Here’s a lesson about dashboards:

When I was 17, I was driving my '93 Camry from California to Salt Lake for school. I was somewhere in the Mojave Desert, windows down, singing "Hotel California" at the top of my lungs, when the music suddenly cut out.

A second later, every light on my dashboard lit up at once. Check engine. Oil pressure. Brakes. Tire pressure. Hazards.

Just as the power steering gave out, I pulled to the side of the road and turned off the car. It never turned back on.

Twelve hours of sweating in the desert heat later, we found out it was the alternator. By the time it went out, it took the cooling system with it, and the engine blew a head gasket. The car was toast. The warning lights had told me something was wrong, but not soon enough, and none of them pointed to the actual problem. Thankfully, my parents drove out, towed me home, and consoled me with a Double-Double and a milkshake.

I think about that day a lot because it's exactly what I see inside businesses. Every dashboard is lit up. Costs are rising. Retention is dropping. Customer acquisition cost is climbing. But when revenue or margin breaks down, none of these dashboards tell you what’s actually going on. They show you symptoms, not the failures in the system.

At 28, when I stepped in as the CMO of a CPG brand, I was taken right back to the day I said goodbye to that Camry.

They had just dropped from $10 million a year to $7.5 million. Amazon had launched a white-labeled equivalent of their flagship product, and a competitor had pushed them out of their second-highest-ranking spot.

The warning signs weren’t in any of their reports. They were trapped in raw, unstructured datasets: the exact kind of data most businesses think they have to wait until the “Predictive Intelligence” phase to use.

If customer satisfaction is tracked at all, it usually falls under CSAT, NPS, or reviews. But a single score is static. Someone who is a promoter today becomes a detractor the second they feel ignored. Years earlier, while manually coding 15,000 surveys three times a week at 1-800 Contacts, I learned that the real intelligence comes from connecting what customers are doing to how they feel and exactly what makes them choose a competitor.

Doing that by hand at scale is virtually impossible, which is why machine learning is foundational for this work. But at this CPG brand I now had to untangle, none of this was tracked.

Far from having a unified data lake, every business channel reported on separate proxy metrics. Finance reported by closing out the month. The ad agency measured directly out of the platforms. And the SEO vendor told me to my face, "It's your responsibility to figure out how traffic converts into a customer. I just get you clicks."

That was on top of an internal team handling six social channels, two social media managers, a full-time video production team, and sales still in free fall.

Every vendor and internal player claimed “all systems normal,” but the real intelligence that tells you what’s working (and importantly what’s not) was completely missing.

As I worked to collect that data and identify the gaps in the customer experience, the picture became clear: nearly a quarter of their revenue had vanished because their customers were more loyal to reviews and rank than to the brand itself.

When I pressed on what each team member contributed day-to-day, the chain was: one social media manager requested content from a contract writer, who sent it to an offshore design team, who sent it to a different contractor, who loaded it into Hootsuite to automate the posting schedule. Four handoffs, and not one of them was scoped to answer what any of this content did for revenue.

So we tested it.

We shut down organic Instagram, organic Facebook, and organic YouTube. Revenue stayed flat.

That meant the video production work, the social media managers, and the contractors were all expenses with no measurable return. Given that people stopped buying once the product rank slipped, we knew brand equity meant very little at this point either. The people who kept buying were buying because the product got them results and they didn’t have a reason to switch.

Unraveling the tool stack was next. They were paying for HubSpot to manage emails, but most of their sales flowed through Amazon. They weren't able to track customer attributes because they didn’t have an accurate record of customers. They weren't segmenting their marketing outside of keywords behind products. They didn't have enough data beyond shipping addresses scraped from Amazon to even engage with them. The tools got cut.

Then came the ad spend.

They were putting roughly $50,000 a month into Facebook. The CEO had suspected it wasn't working, but the agency kept telling them that if they shut it off, all remaining revenue would disappear. That common warning was doing exactly what it was supposed to: hold the agency accountable.

We turned Facebook ads off. The revenue held all the same.

That was the final nail in the coffin. Sales had compressed, but nothing the company was doing meant more or fewer sales. For the team that remained, this was a dream baseline.

Between removing redundant team members, cutting tools that were not useable with the current distribution strategy, and stopping inefficient ad spend, we opened up nearly seven figures of runway.

That’s an important takeaway. We didn't need to keep throwing good money after bad money in the name of scale. We were able to increase profits with straight-forward tests and some honest, albeit uncomfortable, conversations about who was responsible for what.

Most businesses have this same opportunity. The bigger the organization, the bigger that runway.

Inside every company that's been operating for more than a few years, there's what I'd call captive operating expense (captive OpEx): costs that have become so routine, they’re shielded from scrutiny by "that's how we've always done things." Make a note of any time you hear that phrase.

We've seen captive OpEx across tools, integrators, continuous deployments, cloud providers, and sometimes full teams. It’s often the usual suspects:

Two internal projects from separate teams, meant to solve the same problem, are building in complete isolation duplicating each other’s work.

Multiple agencies, all competing for attribution and credit.

Duplicate tools that offer the same functionality: one purchased by marketing, another by operations, and sometimes yet another selected by PMO or IT.

Subscriptions that survived the exit of the person who bought them, now buried in an automation stack that came with no documentation, where turning anything off feels riskier than just paying for it.

None of these get flagged in a standard marketing audit because each one has a rational business case attached to it, or they disappear in the gaps between business silos.

Either way, the result is that 15 to 20 percent of your operating expenses are going to things that either don't work, overlap with something else, or solved a problem that no longer exists. That range is the average we see across our own client engagements, spanning SMBs doing less than $10 million a year, mid-market companies, and Fortune 15 enterprises alike.

At scale, the structure gets more complex, and captive OpEx scales with that complexity. Like the alternator behind all those dashboard lights, it's a small fault that can end up costing you the whole engine.

When we talked about why strategic initiatives fail, the core argument was that tracking metrics in isolation is like watching the check engine light without connecting it to the oil pressure, the battery, and the alternator. The lights go off, but none of them point to the problem. You have to track how your own metrics connect to each other to make sure you're investigating the right initiatives and to get to the root cause.

