April 01, 2015

Catalogers and Co-Ops

I will say this - I would be very frustrated to be a non-Executive working at a co-op. You have too many strikes against you, limiting you from doing a good job.

  1. Few individuals understand statistical models. You have to defend models that you are not trained to understand. That's hard work. The modeler should have to sit in front of the client and address the questions you get.
  2. Catalogers do not give you good information. Catalogers should be fully transparent with you, demonstrating five year or ten year trends across comparable segments, showing you, to the penny, the decline in profit they are experiencing. How can you help them when they will not give you detailed profitability information by segment?
That's why I wrote the Ashley Cole character in last week's Gliebers Dresses (click here). She was being beaten silly by folks who don't know anything about marketing (the CFO), and is held accountable for mistakes that Gliebers Dresses made over a twenty year period of time.

I also wrote the Gliebers Dresses story (more coming, folks, more coming) to defend catalogers. The speaker at NEMOA wouldn't have offered a passionate ten minute argument (from what many of you have told me) unless his client base (like mine) experience frustrations when working with the co-ops. The late Don Libey went through this in 2006 - he addressed client concerns in his newsletter and at a conference - and was promptly demonized by some in the vendor community ... the predictable response of an article in a trade journal that sponsors conferences and gets ad revenue from the very co-ops in question is a time honored tradition. Notice that trade journals do not ferret out cataloger stories to validate if there are challenges or not ... isn't that what a journalist would do? 

By the way, Mr. Libey's concerns were not fundamentally different than the concerns outlined by catalogers in 2015 - nearly a decade later.

In the article above, the author suggested that no solutions were offered at NEMOA. I'd like to offer a few solutions. Since employees from all co-ops read this blog on a frequent basis, let's see if we can't get a few solutions generated.

Solution: Give every catalog client the specific equations used in their model. Housefile modeling organizations like Clario happily do this for clients. Please do this, it's not hard, right? And have the modeler Skype with the client, explaining why the modeler is using the methodology the modeler is using. This is a great first step in being open and transparent.

Solution:  For each select, list the percentage of the select that have purchased from the client's product category in the past ninety days. When the percentage is low (i.e. fewer than 50% of the select buys widgets), explain why. Use prior results to clearly demonstrate that non-widget buyers are worth more than prior widget buyers. Or if you cannot do that, make sure that widget buyers are in each select!

Solution: We all understand that all households are identified by all co-ops. Let's try something different. On a monthly basis, could the co-ops please publish a count of what I'd call "Outstanding Catalog Buyers" on a public blog? Nobody cares that 100,000,000 households are being captured and that a low minority of e-commerce transactions are captured. Everybody cares that there are 8.1 million "Outstanding Catalog Buyers", and that the total has dropped from 8.7 million last year and from 9.1 million two years ago. This helps catalogers an awful lot. The co-ops then raise the issue that the "Outstanding Catalog Buyer" audience is shrinking, and therefore, it isn't the co-ops fault that the audience is shrinking, but instead, it is a reflection of the catalog industry. Similarly, comparable metrics for the e-commerce ecosystem, social ecosystem, and mobile ecosystem should be published monthly, so that catalogers can see that the world is changing.

Solution:  Every month, each co-op publishes a private report for each cataloger, telling how many times cataloger contributed names were mailed by co-op members. Also, for all the names taken by a cataloger, a report outlines how many times those names were mailed by the cataloger in the past year, and how many times those names were mailed by other co-op members in the past month. All reporting is outlined in a this year vs. last year vs. two years ago format, for comparison purposes.

Solution:  Create an online selection system that allows catalogers to pull their own names. If catalogers are not obtaining acceptable performance, then let catalogers choose the names themselves, using the attributes that co-ops typically model against. This serves two purposes - first, you cannot be accused of not being transparent - second, catalogers will quickly see how hard your job is after their selects perform at 80% of your modeled results.

Solution:  Demand that all catalogers share a common set of metrics with you. Response Rate, Average Order Value, Dollar per Book, and Profit per Order. Profit per Order is most important. Demand that every cataloger share profit per order with you, on every select, every segment, every decile, for the past five years. This way, you can compare performance equally across time. If catalogers are being honest with you, and most are, then you will have clear proof that performance is suffering. But demand that profit per order is evaluated, across all marketing activities. Also demand that catalogers share Google performance, retargeting performance, you name it. If you are generating more new names at a reasonable cost, make sure folks know.

