Catalog Matchback Analysis – Part 1

With software like Hubspot, you can track opens on emails, gain metrics on clicks, and measure the response rates of campaigns — which tells you the level of success and profitability you can expect from a conversion.

But what did direct marketers do before the era of digital metrics and tracking? Catalogers relied on key codes to track response by customer segment.  Most orders arrived either by mail or via phone.  Customers were asked for a key code and roughly 90% of a catalog mailing could be tied back to a key code.  The 10% untraceable orders were usually allocated back to the catalog.  It was easy back then!

But over time, orders started migrating online and the ability to capture key codes diminished. Dial-up modems were replaced with high speed internet access, websites became less primitive, and online inventory expanded and reflected real-time availability.  Today, there are so many channels available for placing orders that it is virtually impossible to rely on key code capture to track catalog sales.

Besides standing outside someone’s house or waiting stealthily in the hedges, how can marketers track the effectiveness of direct mail or catalog campaigns?

It’s called the matchback process and analysis. However, whereas digital solutions offer one standard way to measure and track metrics (“cost per click” or “email open rates”) across most software, the matchback process is a little less standardized.

There are multiple ways to track. But track you must because most catalogs mailed today end up having match rates for unaccounted-for orders that range between 60% and 95%!

This means marketers have no idea where the orders came from or from whom.

A matchback analysis can solve this and more. Catalog matchbacks help you analyze catalog sales, evaluate test results, and monitor prospect list performance.

What is a Matchback?

Matchback analysis is a controversial subject.  It is a process where you take all orders within an active catalog window and compare the address on the order file to the address on the catalog mail file.  Orders can be matched in multiple passes.  Typically, you try to match on key code first, then customer ID, and finally by name and address.

All matches are reported in the catalog matchback analysis as catalog sales.  And this is where it becomes controversial.  Many companies that mail catalogs or direct mail rely on the response analysis and assume 100% of the sales are attributed to the catalog.  But we know that 50% to 90% of those sales are digital on origin and would have taken place without a catalog being mailed.

Catalog Attribution

The percentage of orders that can be attributed to a catalog varies by brand and depends on multiple factors:

• B2B brands tend to see a higher % of orders tied to a catalog.
• Consumer brands with an older customer base have a higher reliance on catalogs.
• Brands with relatively unsophisticated digital programs have a greater need for direct mail and therefore a higher catalog attribution rate.
• Prospecting catalogs are ideal for matchbacks because they represent mailings to records that are not in your database.
• Higher catalog attribution rates are also observed among inactive customer segments, especially if they have opted out of email.

Source: Unsplash

Matchback Strategies

Let’s look at, and disect, a few creative ways other companies have tracked catalog sales:

• Match sales based on product SKU. If it’s not in the catalog, do not count the sale. Although this seems reasonable, many catalog recipients often go online and discover additional products not featured in the catalog.

• Eliminate email click-through orders from the transaction file sent for matchback processing. Multiple touches generate incremental sales; therefore, this approach is not optimal.

• Assign promo codes that are specific to a catalog campaign and not found elsewhere. Great idea – but customers love to share promo codes, so it’s not an effective approach.

• Assign the order to the most recent catalog mailed. This approach does not take into consideration the sales cycle or type of product purchased. B2B brands often use the initial catalog received to plan their orders for the season and then wait a few months before placing the order.  Sending multiple catalogs may have little influence on when the order is placed unless a strong promotional offer is used.  On the other hand, a B2C fashion catalog can influence me to buy a cute top or pair of shoes!  In this instance, assigning an order to the most recent catalog does make sense.

• Matchback by segment. Based on where a customer is in the contact cycle, the marketing team may define the time of response as a variable that differs based on segment (e.g. inactive versus 12-month customer). This is a good foundation for assigning a catalog attribution rate.  Based on my experience, the longer a customer has been inactive on file, the greater the catalog attribution %.  In addition, new-to-file customers are more likely to respond to a catalog than established customers who are much more familiar with the products.

So how can you effectively evaluate a catalog’s role in sales using a matchback?  At Hansel Group Marketing, we like to use hold-out tests.  In our opinion, a hold-out test is the best way to measure catalog sales attribution in today’s environment. We will discuss this in part 2 of the post.