Those projects, tools, or legacy processes that are causing problems are often the hardest to see, because most existing work has no metrics for observability beyond abstract OKRs or project statuses inside Jira with deadlines that keep getting pushed out because of a new dependency that's discovered. They become just "the cost of doing business."

But here's what the data confirms. The CMO Survey (Fall 2024) found that only 50% of purchased martech tools¹ are being used in operations, down from 56% just six months earlier.ᶜ Gartner's 2025 Marketing Technology Survey paints an even bleaker picture: only 15% of organizations qualify as high performersᴳ who meet strategic goals and demonstrate positive ROI. So the expensive tools aren’t enabling even the teams who do use them.

That's why it's important to look not only at sales or marketing ROI, but at the return on every system you implement. It's why we always evaluate upfront what impact the systems we build will have on profit, and on the team's ability to generate more revenue. Even when we're building machine learning, it should unlock insights that help the team move faster and target the needs of your customers with more precision.

When you look at how much of each tool's capability is being used, it's less than a third. The licenses are priced for the full feature set, the team needs one or two features from this tool, and one from another, but the renewals are automatic. Vendors count on nobody having time to audit the overlap between their data enrichment tool and your CDP or tag management software that all do the same thing.

All of this shows up as SG&A² or operating expenses that, for some of our clients, have represented millions of dollars in missed profit. When we first met with a CIO at one of our clients, they were unintentionally spending $10 million in duplicate data and tools alone, representing half of the IT budget.

Then there’s the matter of attribution tools. These are like having a computer for your car: they spit out all sorts of codes, but no matter what they can tell you about an o2 sensor or your braking system, it won’t tell you your head gasket is about to blow.

Every attribution platform we’ve seen can't track the real customer journey. Companies spend significant money on platforms like Northbeam, Triple Whale, or an entire customer data platform, all built to track the path a customer takes from first seeing your ad to making a purchase. They promise to track every dollar, but they don't disclose that they're beholden to ad blockers,³ cookie consent managers,⁴ and privacy laws that make the data incomplete. If they were fully transparent, they’d tell you the data behind every trend line or stat is directional.

Part of this, as we’ve discussed in our article about marketing measurement, is that not every sale starts and ends online. So a pixel firing means someone saw an ad and visited the site, but it might have been the conversation with their colleague that made them buy. And even the “how did you hear about us” surveys won’t give you the detail of the moment the prospect decided they needed a solution, how they went about researching it, all of the channels they interacted with when comparing you, and what ultimately made them decide to buy. That qualitative data is the biggest miss in all of these platforms and you pay for not having it twice: once to the platforms that tell you quantitative data is the extent of data-driven, and secondly in rising CAC and declining profits.

To see it in practice, consider the prospects of our data center clients:

How does a CTO actually buy a customer data platform?⁵ The conversation starts with a peer. Another CTO mentions what they're using. That happens on Reddit. Or now, people are asking AI. The CTO reads reviews, visits the company's website directly, searches for alternatives, finds an affiliate review or an organic search result, and then maybe clicks an ad. That entire journey started in a channel you don't own and can't track. None of it shows up in the attribution platform.

When it comes to physical products sold in retail, as we'll look at more closely in a moment, that situation is even worse for these attribution tools. The sale is fulfilled in person and may never touch a digital platform, especially if it came from a recommendation from a friend.

These tools are popular because they give you a better understanding of what’s happening with your budgets than looking at an ad or email platform alone. But, without behavioral data, your teams may over-index what works at one stage of the customer journey, while killing the pipeline upstream.

But marketing isn’t the only department where software contracts lock you in before they can prove value.

A company we work with handles logistics for companies like Amazon and UPS. Before we arrived, their capacity reporting was calculated intermittently and relied on hours tracking with varying reliability. That meant HR struggled to know when to hire, and sales didn't know whether they'd have the team available to fulfill. The result: they were turning away $20 million a year in new business because they couldn't confidently say yes.

In the rush to solve this, they purchased a middleware tool, a connector that ties systems together but doesn't do anything on its own. A $65,000 a year contract locked in for three years. That's multiple six figures before they could even use it. And that's before they found out they still needed a team to come in and implement it. This is one of the big realities about platforms like Salesforce: the total cost of ownership is always more than the seats. You still need engineers before the system can work in your business. Your team is going to spend significant time cleaning data, customizing the system, and hiring more vendors to build on top of it. It doesn't work out of the box.

By the time we finished our initial audit, we found that the same functionality they had just selected a dedicated tool to do was already integrated into the cloud platform used, which would have only cost thousands of dollars a year to run. They wouldn’t have had to keep paying for a software license and if they’d ever migrated their cloud, that functionality would have ported into any new system they’d choose.

This happens often because the sales team that sold the middleware didn't understand the company's business model, let alone how it earned profit. They were focused on their own quotas and how easy the software was to use. Even if they knew you could do the same thing with tools you already had, it’s not in their best interest to say so. A clear misalignment of incentives.

Buying a tool you won’t need for a while, or that requires work from a vendor before it can be used is the same concept as investing in several years of product inventory before you have any buyers. You want a hungry market before you stock up. You want your data systems clean and ready before building AI applications. All because you want to make sure you have a high likelihood of getting a return on your investment.

For teams outside of finance, this is called sequencing risk, and it shows up almost everywhere in business, especially as they scale.

But just as difficult to see as sequencing risk is overfitting. Building too much for simple solutions, or building in one area to compensate for lack in another.

How many dashboards have you been pitched or purchased?

Dashboards that seem to speed up your team but don't improve results. Some aren't actionable enough for team members to use because they're roll-up reports, so the underlying data gets dumped into a spreadsheet anyway. Others never seem to end because they’re at a level of granularity that attempts to track every tactic on every channel, built with so much cognitive overload that your team feels genuine anxiety opening them up and again ends up just going to Excel anyway, because they prefer pivot tables. It's a design flaw pushed by most Tableau or Looker templates that think more metrics means more insights, when they are missing the qualitative insights that help you connect more with your customers.

Just the same, in workforce management, it really doesn't matter how many people call out sick if you would have ended up sending people home early anyway. I'd rather know how many people I need to call in to meet the SLAs.

