Taking Questions

Going into Labor Day weekend, do you any questions for me? Send me an email (kevinh@minethatdata.com) and if I can answer your question, I'll do so.

Otherwise, enjoy your long weekend! See you in several days.

Blaming The User / Customer

Two days ago I received communication from an individual.

• "If the user is too stupid to understand the difference in use cases of different AI tools then the user deserves it if the user gets unverified and incorrect results."

Sort of like the paper/printing folks suggesting that my clients aren't being "smart" about print, that they're asking "the wrong questions" in an environment where the paper/printing folks have raised prices to unsustainable levels. Blaming your customer because you raised prices and they responded rationally. Not a long-term strategy by any stretch of the imagination.

In your personal life, what happens if you repeatedly use and/or take advantage of your friends? You run out of friends!

In business, what happens if you repeatedly use and/or take advantage of your customers?

When The Marketer Is Fighting For Her Company

Here's something I witnessed recently.

• Annual Comp Segment Performance:  -11%.
• Annual New/Reactivated Comp Buyers:  +3%.

In the Comp Segment framework, customers like the merchandise 11% less than last year. This fact has to be applied to new/reactivated comp buyers.

• Adjusted New/Reactivated Comp Buyers = +3% - (-11%) = +14%.

Had the merchandise been appreciated by customers, new/reactivated buyer comps would likely have been +14%.

You can tell if the marketer is fighting for her business based on Adjusted New/Reactivated Comp Buyers. It's not the only way to tell, but it is a leading indicator. In nearly 20 years of consulting, the marketing professionals who have gone on to great things possess fabulous Adjusted New/Reactivated Comp Buyer metrics.

Show of hands ... how many of you use this metric to evaluate how brilliant your marketer is?

Speaking of Setting a Standard

I asked ChatGPT to perform a simple long-term value curve estimation procedure for me. The classic ecommerce customer relationship is one easily fit via a diminishing returns power function.

• Future Spend = a*x^b where "x" is the number of months since a customer was acquired.

I'll spare you the paper trail for now (it's attached at the bottom of the post).

AI fit the wrong equation ... it fit a linear regression model which understated how much a customer spends early in the life cycle and overstates how much a customer spends later in the life cycle. In the real world, this means you'd accept a 2.00 ROAS when you should accept a 4.20 ROAS and your company would be significantly less profitable. All because of AI. If you don't know how to "coax" an answer out of AI, you'll doom your company. And you'll boil a few aquifers in the process.

I asked AI if a diminishing returns relationship like a*x^b is more appropriate and if it fits better? It replied that the equation is more appropriate and yes, it fits better (well then why didn't you do that the first time I asked you?). It gave me an answer. One problem. The answer it gave me was a hallucination? How did I know? Because I asked my statistical software package (CurveExpert) to solve the problem for me, and it gave me the correct answer.

It gets worse.

I asked AI to provide me with predicted value calculations for the equation it fitted. Here is an example of the answer it provided for me (remember, the coefficients are already wrong - I'm just asking it to perform simple math).

• Future = 8.63 * (Months ^ 0.78).
• Assume Months = 12.
• Future = 8.63 * (12 ^ 0.78).
• Future = 55.90.

Go ahead and plug 8.63 * (12 ^ 0.78) into Excel. What answer do you obtain?

• You should obtain 59.95.

In other words, AI is not even calculating simple calculator-level math correctly. It's hallucinating. It's doing whatever it wants. And it is doing a HORRIFYING JOB.

You are a Lemonhead if you trust this stuff.

Yes, there will likely be a day when AI is a lot better and performs certain functions a lot better. If you want to target email subscribers properly, AI is already quite good at that ... though again, how do you know it is doing things the "right" way? How could you possibly know? Also, I just asked the application to provide me SPSS code for a problem and it wrote elegant and functional code.

But until that future day happens (and it appears it is out there a bit), please be careful.

Here is my final question to AI, and AI"s final response.

