Database : Close the Loop Properly
Three major analytical gaps that lead to faulty data decisions
January 2008 By Stephen Yu
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We’ve all seen it: that circular diagram with all the steps direct marketers live through, starting with campaign planning and list acquisition, passing through inevitable data processing steps, and concluding with selection and source coding steps. Then there is a little arrow that connects the last step back to the beginning, capturing the responses we all hope to get out of campaigns. Anyone exposed to one-to-one marketing must have seen at least a few versions of “closed-loop marketing” diagrams. The diagram could take a different form depending on the presenter’s agenda, but the core of the idea is represented in a box called “response analysis.”
That should not be news to any marketer; after all, the foundation of direct marketing lies in the concept of measurement. Lester Wunderman, known as the “father of direct marketing,” did not invent the one-to-one delivery medium, but he first attempted to systemize the result analyses and key measurement metrics. Unfortunately, not everyone in the direct marketing industry today measures their business via scientific and methodical approaches all the time.
Matchback: Do It Carefully
It would be ideal if all marketers had the luxury of properly designed marketing databases with a built-in “closed-loop” mechanism; however, the reality is many still rely on anecdotal information on a campaign level. The trouble with that approach is marketers cannot examine the results based on detailed data points such as name source, selection logic and creative versions tagged in the individual/household-level source codes. The match process becomes essential since the collection rate of source codes at the time of transaction can be unacceptably low when dealing with newer response channels such as the Internet.
A simple matchback process can provide all the necessary result details, granted that the master mail file was maintained properly and the response data contained usable name and address information. It’s hard to believe this step is overlooked by many mailers due to extra costs and resource requirements. Nonetheless, once committed to the response matchback, it is important to pay attention to the details of the process:
• Campaign and time window. Allow a consistent time window for responses if multiple campaigns are analyzed.
• Match logic and rules. Soft-match logic using names and addresses must be tweaked in advance to prevent over- and under-matching situations that can affect the validity of subsequent result studies.
That should not be news to any marketer; after all, the foundation of direct marketing lies in the concept of measurement. Lester Wunderman, known as the “father of direct marketing,” did not invent the one-to-one delivery medium, but he first attempted to systemize the result analyses and key measurement metrics. Unfortunately, not everyone in the direct marketing industry today measures their business via scientific and methodical approaches all the time.
Matchback: Do It Carefully
It would be ideal if all marketers had the luxury of properly designed marketing databases with a built-in “closed-loop” mechanism; however, the reality is many still rely on anecdotal information on a campaign level. The trouble with that approach is marketers cannot examine the results based on detailed data points such as name source, selection logic and creative versions tagged in the individual/household-level source codes. The match process becomes essential since the collection rate of source codes at the time of transaction can be unacceptably low when dealing with newer response channels such as the Internet.
A simple matchback process can provide all the necessary result details, granted that the master mail file was maintained properly and the response data contained usable name and address information. It’s hard to believe this step is overlooked by many mailers due to extra costs and resource requirements. Nonetheless, once committed to the response matchback, it is important to pay attention to the details of the process:
• Campaign and time window. Allow a consistent time window for responses if multiple campaigns are analyzed.
• Match logic and rules. Soft-match logic using names and addresses must be tweaked in advance to prevent over- and under-matching situations that can affect the validity of subsequent result studies.
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The Business of Database Marketing