The metrics on these dashboards are a problem in and of themselves. In all honesty, who cares how many likes, comments, or shares you have? If sales aren't going up, I'd much rather know if we're getting impressions from the right audiences and what makes them act or ignore what we do.

When it comes to internal operations, instead of just knowing the line item of how much my tools cost, a report on how much of them are being used and by who would give me a monthly hit list on which subscriptions to cancel.

Proxy metrics lead to data quality issues that lead to poor strategy and worse outcomes.

One of our clients had 170 tool subscriptions and proposed 56 new initiatives internally to address issues their current dashboards couldn’t answer, even though those automated reports had cost them millions of dollars over the years to build between multiple external teams, dozens of internal team members, and hundreds of thousands in cloud costs.

As we covered in our article on why strategic initiatives fail, the underlying problem was that all of these dashboards were built with the wrong metrics in the first place. When it came time to answer why customers were canceling and why their customer acquisition cost had doubled, all of the ad-hoc work the team normally did to figure out what was happening and why suddenly became urgent. If it had been automated to surface cancellation warning signs before customers called in, they could have prevented tens of millions of dollars in churn.

This is a legitimate case for building out automated reports that tie in metrics, marrying leading to lagging indicators. Most importantly, tying each to qualitative data gives you a sense of which customers are canceling and why. An early warning system.

That's exactly what we built at Signs.com. After finding out that only 8% of their customers purchased a second time, we really only cared about customer attrition within that 8% segment because they represented 80% of the company's revenue and all of the profit. Finding out exactly why they were leaving was as easy as using a tool that predates the internet. We picked up the phone and called them.

We found out that the person who had originally set up the account was no longer with the company, and by the time they needed new signage, the new hire called a different vendor. Finding that out took minutes.

The tool we built was a custom attrition score that predicted attrition based on the individual customer's buying cycle, so that the business development reps knew exactly who to call as part of their ongoing retention efforts. We also added the simple behavior of ensuring they had two to three contacts within each account to preserve continuity if one of their buyers leaves.

As for those 56 initiatives proposed by our other client? Only four of them would have actually answered anything that ties back to revenue or profit.

With the right guidance, the team was able to focus on revenue generating activities with full focus, instead of spreading themselves thin in the name of productivity.

And it’s important to note, this wasn’t their fault. They had the right orders from the top, and each person worked hard to try to solve complex problems. The issue was they were all working in silos focusing on their own team’s metrics, not realizing they would have worked against each other.

Take a moment to identify whether your team has the correct metrics in place.

Do you know how your KPIs tie to generating revenue or improving profit?

Does your team track proxy metrics such as ROAS, CSAT, NPS, or ESAT instead of true marketing ROI or employee retention levers? What do each of those metrics tell you?

Does your team have adequate qualitative intelligence to answer why your metrics change on specific dates and quarters beyond the economy or seasonal demand shifts?

Keep those answers in mind as we look at how to recover captive OpEx.

Hearing about these issues for the first time often feels uncomfortable. There's always concern that if I reduce my budget this year, maybe I won't have as much next year. There's concern that someone might think I've made a mistake. All of that is normal, but it allows the waste to continue.

Gallup's research found that roughly 70% of decisions are based on emotional factors and only 30% on rational ones.ᴺ That applies to your customers, but it also applies to the decisions being made inside your company when selecting vendors and tools.

So what happens when someone in the organization is sitting on these types of commitments?

Besides the mix of hope or fear that an engagement will go well, there could be hundreds of thousands of dollars for tools or agencies that duplicate each other. It’s almost certain to happen in organizations as small as $10M in annual revenue or larger. There are often two types of managers when they learn about these overlaps.

The first just keeps paying it. Canceling means admitting the decision was wrong, paying cancellation fees, and having to inform the board or the executive team that the company will get zero utilization out of money that's already been spent. That can understandably feel worse than just riding it out. There could be a story about how that tool was no longer the right fit and something better came out that will allow the team to consolidate. That manager gets to look like a hero. That type of obfuscation happens every week on all-hands calls.

The decision to pay for the tool was emotional in the first place, hoping it would improve the quality of life for the team, improve results, and improve recognition. All of that's fair. The intentions were good. And the intention not to say anything isn't necessarily malicious, either.

Sure, the sales rep was convincing and likable and just doing their job, so the deal got inked. Gartner's research on B2B buying confirms that most digital content from suppliers is neither memorable nor meaningfully valuableᴿ, which means the pitch that got someone to sign a three-year contract was probably more persuasive than the product itself.

The other manager catches the waste early. They package the decisions that led to the initial agreement, the cost, the findings, what could replace it now, the benefits of doing so, and they map out the long-term savings and opportunity cost. Because long-term profitability for the company is more important than short-term saving face. And we all know how bringing solutions instead of only pointing out problems comes with more political capital than letting someone else find the waste later.

Even Steve Jobs learned this lesson after his famous firing at Apple in 1985, coming back with a fresh perspective when stepping into the role as interim CEO in 1997. Where previously, he had directed the company to scale up its product mix resulting in massive company failures, Apple was now on the verge of bankruptcy as it planned to “test” dozens of products in the market. Unable to explain to his friend what the business was planning, he returned to the office telling the team to cut all but four of them.

It’s a critical lesson of handling downward inflection points in the S-Curve of growth. But we don’t have to wait for revenue to drop to apply it.

That second type of manager that proactively acknowledges when something isn’t working and winds it down is rare. But it would be unfair to blame a lack of character. The psychology at play is almost completely invisible inside organizations. We cover this in our series about perverse incentives in depth.

As a closing note for what to learn from turnarounds, let’s briefly look at a business that went from on track to $1B in revenue, to nearly filing for bankruptcy all in a matter of 6 years.

Many of us have seen problems inside our own organizations that didn't get noticed right away, didn't seem like a big priority at the time, or otherwise just got lost somewhere in a backlog that someone meant to "get to" eventually.

That's exactly what we encountered when we met with a senior leader at a publicly traded mattress company in 2023. Their fulfillment was in retail: Costco, Mattress Firm, and even their own stores, in addition to web sales. After initially seeing a boom in revenue during the first few years of this expansion, something was bubbling under the surface that wouldn't be caught for years.

