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.