Matchback Analytics: Tools to Understand the Relationship Between Catalog Mailings and the Flood of Web Buyers
In the first of a three-part series over the next three weeks, catalog circulation consultant Jim Coogan takes an in-depth look at the data catalogers should be obtaining from their matchback analytics.
In today’s multichannel commerce environment, the Web is producing a flood of orders. And typically these orders come without source codes. A growing number of catalogers are recognizing the need for matchback analysis to understand response to their catalog mailings.
Matchbacks simply are matching mailing files to order files to see who responded to a catalog mailing vs. responded online. As more catalogers recognize the need for matchbacks, they need to dig deep into this new type of analysis to understand what matchback analysis can tell marketers about optimizing their circulation.
Your matchback results provide a much richer and deeper source of data than merely telling you the total response of each list segment mailed, as well as those list segments that performed above breakeven. With the flood of Web orders lacking source codes, catalogers are using matchbacks to get more complete mailing results.
The Basic Analytics
When running a matchback, you get full response data for each list segment with both the source-coded orders and the matchback orders where no source code was given but the order matches a household that was recently mailed. This data is the basic response data catalogers use to track each mailing and each segment mailed in a catalog drop. Listed below are eight key factors involved in helping you get the most out of your matchback data.
1. Total response of each list segment mailed, sales per book, response rate and average order value: This comprises the basis of measuring list response. The difference with matchbacks is you get all the orders that match the mail file, not just the orders that come with source codes.
2. The number of lists above breakeven: This is the first metric examined when looking at which lists fell below planned response.
3. The percentage of total circulation above breakeven: This metric tells you how much of your total circulation was unprofitable. If you cut out the circulation that is below breakeven, you’ll maximize your profitability.
4. The housefiles above breakeven — the number of list segments above and below breakeven and the percentage of circulation above breakeven: Catalogers rarely mail housefiles below breakeven because it’s always difficult to get back to profitability with those reactivated buyers.
5. The prospect segments above breakeven — the number of list segments above and below breakeven and the percentage of circulation above breakeven: Catalogers may choose to bring in new buyers below breakeven, especially if their lifetime value justifies the investment. But you need to know if your planned investment in prospecting is being realized by comparing your plan to actual campaign results. “How much circulation was below breakeven?” and “How much did that investment cost?” are critical questions.
6. Incremental sales from matchback orders, overall percentage increase from matchback orders and percentage increase from house and prospect circulation: Matchback orders are responses without a source code that match addresses on the mail file. Source-code orders are responses with source codes. Nonmatching orders are responses with addresses that don’t match the mail file. This data shows the responses from orders with source codes and the responses from the mail file without source codes. This is the heart of your matchback data.
7. Mix of source-code orders, matchback orders and nonmatching orders: This data tells you how many orders can be linked back to a mailing, how many orders are coming from the Web and how many are coming from retail during the life of a mailing.
8. How big is the variation between different list segments mailed between source-code buyers (with source codes) and matchback orders (without source codes)? Are all the lists showing about the same portion of uncoded orders, or are your Web buyers showing a much higher portion of orders without source codes?
Next week: In the second part of our three-week series, Jim focuses on effectively calculating revenue streams and measuring sales by channel, particularly the Web vs. the call center.
Jim Coogan is president of Catalog Marketing Economics, a Santa Fe, N.M.-based consulting firm focused on catalog circulation planning. You can reach him at (505) 986-9902 or firstname.lastname@example.org.