Let’s look only at what all of us can see publicly:

Just as the company became one of the largest advertisers in the entire mattress category, spending roughly 25x the digital ad budget of the category leaderˢ, revenue was in free-fall. They posted a net loss of $89.9 million in 2022, followed by $120.8 million in 2023.

Any CFO reading numbers like that would understandably be excused for a moment to settle their nerves with a glass of whiskey.

On paper, they had done everything right. They expanded into retail channels. They addressed problems that came up with plans to fix them: updating the website, changing the ads, testing different channels, running promotions. Everything teams are trained to do.

They even expanded their product mix with the intent to reach different segments of the market.

But the results never caught up to their effort. The decline could be traced back to issues that had started simmering four years prior.

We traced it to a common structural problem we've seen with companies running multi-channel distribution. The sale was being fulfilled in a channel that wasn't tied back into the attribution platforms. Once you scale into major retailers, attribution gets much more complicated than checking Google Analytics or an off the shelf tool. Pixels don't fire in a third-party point-of-sale system. And even when you do import that POS data, when the wife sees the ads but the husband pays with his credit card, that sale never gets counted back into the ad platform.

None of it shows up as Return on Ad Spend⁶ (ROAS) in any digital platform, even if ads are the first place customers learn about the brand.

The marketing team's focus on ROAS had led to changes to their website and content that inadvertently diluted their positioning. They went from focusing on the unique selling points of their technology to simply saying "a better night's sleep." Cooling material. Free shipping. Features instead of benefits. The messaging that had once differentiated them from every other mattress company had been replaced with the same language everyone else was using.

And with AI tools that will “audit your competitor” all over LinkedIn and YouTube, more businesses are in for a rude awakening for what happens when all brands look the same. Not every industry has a sort by price button, but the phenomenon of a race toward the bottom has already wiped out equity in brands like Airtable who just got under a 3x valuation.

Positioning, just like your own reputation, can be destroyed in a matter of seconds.

Their breakout commercial had opened up the entire category of direct-to-consumer mattresses by underscoring the pain points of traditional mattress technologies. But new entrants were easily able to leech off of all the work they had done to educate the market. When customers become aware of their own problems and the solutions to solve them, it always bolsters competition.

Their creative misses directly led to their operating expenses growing disproportionately to their revenue. This type of decline is difficult to predict except in very specific dashboards that most teams never expect they even have to build. Yet, everyone should. With that data being clear and present, the underlying issues impacting their numbers compounded as they were focused on putting out the internal fires shown in their metrics.

Many reading this article will remember the "adpocalypse" that happened when Apple's privacy changes impacted advertising on Meta. But unlike many businesses, during the pandemic they got a brief reprieve. All sales channels outside of the web were paused, consolidating back to their website.

At the same time, stimulus checks provided more discretionary income while more people wanted to feel more comfortable in their homes, where they were now forced to spend nearly 100% of their time. That allowed revenue to continue to go up, even as their creative messaging continued to decay.

All of the changes that had been made unintentionally diluting their positioning set them up for a rapid decline. Once stores returned to full operation after 2021, the structural cracks were exposed. Customers went back to shopping in stores where they could try out the mattresses they wanted for themselves, and didn’t rush home to buy it online.

All of this sounds like a nightmare, but the solution to turning them around was simpler than you might expect. Since we were only acting in an advisory capacity, we can’t take credit for anything that went right or wrong, but we delivered the recommendation to their leader to begin restoring their positioning across each customer touchpoint and unify their tracking into a single system. It would only take a few months, including aligning the website, the ads, the brochures, and the salespeople inside the stores.

Sometimes, when we identify these solutions, our stakeholders are hesitant. They might feel afraid to take this information to their counterparts in marketing or sales because it would feel like telling them "your baby is ugly."

This is a real hurdle. The data is there. The warning lights are on. But sometimes organizational structures can make it feel intimidating, if not impossible, for anyone to act on what they're seeing without the fear of backlash.

Fortunately, that senior leader did have the courage to deliver the message.

Their entire media strategy over the past year and a half now focuses on what makes them unique: the benefits of their proprietary technology. That positioning is actively being restored. And the fear that almost kept the message from being delivered? It turned out to be a misconception. It wasn't even true in their own organization.

But in other organizations, it's more often that these issues persist because of a phenomenon that ties back to a much deeper set of incentive structures, both external and internal, that we've written about extensively in why your vendors, agencies, and even your own teams are structurally incentivized to protect the status quo.

For your business, there are warning signs of this phenomenon too. They may not light up because there's almost never a bulb wired in your reports. Much like my dash couldn't have predicted I'd be stranded in the desert on the side of the road for half a day in the scorching sun, your business has the same blind spots. The question is who's coming to get you when your business is pulled over on the side of that road.

Unlike a standard marketing audit, Fourth Order Intel's diagnostic identifies 15 to 20 percent of operating expenses as recoverable within 30 days. The average engagement pays for itself in the first month.

Notes:

¹ Martech tools: marketing technology software used for analytics, automation, CRM, attribution, and campaign management.

² SG&A: Selling, General, and Administrative costs. The overhead that includes salaries, rent, and operational expenses.

³ Ad blockers: software that prevents tracking code from running on a visitor's browser.

⁴ Cookie consent managers: the popups that ask whether you agree to be tracked across websites.

⁵ Customer data platform: a tool that unifies all customer records from multiple sources into a single profile.

⁶ Return on Ad Spend (ROAS): revenue divided by advertising cost.

ᴺ Gallup, "Customer Brand Preference and Decisions: Gallup's 70/30 Principle". Gallup's own research finding roughly 70% of decisions driven by emotional factors.

ᴿ Gartner 2024 Survey. “Boost Sales by Addressing the Emotional B2B Buying Journey.”

ᶜ The CMO Survey, Fall 2024 Highlights and Insights Report, Duke University Fuqua School of Business, Deloitte Digital, and the American Marketing Association.

ᴳ Gartner, "What High-Performing Martech Teams Get Right," 2025 Martech Survey.

ˢ Based on Fourth Order Intel's own analysis of category digital ad spend data (Statista) versus their ad spend during our meetings.

When I was 19 and in college, I got a phone call that my dad was in the hospital. He couldn't keep food down. His blood pressure was so high you'd be surprised he didn't have a heart attack.

