Kevin Hillstrom: MineThatData

It's Time!

2026-05-21T20:10:00.000-07:00

Once again, we've made it to the next run of the MineThatData Elite Program!

In our study of "Beans: The Internet's Only Variety Store!" we covered a handful of elements from a typical Elite Program Run (click here or click here or click here). The analytics are helpful, and you won't find them in on your Agency-Generated Shopify Dashboard.

Cost? $1,800 for first-time participants, $1,000 per run thereafter, participation is fully voluntary. If you don't want to participate in the October run, don't participate!

Deets:

Data due by 6/15 (let me know if you need to see the file format I use to run the analysis).
Payment due by 6/15 (I'll invoice you ASAP).
Analysis completed by 6/30.

Contact me now (kevinh@minethatdata.com) to participate in the June run of the Elite Program!

Case Study: A Marketing Plan

2026-05-20T20:10:00.000-07:00

I ran a logistic regression equation to understand how likely multi-category buyers are to purchase again in the next year ... after controlling for purchase frequency and AOV ... analyzing twelve-month buyers. Here's the results of the Logistic Regression equation ... nerdy stuff you can ignore (though I do want to show my work).

The variable "multicat" has an Exp(B) of 1.361 ... this means that any customer that historically purchased from more than one category was 36% more likely to buy again in the future AFTER controlling for purchase frequency and AOV.

Separately, I was able to demonstrate that "multiple" is important ... whether the number is two categories or seven categories is much less important.

In a prior analysis, we demonstrated that customers are responsive for a few months following a first purchase, then are dormant, then come back at months 11/12/13 and again at 23/24/25 following a first purchase.

A marketing plan becomes obvious.

Separate email streams for first-time buyers within three months of a first purchase, attempting to generate a quick second order (preferably) within a second category.
Personalize the merchandise in email campaigns for first-time buyers to increase the probability of a second purchase (preferably) within a second category.
When a prospect visits via search, the goal is to convert the prospect to a first order and hopefully cross-shop the customer into a second item and/or second category.
When a customer reaches recency = 11/12/13/23/24/25, we ramp-up messaging and frequency.
Personalize the home page and/or landing pages to show customers what they "need" to see to maximize their future value.

Now, this requires "Beans: The Internet's Only Variety Store" to do things differently. They love to "batch and blast" their email campaigns, they send 12 catalogs per year to customers, they under-invest in social. In other words, they're like any other brand. And when you point out that somebody needs to do things differently and they are simultaneously like any other brand ... well ... the message isn't always well-received.

Which brings us to next week's post ...

Case Study: All Paths Lead to Apparel Tops

2026-05-19T20:10:00.000-07:00

Yesterday I talked about Entertainment customers shifting dollars over time to Apparel Tops.

By the time the customer orders for the third time, 34% is in Apparel Tops, just 9% in Entertainment.

Does this happen to all categories? In this case, yeah, it happens to all categories.

Here are first order categories and migration through a third order.

Apparel Bottoms: 47% / 21% (Bottoms vs Tops) in first order, 16% / 35% in third order.

Fashion: 34% / 21% (Fashion vs Tops) in first order, 9% / 33% in third order.

Home: 47% / 19% to 18% / 32%.

Jewelry: 40% / 17% to 6% / 35%.

Workplace: 27% / 21% to 3% / 36%.

Outside: 51% / 12% to 17% / 27%.

Having Fun: 37% / 20% to 15% / 32%.

Seasonal: 34% / 19% to 7% / 31%.

Decorations: 31% / 20% to 9% / 32%.

Two interesting things happening, of course. First, customers on a first order do buy from multiple categories, with Apparel Tops being right there at the top. Second, as the customer evolves, the customer spends more and more money with Apparel Tops, less and less money with the category of a first purchase.

As a marketer, tell me how you plan on using this information to make different decisions? If the answer is "I'm not changing anything", that means something.

Tomorrow, we'll explore an email exchange discussing "doing something different" given we have knowledge of customer behavior.

Case Study: Entry Points Into A Brand And Subsequent Migration

2026-05-18T20:10:00.000-07:00

It's so much fun to study categories ... you learn how your business actually works in ways that Spotify reporting just can't replicate.

Let's look at Category = Entertainment. I selected all first-time buyers who purchased from Entertainment in the first order, measuring how their merchandise preferences evolve as the customer advances down the customer life cycle.

Tidbit #1: If the customer purchased Entertainment in a first order, the customer only spend 33.6% of first order AOV on Entertainment. In other words, the Entertainment item was likely an add-on (Apparel Tops and Home comprised 31.3% of first order AOV).

Tidbit #2: As the customer evolves, the customer leaves Entertainment, shifting into Apparel Tops. There's not much sense to keep hounding this customer to buy Entertainment when the customer has a natural gravity toward Apparel Tops.

Every brand has gravity. If we see more examples of new buyers ultimately gravitating to Apparel Tops, we know something valuable about how to market to customers in email marketing (for instance) moving forward.

Case Study: Off-Season Purchases

2026-05-17T20:10:00.000-07:00

We've learned that "Beans: The Internet's Only Variety Store" is heavily skewed to the November/December timeframe.

I've learned across nearly twenty years of consulting project that it's not healthy for a business to skew so heavily to Christmas. You want to offer products that customers purchase all-year. If there's a reason that subscription-centric brands are coveted by investors, there's the opposite reason that Nov/Dec businesses aren't coveted.

When I see a skewed business, I run a regression model to test the dollar contribution of Oct/Nov/Dec orders vs. orders generated during the rest of the year. For twelve-month buyers, spanning four years of purchase history and one year of "prediction", here's the simple regression equation.