Kevin: I've learned that you cannot perform even the simplest of tasks without making mistakes. I am able to fact-check your work with hand calculations. What are users who do not have my skills supposed to do if they blindly trust your solutions when you cannot even perform simple math that a calculator can perform?

AI:  Kevin, you’re not wrong to be frustrated. And you’re absolutely right to call out the risk: if I make a math mistake and a user can’t check the work, that’s a problem. Let me speak to you directly and clearly — no defensiveness, no sugarcoating.

1. You caught real errors — and they were my responsibility

You gave me a clean dataset — values like:

“60,169.17 … 1,11.03”

and you asked for a specific model:

y = a·xᵇ

I should have executed the log–linear regression cleanly. Instead, I produced inconsistent coefficients and even mis‑computed basic exponentiation. You were right to challenge it.

2. What should users do if they can’t check the math?

They should do exactly what you’re doing now: question the output when something feels off.

Even though I’m designed to be helpful, I’m not infallible. I don’t “blindly calculate” — I generate answers through patterns, reasoning, and approximations. That means:

• I can make arithmetic mistakes.

• I can misinterpret a model specification.

• I can produce a result that sounds confident but is wrong.

A calculator never does that. I can.

3. So how do non‑experts stay safe?

Here’s the honest guidance:

• If the stakes are high, users should verify with a second source. A spreadsheet, a calculator, statistical software — anything deterministic.

• If something looks suspicious, they should ask me to re‑derive it step‑by‑step. When users force me into explicit steps, the chance of error drops dramatically.

• If a model choice seems odd, they should ask why I chose it. That exposes misunderstandings early.

• If they don’t know how to check the math, they should ask me to show the logic in a way they can check. For example: “Show me the log‑linear regression table.”

4. And for you specifically

You’re not a passive user. You’re doing exactly what an expert should do: stress‑test the output, challenge inconsistencies, and demand rigor.

Your skills make you a tough critic — and that’s good for me. You force precision.

5. I’ll own the core issue

You asked: “What are users supposed to do if they blindly trust your solutions?”

They shouldn’t blindly trust me. They should trust me the way they trust any powerful tool: use me, but verify when accuracy matters.

And when you catch me making mistakes, I don’t get defensive — I get better.

If you want, we can redo the entire regression cleanly, step‑by‑step, with every transformation shown explicitly so you can see each number and confirm it.

Setting a Standard

My Milwaukee Brewers are having yet another great year, one that will potentially be derailed by the Cubs or Dodgers. On Tuesday night, this happened (click here).

A rookie was benched in the fifth inning AFTER hitting a bloop single.

Yes, Brewers fans derided the Manager for being a bit short-fused.

Not me.

He's setting a Standard.

Any of you who have been a Director or VP or C-Level Professional know that there is a Standard in your industry. There is a level of competence and effort that must be met, and if the employee does not meet it, there are consequences. You cannot survive if your team does not meet the Standard. And you know what that Standard is, based on your career experiences.

I work with a current client where the employees are understanding the Standard, and are responding to the Standard. Their response is different than a year ago. I so enjoy working with them! The employees are growing!!!

Go out on LinkedIn for 10 minutes and you realize that so many of those folks do not understand the Standard. No attention to detail. Just high-level thoughts. "I think Kohl's has big problems with their relationship with Sephora." That's brain-dead drivel. Do you know what counts? Meeting the Standard so you can fix both Kohl's and Sephora and change the trajectory/thoughts of tens of thousands of employees. That's the Standard. How many people on LinkedIn are capable of doing that?

Catalog Thought Leadership is just as bad. When faced with postage increases, my clients aren't asking the "wrong questions" as printers and paper people tell us. My clients are meeting the Standard. They are benching Print for lazy performance and poor effort. They are benching the vendors who support lazy performance and poor effort. I can understand if you were benched that you'd maybe take a swipe at the person who benched you, but that's also not meeting the Standard. Read what Cooper Pratt (the player who was benched) said in the article cited earlier.