When I got there, they were running everything. Blood pressure, A1c, fasting glucose, kidney function, lipid panel, weight. They checked his eyes. They checked his feet. It was a cluster of measurements that, taken together, told a story no single number could tell on its own.

He was diagnosed with type 2 diabetes.

There's a reason doctors look at symptoms as a system before they make a diagnosis. Blood pressure alone could mean a dozen things. A1c alone could mean a dozen more. But blood pressure and A1c and fasting glucose and kidney function, measured together and tracked over time, narrow the diagnosis to something actionable. And even then, doctors still test before they prescribe. They don't guess. They don't skip to treatment because the first number looked bad. Prescription without diagnosis is malpractice.

Your business works this way too. If you've been running one long enough, you'll recognize a similar pattern.

When your cost to acquire a new customer starts rising, it could be an audience problem, an offer problem, or a bigger external trend that has nothing to do with your current tactics. A single metric can't tell you which one. But if your costs are going up and your cancellations are too, now you might have a product problem or a competitor problem.

And if you're not tracking the impact on contribution margin, you're going to get a stern chat with your CFO in the same tone my dad's GP gave him at his six-month checkup. Profitability can be down even when marketing is reporting higher ROAS than ever. Both can be true at the same time. That's the problem. Your metrics need to track profitability after all expenses are paid.

This happens with tech solutions and operations too. Digital transformation initiatives, martech implementations, platform migrations. They get purchased and configured at significant cost but end up with low adoption. Automation speeds up a process but results don't improve because the bottleneck is somewhere else. The symptom looks different in every department. The structural cause is the same: one metric, tracked in isolation, treated as the whole story. And a guess with a $3 million budget behind it is expensive malpractice.

In 2000, Harvard Business Review published a now-famous line: "The brutal fact is that about 70% of all change initiatives fail."¹ McKinsey's own transformation surveys back that up from the other direction: across 2012, 2014, 2016, and 2018, no more than 30% of transformations succeeded, and in their most recent survey, only 16% did.² And in 2025, MIT's NANDA initiative published findings that 95% of generative AI pilots delivered zero measurable impact on the P&L, despite $30 to $40 billion in enterprise investment.³

The failure rate is accelerating. And the reason is the same one that makes a bad doctor dangerous: single-symptom diagnosis leading to premature treatment. Teams build in isolation because that's how the work is divided. Each one measures the metrics assigned to its own function. And initiatives scale before the teams understand how their metrics impact the ones outside their dashboards.

Here's a case your team should study. These mistakes are more common than you think.

At Signs.com, the team believed 30% of their customers were repeat buyers. That number had been sitting in their reports for years. It came from Google Analytics (Google's web analytics platform, which tracks website visitor behavior), which was looking at a rolling month-long window of returning visitors.

Based on that assumption, they built an entire growth strategy around personal projects and gifts. They found new vendors. Designed new landing pages. Ran competitive pricing research. Launched 20 new products targeting people who were buying for personal projects and gifts, figuring that if 30% were coming back, even losing a little on the first purchase would pay off on the second.

It consumed the entire bandwidth of their marketing team for the better part of a year.

But here's the kicker: that 30% repeat purchase rate was wrong.

When we looked at past purchase data, the all-time repeat purchase rate was 8%. Google Analytics was measuring return visits in a 30-day window. The actual purchase history told a different story. At 22 percentage points, that gap isn't small. And most CRMs wouldn't have caught it either, because they aggregate single-point metrics the same way.

They'll tell you averages. They won't tell you that the average blends together a customer who buys every month and a customer who bought three years ago and never came back. Every forecast they had built on top of that number relied on false assumptions about acquisition costs, repeat customer behavior, and even the average order size. The math all looked great on paper. Then December 2017 hit and they posted a $100,000 loss on their P&L.

Those 20 product launches added almost no new revenue that year. Most of them lost time and money. And this was a company with enough search engine credibility to rank number one on Google for virtually any product they launched. Ranking first didn't save them because the underlying assumption about who was buying and why was wrong.

When we segmented the customers, the picture changed completely. The 92% who had only purchased once were those personal buyers and gift shoppers. The 8% who kept coming back were franchise owners buying signage every single month. The entire growth strategy had been built around the segment that would never return, while the segment that was actually driving repeat revenue had been invisible in the averages.

Only 17% of senior marketers say their firm tracks lifetime value well.⁴

Every business has a customer lifetime. Every business needs to understand what that lifetime is worth and how long it takes to recoup the cost of acquiring that customer. But customer lifetime value isn't the only place this thinking applies. Every initiative in a business should be evaluated against payback period or total cost of ownership to understand whether it's actually going to impact the bottom line. Often it doesn't. And when teams don't know this, initiatives are dead long before the kickoff meeting.

Most initiatives fail at inception. When cascading effects don’t get mapped ahead of implementation, execution gets the blame. But, sometimes, upstream, a few metrics out of place pushed a strategist to focus on something other than the root cause and took the rest of the chain with them.

They’re looking at the surface-level problem or opportunity because it is a quick-win or an emergency fire drill.

If we went by volume of orders, this customer segment made sense. But when we got outside the proxy measurements and into actual profitability by customer type, the story completely shifted. The growth strategy was wrong because it was built on a surface-level assumption, and no quality of execution could have fixed that. The real answer was three layers deeper.

Unfortunately, this happens in every organization as it grows, no matter how senior the team. Teams, at times out of necessity, build within a silo and don’t realize how things are going to connect, what other information they need, or even what metrics really matter when they are prioritizing the needs of their own department.

We’ve seen organizations with 56 planned initiatives, submitted to PMO from each business unit, where only 4 were investigating what drove costs up and retention down. The rest were consuming budget and bandwidth without moving revenue or profits, but instead were one-off reports for campaigns or projects that would have benefitted from a one-time structure so each new initiative could be compared from the same baseline. But, different teams had proposed the same project using different language and KPIs. Their metrics were never connected to the rest of the business.

This is a structural problem, not a people problem. When every department measures something different and the numbers only get reconciled in an emergency, initiatives get built on assumptions that were never validated. We’ve written extensively about where this waste accumulates and what it costs.

