Kevin Hillstrom: MineThatData

"Categories" Project and Customer Advocate Mentoring

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

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.

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

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.