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July 21, 2002 at 08:00 PM
Smart Use Of Data Can Improve Bank Insurance Marketing
Customer data may be one of the most underused assets banks have in marketing insurance and other financial products and services.
Many banks–and insurers as well–have focused on applying the data to meet operating requirements such as workflow management, risk management, claims adjudication and processing, and customer care. Few, however, have leveraged the power of the data to find new customers, align the appropriate agent with a pool of prospects, cross-sell existing relationships or manage customer retention.
The good news is that methods now available can make it easier to exploit the marketing value of the data. And many banks have an exceptionally rich base of customer data to develop.
The obvious question is, “If its so easy, why arent banks routinely doing it?”
The problem is that the best approaches, while effective and productive, are not well understood or easy to implement. No data-mining tool or customer relationship management system will magically convert the data into marketing results.
Banks–and, for that matter, all financial services–need a systematic program with a series of steps that can be applied over and over again to get effective results.
The core purpose of the most successful programs is to grow the value of the customer portfolio by systematically targeting financial services and product offers to the most potentially profitable customers. These approaches work well across the full range of insurance and financial products.
The following highlights seven of the steps that represent best practices in exploiting customer data for profitable growth.
Start with the customer data that are available. The conventional wisdom was to focus initially on building a comprehensive customer database that captures all potentially useful data. While data systems integration is commendable, it is both costly and time consuming, and it can fail to meet todays marketing objectives.
Virtually all banks are well served to begin with the data thats available: basic information on products held by customers, purchase dates, transaction frequencies and transaction size. Over time, the results can be improved further with additional and more accurate data. This learn-by-doing approach can help prioritize the value of new data and build the business case for making a richer investment in data mining.
Calculate the value of individual customers. A simple marketing measure of customer value can provide extraordinary returns. The calculation can be as basic as the revenue accruing from each product held by the customer. The measure can be enhanced by capturing variable marketing costs and behavior-based servicing costs, such as costs associated with customers visits to a branch or calls to customer service.