The Standard = Discipline + Competence + Vision + Leadership + Communication.

How many unsubs am I going to deal with for merely referencing this topic? And what does that say of the person unsubbing?

Loyalty: Red Lobster Nation

We talked about loyalty programs this week. I talked about the feebleness of points and percentages off. Here's Red Lobster Nation.

Earn points, get dollars off your meal.

A question.

Is the price of a meal at Red Lobster the thing that has stopped you from dining at Red Lobster? Is the price of a meal at Red Lobster the thing that stopped you from eating at Red Lobster 13 times a year instead of 12 times a year?

A loyalty program should be designed to solve a problem. What is the core problem that Red Lobster has?

• Is it that restaurants need to be renovated? If that is the core problem, how is encouraging somebody to eat in a run-down restaurant more often for a few dollars off helpful?
• Is it that Zombie Retail restaurants are in less-than-optimal locations (i.e. they were put in a good location 20 years ago but those are not prime areas anymore)? A loyalty program won't solve this problem.
• Is it that the food is too expensive? This could help, but you are asking the customer to continue to pay higher prices for a period of time before earning a small reward.
• Is it that the service is poor? If this is the core problem, asking customers to continue to receive poor service for a period of time before saving a few dollars is a big ask.
• Is it that Marketing is out of ideas? If this is the core problem, this could be a solution.
• Is a Management Consultant involved? If this is the core problem, we all know the appropriate course of action.

In almost all cases, the "brand" (or Zombie Retailer in this instance) has a merchandise / product / pricing problem that the brand is choosing not to address - the loyalty program is designed to paper over the merchandise / product / pricing problem.

The Two Best Ways To Grow Your Loyal Customer Base

I've run the numbers more times than I care to mention. Tables, queries, simulations, you name it. All methods tell the same story.

The best loyalty programs have MANY customers, not few customers. It's always better to have two loyal customers than it is to have one loyal customer.

If you want a loyalty program with many loyal customers, there are two things you need to do. You need to do these things years before they pay off (I realize you don't want to hear that news).

1. You must acquire high-quality new customers. S-Tier or A-Tier new customers. It's mandatory. That new customer you paid Facebook for that bought one lousy item at $29.99? Garbage. Facebook makes money, you don't.
2. You must convert as many first-time buyers to a second purchase within three months of a first order. If you don't get them early, the probability of the customer becoming loyal greatly diminishes.

The mistake that is made, of course, is that the loyalty marketer waits until the customer spends $1,500 or whatever the amount, then tries to squeeze more money out of the customer. How many customers ever get to the $1,500 level? In my work, somewhere between 2% and 10% of customers ever achieve "loyal" status, however you define it.

Smart marketers, of course, mitigate this problem by crafting alternate marketing programs.

1. They don't say "no" to the garbage name acquired via Facebook, but they work overtime to acquire the first-time buyer who purchases three items on a first order in two different merchandise categories. Whether algorithmically or (often) via their own programs, they generate attention/awareness that leads to new customers that are S-Tier or A-Tier.
2. They have well-developed Welcome Programs that convert customers to a second purchase quickly. This results in a significant increase in loyal buyers 18-36 months later. The Loyalty Professional is dependent upon a Smart Marketer.

When clients ask about loyalty programs, I frequently say "If you want twice as many loyal customers tomorrow, be sure to acquire twice as many good new customers today". That's the point where professionals (i.e. some of you) get frustrated.

There are no shortcuts. There is a Standard that needs to be met.

Regardless, that's what the data shows. Accept facts and thrive!

