Before any product is retired or changed, pull the list of customers who rely on it. Its sales figure tells you what the product earns. The list tells you what its loss would cost.
A product’s revenue is the wrong number for deciding whether to retire it. What matters is who buys it and what else they’d stop buying if it went. For most products the answer is “nobody much.” For a few, it’s a group of customers for whom that product is the reason they’re a customer at all.
When the online grocer in Peter Boatwright and Joseph Nunes’s study cut its range, of the households loyal to a brand or size that was eliminated, “nearly half continued purchasing within the category” Published. Read the other way: about half stopped buying in that category from that grocer. And that’s at a store that still sold other brands in the category. At a single-brand DTC store, losing the product is often losing the brand.
Laurens Sloot, Dennis Fok and Peter Verhoef found the same at a Dutch retailer that removed a quarter of its detergents: the short-term losses were “caused mainly by fewer category purchases by former buyers of delisted detergent items” Published. The damage concentrates in the people who bought what was cut, which is exactly what an average across all customers hides.
Call a customer dependent on a product if any of these is true:
Customers who pass two or more tests are core dependents; they get the most personal version of every message in the next chapter. Everyone else who bought the product in the last year is a trier: they’re told, but they’re not the risk. The query is in Appendix A, and it should be a saved segment you can run for any product in minutes.
A product’s sales tell you what it earns. Its dependents tell you what its loss would cost.
When a dependent leaves, you lose their whole spend, not just the product’s share of it. So the revenue at risk from retiring a product can be far larger than the product’s own sales, and the decision should weigh that against what retiring it saves: storage, minimum orders, write-offs, the time it takes to manage. The tool puts those side by side, with and without the playbook from chapter 8.
With the defaults, 2,000 buyers include 500 dependents. Without a plan, 325 customers leave, taking $59,000 a year of revenue and $35,400 of contribution, so dropping the product loses $10,400 a year despite saving $25,000. With a playbook that prevents half those losses, the same decision gains $7,300 a year Derived. Dependents are a quarter of the buyers and 85% of the revenue at risk. The decision flips on how well you handle a few hundred people.
This is one chapter of The Catalog, which is free and readable in full on a single page with no form in front of it.