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Direct marketers can improve campaign efficiency in two in ways. One is reducing costs by sending out fewer promotions or cutting the physical promotion’s cost (e.g., reduce the number of creative package inserts or switch to less expensive telemarketing shops), without dramatically reducing prospective buyers. The other is increasing the number of prospects likely to respond and purchase. This article assumes that direct marketers are using one or more targeting models and that these tools are up-to-date. If not, then bringing them up-to-date is the first step.
Right Person, Right Time, Right Offer, Right Channel
To reach your marketing goal, put greater effort into reaching your best prospects (i.e., right person). To find more customers, marketers tend to promote too deeply into their prospect files. The marginal cost of acquiring the last customer can be 10 times the first because of the vast differences in response and conversion rates. By reallocating resources from the worst-responding cells to the best, a marketer can achieve greater results. While a portion of the top decile will have been targeted incorrectly as likely responders, the rest likely did not respond for other reasons (e.g., perhaps the recipient never opened the package). Rather than mail the same promotion to the lowest-performing deciles, direct marketers might consider remailing the top decile with one or both of the following changes:
* Make the second offer more enticing than the first.
* Make the creative package more interesting (e.g., 3-D, interactive pull tabs or scratch-off) by increasing the creative budget and offsetting the cost by reducing the overall mail quantity.
Improve the timing of your promotions at the individual level (i.e., right time). While seasonality can affect response rates, most marketers believe that models can predict the best time to target someone based on the underlying data. If a large change in credit-card balance, for example, is correlated to response, then a balance increase in December resulting from holiday shopping would be expected to affect the model the same as a similar balance increase in June. However, the June increase might result in more response than the same behavior in December.
Two approaches can help align timing with the individual. The first is to create separate variables for each time period and use them in developing the model. Instead of a single variable, such as “change in credit-card balance,” create 12 “change in credit-card balance” variables corresponding to each month targeted.
Right Person, Right Time, Right Offer, Right Channel
To reach your marketing goal, put greater effort into reaching your best prospects (i.e., right person). To find more customers, marketers tend to promote too deeply into their prospect files. The marginal cost of acquiring the last customer can be 10 times the first because of the vast differences in response and conversion rates. By reallocating resources from the worst-responding cells to the best, a marketer can achieve greater results. While a portion of the top decile will have been targeted incorrectly as likely responders, the rest likely did not respond for other reasons (e.g., perhaps the recipient never opened the package). Rather than mail the same promotion to the lowest-performing deciles, direct marketers might consider remailing the top decile with one or both of the following changes:
* Make the second offer more enticing than the first.
* Make the creative package more interesting (e.g., 3-D, interactive pull tabs or scratch-off) by increasing the creative budget and offsetting the cost by reducing the overall mail quantity.
Improve the timing of your promotions at the individual level (i.e., right time). While seasonality can affect response rates, most marketers believe that models can predict the best time to target someone based on the underlying data. If a large change in credit-card balance, for example, is correlated to response, then a balance increase in December resulting from holiday shopping would be expected to affect the model the same as a similar balance increase in June. However, the June increase might result in more response than the same behavior in December.
Two approaches can help align timing with the individual. The first is to create separate variables for each time period and use them in developing the model. Instead of a single variable, such as “change in credit-card balance,” create 12 “change in credit-card balance” variables corresponding to each month targeted.
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