Around 90% of the original Fortune 500 list from 1955 have one thing in common: they're not on the list anymore.

Only 49 companies have remained on the Fortune 500 continuously since its inception.¹ The other 451 are gone. Bankrupt, merged, reorganized, or contracted to a fraction of what they were. And the rate is accelerating. According to Innosight's corporate longevity research, the average tenure of an S&P 500 company fell from 33 years in 1964 to 24 years by 2016, and is forecast to shrink to just 12 years by 2027.²

Apple almost became one of those statistics at the end of the 90s.

Apple hit $118 million in annual revenue and went public with a $1.2 billion valuation before Steve Jobs turned 26. At IPO, his stake was worth $217 million. He'd taken the complete opposite approach of IBM and Windows. Something approachable, usable, sexy, exciting. It just worked.

Five years later, the same board he'd built and popped champagne with years prior, pushed him out. The CEO he'd personally recruited from Pepsi, John Sculley, sided with the board. He had no allies in his own company.

So what went wrong?

Apple had done what a lot of companies do when things are going well. They started developing too many products too fast without understanding what the market wanted.

Apple had confused momentum with vision. And by the time Jobs came back twelve years later, he said he had more "scar tissue" about getting that wrong than anyone. That you had to read between the lines to understand what the market wanted, but that didn't mean forcing it on them. It meant instead of building a product and finding a market, you find the market first and build the product around them.

That lesson cost Apple a decade and nearly the entire company.

Apple’s deep trough following massive success isn't unique. Every business that's ever grown follows the same pattern of ebbs and flows, though not always as dramatic. Whether you're hitting your first $10 million or your first billion, the trajectory looks the same. Slow start while you're proving the product works. Steep climb while you're scaling what works. Then a flattening, where growth seems to slow and eventually stall, unless there's an intervention.

That's the S-curve of growth. It describes the trajectory of every product, every company, and every market. And nothing in a standard reporting stack flags the flattening until revenue is already falling.

In reality, early signals can warn you 18 to 36 months in advance. You just need to know where to look.

The S-curve's meaning, at its core, is simple: slow start, fast middle, flat top. Consulting decks and academic papers might inadvertently portray the S-Curve as a literal trajectory. But it’s not as simple as you launch, you grow, you mature. It represents a cycle, rather than a one-time trajectory.

It’s important to remember, there’s no guarantee that once you experience a revenue decline you’ll always recover. As we mentioned at the start, most Fortune 500 companies no longer exist on that list. And brands like Circuit City, Blockbuster, and Enron no longer exist, at all.

When analysts talk about the market flowing in cycles, it oversimplifies the reality that there are many cycles that drive the S-Curve for your business.

Apple has been through at least four distinct eras of product category creation: the Apple II, the Macintosh, the iPod/iTunes era, and the iPhone. Each one required a fundamentally different understanding of what the market wanted, and shifts into each subsequent one change the way they thought about the others. And each one caused a chain of S-Curve like effects for their competitors.

Before the iPhone, you typically needed three separate devices to browse the web, listen to music, and talk on the phone. But once the iPhone launched, now Sony’s Walkman CD player felt instantly obsolete, as did a cellphone with 10 buttons that couldn’t check your email. There were other devices that filled this use case like Palm phones and Windows Mobile, but the Apple UX/UI replaced the stylus with multitouch, making it the easiest device to adopt. And that kicked off a smartphone boom that would last nearly 20 years.

But now, in 2026, iPhone sales are finally slowing. People still want them, but they’re finally hitting saturation as even fervent Apple users classify the latest phones as iterative, over innovative.

From the iPhone alone, we see the effects of product saturation, ease-of-adoption, innovation, and obsolescence impacting dozens of categories all in a matter of two decades.

But the saturation of iPhone adoption didn’t completely catch Apple off guard. Their launch of Apple Silicon, with neural engines that allow powerful AI models to run on local hardware, is one of the first times in decades that they've been far ahead of the market's needs. And the sudden surge in Mac Mini demand to run agentic workflows with OpenClaw is just one of the ways it's paying off.³

From music producers that enjoy running hundreds of channels at a time without lag, to colorists exporting Hollywood length films in the time it takes to order food on DoorDash, the latest Apple chips attract creators and professionals alike, all with the same hardware, with tailored messaging for each cohort.

