During the past decade, matchback analytics have become an indispensable part of catalog marketing. Without matchback analytics, it is possible you would not have a catalog channel.
The age of the "matchback" changed our perception of marketing.
Do you remember the good 'ole days, like way back in 1994, before we had an e-commerce website, back in the stone age of catalog marketing?
Back in 1994, we cared a lot about the concept of "cannibalization". We executed a lot of exotic multi-variate tests to detect cannibalization. For instance, say we mailed two catalogs, one on September 1, one on October 1.
- September 1 Catalog = $5.00 per catalog.
- October 1 Catalog = $5.00 per catalog.
- Total Demand = $10.00 per customer.
And then, we got excited! If we could generate $5.00 per catalog, maybe we should add a third catalog. So in 1995, we added a third catalog, on September 15.
- September 1 Catalog = $4.00 per catalog.
- September 15 Catalog = $4.00 per catalog.
- October 1 Catalog = $4.00 per catalog.
- Total Demand = $12.00 per customer.
Remember what we thought? We were happy with the new catalog, but we were concerned with the performance of the two existing catalogs. It was almost like they weren't working well anymore! And in fact, if we ran a profit and loss statement, we found that $12.00 of customer demand across three catalogs was less profitable than $10.00 of customer demand across two catalogs.
We thought about cannibalization, a lot. We were genuinely concerned about how one marketing activity cannibalized another activity.
Then matchback analytics came along. The data enabled the catalog vendor community to change our minds about how we thought about marketing activities.
We stopped thinking about "cannibalization". Heck, these catalogs didn't cannibalize business. Nope, these catalogs "added" business, they "drove" business to other channels.
The industry script (supporting an additive model vs. cannibalization) benefits the catalog ecosystem. The co-ops and database vendors created reporting that illustrated how catalogs drove sales across all channels. Their reporting supported the notion that we should rent more names from the co-ops. In other words, matchback reporting fuels the financial success of the co-op and list industry. The paper industry supports the concept of matchbacks. Printers support the concept of the matchback. The USPS supports the concept of the matchback. Your favorite Catalog Consultancy that helps you with mailing plans benefits from the matchback. Even third-party opt-out services benefit from matchbacks ... without matchbacks, they serve fewer customers who are getting unwanted catalogs.
The industry script benefits the entire catalog ecosystem.
Now let's focus on you, the Catalog CEO. Do you benefit from matchback algorithms?
As we head into the Holiday season, I'd like to ask you to do our industry a favor:
- Randomly sample 5,000 or 10,000 customers from the universe you would mail your best-performing Holiday catalog to.
- DO NOT mail these customers your best Holiday catalog.
- Code these customers as a unique segment, and enter these customers into your matchback routine with your favorite matchback vendor. Remember, these customers were not mailed a catalog, so your matchback vendor should show that no orders are matched back to the catalog that you did not mail.
If your matchback vendor matches online orders back to a catalog that was not mailed, then you have an estimate for how much your matchback vendor is over-stating the results of your catalog mailings.
Catalog CEOs, this is a very important topic. If your matchback vendor is over-stating your catalog performance because your vendor fails to take cannibalization into account, then you are over-mailing your customer base, and in all likelihood, you are wasting marketing dollars, squandering profit.
Increasingly, I am hearing of big discrepancies between matchback results and real-world results obtained via holdout tests. One company told me that every phone order was paired with one online order matched-back in their matchback algorithm. And yet, when they executed a holdout group, they only saw a 5% drop in total demand --- almost no phone demand or online demand was lost when the catalog was not mailed.
In other words, cannibalization was so significant that the catalog was basically adding no incremental demand. This is an important concept --- cannibalization testing shows no additional demand, while matchback algorithms show that catalogs drive online business, forcing you to mail more catalogs.
Do you understand the distinction?
Catalog CEO's, please ask your marketing folks to give this test a try. The entire catalog industry ecosystem benefits from matchback algorithms, and they aren't supporting matchback to be evil ... it's the best available reporting folks have. I'm asking you to question your results, to execute a test and validate that the matchback algorithms are giving you honest results. I don't benefit from doing this test, I have no financial interest in positive or negative results. Only you will benefit if you find that orders are being mistakenly attributed to catalog mailings.
So see for yourself! Run a holdout test, code the customers as a segment, run them through your matchback algorithm, and see if there is a bias that is causing you to over-state your results.
Thank you for your consideration,
Kevin Hillstrom, President, MineThatData