Finding the Missing Bridge Between Customer Metrics and Corporate Value
Dallas Area Rapid Transit put out a press release this month with three records in it: highest-ever NPS, highest satisfaction score in six years, record share of riders who say they’ll keep riding.
All three came off one survey. The survey was tied to a sweepstakes. You had to complete at least 70% of it to be entered. So the sample is whoever wanted a shot at the prize badly enough to finish the form. Nothing in the release connects any of it to ridership, fare revenue, or the budget.
Five days later the American Customer Satisfaction Index dropped its Q2 report. Steepest quarterly decline outside COVID. Complaints at a record high. Corporate profits at a record high, same quarter. And ACSI’s own founder said what nobody in this business says about their own numbers: a lot of the metrics companies use to track customer experience predict stock returns or profit “about 50% of the time, so would a coin toss.”
Same week. The country’s most credentialed satisfaction index admitting its category is a coin flip, and a transit agency running a victory lap on three numbers nobody checked against anything.
Is everybody missing the point?
“Record NPS” tends to get covered as good news on its own terms, the same way “record satisfaction” and “record engagement” do – the question of what the number actually predicts rarely makes it into the coverage. DART isn’t unusual here. It’s a clean example of a pattern that runs across NPS, CSAT, engagement rate, and sentiment score alike: metrics that are cheap to collect, and that get reported as the outcome rather than as a stand-in for one.
That doesn’t mean the metrics are useless. And it certainly doesn’t mean the people using them aren’t doing serious work. Quite the opposite. There are very smart people inside customer organizations trying to understand what makes customers stay, leave, buy, engage, advocate, redeem, renew and everything else that happens inside a customer relationship. NPS and CSAT and engagement and sentiment can all tell them something.
The problem is what happens when we ask those numbers to tell us something they don’t.
The fine print
A sweepstakes pulls in people willing to sit through a survey for a shot at winning something. A 70% completion floor screens out anyone who got bored or annoyed halfway through – which tells you something too, just not something that makes the press release. None of that makes DART’s numbers fake. It makes them the wrong evidence for the claim being made with them: that a record score is an achievement, full stop, rather than a survey artifact that may or may not have anything to do with what riders actually do.
Swap the transit agency for any company’s quarterly CX deck and you’re looking at the same move.
How a proxy becomes a mandate
These metrics get cheap to produce, easy to fit on a slide, easy to stack against a competitor’s number. That’s the whole reason they became the default language for “customer health.” So teams start managing to the number instead of the outcome – chasing NPS lift, chasing engagement lift – because that’s what’s on the dashboard and what gets reviewed.
But there’s a more generous explanation for how we got here than simply saying companies have been measuring the wrong things.
For a long time, these were the things we could measure. We could ask customers how they felt. We could track opens and clicks. We could measure satisfaction and stated intent. And as the customer discipline became more sophisticated, so did all of those measures.
What’s changed is our ability to see what happens next.
We can increasingly connect customers to transactions, transactions to behavior, behavior to retention, retention to margin, and all of it back to an individual customer or cohort. The customer work has moved a long way downstream from where it was when a lot of our standard metrics were created.
The reporting hasn’t moved nearly as far.
The signal behind the signal
Here’s the distinction that keeps getting lost: mechanism versus measure. Trust and satisfaction are real, and they’re often how the economics happen – a satisfied customer renews, a trusted brand holds its price. But the mechanism isn’t the measure.
Whether any of it worked comes down to one thing, and it’s economic: did the customer’s spending behavior change, did the relationship hold, did it show up in what they paid or kept paying or cost you to keep.
This isn’t theoretical. A promoter on an NPS survey isn’t under contract to buy again, and plenty don’t – the score is a mood at the moment you asked, not a commitment. Strong sentiment numbers can walk right into a renewal cycle and lose the account the second a competitor undercuts on price, because sentiment was never what was holding the customer there. Switching cost was. Or contract terms were. Or nothing was, and you were about to find out.
