Kevin Hillstrom: MineThatData

A discussion about Home merchandise. The discussion started after I previously shared the following "Class Of" table.

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From: sloane.montgomery@beans.com
Sent: Monday, May 25, 2026 3:19 PM
To: Paisley Ingram <paisley.ingram@beans.com>
Subject: RE: FW: Rebuilding Plan

        

Ok, I've listened to your requests over the past year ... those requests resulted in two new project opportunities for all of you. Contact me (kevinh@minethatdata.com) for details.

You've read my Case Study series (plenty more coming from the world of "Beans", FYI). Those analytics are part of a new product offering called "Categories" ... where you will learn how your customers interact with Merchandise Categories/Departments. The analysis is designed to explain why your business is thriving / struggling, pointing out actionable insights for marketing and merchandising purposes. As you can see, if you are a prior client or are a blog subscriber (hint - that's you) there is a substantial cost incentive to work on this project.

The most popular request in 2026 has been a form of specialized "Mentoring" ... either weekly video conferences or weekly video conferences fused with ad-hoc analytics designed to help your Management Team navigate customer behavior in 2026. Here are your two "Mentoring" options ... I call this offering a "Customer Advocate Mentorship" opportunity. Think of it as me being a "Chief Customer Officer" of sorts.

Click here for data layouts as well as other project offerings for the remainder of 2026.

My July / August / September calendar is likely to fill up quickly after this announcement, so please reach out soon to reserve time for your project.

Thanks,
Kevin

        

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.

        

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.