More Hints for Marketers

Last month's column discussed the basic challenge of response attribution, which is linking customer behavior to marketing activities. From an information management perspective, the basic questions are whether the system can identify: a) the people who receive the marketing message and b) the people who respond to the message through actions such as making purchases.

Four Situations

Because each basic question of response attribution can have a yes or no answer, there are four possible situations as follows:

Known receiver, known responder. This is the most favorable situation. Because the receivers are known, their long-term behavior can be compared with non-receivers to measure the full impact of the promotion. Ideally, the responses carry an identifier, such as a source code, that links them directly to the original promotion. Otherwise, as long as responder names and addresses are captured, it may be possible to match the responder list against the original promotion list. Common examples are direct mail, catalog and targeted e-mails with a response mechanism.

Known receiver, unknown responder. This is the situation when promotions are sent to specific individuals, but the responders either cannot be identified at all or cannot be connected to the list of receivers. Common examples are mailings that drive traffic to retailers or dealers. These mailings often use anonymous response devices, such as non-personalized coupons redeemed at the store, to measure results without linking them to individuals. In such cases, analysts can measure the volume of sales directly tied to the promotion, but cannot include purchases by promotion recipients who did not redeem the coupons. Marketers may still be able to compare overall behavior of recipients versus non-recipients, but only if there is some other mechanism to tie sales to individuals.

Unknown receiver, known responder. Here, the marketer doesn't have the list of people who will see the promotion, but still gets the names of responders. Examples include any type of direct response advertising through mass media such as TV, radio, newspaper inserts, billboards and Web sites. It's often possible to identify the promotion the responders are reacting to through codes printed on advertisements or by using different telephone numbers or addresses for different advertisements. Such information measures immediate advertising results; however, it does not capture the effect that seeing the advertising had on subsequent behavior. This cannot be measured without knowing who received a promotion even if they didn't react immediately.

Unknown receiver, unknown responder. This is the classic situation of marketers selling through retail channels. They advertise in mass media such as print or broadcast, which means they have no list of the receivers. Sales are then made to essentially anonymous consumers, either because their names are literally unknown or because the retailer or dealer will not share them. Marketing results can only be inferred from changes in total sales after the advertising is executed. Even this crude inference is possible only if marketers hold back advertising to another, roughly comparable group so that differences in results - presumably attributable to the advertising - can be measured. Because forms of mass media are inherently nonselective, such tests are frequently conducted by comparing different geographical markets - a very blunt approach, and one that is increasingly unreliable given today's eroding barriers between local and national media. Figure 1 summarizes these situations.

Figure 1: Four Situations of Response Attribution

The more closely responses can be linked to recipients, the more precisely the impact of specific advertising efforts can be measured. Over time, such measurements enable marketers to learn how to improve their results. Thus, one strategic objective for marketers is to shift their efforts to techniques that allow better measurement. For example, adding a loyalty program not only enables an airline to reward its most valuable customers, it also enables the airline to assess the results of different promotions on long-term behavior. These promotions may be alternative reward schemes or incremental revenue generators such as incentives to purchase seats on lightly booked flights. Even small improvements in these areas can have a material impact on airline profit margins.

Other techniques to make responses more measurable include substituting personalized coupons for anonymous ones, using tracking devices such as cookies or tracking codes on Internet promotions, and capturing caller ID information from inbound telephone contacts. New technologies such as RFID (radio frequency identification) tags may provide still more identity information, although privacy concerns may limit their application.

New Elements

Some of these techniques are new because the underlying technologies are new. Others have long been available but have become more practical as costs have fallen. Thus, one new element in response attribution is the wider variety of measurement techniques available.

Another element is the greater mix of channels. Internet marketing plays an increasing role in many businesses. Other channels, such as direct mail and telephone marketing, are also being added by many firms that originally sold elsewhere. Thus, organizations must now measure responses across multiple channels to assess the full impact of a particular promotion. This makes it even more important to identify specific promotion recipients and responders.

Marketers are also increasingly concerned with long-term relationships, which can only be tracked by linking multiple interactions. This means that the gold standard of traditional direct marketing - capturing a response code that links each order back to the original promotion - is no longer adequate. Instead, marketers need to assemble a comprehensive history of all promotions and responses for specific individuals.

Building such histories is the role of a marketing database. Such databases started appearing twenty years ago; therefore, the basic design principles and technical challenges have long been known. However, even today, most marketing databases serve primarily to simplify creation of customer lists for targeted promotions. Response attribution is still mostly conducted with an eye toward linking specific responses to specific campaigns.

This is starting to change as marketers come to grips with the fact that long-term relationships cannot be analyzed as a series of unconnected interactions. Marketers must abandon the traditional goal of response attribution - assigning a specific cause to a specific event - and adopt a broader approach organized in terms of customer treatment policies. These policies govern large numbers of interactions over long periods of time. Their results are measured in terms of overall customer behavior, not specific events. In fact, because the behavior of a single customer is not statistically significant, the scope of analysis actually shifts from single transactions all the way to customer groups.

Conducting effective analysis at the customer group level requires new techniques for data gathering and analysis. Data gathering must track customers over longer periods and must capture the policies in effect at specific points in time. Analysis must employ sophisticated statistical methods to infer the impact of different policy components. Both the data and the results are likely to be less precise than traditional response attribution techniques; however, the business benefit - truly measuring and ultimately optimizing long-term customer results - makes the adjustment worth the pain. 

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