It's Time!

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

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.

1. 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.
2. Personalize the merchandise in email campaigns for first-time buyers to increase the probability of a second purchase (preferably) within a second category.
3. 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.
4. When a customer reaches recency = 11/12/13/23/24/25, we ramp-up messaging and frequency.
5. 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

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

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

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

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

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

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?

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

I'm writing this back on Sunday night.

Here are the two shows I just watched on YouTube.

• AI Wasn't Built for You.
• English Football is Absolutely Ridiculous.

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

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.

1. Apparel Tops:  We know this is a high-volume category, and it is all by itself meaning that many customers ONLY purchase Apparel Tops.
2. 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.
3. 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

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

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

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.

1. The merchant is killing the category on purpose (which happens all the time).
2. 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

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

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