It runs the other way too: a company with mediocre satisfaction scores can grow for years on lock-in or high switching costs, which is a bad story for the customer and a fine one for the balance sheet, and no sentiment metric will ever catch that gap, because sentiment isn’t measuring the thing actually keeping the customer in place.
That’s not an argument for getting rid of sentiment. It’s an argument for finding out what the sentiment is connected to.
So keep going
That’s the question to run against every metric before it earns a spot on the report: not does this feel like it should matter, but has this number, in this business, been checked against what customers actually did.
If NPS goes up, what else goes up?
If engagement improves, what changes?
If satisfaction falls, do customers actually leave?
Do highly engaged loyalty members spend more because they’re highly engaged, or are they highly engaged because they already spend more?
These aren’t small distinctions. They’re the difference between knowing something about a customer and knowing something about the economics of that customer.
And this is where I think the conversation around customer measurement has been stopping too soon.
We’ve spent years building increasingly sophisticated ways to look down into the customer relationship. Maybe it’s time to lift our heads up and look at what all of that work connects to.
The gap above the dashboard
Dan McCarthy and the team at Theta have been coming at this from the other direction.
Their Customer-Based Corporate Valuation (CBCV) work starts with the customer and works its way up to the value of the company. Customer acquisition, retention, spending patterns, contribution economics – understand those well enough and you can begin to model the future cash flows of the customer base and, from there, what that customer base says about corporate valuation.
It’s a compelling idea for a pretty obvious reason: companies are ultimately collections of customers producing cash flows. If you can understand the economics of those customers, you ought to be able to understand something important about the economics of the company.
But there’s a fairly large space between CBCV and the average customer dashboard.
On one side you’ve got NPS, CSAT, engagement, sentiment, redemption, frequency, churn and retention. On the other you’ve got cash flow and corporate valuation.
The missing piece is customer economics – the connective layer between the metrics we’re already producing and the financial outcomes the business is trying to produce.
The bridge
The sequence isn’t especially complicated.
What did the customer say?
What did the customer do?
What was that behavior worth?
What does that value mean to the business?
NPS, satisfaction, sentiment and engagement sit toward the front of that sequence. They’re signals about the relationship.
Then comes behavior – buying, renewing, redeeming, increasing frequency, consolidating spend, leaving.
Then the economics of that behavior – revenue, margin, retention value, acquisition efficiency, cost to serve, lifetime value.
And eventually the aggregate effect of all of those customer economics starts telling you something about the quality and value of the business itself.
Customer signals to customer behavior to customer economics to enterprise value.
Most organizations have pieces of this already. Some have nearly all of them. What they generally don’t have is the connective tissue that lets somebody follow the line all the way through.
That’s the part that seems worth fixing.
Back to Dallas
So take DART’s record NPS at face value.
Maybe riders really are more satisfied than they’ve ever been. That’s useful information.
Now keep going.
Do the more satisfied riders ride more often? Do they remain riders longer? Does stated intent to continue riding predict actual continued ridership? Are there particular parts of the experience that have a stronger relationship to those behaviors than others? And what do those changes mean economically?
Answer those questions and the survey becomes more useful, not less. It stops being the end of the measurement exercise and becomes the beginning of it. Which is probably the larger point here.
The customer discipline doesn’t need to throw away the measurement systems it’s spent decades building. It needs to connect them to the next layer.
What customers say matters because it can help explain what customers do. What customers do matters because behavior has economic consequences. And those economics, aggregated across a customer base, eventually show up in the value of the business.
That’s the line we’re trying to draw.
Customer value economics sits in the middle of it.
And if McCarthy and Theta are right that we can work from customer economics all the way up to corporate valuation, then the opportunity for the people running customer organizations is fairly significant. They already own much of what happens upstream.
They just need to keep going. Because the real opportunity isn’t to produce a better customer score. It’s to show where the score goes.
Photo by Jametlene Reskp on Unsplash
