CONTACT STRATEGY DEALS with using data about customer purchases, promotion patterns, interests and preferences to not only profitably regulate the sequence and frequency of customer contact, but also appropriately customize the offer, creative thrust and positioning of those contacts.
Regardless of the tools a marketer uses to help develop and implement contact strategy, the key variables to consider include purchase data (recency, frequency, monetary value and product) and promotion history data. Demographic and survey data can also help you understand your customers’ and prospects’ perceptions of your product or service and your competition.
Employing the dimension of time might be the easiest way for marketers to begin using data to segment customers and develop customized contact strategies. For example, first-time buyers can be isolated from the rest of the file and treated as a special segment to receive a “welcome” package. Or a new-customer kit and brief survey could be included with initial shipments to help stimulate repeat purchases.
For longer term customers, some marketers have found that an excellent contact opportunity is a special effort designed to celebrate the anniversary of their original purchase date. It acknowledges their status as longtime, valued customers. Segmenting the file by how long each individual has been a customer can-in and of itself-lead to different contact strategies.
Total sales dollars is probably the most often used criterion for deciding who is or is not a good customer, since it shows how much money a customer has spent with the company. Evaluating customers according to their total sales dollars over a period of time can easily become the basis for a customer contact strategy. One way marketers do this is to take total dollars spent and divide it by time, say total months on file. This results in a revenue velocity indicator for each customer.
For example, a customer who has spent $100 with you and been on your file two months would have a different score than a customer who has spent that same $100, but over 20 months. It would seem to make sense that the customer with the higher revenue velocity score (100/2=50, 100/20=5) would be a better repeat customer, and thus a better candidate for more frequent contacts.
In addition to looking at dollars, it is also important to take product ownership into consideration when developing a contact strategy, if for no other reason than to avoid offending a customer by recommending an item they already have.
For financial services companies, insurers, automobile manufacturers and other marketers that promote big-ticket items, contact strategy must include the ability to carefully regulate both the frequency and the sequencing of single-product promotions.
Take insurance direct marketers as an example. Their contact strategy focuses on first defining a segment using a combination of product ownership, eligibility and suppression rules, then using model scores to access the probability of achieving the highest return on investment for each customer and for each product.
Eligibility can be determined for each customer by analyzing if an individual is a resident of a state where the company is licensed to sell, doesn’t have a “do not contact” code and doesn’t have a last contact date inside the “too soon to contact” window. The resulting group of customers can then be assigned a probability score for responding to any and all products for which they are eligible.
Once these marketers have a probability score for each product and for each person they can assign values to the products. This is done by multiplying an individual customer’s estimated response rate by the expected first-year revenue rate for each product. These return-on-investment values then become the basis for selecting which product gets promoted to which customer.
Aside from looking at ownership, another helpful way to use product information is to evaluate customers according to the number of products they own and how long they have been customers.
Using this approach, one insurance DMer was able to identify certain customers who were too responsive to cross sells and upsells. While at first they seemed to be highly profitable, they proved to be just the opposite. In fact, many of these less-than-12-month multibuyers had actually canceled all of their policies.
Subsequent research revealed a primary reason for canceling was the exorbitant combined monthly premium. When these customers had only one policy they didn’t notice the relatively small amount each month on their credit card. However, when they began seeing the “sizable” amount for three different policies each month, they had to cut back.
Based on this situation the insurer developed a more targeted strategy. Customer promotions were carefully regulated depending on the length of time people owned a particular policy, their payment history and the balance-to-credit- limit ratio on their credit card.
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