Know Your Clients, and Serve Them.

Sophisticated statistical models can help insurers understand and anticipate customer and prospect behavior, which can lead to more sales opportunities.

While many people expect direct-selling models to gain market share, the need for advice and counsel seems only to have increased. More than 95% of annuity and life insurance premiums are sold through advisers, such as agents, stockbrokers and financial planners. And in a Tillinghast-Towers Perrin survey of chief executive officers, 86% of life insurance CEOs agreed or strongly agreed with the statement, "Advice, planning and counsel will grow in importance as a means of financial-services providers' acquiring, retaining and developing customers."

Yet, the current adviser-based distribution model is not perfect. Life insurers must find a way to make the customer acquisition and development process more effective for the adviser and less costly for the company, while delivering greater value to the customer.

Customer value management techniques can help meet the challenge. These techniques apply a disciplined, information-driven marketing approach to the customer life cycle.

Importantly, these techniques can be employed as a complement to, not as a substitute for, the adviser in the sales and servicing process. By providing the adviser with a more qualified lead stream and by identifying those customers with the greatest potential for up-sell or cross-sell, customer value management frees the adviser to focus on the most productive customer-development opportunities.

Customer value management relies on sophisticated statistical models to understand and anticipate customer and prospect behavior. Using a combination of demographic, census tract, block group, product ownership, transaction and behavioral data, models enable insurers to improve targeting efficiency by enhancing the response rate for a particular marketing effort. Although the application of such techniques is relatively new to the insurance industry, it has been quite successful in the banking, consumer-credit and retailing industries.

A major function of customer value management is to match products and services to customer needs. Predictive statistical models--sometimes referred to as product propensity or, in the case of cross-selling, next-product-to-buy models--can be used for acquiring customers and selling new products to existing customers. These models identify the drivers of marketing response and quantify the influence of each driver. The models also assign a probability that a prospect will purchase a given product or service. This probability can then be used to select high-potential prospects for a marketing campaign or to help an adviser anticipate a latent customer need with the "right" product or service. By employing this enhanced targeting approach, insurers can lower marketing expense by reducing unproductive mailings and by directing the adviser's time to higher-likelihood sales opportunities.

Enhancing customer-response rates is only one part of customer value management. To provide maximum value to the company and adviser, the response models must consider anticipated customer profitability. Relying on response models alone can bring undesirable side effects, such as adverse selection and product cannibalization. At their most sophisticated, these models are developed based on customer lifetime value. This approach identifies customer targets by weighing the time-adjusted future cash-flow stream by the probability of response.

Applying proven customer value management techniques has significant strategic and tactical challenges, however. For many insurers, this approach entails a fundamental shift in their approach to life insurance distribution. At its core, this strategy is a marketing-driven approach to customer acquisition and development vs. the more traditional sales-driven approach. Adopting such an approach requires that a company institutionalize processes and information collection that more traditionally have been left to the field.

There also are important tactical considerations. First, there is a need for rich customer data. Behavioral, transactional and product ownership histories have proven to be the best predictors of future sales. Many insurers will need to access and institutionalize such data that resides in the files or heads of its agents. Second, statistical-modeling expertise is a must. Third, field commitment is an essential ingredient. If viewed as just another "home office marketing initiative," customer value management probably will fail. A pilot program can demonstrate the value of this approach to a skeptical field force.

By embracing these techniques, insurers can unleash the selling potential of their field forces, improve overall distribution economics and create greater value for their customers.

Richard K. Berry, a Best's Review columnist, left, and Victor J. Hoffman are consultants in the New York office of Tillinghast-Towers Perrin.