Originally published by Eva Keller in CEO Weekly

A customer walks into a store and spends $45. Ask most owners what she’s worth, and they’ll tell you $45, because that’s what the receipt says. In a new CEO Weekly feature, Tim Shea, founder and CEO of Latticework Insights, calls that answer the most expensive mistake in retail, and one almost every store owner makes without realizing it.

A receipt shows what a customer spent today. It says nothing about what she’s worth. Tim has spent 25 years building data infrastructure for brands including the NBA, UFC, Sweetgreen, and Reddit, and he says the gap between those two numbers, spend-to-date versus true value, is where most retailers leave money on the table.

“Your LTV is not $100 just because a dashboard says so,” Tim says. “You don’t have one type of customer. You have many, and most businesses are averaging them together, which erases the exact information that would tell you where to spend.”

The piece walks through the math behind that claim. A common figure Tim sees across e-commerce and DTC brands is that roughly 80 percent of customers never make a second purchase. The 20 percent who do are carrying the business, and inside that group, the timing of the second purchase predicts almost everything that follows. Customers who buy again within 30 days are far more likely to reach a third, fourth, and fifth purchase than customers who wait 60 or 120 days for the next sale.

“We’ll look at a company’s data and find that half their revenue comes from customers who bought three times or more,” Tim says. “Get someone to a second or third purchase, and a meaningful share of them go on to buy ten things. That’s not a rounding error. That’s the business.”

The article also covers the SMART framework, the starting exercise Tim runs with every client: Speed, Margin, Attribution, Retention, and Tiers. Speed asks how quickly a customer buys again. Margin asks what it actually costs to acquire and serve her, not just what she paid. Attribution asks where she came from. Retention asks what it takes to get her to buy again. And Tiers asks us to accept that we do not have only one type of customer, because a $45 customer who needs a discount to buy and a $450 customer who buys every few months should never be averaged into a single number.

The obvious objection is that this kind of analysis used to be expensive: a six-to-twelve-month build, a data warehouse, a pipeline, a hired data science team, often running well into six figures before a single insight came out the other end. Tim says that barrier is the thing that’s actually changed, not the underlying math. AI has collapsed most of the stack that used to require a dedicated technical team to build.

“The math hasn’t gotten easier,” Tim says. “The cost of getting to the math has.”

His advice for a retailer this week is narrow and specific. Pull last year’s purchase data and look at one thing: how long it took repeat customers to buy the second time. The customers who came back fast are the pattern the rest of the business should be built around, and finding them no longer takes six months or six figures. It’s the same starting point Latticework describes in How Elite Brands Use LTV:CAC Analytics to Outsmart Their Competition.

Read the full article on CEO Weekly →

If you’re still valuing customers by what the receipt says, get in touch. Finding the customers your business should be built around no longer takes six months or six figures.