investigation, could you turn that information to your advantage? Or in product development, once you've discovered that your customer is really a household and you understand the characteristics of your target household, could you develop a set of policies and options that better fits the needs of your target customers? You may find that your target customers prefer the high-touch personalized service and expertise they get from a local insurance agent, or you may find the opposite—they demand the instantaneous 24/7 self-service options that the Internet provides. Analytics can also help inform investment decisions—for example, whether to spend more resources on expanding an agency force or improving the company's Internet presence.

The point is there's plenty of opportunity for insurers to find their n+1 by focusing analytic efforts in those areas that have been traditionally under-resourced in the insurance area. Insurers that can be effective first-movers in those areas will find they may develop a competitive advantage—for a time. But the landscape will continue to change. Another good thing about being a first-mover is that it doesn't take size—in fact, being small, agile, and adaptable can be a definite advantage.

Phil Hatfield is modeling data services executive at ISO Insurance Programs and Analytic Services. ISO is a Verisk Analytics (Nasdaq:VRSK) business.

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