Catalog Matchback Analysis – Part 2

In our previous catalog matchback post we discussed matchbacks and how we should not attribute 100% of the sales results to the catalog mailing because many of the sales are digital in origin.  Customers are often touched multiple times (print, email, retargeting) along their purchase journey. Today we will look at catalog hold-out tests and how they can be used to modify your catalog matchback reports and provide a better understanding of a catalog’s role in generating sales.

Catalog Hold-Out Test

A catalog hold-out test is where you set up your RFM segments for the mailing and hold out a % of records from each segment. You should hold out all customers at the same address.  This is especially important for businesses who often mail catalogs to multiple contacts at the same location.

During the hold-out period, make sure that you do not mail any subsequent catalogs to the hold-out group.  The hold-out panel should be placed in top priority during the merge purge to ensure all matches to the hold-out panel are dropped.

Keep your “mailed group” intact.  Even though they might move into a different RFM segment during the testing period, keep the coded segments together and assign them 2^nd highest priority during the merge.

We prefer a long term hold-out test to avoid seasonal biases and other variables that might skew results. A long term catalog hold-out test should also be used for brands with small customer files or low response rates.

At the end of the testing period, measure sales and response between the two groups and look at the incremental lift in sales.  The hold-out group will have lower sales but it will also have lower costs.  Measure profit. Assign a catalog attribution %.

If you segmented your file in 0-12 month increments, make sure you assign a catalog attribution % based on these increments. Now apply your attribution % to the matchback analysis.  Voila!  Now you have a more accurate catalog response report to help plan your catalog circulation going forward.

If you are a brand that is experiencing increasing migration of orders to online, you should repeat the hold-out test in another year or two because your catalog attribution % will decrease over time.

Using Lookalike Audiences within the Matchback Process

Companies that are looking to integrate a direct mail or catalog mail component to their marketing strategy usually use co-op and other databases or audiences for leads.

LinkSoul, the e-commerce apparel company, focuses on using data from both external providers/databases as well as those already within the company’s transactional history to generate new “lookalike” audiences that may display the same behavior or have demographic data similar to already-present customers.

LinkSoul also feeds these into the backend of Facebook to gain new, high-quality leads for their direct mail campaigns. Then, using a B/E analysis, they can figure out which customers might benefit from a catalog and respond, making it profitable for them to send out a piece of brand collateral.

Source: Unsplash

The matchback process is key in when creating these new leads. Even if companies are using a “self-serve” provider like Facebook Ads to generate leads from a “Lookalike audience,” marketers will still need to know which customers actually responded to which mailers.

Monitoring the Customer’s Behavior

Matchback analysis is a process on your end to be able to track response rates. But, when coded and segmented in the right way, you can gain insight from this process that allows you to view your overall customer behavior…especially if you incorporate hold-out test catalog attribution data in your response reports.

Your POS system — whether in-store or online — is one of the most important “stops” or “terminals” that capture this data and you should be using it to reduce the percentage of unmatched orders.

Matchback analysis is closely related to the breakeven calculation. Breakeven analysis should be performed at the beginning of each campaign, to determine profits, and then at the end, to see how much of the forecast actually aligned with the responses coming in.

From here, matching those responses back with specific customers can help you perform even more sophisticated A/B tests. You can get a sense of which segments of customers will most likely respond to a catalog, which means that it’s well worth your investment.

For example, you might then decide to do a triggered postcard for a customer that may not generate enough incremental sales to qualify for a catalog but can easily support a postcard touch.

The point is that direct marketing has built within it exercises like matchback analysis — and it’s a method that really allows you to track results, using real data to create future projections.

It’s the granular level of accuracy that makes matchback analysis so powerful. Expect improved tracking, better use of marketing dollars and a more effective circulation plan for your next round!