We all can learn a thing or two from Apple about looking ahead and keeping in touch with how different groups use and need our products.

The businesses that survive long-term are the ones that build the next curve before the current one flattens. The ones that don't end up like Blockbuster pretending the internet is a fad, or Kodak convinced that digital cameras would never catch on, or Circuit City assuring shareholders and customers they weren't going bankrupt, only for everyone in the world to get a little uneasy when they hear the word "warranty" because they bought something that was never delivered.

I had my own experience with a flattening curve as a new CMO working with a small CPG brand.

Knowing my background with turnarounds, they needed help addressing a real problem: the year after they hit $10 million in revenue, they suddenly crashed 25%. The team was confused. The product was the same, the same ads were still running, and the numbers had been climbing for two straight years. Now, millions in top line were gone. So what happened?

While nothing in the core business had changed, the market had, and the team's focus had started moving elsewhere. A competitor entered with a better offer and faster delivery. Then another one with a better product altogether. And my previous client had launched an ad campaign that started to take market share away from them too.

Customers who had been loyal started comparing. Then churning. And because the brand had never built systems to monitor those customer forces, they didn't see it coming. By the time revenue dropped, the early signs of their revenue decline were already 18 months old.

The S-curve formula is a logistic function: growth starts exponentially but is bounded by a carrying capacity, which in business terms is your total addressable market, your operational capacity, or the limit of your current product-market fit. But the formula isn't what matters. What matters is this: the plateau shows up in your revenue last. It shows up in your leading indicators first. And if you're treating product-market fit like a one-time event, the S-curve has likely already started in your business.

I remember getting my first Blockbuster card in college. Everyone at the store was kind, and all of the latest movies were available to rent, along with many of my favorite classics.

But by the next year, my friends and I were browsing what to rent on Netflix. No driving to the store, hoping what we wanted to watch was available, and no late fees when we were done. Everything that was a normal frustration of the old paradigm was gone.

That’s what catches a lot of these companies off guard. Their people are working hard, the product didn’t get any worse, and the plateau happened anyway. I’m sure if I was asked in the Summer of 2008 if I’d planned to rent a movie from Blockbuster in the next 12 months, I would have said yes.

The product is just one part of how your customers experience your brand. We’ve already seen that product-market fit can expire with nothing seeming to change in the business.

But when looking at your business from the outside, your product isn’t real to your prospect until they’ve experienced it or seen what it can do. It’s a concept. That concept can be incredible, exciting, interesting, or boring and forgettable. In short, it’s the promise of a feeling.

Before they buy, they view your products or services as an offer. It's a value exchange from the very first moment they decide to pay attention to your content or sales reps, all the way to the moment they debate whether to hand you their credit card.

Up to that moment, they rely on signals that have less to do with the literal features of your product and more to do with everything they hear about it from others.

For Netflix, that concept sounded strange at first. Give my credit card information to some random business online and they send me DVDs in the mail? But the mail is slow and what if my identity gets stolen?

Then some brave early adopter heads out into the unknown: they hand over their AMEX details and wait for Fight Club to show up in the mail.

Once they purchase, they're often anticipating a specific outcome or feeling when they buy. Even if you have the best product in the market, delays in shipping, poor onboarding, or unclear instructions can mean a refund before the revenue even gets tracked in your system.

All of these touchpoints need to work together seamlessly, because your customer experiences all of them as your brand. And Netflix systematically removed each point of anxiety-inducing friction, one touch point at a time.

From their product of movie rentals, to their offer of unlimited rentals, to delivery of making the online app easy to use and making good on their promise, all the way to its evolution into a streaming platform, Netflix nailed three important fits. Together, we call them Continuous Market Fit.

Let's look at each fit as possible failure points a little closer:

Product Market Fit. Product failure is the most obvious but the hardest to accept. A SaaS client of ours is one of the best examples we've seen. Before we transformed their app and their customer journey, they had a desktop product in a mobile-first world.

A new entrant launched an all-in-one mobile platform, and our client's product immediately felt obsolete when the new product addressed many concerns and usability issues we solved as a first step to slowing down customer churn.