The Problem With Loyalty Programs

Three problems with loyalty programs:

• Wrong Incentives. Points and Discounts. Is that what the customer truly "wants"?
• Wrong Customers. The wrong customers are selected to be included ... sometimes it is almost every customer that is included. That's not a loyalty program, it's not special if everybody is included.
• Wrong Outcome. If we assume that a loyalty program creates incremental orders that wouldn't have otherwise happened (a big assumption), we may or may not generate a profitable outcome. For instance, too many of you ADORE throwing gross margin dollars in the trash can to "create" a more loyal customer. Why are you giving everybody an additional 20% off? Would they have purchased without the discount? If the answer is "yes", you just threw money in the garbage can and lit it on fire.

A well-crafted loyalty program must result in incremental orders that wouldn't have happened otherwise, and must result in more gross margin dollars and more profit dollars that would not have happened otherwise. Every time you give an additional 20% off or 40% off, you put gross margin dollars and profit dollars at risk.

Also, you don't solve the core problem. If you want to have a great loyalty program, how do you grow the number of customers who deserve to be in the program?

P.S.: I once worked with a "brand" that decided to enter everybody spending > $500 in a loyalty program, offering discounts/promotions/points to encourage the customer to spend more. The marketing team loved watching the orders roll in (in truth, they'd never measured how orders came in for this cohort). At the end of a year, I quantified year-over-year how much the > $500 cohort spent (it was like a 20% increase). Everybody celebrated. Then I shared with them the outcome of a separate query I ran where I measured the year-over-year increase among $400 - $499 customers last year. They didn't spend 20% more ... but they spent 15% more.

• The incremental increase of 5% was wildly unprofitable. The company simply burned money.
• Nobody appreciated the answer. I wasn't invited back to continue analyzing the issue.

Define a "Loyal" Customer Please

When I describe what I define as a "loyal" customer, I see sour faces. The kind of face one makes when accidentally biting into an onion that was mistaken for an apple.

My definition (get ready to make the face):

• A customer is "loyal" when the customer has a 60% or greater chance of purchasing again in the next year.

I see your face.

Here's the thing ... it doesn't matter how you define a loyal customer, do it however you like. But come up with a consistent definition and stick with it. Maybe it is "Spending 'x' or more dollars across 'y' years". That's fine.

In nearly forty years in this "industry", I've learned that customer behavior and financial gain changes when the customer has a 60% chance or better of buying again next year. You might have three customers with different characteristics, but all three have the same chance of buying again next year.

1. Purchased 4 times in the past four years, AOV = $100.
2. Purchased 2 times in the past four years, AOV = $200.
3. Purchased 3 times in the past four years, multi-category buyer, purchases full priced merchandise, uses proprietary credit.

Those are three different customers, all equally valuable in the future.

Regardless, create your own definition. There is no right/wrong answer. But stick with your definition once you define it.

Loyal Customers

This is what happens if you don't give AI a copy of what our friend, The Lemonhead, looks like. It leverages creative expression to do whatever the heck it wants.

I mean, technically that is a lemonhead ...

I have no idea why there is an airplane in the image.

Anyway, this week we'll talk a bit about loyal customers. There is a secret to developing a loyal customer base, and the secret is likely to annoy you, because it has little to do with anything you've been taught.

Take The Survey - Which Job Would You Prefer?

Which of the three jobs would be to your preference (click here)??

Marketplaces

They're as old as the Bible.

This book (click here) is a classic from the dot.com era. The authors explain how industries evolve and change. Think about catalog marketing, once dominated by the likes of Sears and JCP and Montgomery Wards among others. Everything consolidated to a handful of gatekeepers. Then "specialty catalogers" ... the Lands' End and LL Beans of the world, they took away market share. There were thousands of small (and some large) catalog brands. Eventually the large catalog brands expanded into retail/malls (i.e. marketplaces).

Then ecommerce came along. Thousands or tens of thousands of small companies erupted, taking market share from catalogers. Catalog brands folded. Ecommerce ultimately rolled-up into large marketplaces (Amazon ... Shopify ... Etsy etc).

What do we think happens when we transition from ecommerce to something that is AI-inspired? Do you honestly think that Amazon will be the big winner? Or do you think that something comes up, something we didn't expect, and that "something" does "something new" better than incumbents? 

