As a society, we’ve started letting the metric make the decision instead of informing it.
I’m a recovering NCAA athlete. I spent the first 24 years of my life chasing progress on and off the field. Weight room numbers, 40 times, shooting percentage. All of it mattered. But when you got down to it, those measurables rarely defined who was good versus who was truly great. They didn’t define who emerged as leaders, or who drove culture. Effort wasn’t quantifiable. Neither was having an “it factor” when it mattered most. But everyone knew who had it.
That’s not to dismiss measurable goals. The team with better average athletic measurables is a safer bet in almost every sporting scenario. There needs to be balance between the measured and the felt.
I was out to dinner with an old teammate recently, inflating our glory days, as one does. Conversation turned to how we stay in shape these days.
“What’s your gym and cardio routine?”
“What’s your diet?”
“Do you use a wearable to track your metrics?”
Bedtime. Feeding windows. How much you drink. Hell, even bedroom temperature.
I’m into this stuff too. I train daily. But I was overwhelmed by every data point he was optimizing for.
Finally, I asked, “How do you feel?”
“Man, I feel like I can’t keep up.”
That’s when it hit me. The numbers were telling him how he should feel rather than how he actually felt. The tracking had stopped being fun and started being a permission slip.
Same Trap, Different Spreadsheet
I got into marketing during the boom of performance marketing. Using data to dictate strategy, tactics and budget. SEO, digital advertising, analytics, attribution modeling, all of it. I’ve loved it. I studied economics. I’m a type A, left-brain data jockey. It turned marketing into a science for me, and I’ve built a career on that industry trend.
But it’s gotten harder to keep up with the sheer number of measurable metrics, let alone figure out which ones are actually moving the needle.
We create content to drive rankings. We improve rankings to drive traffic. We drive traffic because more volume means more leads, and more leads means more closed sales. Meanwhile, budgets are tightening, so we go hyper-ABM. Target the 500 people who could buy today. Clay lists, targeted ads, sales calls, postcards. Less wasted spend, more targeted budget.
Both of these are sound strategies in isolation. Together, they contradict each other.
The purpose of content is to provide value to the reader, period. Write for rankings instead, and the content goes soulless. Nobody comes back, and there’s no more traffic to show for it anyway. Narrow the audience for ABM, and CPCs and competition both spike, because now you’re one of nine emails and eleven ads hitting the same VP. Eventually they go numb, or they block you.
When the Data Said Quit
This played out in real time with a warehouse management software client of ours. The WMS space is saturated with players and has no dominant market leader. There’s always opportunity for capture, but upending an entire warehouse’s operations and tech stack is a daunting move.
Historically, we focused their content on warehouse optimization, integrations to tech stacks, and handling new brands and vendors. The broad stuff, with high search volume. But with growing competition in search and prospects looking for more specialization, we changed the content strategy six months ago. We shifted to be vertical-focused: ecommerce, cold-chain, cannabis, wine distribution. We built web pages instead of blog content, corresponding email campaigns, paid distribution, and social collateral.
The data said it was a bad move. There was no search volume for these hyper-specific audiences, social engagement on the posts dropped, and the ad targeting was so specific that reach plummeted and CPCs rose.
The results: far less reach, far fewer clicks, far less traffic, and early on, fewer leads than we were used to. Every signal pointed to quitting the strategy. But prospect calls started referencing finding the company through AI, and a slow but steady volume of opportunities in these verticals started coming in. We stuck with our gut despite what the data was telling us.
Reviewing deals from the last six months against the prior six, MQLs sourced from organic search, AI referral and website conversions are up 62.5%. Estimated deal value tied to those sources is up more than 300%. We’re also seeing real signed contracts from this channel for the first time in a while. The number of deals that have fully closed is still catching up, since a lot of this pipeline is newer, but the deals coming through are bigger and further along than what we had before.
By letting the metric dictate the decision, we put ourselves in conflict with another metric. More search content, fewer search clicks. Smaller audiences, higher CPCs. Sometimes the math still works out. It rarely accounts for what actually matters.
The Signal That Didn’t Have a Dashboard
Here is the part worth sitting with: The pages that no analytics tool would justify building are the ones prospects are finding.
We built them for audiences too small to register as search volume: cold-chain operators, wine distributors, cannabis warehouses. A keyword tool looks at those and reports nothing worth chasing. What it cannot see is that when someone asks an AI assistant which warehouse management system handles cold-chain compliance, a page written specifically about cold-chain compliance is the one with something to say.
We did not have a report telling us this was happening. We had prospects on calls saying it out loud. That signal arrived in a sentence during a discovery call, not in a dashboard, which is exactly why a metrics-first process would have thrown the strategy out before it had a chance to work.
Search volume measures how many people type a phrase into a box. It was never a measure of how many people have the problem. For most of the last decade those two things were close enough to treat as the same number. They are drifting apart now, and the tools haven’t caught up.
Some Things Don’t Show Up in the Data
The data didn’t say to keep going. It said to quit.
What made us stay the course wasn’t ignoring the numbers. It was refusing to let one metric veto a decision when a different, harder-to-measure signal said otherwise. Real prospects, on real calls, telling us how they found us.
You already know this pattern from somewhere else in your business. Your best salesperson doesn’t send the most emails or make the most calls, and probably can’t be trusted to update the CRM. Nobody manages relationships better. Every metric you have on him is unremarkable. You would never trade him.
The brands, the operators, and the athletes who win in a world obsessed with over-optimization aren’t the ones who throw out the data. They’re the ones who know when a metric is telling the whole story and when it isn’t, and who trust their read on the difference.
“It’s been awesome catching up. Want to grab a nightcap somewhere else?”
“I would, but it’ll affect my sleep score.”