1.257 + 0.079*($ Spent in Oct/Nov/Dec) + 0.095*($ Spent Jan-Sep).

In other words, dollars spent in "off months" are 20.2% more valuable than are dollars spent in Oct/Nov/Dec.

This fact supports encouraging the Merchandising Team to invest more effort into January - September.

Case Study: Simple Forecasts

2026-05-14T20:10:00.000-07:00

If we're going to analyze the intersection of category performance and customer performance (which - FYI - is really what our businesses all come down to), we should also be able to forecast how a category is likely to evolve next year based on current customer trends.

Here's a simple forecast I put together for Apparel Tops ... the largest category that Beans manages.

I know, small numbers ... click on it for details, ok?

Categories yield Teachable Moments. Look down the "PctDmd" column. This is the percentage of annual demand likely to be derived by four customer segments.

9.4% from last year's Apparel Tops AND Other Category buyers.
6.4% from last year's Apparel Tops only customers.
4.7% from last year's Other Category buyers.
79.5% from New/Reactivated buyers this year.

In essence, the Merchant responsible for Apparel Tops is generating 80% of his/her revenue from customers who haven't purchased in the past year.

If you know that fact, how would you work with a Marketing Professional to grow Apparel Tops? Yes, you know the answer. Hint - the answer applies to most of you.

We can see the performance difference for three twelve-month buyer segments.

Category Yes, Other Yes = $5.98 in the next year.
Category Yes, Other No = $4.58 in the next year.
Category No, Other Yes = $1.44 in the next year.

If the customer didn't buy Apparel Tops last year but bought something else, the customer is worth 1/3 as much to 1/4 as much as a customer who did buy Apparel Tops last year.

Look at the demand forecast on the far right - if everything is similar to last year, the category will contract by 4.4%. No bueno.

Your Chief Merchandising Officer and Chief Financial Officer need to know what is coming ... by category. If categories are forecast to contract, inventory buys need to be adjusted accordingly.

These category analysis are darn important.

Case Study: When The Crabby Merchant Is Right ... And Horribly Wrong At The Same Time

2026-05-13T20:10:00.000-07:00

In the email dialogue shared yesterday, the Chief Merchandising Officer suggested that her customers had long repurchase cycles, therefore, it's not fair to measure future value across only twelve months.

For "Beans: The Internet's Only Variety Store", she is both correct and horribly wrong at the same time.

Her customers have long repurchase cycles. I use my Life Table Methodology to measure repurchase activity/cycles. Here is what the data looks like.

The green table shows incremental repurchase probabilities by month, as well as cumulative repurchase probabilities by month, for 1x buyers, 2x buyers, 3x buyers, and 4x buyers. The graph next to the green table maps out incremental repurchase probabilities for first time (1x) buyers. Look at that stupid-high spike at month = 12. What do you think that is?

It is customers coming back and repurchasing at high rates exactly one year following a first purchase.

In other words, there are a metric ton of customers who buy in November/December, are responsive in Dec/Jan/Feb, then are inactive for most of the year ... then they magically reappear 11-12 months following a purchase to buy again. The customer then disappears for nearly a year before reappearing in months 23-24.

It's a hugely seasonal business ... the Chief Merchandising Officer is CORRECT in suggesting that her customers have a long purchase cycle and measuring things over time is more appropriate.

The Chief Merchandising Officer is also HORRIBLY WRONG at the same time. Her customers are simply not generating reasonable amounts of future demand no matter the timeframe looked at. Remember what I shared yesterday?

New customers spend maybe $11 in year one, $7 in year two, and $4 in year three. In total, that's $22 of future demand ... it's nothing.

It is terribly difficult to run a business when customers have virtually no future value! The secret to a successful business is to manage acquisition costs while maximizing future profit yielding a wildly profitable relationship. If you cannot maximize future profit? No bueno.

The job of a great merchant is not to smoosh all sales into November/December. A great merchant creates reasons for customers to buy products all year long. The Nordstrom Anniversary Sale proves this is possible. Amazon Prime Days (where do you think they got that idea from) prove it is possible. The merchandising team at Beans should also know it is possible ... and chiding an analyst for not viewing customer response on an appropriate time horizon does not absolve the merchant for failing to create a thriving business in, say, June.

Does that make sense to you?

Case Study: An Email Correspondence

2026-05-12T20:10:00.000-07:00

Maybe the most important finding in the past week is that "virtually nobody" is repurchasing when acquired by "Beans: The Internet's Only Variety Store!", regardless of merchandise category. Discovering the fact is one thing. Communicating the fact is quite another thing. And sometimes, the communication results in a reshaping of the message I convey.

This is why I send "tidbits" in my projects ... the back-and-forth interaction is useful and helps shape the outcome of the project.

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Monday, May 11, 2026 3:22 PM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: RE: FW: RE: FW: Repurchase Activity

Does Sloane have a point? Sort of. But the point doesn't change the fact that your customers have minimal future value.

If I measure repurchase activity over three years instead of one year, repurchase rates improve from maybe 18% to 33%. In that manner, Sloane is right.

Let's look at annual spend for customers acquired four years ago. Year1 = $10.68 in sales. Year2 = $6.92 in sales. Year3 = $4.08 in sales.

The story doesn't change ... the customers you acquire have virtually no future value when converting sales to profit. You have to generate a lot of profit on a first order to stay in business.

______________________________________________________

From: Paisley Ingram <paisley.ingram@beans.com>
Sent: Monday, May 11, 2026 2:56 PM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: FW: RE: FW: Repurchase Activity

Does Sloane have a point regarding a longer repurchase cycle?