Before we arrived, "our product is the problem" was the one sentence everyone avoided like the plague. As we talked about in our installment on perverse incentives, we know there is a chilling effect on delivering bad news in the workplace. But the data held up.

Marketing ran a six-month free promotion. Then a twelve-month free promotion. Sales reps burned out trying to close deals against a superior competitor that offered nearly twice as much commission. So they started leaving.

Junior reps that were just learning the product and needed to hit their quotas made promises the product didn’t deliver. Older customers started to leave negative reviews about bugs that had existed for years, while new customers echoed with feeling misled.

Every department responded to the symptoms they could see. But, each fix worsened problems in the next department over.

They'd been giving the product away free for a year. Customers were still leaving, and CAC was going up. That's product failure.

Offer Market Fit. Offer failure is more subtle. Your product can be excellent, but if the positioning, pricing, or messaging doesn't align with how people actually discover, evaluate, and buy, the curve stalls, assuming it gets off the ground in the first place. Take the charting software we use. The conditional formatting, the data architecture capabilities, and the ability to build complex system diagrams are unmatched.

But their ads focus on an automated org chart feature. Power users don't care about org charts. It's not the problem their best customers are trying to solve. The offer and demo are misaligned with the market demands, and that means two things: the sales cycle is longer than it needs to be because they rely on cold outreach, and CAC is higher than it should be because they waste money on ads that don't convert into leads.

At a large insurance brand we worked with, the offer failure was structural. They were spending $36 million a year on advertising, and their entire lead generation model presumed a deliberate, research-driven buyer moving through a long, linear nurture funnel, making a rational decision.

The reality was that the vast majority of purchases were impulse-driven by external events. The entire offer was optimized for a buying behavior that accounted for a fraction of actual revenue. That's a $36 million misalignment that, with better messaging, targeting, and timing, meant a 47% reduction in CAC within the two cohorts that also stayed the longest.

Delivery Market Fit. Delivery failure is the easiest of the three to miss, because it happens after the sale is already recorded as closed/won and it’s handed off to customer success. You can have a great product and a compelling offer, but if the experience of buying, receiving, and using the product lacks endorphin loops, is more frustrating than rewarding, or the customer feels like they're left hanging, the curve stalls.

Think about all the incredible products advertised online as cheap alternatives to flagship brands. Headphones, car accessories, kitchen tools, at prices the flagships can't touch. But when something goes wrong, and you need support, the experience can be awful. No phone number. No real customer service. A chatbot that gets stuck in loops (or that says off the wall things with a little prompt injection).

That delivery failure caps their growth curve regardless of how good the product or offer is because poor reviews and refunds drag down a great product that had poor support.

Imagine if Blockbuster had exclusive movies today, but you still had to drive to the store, hunt for it to be in stock, and pay for the monthly membership at today’s rates. The offer is good, the product is good, but the delivery is a pain.

Optimizing the three components of Continuous Market Fit together can help identify and mitigate the forces that cause the S-curve of growth years ahead of revenue decline.

It's continuous because the forces that affect fit aren't static. Competitors launch new products. Regulations change. Customer tastes or expectations evolve. Cultural moments shift what people care about. Technology creates new channels and destroys old ones.

Porter's Five Forces⁴, which we’ll look at in detail momentarily, point to this, but they're typically treated as static in practice. This is exactly why we encourage you to look outside your business when a metric unexpectedly shifts. We’ve developed our own model that connects macro to micro forces and focuses on buying from the customer’s perspective. These data points are rarely in any business dashboards or consulting decks, and it gets worse the larger the organization is.

Siloed businesses are the most at risk and ill-equipped when it comes to traversing the S-curve of growth. In larger organizations, even marketing becomes multiple separate departments split into product, fulfillment, with separate teams managing paid search, media, creative, and the web experience. Their internal processes are fractured and it shows in their customer experience.

Your Facebook ads say one thing, like how well you’re rated in G2. Your Google ads say another, like how you’re better than a competitor. But neither says what you actually do. So, prospects discover you on social, research you on Reddit, read what your affiliates have said about you, and then come back on branded search when they're ready to buy.

