Easier Said than Done
Response attribution is one of those pesky details that can get in the way of grand visions. The vision in this case is optimization of enterprise marketing programs, which means ensuring that marketing resources are allocated to the most productive possible uses. This implies a vast, all-knowing marketing management system that identifies all spending options, attaches a value to each and chooses the most effective. It sounds great in theory, and it's even possible to build process models that can simulate the results of different strategies. However, how do you measure the actual results of different choices so you can assess and refine your assumptions? That's where response attribution comes in, and that's when things get ugly.
The problem is that marketing systems are not all-knowing. (You read it here first.) Response attribution is the process of determining which marketing activity caused a customer action. At the most basic philosophical level, causation is always problematic. However, marketing adds its own wrinkles. When individuals are exposed to multiple offers for the same product, which gets credit for the purchase? How should attribution account for exposures that are not tied to a specific individual, such as broadcast television advertising? How can it include the impact of non-marketing interactions such as purchase, product usage and customer service experience? Should it take into account competitive and environmental influences?
Response attribution was originally applied to direct marketing promotions. In these situations, each response is tied directly a specific individual, and most promotions can be linked to individuals as well. The simplest case is direct mail, where marketers typically print a promotion identifier on the order form that will be returned by the customer. This identifier, called a source code or key code, is recorded when the order is processed, and thus the connection is made. Slight variations are possible with media where there is no physical order vehicle, such as telephone orders (e.g., having the customer read the source code aloud when placing a telephone order).
However, complexity mars even the edenic simplicity of direct marketing. When a customer receives several catalogs offering the same product, does it make sense to credit the response to just one? How should you treat pass-along orders from people who were not sent the original promotion? What about orders that cannot be linked to any particular catalog? Should consideration be given to what might have happened if a promotion had not been sent?
The answer will often depend on the purpose at hand; deciding how many catalogs to send to one person requires a different analysis than determining whether one direct mail letter is better than another.
Outside of direct marketing, things get complicated fast. Retailers may have customer lists and even use them to send catalogs; however, connecting these to in-store purchases ranges from difficult to impossible. Simply identifying the customer is a challenge: retail transactions involving cash or even credit cards (depending on privacy rules) can be effectively anonymous. Many merchants have loyalty programs largely to give customers an incentive to identify themselves at the time of purchase. Even when the customer is known, identification of the promotion they are responding to may not be clear. One common approach is to send coupons marked with both customer and promotion identifiers, which are presented at time of purchase. However, this can be expensive, and many customers will not redeem the coupon even if the promotion did, in fact, influence their behavior. A more universal solution relies on inferred response: if the customer received an advertisement for an item and later purchased it, the marketer assumes a connection. This is usually implemented through queries that compare lists of customers with purchases of specified products in specified time periods. The products are those included in the promotion; the time period is the range during which the promotion is assumed effective. Often, marketers look at several different queries, incorporating different date ranges and groups of products, each implying a different definition of response.
Companies selling through distributors, retailers and other third-party channels have an even more difficult time collecting customer purchase data. Warranty cards are one common source of information for manufacturers, but coverage is far from complete. Other consumer goods manufacturers often rely on product movement data or third-party surveys to get some idea of sales changes and how these correlate with promotions. Of course, such data rarely identifies individual purchasers and is affected by many factors other than the company's own promotions.
Next month's column will describe new challenges in response attribution and how today's marketers are responding.
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