______________________________________________________

From: sloane.montgomery@beans.com
Sent: Monday, May 11, 2026 10:39 AM
To: Paisley Ingram <paisley.ingram@beans.com>
Subject: RE: FW: Repurchase Activity

His experience is limited - those companies are too big to matter. $20,000? Highway robbery. I'll get you better answers from AI for minimal cost. Let's focus on the future, not an antiquated business model where some dweeb is paid a premium for something software can easily generate for free.

Ask Goober if customers have a longer repurchase cycle? I think he's looking at the issue the wrong way. Marketers are marketers for a reason, they're simpletons who are too narrow-minded to have the world-view you and I have to have to run a business.

______________________________________________________

From: paisley.ingram@beans.com
Sent: Monday, May 11, 2026 10:33 AM
To: Sloane Montgomery <sloane.montgomery@beans.com>
Subject: RE: FW: Repurchase Activity

He was part of the Management Teams at both Eddie Bauer and Nordstrom back in the day. We're paying him $20,000 for his work.

Also, we're breaking too many eggs. Sales are down 20% since your arrival. We can't survive if we go below $16 million in annual sales.

______________________________________________________

From: sloane.montgomery@beans.com
Sent: Monday, May 11, 2026 9:56 AM
To: Paisley Ingram <paisley.imgram@beans.com>
Subject: RE: FW: Repurchase Activity

First of all, who is this propeller-head you are working with? You can tell this Goober never worked for a real business, he's just out there wandering aimlessly in the Land of the Theoretical. How much are you paying for his "insights"?

Anybody with half a brain knows you don't measure lifetime value within twelve months. It's called Lifetime Value for a reason. You measure the Lifetime. That's what I'm working toward. And if we have to break a couple of eggs along the way, so be it.

______________________________________________________

From: Paisley Ingram <paisley.ingram@beans.com>
Sent: Monday, May 11, 2026 9:44 AM
To: Sloane.Montgomery@beans.com
Subject: FW: Repurchase Activity

FYI Sloane.

Best,

Paisley

______________________________________________________

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Monday, May 11, 2026 9:12 AM
To: paisley.ingram@beans.com
Subject: Repurchase Activity

The analysis suggests that the future value of customers recently acquired is well below what I'd expect. Apparel Bottoms, Apparel Tops, and Outside are the three categories that do comparatively "well" ... even then, those categories deliver customers who spend just $11.00 on average in the next year (about $2.00 of profit after subtracting marketing costs). Fashion / Seasonal / Having Fun generate customers who spend about $8.00 or less on average in the next year (maybe $0.50 of profit after subtracting marketing costs).

This puts a lot of pressure on your p&l, because you have to generate a lot of profit when acquiring a new customer for your business to be profitable.

Thanks,

Kevin

Case Study: Which Categories Bring In Valuable Customers?

2026-05-11T20:10:00.000-07:00

I ran an analysis, measuring how much future value (demand/sales over the next twelve months) a customer will generate after being acquired within each merchandise category. If a category does a great job of bringing in new customers and those customers don't spend money in the future, well, we've got a problem.

Here's a summary table of what I learned.

Apparel Tops is a high-volume category, and fortunately it is a top-three category in terms of future value (NY Value).

Notice that most categories generate customers that buy from nearly two categories in the next year if the customer repurchases.

However ...

However, there is a problem in this table.

When a customer is acquired, regardless of category, the customer spends very little in the next year. Seasonal is worst ($6.40) ... Outside is best ($11.33).

In other words, there is very little customer loyalty associated with this brand. Newly acquired customers have a low chance of buying in the first year with the brand (between 14% and 18%), and if they buy the don't spend much ($46.00 to $63.00).

This is one of those moments when the Consultant realizes s/he is about to anger people. Yes, you can run a profitable business with a customer base that simply doesn't spend much, but it means everything regarding the p&l is dependent upon generating profit off of a first purchase.

Tomorrow, I'll share what the conversation looked like regarding exceptionally low future value metrics.

Brief Case Study Break: What Your Media Might Look Like In The Future

2026-05-10T21:30:00.000-07:00

I'm writing this back on Sunday night.

Here are the two shows I just watched on YouTube.

The former is the trendy stuff ... well produced, powerful people explaining powerful issues. It's easy to see how people are attracted to the content. Unfortunately, you'll never appear on this show. It's the evolution of a Ted Talk pushed 20 years into the future.

The latter is your future. That's Nieve. About 15 years ago, Rog and Davo brought the Premier League to me. Rog made it accessible to somebody who only saw a handful of televised Premier League fixtures each year. His storytelling was all audio back then, a completely different style of storytelling, but appropriate for his (my) generation.

Nieve is able to take the concepts of the Men in Blazers and apply them to the entire English Football Pyramid, via video ... I mean, in the clip above, she's at the final game of the National League season (5th tier), and she captured the pure madness and drama of the last ten minutes of that match. Poor Rochdale (hint - it would end up a fairy tale for them today at Wembley Stadium).

She exudes raw, unadulterated humanity. There is no way AI is going to replace her, she's a force of nature, and if you have any empathy for the fans Exeter City FC, Nieve is going to help bring that empathy to you via humor and sadness in a way that modern "polished" / "scrubbed" activities cannot possibly achieve.

Nieve is your future. I realized you work for "Beans: The Internet's Only Variety Store". But every single one of you has a "Nieve" working for your brand ... a person uniquely qualified to bring storytelling to your customers. I am always amazed that almost none of you are willing to take that risk, and if you are willing to take the risk you shut it down long before anything positive could happen.

