Customers who buy from two categories spend more than customers who buy from one. Most of that gap was there before they bought the second category.
The slide is built the same way everywhere. Split customers into those who have bought from more than one category and those who haven’t, compare a year’s spending, and divide. Then comes the leap: get single-category customers to buy a second category and they’ll be worth that multiple.
To buy from two categories, a customer has to buy at least twice, or buy a bigger basket. Customers who buy often, and have been around longer, have more chances to try something else. So “multi-category” is partly another name for “already buys a lot,” and the slide reports that the group who buy more buy more.
The research says the same. V. Kumar, Morris George and Joseph Pancras studied what predicts cross-buying at a catalog retailer and found that, alongside the firm’s marketing, it tracked how customers already behaved: how often they bought, how much they returned, how focused their buying was, and which category they started in Published. Werner Reinartz, Jacquelyn Thomas and Ganaël Bascoul tested the direction directly, on two data sets, and concluded that “cross-buying is a consequence and not an antecedent of behavioral loyalty”: loyal behavior drives the number of categories people buy from, not the other way around Published.
Annual reports make the claim the other way. Warby Parker’s 2024 10-K says customers who shop “across product lines and channels tend to convert to highly loyal returning customers” Filed. That may be true, but as written it’s a correlation, and it would read the same if loyal customers simply went on to buy more lines.
Multi-category customers aren’t valuable because they buy more categories. They buy more categories because they were already valuable.
Recommendations have the same problem. Amit Sharma, Jake Hofman and Duncan Watts used natural experiments on Amazon and estimated that at least 75% of the clicks through recommendations would likely have happened without them, through search or another route Published. Attribution credited the recommendation for traffic that was coming anyway.
You can’t remove selection from observational data, but you can size it. Compare what the two groups spent before any of them bought a second category, then compare growth, not level.
Say a brand’s multi-category customers spent $450 in their second year and its single-category customers spent $150: the slide says 3x. Now look at their first six months, before anyone crossed categories. The future multi-category buyers spent $200; the others spent $100. The gap was already 2x. From that start, the multi-category group grew 2.25 times and the single-category group 1.5 times, so the most cross-buying could explain is a 1.5x difference. A converted single-category customer would go from about $150 to at most $225 a year: worth up to $75 more, not $300 Derived.
DerivedFrom the example’s made-up round numbers. Your own split comes from the query in Appendix A.
With the defaults, the 3.0x becomes at most 1.5x, and a converted customer is worth up to $75 a year more, a quarter of the $300 the slide implies. That’s still worth having. It’s a different budget.
This is one chapter of The Next Category, which is free and readable in full on a single page with no form in front of it.