Measuring a direct marketing campaign’s effect on sales

Never have consumers been courted and wooed as they are today, surrounded by branding, PR, advertising, and marketing campaigns urging them to try — and remain faithful to — a panoply of products and services.

Consumers hear so many messages from so many media sources that it’s often difficult to tell which message has motivated them to respond or to make a purchase. Was it a memorable television ad? A recommendation from a trusted co-worker? Or an email offer that landed in their inbox at just the right moment?

When it comes to causality, the effect of a direct marketing campaign (as opposed a PR or brand-awareness campaign) on sales is relatively easy to measure. But accurate measurement of consumer responses and sales doesn’t just happen. Good data tracking tools need to be built into your campaign so you can test and refine your offers, your direct mail formats, and, of course, your mailing lists.

The Credit Union National Association article “Tracking the Results of a Direct Marketing Campaign” offers tips for getting started with measurement. Developed for the credit union industry, these can easily be adapted for campaigns for a wide range of products and services:

  • Collect “front-end” data. How many consumers responded compared with how many actually carried through and made a purchase or signed up for future contact?
  • Determine the causes for the difference between the two rates. For a web offer, you can tell if a click-through resulted in a long visit or just a second or two on the site (indicating that the website itself is not compelling enough). For a mail campaign, review mail-in forms or ask front-line staff for their feedback on why the inquiries they received didn’t evolve into sales or sign-ups.
  • For measuring results by product line, use historical data. If you usually sell 1,000 units of a particular product a month, and during the campaign response period you sell 1,050, you have an indication of effectiveness. (When using historical data, be sure to take seasonal variations in sales activity into consideration.)
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