Instead of worrying about AI, why not apply stuff AI cannot possibly mimic (like a parrot) to your marketing efforts?

Case Study: Dispelling Legends

2026-05-10T20:10:00.000-07:00

At "Beans: The Internet's Only Variety Store" there is a legend ... that a broad assortment "holds the brand together".

It's been my opinion that Leadership is violating this "legend", assuming the legend is true.

So ... I performed a classic Factor Analysis to demonstrate categories that customers like to purchase from. If dots on the image below are close together, customers like to purchase from the categories "near each other" on the image. If categories are far apart, it means different customers prefer the categories.

Here's the image from the Factor Analysis.

There appear to be three reasons why customers buy from this brand.

Apparel Tops: We know this is a high-volume category, and it is all by itself meaning that many customers ONLY purchase Apparel Tops.
Home / Outside: There are clearly customers who view this brand as a Home / Outside brand. There's some bad news here ... some customers clearly come for Home / Outside, but a few weeks ago I showed you that Home decreased from $6.0 million to $2.9 million over four years. No bueno.
Everything Else: There are customers who view this brand as an eclectic mix of categories (the right/middle side of the image).

Of interest ... Apparel Bottoms does not align with Apparel Tops ... it aligns with "everything else".

I have to analyze new customers by these three groupings ... if the future value of a customer who aligns with "Everything Else" is higher than other categories, we have a lot of freedom as marketers to be clever. If "Apparel Tops" drives new customers, we have to concede that those customers might not appreciate the "entire assortment". Messaging to Leadership doesn't always go well on that topic.

P.S.: If you like what you are seeing here and are interested in a Category/Customer centric analysis, send me an email (kevinh@minethatdata.com).

Case Study: Customer Response to a Dying Category

2026-05-07T20:10:00.000-07:00

We talked about Apparel Tops yesterday. We've previously mentioned that Fashion is a dying category, largely because the merchandising team appears to be killing the category. How does customer response change when a category is being killed off?

Similar to Apparel Tops, most demand comes from new/reactivated category buyers (80%). Again, the marketer has to know this, because the marketing plan has to include a lot of $$$ and attention in awareness (organic social) and search (product listing ads). If the marketer doesn't acknowledge this fact and act upon it, well, the marketer is equally culpable with the merchant at killing off the category.

This likely applies to your business as well. Most of your categories offer products that largely appeal to new/reactivated buyers and/or prospects. A marketing department that does not understand this dynamic is a marketing department that sub-optimizes the potential of the category/business.

Ok, what have the merchandising team done with their assortment-contraction initiative?

Rebuy Rates over time.

12-Month Fashion Buyers = 2.0% to 3.0% to 2.8% to 1.5%.
All Other 12-Month Buyers = 1.6% to 2.5% to 2.2% to 1.2%.

What happened in the past year is telling ... 40% or greater decreases in rebuy rates (albeit very low rebuy rates). With less merchandise available, existing buyers become less likely to repurchase.

The astute reader should say "Hey, Goober, you just told us that almost all demand comes from new/reactivated buyers, please tell me how many new/reactivated buyers the category had over time".

I can do that.

36,236 to 54,438 to 52,418 to 27,308.

We see the same (ugly) trend with new/reactivated buyers ... counts are down nearly 50%.

This comes up repeatedly in my work ... if you trim the assortment, you harm demand/sales. If you grow the assortment, you increase demand/sales but introduce other challenges (inventory / liquidations / margin erosion).

I'm going to hold off on communicating this fact to Paisley Ingram (the owner) until have a few more data points. I need to find a simple way to tell a complicated story.

Case Study: Customer Response To Merchandising Changes

2026-05-06T20:10:00.000-07:00

Let's approach this discussion in bite-sized pieces.

This table reviews repurchase activity for Apparel Tops ... the best-selling category that Beans: The Internet's Only Variety Store sells.

Yes, there's a lot going on here.

An introductory tidbit ... in the past year, 79% of demand in Apparel Tops comes from customers who haven't bought in at least a year or are first-time buyers. Only 16% of demand came from last year's Apparel Tops customers and just 5% of demand came from other twelve-month buyers.

If you are the marketer trying to grow Apparel Tops (your best-selling category), what might your approach be?

Awareness (organic social) and Search (product listing ads).

You could try to squeeze more out of existing buyers, but what is the point? If your efforts were positive and increased sales among existing Apparel Tops buyers by 10%, total demand would grow by 16%*10% = 1.6% ... meaningless.

When a merchant tells you that the marketer is not getting her product in front of the "right customers", the merchants is both wrong and right at the same time.

Wrong, in that the merchant usually has minimal experience with marketing techniques.
Right, in that most categories benefit from exposure to prospects who haven't bought the product previously.

Pay attention to rebuy rates over time.

16% to 15% to 14% to 11% for existing buyers.
7% to 6% to 6% to 4% among all other twelve-month buyers.

This is a mystery that requires professional levels of communication.

The category is stable because new/reactivated customers are buying the product.
The category is stable because new/existing items are being managed reasonably.
The housefile ... twelve-month buyers ... are increasingly less likely to buy this product, but their share of total demand is not sufficient for management to notice there is a problem.

Case Study: Hints of Discontinued Items

2026-05-05T20:10:00.000-07:00

Remember our "Class Of" table for the Fashion category? I do.

There are "tells" in this table that help me understand how a merchant approaches the business. In this case, the merchant in charge of Fashion discontinued existing items. Yes, the merchant failed to introduce enough new items to grow the category. Acknowledged. But the merchant also decided to take the hatchet to existing items.

How do I know this?