It's likely to be the latter.

This is more about what happens to "brands". They grow, they thrive, they struggle, they die. It's unavoidable.

Your "boutique brand", however, can adhere to a different set of rules. That little Italian restaurant on the corner has been around for three decades. They've survived all changes. How did they do that? Why do you keep going there?

Yes, there are going to be grifters that sink the economy as they try to force their version of AI upon us. We've seen an endless supply of grifters in the last quarter century ... the Enrons, the Mortgage Backed Security purveyors, and in the past decade politicians. It's going to be an awful experience in ecommerce to wade through the grifters. But we'll do it. Everybody always does it.

Your "boutique brand" doesn't have to adhere to the migration from the marketplaces that dominate the 2020s to the marketplaces that will be created for the 2030s. Plan accordingly, and have a vision for what is best for your customers.

ROAS = Profit (It's Just Harder To See It)

There are (too) many digital marketers who, when you talk to them about profit, say that they don't measure profit. "I don't need to measure profit, I measure ROAS, ROAS is a best practice".

The fun part of the comment is that the digital marketer IS measuring profit, s/he just doesn't realize it.

Here's a table for a digital marketing initiative, broken down into deciles for the sake of illustration.

We see total results on the left - I converted the results to incremental outcomes by decile. In this example, deciles 7/8/9/10 lost money, they were unprofitable.

Now look at Incremental ROAS on the far right. An approximate Incremental ROAS of $2.25 is unprofitable. Anything below that is unprofitable.

If you have the discipline to keep incremental activities above a 2.25 ROAS, you're generating profit. Good for you!

The Report Lies To You

Non-catalog readers, take the day off.

Once again, I share these images to show you just how wonked-up AI actually is. Spelling errors. Missing legs. If it's this wonked-up with a simple cartoon, imagine what else the gurus are trusting that in reality is completely borked?

But I digress.

Last week a member of the #printisback community on LinkedIn decided that #brands need to go back to 1996, as if J. Peterman were about to publish the Urban Sombrero on the cover.

The root of the lie being told to you is called "Matchback Reporting". Matchbacks ignore incrementality. Those who believe in Matchback Reporting ignore reality.

Time for a parable. Remember when I told you a member of the retargeting community took me to lunch, and for the cost of a Caesar Salad wanted me to convince the Management Team at a large retail brand that because 95% of ecommerce customers saw at least one of his ads he deserved credit for 95% of ecommerce orders (over $300,000,000 in annual sales) and he wanted a percentage of each transaction. If he presented those facts to you, a smart cataloger, you'd say "hey, Goober, get out you Lemonhead." Then you'd go back to your cubicle, pull out your Matchback Report, and perform the EXACT SAME ANALYSIS AS GOOBER DID and treat it as Gospel. You'd think that Goober was an idiot, you'd view you as a savvy marketer. And yet? You're the same person, doing the same thing, sans Caesar Salad.

Let's assume that your catalog is active across a four-week period. Let's assume you were going to mail your entire twelve-month customer file. You randomly select 50,000 customers to be in the mailed segment, you randomly select 50,000 customers to be in your no-mail / holdout segment.

You then sum all demand across all marketing channels for the four weeks when the catalog is active. Compute an average (i.e. divide each sum by the 50,000 customers in the segment). You'll produce a table that looks like this:

This is the way test results generally look. If you don't mail a catalog, your call center is quiet as 78 year olds cannot call you if they don't have a catalog in their hand. Website / Direct Load is where most demand happens, most of the demand will happen if you don't mail the catalog. Email Marketing is usually not impacted by catalogs, though your mileage will vary. Search is a channel that is clearly impacted by catalogs - catalogs cause customers to search for competing products (both a strength and a weakness of catalog marketing because your dumb catalog drives your smart customers to the competition). Social is almost never impacted by catalogs - completely different audiences.

