Catalog ROI Is Overstated Because Of Search

Last week, we chatted about how E-Mail ROI is mis-calculated. My stats tell me that you found the article interesting.

Catalog advertising causes the same issues that e-mail marketing causes, often on a larger scale.

The typical catalog marketer matches paid search orders that occur within 30/60/90 days of a catalog mailing back to the catalog that the circulation team believes is responsible for creating the order.

However, the typical catalog marketer does not match back unconverted paid search expenses to the catalog responsible for causing unconverted paid search to happen.

Take a look at this profit and loss statement.


This is a fairly typical catalog profit and loss statement.

Notice converted paid search orders. These orders are matched-back to the catalog. Some catalogers match the paid search expense of those orders back to the catalog.

Almost nobody matches the unconverted paid search clicks back to the catalog that caused paid search to happen. In this example --- a reasonably honest assessment of a catalog profit and loss statement, the catalog caused 3,200 paid search orders to happen. However, at a 3% conversion rate, the catalog caused about 100,000 paid search clicks to happen.

The average cataloger does not allocate the cost of the incremental 96,800 unconverted clicks back to the catalog that caused the clicks to happen.

So three things happen.

1. The cataloger significantly over-circulates the catalog, because the additional expense is not allocated to the catalog driving paid search. The catalog marketing effort is less profitable than it appears.
   
2. The cataloger significantly mis-understands the impact of catalog marketing. In this case, circulating 1,000,000 catalogs caused 100,000 paid search clicks. The marketer fails to see that the catalog caused a 10% "engagement rate". This is a big deal --- the catalog is causing far more customer engagement than is typically measured.
   
3. A portion of the 100,000 paid search clicks result in purchases with the competition, reducing your Net Google Score.

Eventually, we'll create a database infrastructure that allows us to capture appropriate customer interactions. This will fundamentally change how we market to customers.

• We will attribute unconverted paid search clicks back to the customer/catalog combination, in our promotional history files. Instead of recording an $0.80 cost for the catalog, we'll record a $0.80 + $0.50 = $1.30 cost to the customer, incorporating the cost of the search. Ask your database, co-op, or web analytics vendor if they are able to do this for you.
  

• When we make mailing decisions (e-mail or catalog), we will make the decision based on the historical paid search expenditure of the segment we're considering. We won't send as many catalogs or e-mails to customers who augment their experience with unconverted paid search. This is a big deal, folks ... we'll be much more profitable when we make this transition.

• Example: Say your break-even on an $0.80 catalog is $2.50. Now you have a customer who loves to click on paid search ads when she receives a catalog. Your "real" cost of mailing the catalog is $1.30, driving your break-even over $4.00.

• Example: E-Mail marketing is essentially free, until it isn't free! The new e-mail marketing discipline will require us to make e-mail marketing decisions, at a segment level, based on anticipated paid search expense. All of a sudden, e-mail marketing is fundamentally changed --- the discipline becomes nearly identical to catalog marketing.
  

• Another Issue: We have the same problems with Affiliate Marketing and Shopping Comparison Sites. If catalog marketing drives a customer to an affiliate, and that affiliate skims 7% off the top of an order, the catalog needs to receive an expense penalty for driving demand to the affiliate.
  

We've spent a decade doing matchback analytics. Now, we need to provide the vendor community some leadership, so that matchback analytics account for the expense side of the ledger. We are continually making bad decisions because our database infrastructure fails to capture important information.

Who do you see doing this type of work out there, and what was the impact of this style of analysis?