Look at the Class from Three Years Ago. Demand went from $564k to $384k to $160k in Years 1/2/3. Demand trails off after the introduction year, then trails off faster.

Look at the Class from Two Years Ago. Here comes a "tell". Demand went from $860k to $276k to $75k. That doesn't happen because the items fall off faster ... that happens because a merchant says "I don't like those items". Not liking existing items and then not introducing enough new items is one of two things.

The merchant is killing the category on purpose (which happens all the time).
The merchant is committing professional malpractice.

Our job is to understand if (1) is happening or if (2) is happening.

If a merchant is trying to purposely kill a category, the merchant has to demonstrate that killing the category does not impact other categories. In other words, when I worked at Nordstrom, if we killed off Cosmetics we'd be killing off the business because fragrance on the ground floor of a store brought in new customers who shopped the entire store. There's nothing more alluring to customer acquisition than fragrance.

Tell me what you've learned so far (kevinh@minethatdata.com). Are you finding this valuable? My clients find it valuable.

Case Study: The Virus Is Spreading

2026-05-04T20:10:00.000-07:00

Yesterday we talked about the fact that Fashion was being starved by a lack of new items. Because existing items either die off quickly or are discontinued quickly, new items must fill the gap if a category is going to thrive.

I summarized all categories - looking at new items by year, as well as demand across new items and existing items. Here we go.

What does the data tell us? A lot. Your category data will communicate to you as well.

Total Business = Starved of New Items.

Apparel Bottoms = Not Starved.

Apparel Tops = Not Starved.

Fashion = Starved.

Home = Starved 2 Years Ago, Lack of New Item Demand in the Past Year.

Jewelry = Starved.

Entertainment = Starved.

Workplace = Drastically Starved.

Outside = Not Starved.

Having Fun = Starved and a Lack of New Item Demand (might be a metaphor in this category).

Seasonal = Starved.

Decorations = Not Starved (many new items 1-2 years ago).

We have Apparel Bottoms, Apparel Tops, Outside, and Decorations that are being managed consistently. Everything else? Being cut to the bone.

Apparel Bottoms / Apparel Tops / Outside / Decorations?

1,099 new items three years ago, 1,055 new items today.
$7.1 million existing demand three years ago, $7.1 million today.
$6.7 million new item demand three years ago, $5.5 million today.

Everything else?

2,047 new items three years ago, 1,125 new items today.
$7.4 million existing demand three years ago, $5.5 million today.
$7.2 million new item demand three years ago, $2.6 million today.

In the four "preferred" categories, there is still degradation in new item demand on a comparable number of new items.

All other categories are simply imploding.

This business isn't failing. Management is likely failing the business. Four categories are performing acceptably. I emailed Paisley Ingram a paragraph about what I observed.

From: paisley.ingram@beans.com
Sent: Monday, May 4, 2026 11:13 PM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: RE: Four Key Categories

Kevin, we certainly struggled with our merchandising strategy the past four years. We know that Apparel is performing well, so we elected to prioritize Apparel. Sloane's arrival (our Chief Merchandising Officer) has been invigorating! She has us focused on products and categories that performed well historically. She uses actual performance data to show us "what works", then plans her assortment accordingly. She believes that new items are inherently risky, and any downside to not having enough new items can be overcome by a sound marketing strategy to attract customers or speak to loyal buyers. She strongly believes in a marketing/merchandising partnership that is led by product carryover tactics.

You keep asking interesting questions that stray from marketing, focusing on what we sell and how we sell it. Are you suggesting a gap between our current merchandising strategy and our results?

Best,

Paisley

______________________________________________________

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Monday, May 4, 2026 4:57 PM
To: paisley.ingram@beans.com
Subject: Four Key Categories

The data suggest that about half of your business is somewhat healthy ... Apparel Bottoms, Apparel Tops, Outside, and Decorations. All other categories show a disinvestment in new items, new items then fail to become existing items, causing those categories to perform poorly over time, with an acceleration in poor performance in the past year. Was there a plan to contract the business, or was there a plan to approach most categories differently and the results observed were unexpected?

Thanks,

Kevin

Case Study: Killing a Category

2026-05-03T20:10:00.000-07:00

Last week I showed you category performance over time. The result was ... chaos.

When I see chaos, I dig into what might be causing chaos. Apparel Tops have performed reasonably well over time, certainly not exhibiting the dramatic declines observed elsewhere. I produce a "Class Of" report to understand how different merchandise classes are behaving. Here is the Class Of report for Apparel Tops.

Ah. This table has so much information in it. New item development is reasonably consistent over time ... 799 styles to 757 styles to just 637 new styles last year to 748 new styles in the year just ended.

Look at how much demand new items generate in year two. Three years ago, new items generated $4.6 million in year one and $2.0 million in year two. Two years ago, the relationship was $4.6 million / $2.3 million. A year ago the relationship was $3.7 million / $1.7 million. In other words, new items either die off quickly or are quickly discontinued. When this dynamic happens, it is critically important to constantly replenish the assortment with new items. "Beans" (The Internet's Only Variety Store) appears to replenish new items within Apparel Tops at a reasonable rate.

Of course, Apparel Tops is a category that is hanging in there.

Fashion, on the other hand, is struggling. What does the Fashion Class Of report look like?

Do you see the change in management of the category?

Look at new styles by year ... 120 to 175 to 167 to 97.

Look at demand by year ... $1.2 million to $1.5 million to $1.4 million to $0.8 million.

The category isn't dying.

The category is being killed by Management ... or more specifically, by the Merchant in charge of Fashion.