A metric can look right and still mislead you for a lot of reasons. Sometimes the underlying data in a source is broken. Sometimes the system is missing the full context of your business. Or other times, the metric itself is a proxy that doesn't actually track revenue performance.

We see this in OKRs where teams measure the number of things they'll get done but not how any of it impacts the bottom line. At that company, it was a mix of all of these.

This is the foundation of what we call Paired Metrics. A single metric in isolation tells you what happened. It can't tell you why, whether it matters, or what to do about it. Only the relationships between metrics can do that, and which relationships matter changes depending on where a customer is in their journey with you and what the market is doing at that moment. The pairing that explains a first purchase is not the pairing that explains a renewal. A system that measures two customer stages the same way will be confidently wrong about one or both of them.

At a publicly traded company we consulted for, organic traffic (unpaid search) dipped by 2% in a single quarter. Executives panicked. They had just spent over a million dollars restructuring their website. They circled the wagons around their website team, their strategy team, their marketing team, and their technology teams, trying to figure out if the numbers were accurate, what broke, and what they could do to fix it.

When we looked at the full picture, paid advertising had increased by 300% that same quarter, and total website traffic was up 800% year over year. The advertising was driving a massive wave of new visitors, and it had only slightly reduced the share coming from unpaid search. That 2% dip was noise. But because it was tracked as a single number without pairing it against the rest of the system, multiple teams spent weeks investigating a problem that didn't exist while a million-dollar initiative stalled.

Looking at connected metrics over time helps determine whether a change is significant. But even when it is significant, you still need to answer why. Often, answering that question requires qualitative data that links the behavior to the motivations of the market. What are customers actually saying in support tickets, cancellation forms, and reviews? What are they telling each other on Reddit? If you don't have this in your business, it's the single biggest opportunity to add context to your reports and get out of dashboard hell.

Most marketing teams are handed Return on Ad Spend as their North Star, and the platforms reporting it are the same ones selling the ads. Operations teams and IT teams, this is for you too: the next time your marketing team asks to build yet another ROAS dashboard, keep reading.

ROAS, which is calculated as revenue divided by advertising cost, can easily be manipulated by ad vendors that have the wrong incentives. Sometimes they'll reallocate budget toward remarketing audiences at the bottom of the funnel to make ROAS look higher at the expense of bringing in new leads and prospects six months from now. Or they'll offer a discount that lowers CAC but also lowers lifetime value at the same time, because now you're acquiring customers that only want a discount and will continue to only buy when one is offered. That's a direct hit to your margin.

Most teams that want to improve their ROAS immediately reach for attribution software. Platforms like Northbeam and Triple Whale, or a full customer data platform, will measure precisely whatever you point them at. That means they rely on your system of metrics. And if your metrics aren't tied to revenue, the risk becomes buying more measurement precision before you know that precision is accurate.

Accuracy requires organized data and a way to read the qualitative signals that explain why a number moves. The biggest pitfall in media management and attribution is precise measurement of the wrong metrics instead of focus on the right messages. Pulling your sales leads and traffic metrics into a single report you actually read would do more than another layer of tracking.

Consider a 5:1 ROAS. That means you generated $5 in revenue for every $1 spent on ads. Sounds great. But that number doesn't account for your cost of goods, your margin, your return rate, your cancellation rate, or whether those customers ever come back. At one company with a 20% product margin, a 5:1 ROAS meant they were giving the product away for free after manufacturing, shipping, and platform fees. The agency touts success, but finance knows the truth.

Here's where this gets real. At Old School Labs, where I was the CMO, we had two products that told opposite stories depending on which number you looked at.

Product B had the healthy ROAS. It recovered the cost of acquiring a customer in four months.

Product A cost twice as much to acquire a customer, and on the first purchase it came in below 1:1, meaning we spent more to make the sale than the sale brought in. Any team optimizing for ROAS would have killed it immediately. It looked like a loser.

Product A recovered its acquisition cost in two months. Half the time of the product that looked twice as healthy on the dashboard, because those customers came back on a much shorter cycle. They also stayed around three times longer.

Over the life of a customer, product B returned a modest amount of margin. Product A returned orders of magnitude more. At the cohort⁵ level it was the most profitable segment in the entire business, and cutting it would have taken a large share of the company's profit with it.

But even tracking these paired metrics wouldn't have been enough. We needed to understand who was actually buying product A. They were older men who had started noticing their energy and their strength drop off. The obvious play for a supplement company is to go after younger guys who want to be more fit, or the weight loss market, which were two of our other biggest cohorts. This entire segment would have been invisible if we had just cut the campaign with the lowest ROAS instead of investigating who was actually buying and why.

That's why qualitative data matters. Attributing performance to just channels or individual tactics would have missed this completely. Sometimes the message matters more than the metrics.

Let me be clear: none of this justifies a low ROAS. The point is to stop looking at any leading metric in isolation and start looking at everything as a system. This is what will enable your marketing teams to hold agencies accountable. It's also what will prevent your IT teams from getting bottlenecked by long, expensive implementations that only add to your operating expense at the cost of your margin.

Your acquisition cost, your lifetime value, your payback period, your cancellation rate, your margin by customer group. When those numbers are paired, the real story shows up. When they're not, you're making million-dollar decisions on a single data point. And when you add qualitative data to the picture, you find the segments that no dashboard can surface on its own.

Without all of that, most CFOs would rather write a check to one of Zuckerberg's charities than keep spending on the platform.

Everything we've talked about so far has been in the context of customer acquisition. But the same pattern shows up in operations, and the consequences compound just as fast.

Let's look at an example from team resources and workforce management. At 1-800 Contacts, the chat support team was handling two simultaneous conversations plus email. Volumes were high, queues were long, and the team wasn't meeting their SLAs. The obvious metric said the problem was capacity: not enough people to handle the volume. So the company offered overtime. The team was working 50 to 60 hours a week. They were burning out, and they still weren't meeting targets. Workforce management tried to incentivize with even higher bonuses per hour worked and the opportunity to earn more paid time off they could use in the future. The team was at risk of churning some of its top talent, and it would take a month before they could train anybody new on the floor.

One of my counterparts and I looked at the situation differently. The constraint we saw was cognitive load. An agent had to hold a happy customer in one window, a disappointed customer in another, and a logistics issue in a third, switching between all three continuously.

