Sell Customers Their Next Most Likely Product

Recent articles in the financial services press and the direct marketing world tell us that financial services companies don't know their customers well enough to make the appropriate offers. Evidence suggests the financial services firms don't know their customers' specific needs, and most aren't even making offers to a good-sized portion of their customers.

So how can financial services firms overcome this lack of knowledge and make the appropriate offer at the right time? Can you tell what your customer's next most likely product purchase is going to be?

Advances in data warehousing and data mining have helped answer those questions. Start by combining all relevant information in one large database. What is relevant information? The obvious choices are basis demographics on your customer. Customer transaction histories are especially valuable as past behavior often is the strongest predictor of the future.

If your firm has recent marketing or consumer research on its customers, make sure to combine that as well. Credit data, whether it is inhouse or obtained externally, can be an important component in figuring out your customers' next move.

Once you have combined all these pieces of information, the analysis stage can begin. Using sophisticated analysis techniques, it's possible to model customer's propensity to purchase specific products on an ongoing basis. Once you have developed a set of product scores for each customer, how do you tell which one is most likely to be purchased next? Standardizing scores helps to overcome that obstacle. Then products can be ranked against each other to determine which are more likely to be purchased first.

But building the tool to give you the information is half the battle. How do you implement it in order to give your firm the best results? There are many paths a company could choose in using this information.

One of our banking clients incorporated this information into its customer database. This allowed customer service representatives to see which products should be pitched at that particular contact. This type of integration, while neither easy nor inexpensive, is the most effective. After all, a recent survey of 1,000 consumers by Deluxe Corp., St. Paul, MN, found that 67 percent were more likely to accept offers if presented in person rather than by mail, telephone or the Internet. This approach also has the benefit of strengthening the customer relationship by keying the firm into the customer not the product. Customers expect personal treatment and this approach is all about that specific customer.

If you aren't ready to integrate this type of information into your customer database, you can use it in more traditional direct marketing approaches. Make the offer to just those customers who are most likely to purchase a specific product at that time. While your total mailed will be smaller, your response rates and conversion rates will be significantly higher. And whether you integrate the use of this method or use traditional direct methods, your profitability will increase with the use of more focused targeting methods.

The cross-sell potential of this approach also is great. Having amassed this wonderful marketing database, you can perform more analysis to determine product clusters that may be purchased together. For example, financial services firms have long targeted those people who have just bought new homes or refinanced with insurance-related products. The same thinking behind this strategy can be used to find product propensities that correlate highly among customers. Customer service representatives then can tailor a complement of products and services that meet the customers' needs. This builds on the personalization aspect and increases the size and scope of the customer relationship.

The next most likely product approach is a useful way to combat the long-standing problem of not knowing enough about your customer to make the appropriate product offer. It takes advantage of existing improvements in technology to yield real-time information. Used properly, this approach can increase response and conversion rates, improve profitability and strengthen the customer relationship.

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