Obviously there are times when it is good to kill off a category ... customers don't like the merchandise, times are changing, gross margins are poor. I asked Paisley Ingram, the Owner of Beans (The Internet's Only Variety Store), her thoughts about Fashion.

From: paisley.ingram@beans.com
Sent: Sunday, May 3, 2026 8:06 PM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: RE: Category = Fashion

Kevin, thank you for noticing this trend within Fashion. Our Chief Merchandising Officer, Sloane Montgomery, thought it was a good idea to trim the assortment down to winning items, giving customers a focused assortment to browse from. As Sloane says, "It doesn't matter how many new items we sell, most items appear new to customers who haven't seen the item before". She thinks you might want to dig into the problem more carefully, identifying marketing issues that led to the demand shortfall.

Best,

Paisley

______________________________________________________

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Sunday, May 3, 2026 6:15 AM
To: paisley.ingram@beans.com
Subject: Category = Fashion

When I analyzed Fashion, I quickly observed that the number of new items in the past year was cut nearly in half. This reduction in new items led to a significant reduction in demand in the category. Is there a reason why this happened?

Thanks,

Kevin

Case Study: Merchandise Chaos

2026-04-30T20:10:00.000-07:00

There are simple reports I produce to introduce me to category performance. Below is a summary of five year's of demand/sales data by category. Prepare for chaos.

This is a company ("Beans" ... The Internet's Only Variety Store) that is lost.

Why do I suggest this company is "lost"?

There is no merchandise consistency within this brand. It is common for a handful of categories to be the "gravity" that holds a brand together. When I worked at Nordstrom, you'd have some mix of womens / mens / footwear / cosmetics that generally glued customers to the brand. It would have been crazy for footwear to be +40% and cosmetics to be -40% ... long before either category skewed beyond +/- 10% somebody would have been promoted/fired.

Look at Fashion ... Fashion is -46% IN JUST ONE YEAR! How does that happen?

Jewelry? It looks like it is being killed off. Same thing with Office. And Media. And Recreation.

Seasonal merch went from nothing to something to nothing.

Meanwhile, Table Top / Outdoor / Apparel Bottoms / Apparel Tops are not performing poorly.

I asked Paisley Ingram what happened?

From: paisley.ingram@beans.com
Sent: Tuesday, April 28, 2026 2:16 PM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: RE: Five Year Category Performance

Our long-time Chief Merchandising Officer left the company four years ago to spend more time with her family. We wish her well in all endeavors. Her replacement left the company about two years ago to spend more time with her family. We also wish her well in all endeavors. Our current Chief Merchandising Officer is Sloane Montgomery. Sloane firmly believes in a focused assortment of favorites that customers love, and she is a strong believer in our apparel offering.

Thanks,

Paisley

______________________________________________________

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Tuesday, April 28, 2026 11:47 AM
To: paisley.ingram@beans.com
Subject: Five Year Category Performance

I took a look at category performance by year. It is obvious that something unusual happened 3-4 years ago, and again in the past year or two. Is there anything I need to know about your merchandising strategy as I dig into category performance?

Thanks,

Kevin

Case Study: What A Difference

2026-04-29T20:10:00.000-07:00

Yesterday we talked about comp segment reporting. I can slice and dice the reporting ... in this case, I share comps for new merchandise and existing merchandise. What do the two tables tell you?

Starting in April 2024, somebody decided to not invest in new merchandise - comps responded accordingly. Of course, the most important months for Beans (The Internet's Only Variety Store) are November and December - the two-year comp in November is -49.4% for new merchandise. I mean ... that's business malpractice.

The two-year trend for new merchandise is -34.6%.

The two-year trend for existing merchandise is -1.2%.

Obviously I'm going to dig into category data, but I have to share what I've learned with the owner (Paisley Ingram). My email correspondence is listed below.

From: paisley.ingram@beans.com
Sent: Monday, April 27, 2026 9:06 AM
To: Kevin Hillstrom <kevinh@minethatdata.com>
Subject: RE: New Merchandise and Existing Merchandise Comp Segment Results

I shared your tidbit with our merchandising team. They are frustrated with my marketing team. They are frustrated with copywriters. They are frustrated with the website. They are frustrated with tariffs. They are frustrated with AI. They are frustrated with overall business performance. I need to position this work as a way to make them look better instead of the work being an audit of their failures.

Thanks,

Paisley

______________________________________________________

From: Kevin Hillstrom <kevinh@minethatdata.com>
Sent: Monday, April 27, 2026 8:55 AM
To: paisley.ingram@beans.com
Subject: New Merchandise and Existing Merchandise Comp Segment Results

I had a chance to analyze comp segment performance for new merchandise and for existing merchandise during the past three years.

Over the past two years, new merchandise posted a -34.6% comp. Existing merchandise posted a -1.2% comp.

The data clearly indicate that the merchandising team made decisions to not source new products at a sufficient rate to grow your business the past two years. In upcoming days, I will dive into the data and identify categories where new merchandise investment did not meet customer expectations.

Thanks,

Kevin

Case Study: A Marketing Problem is Identified

2026-04-28T20:10:00.000-07:00

We're busy analyzing "Beans ... The Internet's Only Variety Store!". Yesterday I ended the post with reference to a table from my Elite Program runs, called the "Comp Segment" table. There are two sub-tables within the table ... one analyzing new/reactivated buyers by month ... one analyzing how customers with exactly two purchases in the past year spend money in the following month. Here we go.

The top portion of the table shows us three years of comp new/reactivated buyer counts. If a customer purchases in a month, and I don't see a purchase from the customer for the twelve prior months, the customer is "new/reactivated".

Tell me what you see in the top half of the table?