Solution:  Given catalogers the opportunity to not share their names with e-commerce businesses. Co-ops were created for catalogers.

Solution:  If you are not requiring e-commerce businesses to contribute names, and you are offering cataloger names to e-commerce businesses, give the cataloger the opportunity to split list income 50/50 with you - since the cataloger was kind enough to freely contribute the names to your database.

Solution:  Open your books, and if your performance is struggling as cataloger performance struggles, share that fact with catalogers. Demonstrate that you are in this challenge with catalogers. If your performance is improving and the cataloger you work with is struggling and that cataloger is having positive housefile performance, offer to do something to help the cataloger out.

Solution:  Invite each client to your headquarters, so that your clients can meet the modelers and Executives and back-of-the-office staff who make new customer acquisition possible. Hold a day-long conference, and share your vision as to how you plan to use data from all sources to make catalogers profitable for the next ten years or more.

Solution:  Report on the percentage of each select that are names that were contributed by pure e-commerce businesses.

Solution:  For each select, report on the average age of the name offered. Also list 25th, 50th, and 75th percentiles for age. If ages skew old, prove to the cataloger that the older names outperform younger names, via reporting of prior results.

Use the comments section to offer more solutions. And since so many co-op employees read this blog, why not jump on here and leave a comment with your thoughts about the solutions?


  1. Excellent commentary Kevin. I wish you had been at NEMOA. Maybe someday they will give you more than 10 minutes to present this. The co-ops do themselves a disservice by being so defensive and secretive. Thanks again.

  2. This response came in from an anonymous catalog marketing professional, and is reprinted as it arrived in my email inbox:

    What you have proposed is a nice Christmas wish list. I think many of us mailers have been asking for something along these lines for years. In particular, I'd personally love to see 3, 4, 7 and 12 implemented as a start. That should be relatively easy, but perhaps not all as easy as it sounds, I would think.

    As for solution 4, the problem lies in establishing the value of "my" specific transaction data that was contributed as it relates to determining whether or not a particular household record was selected by the co-op as being included in a particular campaign, not the mere fact that the consumer ever had bought from me in the past. A particular consumer may have bought from my company, a food mailer and a woodworking tool catalog, but the presence of one transaction in the household might not have played a part in qualifying or selecting that household in a third-party clothing mailer's model. Perhaps if you could limit the "counting" of times the transaction played more than say a 10% weighting of the model-based selection scoring during fulfillment, that might be useful, although terribly difficult to manage.

    As for solution 7, the problem is trying to define "pure play". While I definitely agree with the basic premise, I am not sure how you could adequately determine what percentage of contributed transactions are required to apply the moniker of a pure play. What would be the acceptable threshold? If they get 3% of their transactions from postal promotions, are they no longer a pure play? And what about a pure play that wants to move into a multi-0channel strategy, as many have started to do recently?

    Or are we as an industry slicing and dicing consumer activity wrongly? Perhaps, rather than pitting catalog vs web, we should consider our sandbox to be simply defined as remote retailing and be channel agnostic.

    I am old enough to remember the flap within the industry when 800-numbers were first introduced. How were these buyers who didn't have to pay for their calls different from those that used toll-free numbers? Were they less loyal, more fickle, or more opportunistic than people who paid their own inbound phone calls? Some companies even set up separate divisions to service those customers because they were not true catalog buyers.

    Sounds very similar to me when comparing the current state of affairs with internet buyers. It is an order channel and if your business is based on one marketing-cost strategy, that is your strategy. As consumers evolve, making distinctions between brick-and-mortar, multi-channel, DM only and web-only as your go-to-market methodology will become as quaint as the distinction between 800-number consumers and toll-call buyers.

    It would be interesting if anyone has ever had a model that showed households with a high affinity for purchasing from web-based companies was a negative predictor and that metric was used to suppress households from being selected from my DM campaign, steering the model away from pure-play internet households and keeping me from mailing them simply because they made a single catalog purchase three years ago. I would imagine that blade slices both ways, if it is allowed to.


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