There is real research on what that costs. Sophie Leroy's work on attention residue found that when you move from one task to another, part of your attention stays stuck on the task you left, and it's worst when you were forced to leave that task unfinished.⁶ Earlier work by Rubinstein, Meyer, and Evans found that switching costs climb as the rules governing each task get more complex.⁷ Three different customers with three different problems are about as rule-complex as a shift gets.

Handle times ran longer because an agent was never fully present with the conversation in front of them. That’s the part a single metric would never show you.

The volume itself was also overstated. A meaningful share of it was the same customers arriving twice, through a second channel, because the first one hadn't answered them yet. We all know how that feels. Your package is missing, or you received the wrong product, or you simply ran out of something you need. It's urgent. You want it solved now. And the fear of never hearing back makes people tenacious. None of that shows up in a capacity metric. It shows up as more work, which reinforces exactly the wrong diagnosis.

The fix was specialization. Chat handle time dropped from 7 minutes to 3.5. Email handle time dropped from 10 minutes to 1. Overtime was eliminated. SLAs were met, and the team was happy. Volume actually decreased, because the duplicate contacts stopped.

That meant over $250,000 in annual costs removed without layoffs. The demands of the customers were met without any new hires or any new tools, and employee retention improved because people stopped burning out.

Capacity was the metric everyone was watching. It was the wrong one. Today, a large share of those chats and emails could be handled automatically with the right knowledge base behind them. But back then, one number tracked in isolation led to the wrong prescription.

Everyone can relate to that feeling of holding an Allen wrench in your hand, staring at the last step of the instructions written in bad English, then looking back at the packaging you excitedly ripped open only an hour ago, wondering how you're going to get all of this garbage back into the box to return it to Amazon.

Just as you were feeling relief about almost being done, having managed to get through every cryptic step, at times being tested on only your most elegant French, you realize the one piece that holds everything together wasn't in the box. That's exactly what cost one of our clients over a year of IT resources and hundreds of thousands of dollars before bringing us in to start everything over from scratch.

At an organization with an operating budget north of $100 million, the marketing team needed to track website analytics through Google's latest platform, GA4. Reasonable ask. But GA4 requires a connection to BigQuery, Google's cloud data warehouse, to access the full dataset. The team set that up. It worked, except for one critical problem: one of the most important data points, the session ID, wasn't available through the API. Google gates it behind their own cloud environment.

None of this surfaced before the commitment. The team was simultaneously transitioning from one data warehouse to another because they wanted something that would automatically scale with their data volume without having to manage infrastructure manually. Also reasonable. But their implementation vendor didn’t map the capabilities against the limitations before they started building.

The cascade: six months building out the platform. Three months designing the dashboards on top of it. Another three months before the dashboards showed them anything was wrong. It wasn’t until the project hit quality assurance that the original stakeholder asked why they couldn’t track data by session anymore. A year of work. Multiple teams involved. And the root cause was an assumption about an API that went unvalidated in week one.

Experienced product managers and engineering teams know you need to test small, early and often, before getting to an MVP and scaling to a solution of this size if you want to prevent these types of issues. But even when you're not building custom solutions, there are other Trojan horses that can sneak into your company and derail your strategy. Most of them start with a free trial.

There are a lot of middleware solutions and SaaS products that work as a band-aid for gaps like this. But none of them should ever be a substitute for doing the homework on how the underlying systems actually work, what’s possible, and what’s not. Most APIs expose all of their endpoints. They don’t force you to buy additional products just to access your own data. But some do. And if those dependencies go unmapped before the first line of code gets written, you end up with a year-long detour that was avoidable with a week of research. And you will be locked into a vendor because their terms and platforms don’t allow you to export your own data. They make you rent it instead.

While this goes a little deeper than paired metrics, it still illustrates the same point when applied to technology decisions. You're not just tracking the cost of a platform. You're tracking cost paired against capability, paired against dependencies, paired against time to value. The same principle that found the hidden customer segment at a supplement company and the division of labor fix at a contact lens company applies here: one number, tracked in isolation, led to the wrong prescription.

Before you start labeling me a Luddite or a deliberate contrarian out to rage bait for attention, hear me out: AI is extremely capable. It's true. Our entire company couldn't run at the scale it does without it. It's unequivocally been the most important invention since the internet... when used correctly.

But many of you are already seeing that AI doesn't fix broken data on its own. There are tons of SaaS companies and data vendors beating their chests on the idea that AI is only as good as the data you feed it. But much like treating diabetes, nutrition is only part of the equation.

Adding fruits and vegetables while continuing to eat candy and drink beer isn't going to fix your blood sugar, no matter how much you start running at the gym. Just the same, adding new clean data into your data warehouse for AI to look at isn't going to do much for your messy Salesforce objects that are disconnected, or your existing tables crowded with proxy metrics that don't track revenue and profitability. Because of context windows, throwing unstructured data at AI without a system to clean it or identify the features relevant to your business doesn't just overwhelm the model. It's one of the fastest ways to get your team reacting to advice that treats symptoms, not the system.

I don't know about you, but when it comes to my health, I'd much rather look to the doctor treating my whole system than the random YouTuber telling me all I need is a shot of pickle juice to enjoy as much cake as I want.

You're probably thinking: isn't that what Scale AI or similar platforms are supposed to fix? They're no different than any other SaaS product built to appeal to the lowest common denominator. They build out features they think everybody should be tracking.

But when it comes to commercial software, we always have to question the incentives. Consider how much of the research you encounter was paid for by a company with a product to sell. The same goes for the safety committees inside large organizations, and for the industry bodies that publish the best practices everyone else adopts. The people funding the study, staffing the committee, and writing the standard all have something riding on the conclusion. When a company's own product is the thing causing the problem, the incentive to go looking for that is zero, because finding the root cause would mean destroying the business model.

So whenever someone hands you a conclusion, the question worth asking is who wins if you believe it. It's like the old Roman poet my dad is named after says: "Who will guard the guards?"

I've seen this firsthand from working with both Equinix and Digital Realty, two companies that on paper do the same thing. Their business models and go-to-market strategies are completely different. Some of the audiences they target overlap, others don't. They use separate tech stacks. The conversations they have with their customers about specific needs use completely separate language, each addressing unique pain points and opportunities.