Here's what I see. In 2024, comp new/reactivated buyers increased by 0.5% on an annual basis. In 2025, comp new/reactivated buyers decreased by 22.7%. This is likely a marketing problem. New/Reactivated buyers come from well-executed marketing tactics. We see modest decreases in November/December 2024, then consistent decreases through most of 2025.

Pay attention to November/December counts. What do you observe?

Counts are very high in November/December.

In other words, this business is disproportionately skewed to November/December, which means that "The Internet's Only Variety Store!" is really a Christmas-based Gift-Giving store. All of the risk of the year is back-loaded into November/December. It is VERY HARD to run a November/December centric business.

Look at November 2025 ... counts are down 42.7%. This is a MARKETING problem. It likely means the CFO told the CMO to not spend money, without understanding how the business actually works. I could be wrong ... but that's the pattern I repeatedly see when I analyze Elite Program clients. When a business has an annual rebuy rate < 40%, the business must focus the majority of all marketing efforts on new/reactivated buyers ... and when the business fails to achieve this mission, the business struggles.

The bottom half of the table shows how much more/less customers who purchased exactly two times in the year spent in the following month. If we have five customers, four do not purchase ($0) and one does ($40), the average is $8.00 per customer. That's what is being reported above.

What do you observe in the table?

From April 2024 to August 2025, comparable customers spent less, consistently, vs. the prior year. This "can" happen when marketing dollars are reduced. This is "more likely" to happen when something changes with the merchandising strategy of a company.

Over the past two years, comparable customers are spending 22.9% less than they previously spent. That's unsustainable.

The two tables within a table tell a story.

It is obvious that "somebody" decided that marketing should spend less money in 2025.
It is obvious that there is a merchandising problem causing customers to spend less year-over-year.

Footnote: When I woke up on Monday morning, I was greeted with three unsubs. All three were industry consultants (not people actually working for a brand like "Beans"). Two of the three were merchandising consultants. In other words, it was clear that I offended them. The same thing happened back in 2014 when I introduced Merchandise Forensics ... the most popular analysis project I ever sold. It is fashionable and easy to blame marketers for missteps. It causes uncomfortable feelings when you suggest that a merchant isn't perfect, but instead, is just like the rest of us.

Case Study: Interacting Challenges

2026-04-27T20:10:00.000-07:00

Category performance comes later ... early in a project, I look for high level trends that help guide me to solutions.

I liberally borrow analytics from my Elite Program runs. The rolling twelve-month view of customer behavior is always illuminating.

There's all sorts of goodies in the table.

Customer counts fall away during 2025.

Annual orders per buyer have declined from about 1.17 per year to 1.14 per year ... in other words, customers are only buying once per year. More on that in a future post.

Items per order collapsed in November 2025.

Price per item purchased accelerated in November 2025. It is usually not a coincidence when items per order decrease and price per item purchased increases ... customers have a budget.

AOV decreased for years, but in the last three months of the year AOV increased at a healthy rate.

I mentioned yesterday the problem with new items. This table clearly illustrates the challenge, and the challenge is a two-year challenge that accelerated late in 2025.

The last two columns illustrate trends among items selling at/above their historical average price point (i.e. a $30 item selling for $30) and items selling below their historical average price point (i.e. a $30 item selling for $20). In the past year, items selling below their historical average price point declined faster than items selling at/above their historical average price point. Though demand collapsed, demand collapsed faster among items selling below their historical average price point. The merchandising team was trying to sell at full price. Good job!

Another important Elite Program table is the Comp Segment table. Here is the table.

There are many tidbits here that shape where the analysis is headed. I'll explain those tomorrow. Interestingly ... there IS a marketing challenge that the table above identifies. Can you see it?

So far ...

A clear merchandising problem with new items.
Customers only purchase one time per year.
Customer counts are decreasing throughout all of 2025.
Prices increased late in 2025.
More full-priced selling in 2025.

Case Study: First Steps

2026-04-26T20:10:00.001-07:00

When you work on a new consulting project, you don't ask silly questions like "Why is your company named 'Beans'?" You wonder, you ponder, but you don't ask.

Instead you do two things.

You listen.
You run client data through the Elite Program Code.

Remember, Beans ... The Internet's Only Variety Store ... was a $31 million dollar business that is now a $20 million dollar business. That's an unacceptable drop, and a drop of that nature is generally self-inflicted. Businesses can collapse on their own, they can collapse because they are mismanaged, and they can collapse for both reasons. Typically ... somebody mismanaged something or didn't understand something.

Somebody at Beans mismanaged something or didn't understand something.

I like to run new data through my Elite Program framework. I want to see how a client compares to all other clients. Within a client, there are many points of view ... there are fables, there are stories employees tell themselves. There are also facts about actual customer behavior and actual merchandise categories.

Let's look at a simple comparison in the data. Depicted below are two graphs, one for annual demand from new items, one for annual demand from existing items. I elected to graph each metric with the same scale on the y-axis because doing so is illustrative of the challenges Beans faces.

What do you observe?

Existing item demand takes a hit in the last months of 2025, sure. Now look at the trajectory of new item annual demand. From fourth quarter 2024 through fourth quarter 2025, we have a catastrophe.

I'm literally 30 minutes into the project, and I already have a potential culprit - it's the Chief Merchandising Officer. In my mind, I prepare myself for the fact that future discussions are not going to go well. If this was a marketing mistake, both graphs would decline at the same rate. If this is a merchandising mistake (and it most certainly is), one graph doesn't look too bad while the other graph looks ... bad!

Yup, we're off to a good start.