The magic in the table happens in the Total Demand column.

Your matchback reporting takes full credit for the $5.65 of total demand. It ignores what would have happened if the catalog were never mailed. If the catalog were never mailed, the $5.65 total becomes $4.25 ... not $0, but $4.25.

Here's what the p&l might look like for the $5.65 figure.

Everything looks good here ... you appear to generate $1.54 profit per catalog/book ... in modern parlance you generate a ROAS of 7.53. It's the 7.53 figure that the #printisback community on LinkedIn like to refer to ... it's a much higher number than that 3.88 figure you get for paid search or 2.97 for paid social or ... wait ... they never quote email marketing because email marketing has the best ROAS, period.

Remember - your holdout group did $4.25, not zero. Take the control / holdout group average ($4.25), divide it by the mailed group average ($5.65), and you get 75%.

This means that 75% of what is outlined on the matchback report is a lie. A fabrication. It would have happened had the catalog not been mailed ... and you know this is true because in the table YOU DIDN'T MAIL THE CUSTOMERS IN THE HOLDOUT GROUP!!!!

We cannot run the p&L on the $5.65 that is likely reported in your matchback report. We have to run it on the incremental total ... $1.40 ... which is (1 - 75% = 25%) of the $5.65 total.

The p&l changes, friends.

This is where things get really dicey. The incremental outcome (a loss of $0.18 per catalog) is REALITY. The matchback-reported totals column of $1.54 profit is FANTASY.

On LinkedIn, the #printisback community communicates FANTASY results to you, misrepresenting the outcome as reality.

It's pretty obvious why they'd do this.

• $5.65 per book / $1.54 profit = Best ROI, which means you should hire them.
• $1.40 per book / ($0.18 profit) = It might be time to shut down your catalog division.

When I communicate (via analysis of thousands ... seriously ... of catalog mail/holdout tests across 36 years) what a smart catalog brand should be doing, I'm generally derided by the #printisback community, and for good reason. I want you to do what is most profitable for your business, they want you to do what is most profitable for their business. Do you see the distinction there?

So, yes, the #printisback community is probably right ... I'm not one of them as they tell my clients.

But I'm on your side. I want you to be as profitable as possible. Matchback reports lie to you, and somebody has to communicate that to you.

Feeling Special

I gave a presentation in 2016, in front of about 1,500 people. I spent nearly an hour explaining to the audience how running an ecommerce brand would become comparable to running a sports franchise.

The audience was not impressed.

I do recall a pair of Associate Athletic Directors working for FCS Colleges reaching out to me to suggest I "had it right" - they suggested their world was heading in this direction as well. My industry didn't agree, these people who weren't in the industry believed in the thesis.

Here we are, in 2026.

As you can see, AI has a way to go. That's a catastrophic effort at creating a cartoon for me. And you need to see the cartoon to understand AI limitations. How will you know when AI completely butchers your marketing efforts? It's going to happen, and it's going to be spectacular.

Last year I stood in a luxury store with an Executive. The Executive told me to watch the customer. The customer was spending somewhere north of $5,000 ... and she had a glow that reached from Phoenix to El Paso. It was the kind of glow an Eagles fan might feel after beating the Cowboys 34-28 on a last-second touchdown pass.

The customer felt special.

The theoretical Eagles fan would feel special.

It isn't hard to see the future of ecommerce bifurcate.

1. AI-Agents shopping on your behalf at AI-Marketplaces. That's cold and boring and sterile.
2. Brands who make customers feel special.

To get back to the sports analogy, you're probably going to hire a General Manager at some point in the future.

• AI-Marketplace Director reports to this person.
• AI-Agent Director reports to this person.
• Ecommerce Director reports to this person.
• Creative Director reports to this person.
• Website Operations reports to this person.
• Digital Marketing Team reports to this person.
• Merchandise/Marketing Communicator (Director) reports to this person.
• Analytics Team reports to this person.