You need to identify your own features in the data and build out measurement strategies that work for your specific business model. It makes no sense to continue renting your insights and your own data back from vendors that are trying to embed themselves in your ecosystem permanently.

The same single-metric problem that led one company to build a strategy on a 30% repeat rate, that caused the team to spend a year building on an API limitation their implementation vendor never mapped, that led an operations team to throw overtime at a specialization problem, is now being accelerated by tools that make it faster to act on bad assumptions.

AI amplifies whatever you feed it. If you feed it a single KPI tracked in isolation, it amplifies the mistake faster, at greater scale, and with more confidence than any team member would have had making the same error manually. That's a whole Texas-sized sheet cake drowning in a family-sized jar of pickle juice.

That’s why the measurement system has to be right before you point AI at it.

You may recognize some of these problems in your own organization, or some that are similar. As we cover in our article on the S-curve of growth, scale naturally causes organizational drift and creates silos where teams start measuring different KPIs and answering to different incentives. The customer experience fractures first, and the employee experience follows it.

If silos are the root cause of these issues, the solution isn't to get rid of your team and try to scale back to being a founder-led organization. That's not what we're saying. While solving some of these symptoms with better systems will naturally correct headcount, the goal is to get all teams driving toward the same outcome and make sure every person in the company understands how they contribute to increasing profit.

Another consideration with paired metrics goes beyond tracking numbers over time. Those metrics have to be shared across departments. Any metric that lives inside only one department's dashboard eventually creates conflict, because two teams end up defending two different versions of the same quarter. If you feed AI paired metrics tracked over time, shared across departments, and validated with qualitative data, it amplifies insight. The system gets smarter because the inputs are connected.

We write in detail about the controlled experimentation methods that actually prove what's working and what isn't. But the experimentation only works if the metrics feeding it are paired and shared.

Marketing and sales need to work together on CAC because marketing controls the upstream activity that drives demand and lead generation. They need to make sure they're capturing the right attention and driving it toward the sale. It's sales' job to close. The entire equation of acquisition cost should be shared by both teams.

Sales also has to make sure they're closing the right customers and setting the right expectations to prevent attrition. That makes lifetime value a shared activity, bridging all the way upstream to marketing and the people responsible for building the product, rather than something customer experience and retention carry alone.

In order to make sure every team understands where these activities break down, it's the responsibility of BI and RevOps to give all of these teams a clear picture of where each activity hands off from one team to the next. That way, responsibility is shared. Accountability is visible. And the metrics that connect them are tracked together, not in isolation.

Without this, the entire organization gets stuck in effortful, deliberate thinking on every decision. Daniel Kahneman, author of Thinking, Fast and Slow, calls this System Two thinking: the slow, analytical, energy-intensive mode your brain uses when it doesn't have enough information to act intuitively.

When your teams don't have shared metrics giving them a clear picture, every decision requires grinding through incomplete data, second-guessing other departments, and competing for resources with a scarcity mindset. It causes stress, leads to overanalysis, and it's exactly where collaboration breaks down, because the numbers haven't earned enough trust for anyone to act without protecting themselves first.

This isn't theory. This is Thinking, Fast and Slow illustrated in your organization in real time. There's a reason he won a Nobel Prize in Economics as a psychologist. When shared metrics give every team the same clear picture, more decisions can move into System One: fast, intuitive, collaborative. That's because instead of trying to understand what moved behind just a single metric, teams can be armed with real insights that span how their activities function within the entire system. The team can direct their energy toward the customer instead of toward defending their dashboards.

Every person in your organization needs a balance of both: the intuition to act quickly and the rigor to validate what's working. But you never want those two modes to turn into business units competing with each other instead of functioning as a single team. That transitions the organization from a fractured group of silos into a single unit competing with actual competitors instead of cannibalizing each other from the inside.

That shift is the difference between treating blood pressure alone and diagnosing diabetes.

No one wants to hear that their department or their organization might be in disarray. It's a fair question to ask "how bad can it really be?" when no one on the team is raising these problems. But nobody's report covers the gaps between functions, so no one's job is to raise them. We've written about why people don't feel safe raising these issues and what it costs when the feedback loop is broken.

My dad didn't want to hear he had type 2 diabetes either. But the cluster of symptoms told the truth before any single number could have. Fortunately, catching it in his late 40s likely prevented many more serious consequences of leaving the disease untreated.

Most companies call us when revenue is declining, which is the last sign that the system has been suffering for a long time. The early warning signs are almost always visible months or years before the revenue catches up. Those early warning signs are your leading indicators. Revenue is a lagging indicator, the last thing to move. It's important not to ignore them. If your current solution is to treat rising CAC with more spend, to cap it at a specific target MER, or to treat declining retention with a loyalty program without ever connecting the symptoms to ask whether they share the same root cause, your business may not just have high blood sugar. It might already be suffering from clogged arteries.

Fourth Order Intel pairs your metrics, maps them to revenue impact, and identifies the 15 to 20 percent of operating expenses that single-point measurement misses. The average engagement produces findings within 30 days.

References:

¹ Beer, M. and Nohria, N., "Cracking the Code of Change," Harvard Business Review, May-June 2000.

² McKinsey & Company, "The keys to a successful digital transformation," and McKinsey Global Surveys on transformations, 2012, 2014, 2016, and 2018.

³ MIT Initiative on the Digital Economy (NANDA), 2025. Analysis of enterprise generative AI deployments.

⁴ CMO Council and Deloitte Digital, "Humanizing + Analyzing Relationships To Drive Revenue, Retention And Returns," 2021. Survey of 150 global marketing leaders.

⁵ A cohort is a group of customers who share a common characteristic or acquisition period, allowing you to compare performance across segments rather than in aggregate.

⁶ Leroy, S., "Why is it so hard to do my work? The challenge of attention residue when switching between work tasks," Organizational Behavior and Human Decision Processes, 109(2), 2009, pp. 168-181.

⁷ Rubinstein, J.S., Meyer, D.E., and Evans, J.E., "Executive Control of Cognitive Processes in Task Switching," Journal of Experimental Psychology: Human Perception and Performance, 27(4), 2001, pp. 763-797.