I'll typically send a tidbit to the project sponsor and anybody the project sponsor wants me to include. I'll clearly communicate that my first view of the data suggests a merchandising challenge, not a marketing challenge. Then I'll duck, because somebody is likely to be angry with my assessment.

P.S.: If you like the comparison above, consider becoming part of my Elite Program, with runs in June / October / February of every year. Cost is $1,800 for the first run, then $1,000 each thereafter. June is just around the corner, friends!

Case Study

2026-04-25T22:22:00.000-07:00

Let's spend some time talking about a case study. I will analyze a business called "Beans". The data is real, the company and scenario is fictional.

The business is owned by Paisley Ingram. She calls Beans "The Internet's Only Variety Store". If you ask Paisley, she'll tell you ... "I sell whatever I want to sell. Always have. My customers love me."

Annual Demand/Sales for the past five years.

Demand 4 Years Ago: $31.0 million.
Demand 3 Years Ago: $28.4 million.
Demand 2 Years Ago: $27.4 million.
Demand 1 Year Ago: $25.6 million.
Demand as of 1/1/2026: $20.8 million.

Woo boy.

Let's dive in and see what a study of merchandise and categories can tell us about the problems at The World's Only Variety Store.

Your Mission

2026-04-23T20:10:00.000-07:00

Years ago I worked with a CEO who had a Mission for his team.

He firmly believed in retaining customers, in loyalty programs. He believed that he could "force" a customer to become loyal. Seriously. He talked about it. Just get the right offer in front of the right customer at the right time. Throw some points at the customer. Let the customer redeem points when he told the customer it was the right time to redeem points. "It works for BJ's Restaurant and Brewhouse!" Good for him ... free pizookies all around.

The Mission? "We're going to thrive by creating a large number of highly loyal customers, and we have the tools to do this outside of the merchandise we sell."

I mostly disagree with the guy, but he had a clear Mission for his team.

(IDEAS) * (TALENT + LEADERSHP + STORYTELLING) = MISSIONS

His team moved in lock step with his Mission. The guy did his job as a Leader. His Team was talented. His Leadership was good. His ideas were poor. His storytelling was non-existent. This meant his Mission, while clearly articulated, had minimal value.

He needed new customers.

It is possible to have a good Mission but a bad outcome.

Stories Support Missions

2026-04-22T20:10:00.000-07:00

I met with a client ... the analyst was struggling to articulate something important. I spent fifteen seconds tying thoughts together ... an Executive then said to the analyst "Why didn't you say that?"

The analyst looked mortified.

Because HE DID SAY THAT!!!

Of course, he didn't say it in a way that resonated with anybody.

Every three or four years, stories re-emerge. They re-emerge because technology moves us in a direction we don't fully understand, leading to a handful of individuals who articulate the future in a way that allows us to feel comfortable, feel knowledgeable, feel confident, and consequently take action.

In my hobby (headphones), people will ask questions about headphones or review headphones on Reddit (https://www.reddit.com/r/headphones/). You can sniff out bad storytelling in two seconds ... it is clear contributors using AI to write, and you know it the minute you see it. AI has a structure that is synthetic and inauthentic. It's obvious. Bad storytelling.

The same thing happens in business.

In catalog marketing, there are no storytellers ... and that is horrifying because the most famous sitcom of all time leveraged catalog storytelling as a core component of the series (Elaine working at the J. Peterman catalog). Seriously ... pick one employee from any ... ANY catalog brand who is a brilliant storyteller. I'll wait for your reply ...

You'd think the catalog vendor community would be able to communicate a compelling story that does not use terms like "digital fatigue", "Gen-Z", or "leading brands".

Human Teams = Talent + Leadership + Storytelling.

Our equation evolves.

(IDEAS) * (TALENT + LEADERSHP + STORYTELLING) = MISSIONS

Strong Teams, Weak Teams

2026-04-21T20:10:00.000-07:00

Last year, my Green Bay Packers were a very good team that was decimated by injuries to key players.

There are three overriding teams on a football team.

Offense
Defense
Special Teams

In the playoff game in Chicago, the Bears scored 25 points in the fourth quarter to rally for a 31-27 win. They entered the fourth quarter trailing 21-6.

On offense, the Packers generated 421 yards and had zero turnovers. That should be enough to win a game.

On defense, the Packers yielded 445 yards, but generated two turnovers.

In terms of penalties, the Packers had 65 penalty yards vs. just five for Chicago.

Here's a fun stat. On special teams, the Packers kicker missed two field goals and an extra point. That's seven points given away in a four point loss.

In other words:

The offense was good enough to win.
The defense was good enough for three quarters then fell apart in the fourth quarter.
Special teams gave up seven points in the kicking game.

One team ... three sub-teams with varying levels of success.

Does the same thing happen at your company?

I'll go out on a limb and say ... yes ... yes it does.

It's easy to pick on the merchandising team ... they source the products that customers buy. If they do a poor job, there's no amount of help that anybody else can provide to prevent a lousy outcome.

It's also easy to pick on the marketing team. It's a discipline that, historically, has been populated by an odd combination of absolutely dim-witted knuckleheads, brilliant business leaders who will run brands in the future, highly competent individuals who execute campaigns flawlessly, and analytics professionals who want to optimize things that nobody else wants to optimize. Try running a great team with that combination of talent!

I've spent a lot of time talking about category performance in the past month, for good reason. When the marketer or analytics professional understands how merchandise categories fit together (and interact with marketing channels), the professional can "be a bridge" between marketing and merchandising ... the professional can be a Leader, growing categories that yield high-value customers, capitalizing on the profit generated by specific categories.

Your business has a lot of teams. Those teams are unlikely to function perfectly. Be the person who helps teams function better.