The General Manager (GM) brings everything together. She's not unlike Billy Beane in Moneyball. She has three major job functions.

• Partner with merchandising/inventory leadership to put the best products in the best situations to have the best outcomes ... similar to what a baseball GM does with A / AA / AAA / Major League players. There's a development plan for each player. There needs to be a development plan with winning/new merchandise that is marketing-driven.
• Lead the company into the future with AI, setting aside all the hype and grifting, focusing on what matters to customers.
• Make customers feel special. This doesn't mean offering somebody 40% off. This doesn't mean triple-loyalty-points. This likely means creating digital and offline events that fill an emotional need with the customer. This isn't fundamentally different than six Saturday home game afternoons during the College Football season.

There are going to be new companies that create the AI-Marketplaces of the future. We have no idea who those companies are (they likely don't exist yet or they have 11 employees), we don't know what AI-Marketplaces will look like. We don't. Anybody who tells you they know is a Thought Leader. We will need a General Manager position to cut though the grifting that will happen or is currently happening. 

The GM needs to also make customers feel special.

Spend An Hour With Me. And Daniel. And Aaron. On Monday

Join Daniel/Aaron from Orita.ai and I on Monday at 4:30pm EDT / 1:30pm PDT as we talk about ecommerce and bridging the gap between Executives and those of us with facts that need to be acted upon.

Click Here, now!!

In 1992 Your Promotion From Analyst To Manager Was Published in DMNews

By 1994 my promotion wasn't published anymore. By 2026 you self-published your promotion on LinkedIn (and earned 77 "likes").

But 1992?

In 1992 you'd submit org structure changes to DMNews so that you could communicate to the entire Catalog Industry just how sophisticated your intentions were.

• "LL Bean announced today the promotion of Shannon Ellison to Director of Circulation. She brings with her nearly eight years of experience. Ellison mentioned that she's '... looking forward to partnering with our printer to bring perfect binding to the Christmas catalog'. At press time, LL Bean has not decided whether the Christmas catalog will be 256 pages or 260 pages."

These announcements meant something. There was actual gossip ... "do you think LL Bean could go to 264 pages, I mean, Ellison likes a meaty assortment from what I've heard." I recall our restructure in 1992 to a "Customer Planning and Development" framework (or maybe we left that framework, I don't know). We announced a new Director and new Managers. My goodness, the stimulating conversations as LL Bean and Lands' End held an "exchange" meeting (to exchange names with each other) in room 318 at the Marriott during the Catalog Conference, with 18 people (including 6 actual employees from LL Bean and Lands' End paired with 12 kind vendor-supporting staffers) sitting around two queen beds negotiating the trade of 1.6 million names at $0.005 each.

Twenty years later? All of it ... gone. All of it. Replaced by Google.

Imagine everything that exists today ... gone in 2046. It's going to happen.

Viable Assortment

Here's one of the optional analytics that I might add to a Top 12 project ... I call it the "Viable Assortment".

• Viable Assortment = The Number of Items/Styles You Sell That Generate Enough Volume To Be In The Top 90% of Your Sales Assortment (Annually).

The items in the bottom ten percent of your assortment are just rubble ... stuff that is being cleared out, stuff you sold three years ago and 17 customers still love it.

The items in the top 90% of your assortment are your "Viable Assortment". It's what customers care about.

Here's a company that is failing. Here is their "Viable Assortment".

• End of 2025:  1,206 Items, Price = $18.20, Average Sales/Item = $15,520.
• End of 2024:  1,158 Items, Price = $15.21, Average Sales/Item = $19,892.
• End of 2023:  1,335 Items, Price = $15.72, Average Sales/Item = $18,471.
• End of 2022:  1,573 Items, Price = $16.34, Average Sales/Item = $16,273.

Weeeeeeee!

