When measuring the effectiveness of a promotion, the focus should always be on the incremental order activity that is generated by the promotion, according to Jim Wheaton, a principle with Chapel Hill, NC-based database solutions firm Wheaton Group. The overall order activity during the promotion’s “response window,” he says, is irrelevant.
Let’s assume that a catalog mailing is followed two and a half weeks later by a remail of the same catalog with a cover change. What you want to determine is how many of the orders that were received after the second catalog’s in-home date were the result of the second catalog. How many would have occurred even if the second catalog had never been mailed? And for those catalogers with an e-commerce site, how many orders would have come in if neither the first nor the second catalog had been sent?
“Catalogers implicitly acknowledge the existence of ‘baseline’ order activity when they say that a remail ‘cuts off the tail’ of an initial catalog’s promotional curve,” Wheaton says. “But without knowing the amount of baseline order activity, you cannot determine the amount of incremental purchases generated by a second catalog, or for that matter, by the first catalog compared with self-generated e-commerce activity. Without this understanding, it is impossible to measure the effectiveness of a promotion.”
To accurately measure incremental performance in today’s multichannel world of overlapping promotions, Wheaton advises using a “longitudinal analytical framework.”
The first element of such as framework, he says, “is a robust repository of complete customer history.” This should include:
Unabridged, atomic-level order and item “demand” transactions and, when appropriate, postdemand transactions such as returns, exchanges, and allowances.
All promotional contacts, including mail, e-mail, and phone.
Scrupulously deduped individual-level data that are properly linked to the household level, in the case of consumer data. For b-to-b, that translated to individual-level data properly linked to the site level, and site-level data to the company level.
The ability to easily re-create past point-in-time customer views, model scores/segment definitions and business rules, and “time-0 snapshots” for predictive modeling and cohort analysis.
“This can be defined as a ‘marketing database,’” Wheaton says. “However, it is much more than what most catalogers refer to as their marketing database.”
The second component is “the implementation and subsequent analysis of continual waves of longitudinal test treatments, in order to fully understand the complex interactions of channels and promotions,” Wheaton says. “All ongoing analysis takes place within the robust repository of complete customer history.”
With such an approach, he admits, “the devil is in the details, and the analytical challenges are daunting. However, the payoff is that true insight can be gained into the behavior of customers and prospects. And the holy grail of contact optimization is achievable.”