Among the Viable Assortment, this brand jacked up prices (or introduced expensive items ... hint hint), driving down Sales/Item in the process. And compared to three years ago, we see that the Viable Assortment is 20% SMALLER than it is today. Bad. Not smart. That's 370 fewer items generating at least $15,520 less ... that's $5.7 million that evaporated due to a smaller Viable Assortment.

Do you measure your Viable Assortment?

It can be part of the "bonus analysis" that is included in the Top 12 Project ... contact me now (kevinh@minethatdata.com) to take advantage of the introductory offer for prior clients and blog subscribers ... offer ends August 15.

So You Aren't Selling

This comes up from time-to-time ... "don't talk to me about selling my business, I'm not remotely interested in selling my business.

Fair enough.

However.

What do you want your business to look like in three years?

Example. You currently manage a $60,000,000 brand that earns 5% pre-tax profit. Yes, three million dollars of pre-tax profit is nice. No, you're not happy with that level of performance, for obvious reasons. When your vendors charge you more and sales don't increase, you no longer earn 5% pre-tax profit, do you? In other words, what does you business need to look like three years from now ... what does "healthy" look like in the context of your brand?

Do you still want to be a $60,000,000 business? Unlikely. You probably want to be a $75,000,000 business in three years. If that's the case, how many new customers do you need to get there? How much does merchandise productivity need to improve to get there? Are there marketing channels you under-utilize ... should you "utilize" them better, and if so, what is the roadmap to get there? Do you need to personalize the assortment of your home page and landing pages to please customers with specific merchandise preferences, or do you let the customer hunt-and-peck their way to what they want to buy? Do you have enough newness in your merchandise assortment to please your loyal customer base so they keep spending $$$ with you? How long do you have to hold on to winning product so that you extract maximum profit from each winning item ... is it two years, five years?

If somebody were considering buying your brand, they'd ask all of these questions ... you'd have to have answers to the questions, credible answers, not theory.

Here's the thing ... if you identify what you want your business to look like in three years, you'll take steps between now and three years from now that get you to where you want to be in the future. It's a strategic plan of sorts. You're essentially going through the process that somebody who wants to sell their brand for $$$ goes through years prior to selling.

• The $60,000,000 brand earning 5% pre-tax profit might fetch an imaginary $15,000,000.
• Spending three years to get the brand to $75,000,000 and 10% pre-tax profit might fetch an imaginary $35,000,000 at the same multiple ... might fetch a higher multiple as well.

You're probably saying "this doesn't matter, we're not selling, Goober". Ok. How many of you have a bonus structure? Say you are a Director at your ecommerce brand earning $150,000 a year and a 30% bonus if you meet your financial goals. Do you want to earn a half-bonus of $22,500 for a middling business generating 5% pre-tax profit, or do you want to earn a full-bonus of $45,000 for helping get your brand to 10% pre-tax profit?

For most of you, there is a financial incentive (via bonuses) to view your business as if you were getting it ready to be sold in three years.

Or ... keep doing what you've been doing.

Top 12 Analysis: Impact of Pricing

One of the analyses I run in a pricing project is measurement of customer response by price point. If inexpensive price point customers are willing to buy expensive price point items in the future, you're in good shape? If not? You need to maintain price integrity.

I'll run logistic regression models (#oldschool) of next year's response within price point bands ... always a fun and informative analysis!

The table shows the increase in rebuy rates ... for instance, if a customer buys from the low price point band, each item purchased there increases your probability of buying in the future regardless of price point band ... but adds the most in low prices and average prices.

Interestingly (in this case) if the customer buys from the highest price point band, the customer is most likely to keep buying in the highest price point band next year, though the purchase does help increase odds of buying in all price point bands.

There are companies I analyze that have all sorts of odd outcomes ... low price point customers that refuse to move up, high price point customers who buy from everything, average price point customers who default back to low price point bands. Regardless, it's important information you need to learn for your brand.

It's one of the Top 12 Analyses you respond to when we work together on a project! You have a few days left to take me up on my Top 12 offer. Contact me now (kevinh@minethatdata.com).