With the right context, prospects can decide much more quickly whether a brand is the right fit for them. Sometimes, in as little as a few minutes, instead of months, especially if what you offer hits them right at the moment of their own plateau.

But for many growth-stage brands, the entire ping-pong journey across channels is a frustrating necessity to gather all of the details required to make a decision to purchase. And if teams are measuring only the last click, that story gets much worse before it gets better. (We’re not fans of nudging so much as jolting, as you will learn as you get to know us.)

No standard tool monitors these three fits on an ongoing basis, so most businesses can't. They evaluate product-market fit when they write the business plan, and then they file it away only revisiting it during a crisis. A consultancy comes in every couple of years and does a snapshot of the current market. But as for how these three legs work together across years of changing markets, it's rare.

When they do, they become the largest companies in the world. Apple. Tesla. Nvidia.

Think about what you're missing with a snapshot every one to two years. Two years of competitive movements. Two years of shifting customer expectations. Two years of warning signs in your metrics.

Most teams have no reason to look here:

Energy costs move the entire cost structure of your supply chain, and watching them has never been part of any marketing job description.

Consumer sentiment and credit conditions shift who can afford your product and how they justify the purchase, and that shift shows up in your CAC months before it is connected to the cause.

Regulatory changes rewrite what you're allowed to say in your ads.

New categories of tools change what your customers expect a "good experience" to feel like, whether or not you've adopted them yourself.

Most of these won’t show up in a QBR, but all of them are already acting on your S-curve.

And looking at your industry, there are guaranteed to be new entrants, new customer pain points, and new opportunities that aren't represented by your offer, product, or delivery.

You don't have to spend $1 to $5 million with a consultancy to get this intelligence every two years. You can have it in-house without burning millions on tokens or hiring an army of analysts. And once it's running, the gains make the cost feel negligible. Because in that world, knowing what works, what doesn't work, and what will work to accelerate the pipeline and win customers becomes obvious without burning the midnight oil on corporate fire drills.

But it does require going deeper than looking at Reddit comments and trusting what ChatGPT or Claude say at face value, regardless of what skills you’ve added.

As we’ve alluded to, growth plateaus don't appear in your revenue first. Revenue is a lagging indicator. It's a representation of latent forces that have already been acting on your business for months or years. The combination of your marketing and sales efforts, the demand of the market, and the satisfaction of your customers.

Before revenue ever declines, watch for any of the following:

The cost of revenue is going up. This is what happened at a publicly traded mattress company. They were spending 25x what the category leader was spending on ads, but revenue was still declining. We spotted that their SG&A curve had a higher slope than their growth for several consecutive quarters before their revenue declined. The causes driving that slope were all outside of their dashboards.

You’ve probably seen this one in your org already: customer acquisition cost (CAC) starts to rise. "The platform got more expensive." "It's seasonal." "We need to update the creative." Those can be true, but they also might dismiss a much larger trend that shows up in revenue a year from now. They also embed the assumption that you are perfectly targeting all possible cohorts with tailored messaging. Just like Apple had to become aware of the use cases that drive adoption, we’ve helped businesses turnaround by tailoring the customer journey to cohorts within their existing customer bases as well as market segments they weren’t yet addressing.

Customer retention wanes, but the explanation is accepted at face value. When somebody cancels and says, "I can't afford it," that's the quick answer to get the retention agent off the phone. They've already made up their mind. They've already found a competitor who offers a better solution. Or a cheaper alternative, that doesn’t necessarily mean your prospect is price conscious, but that they don’t see what you offer as worth what you’re charging. It's manifest sentiment, not latent sentiment: the gap between what they say and what the subtext means.

At the brand I mentioned earlier, spending $36M per year on ads, they watched two metrics move in opposite directions. CAC went up. LTV went down.

Anyone who's been an operator long enough knows this is a slippery slope. Because if you don't turn around your marketing performance, you're spending more just to keep revenue flat. When your marketing is insolvent, a revenue dip isn't far behind. And if you do maintain revenue, that margin compression pushes you to a crossroads.

When customer churn had moved from 5% to 13%, it was dismissed as driven by the economy. But at a 40% cancellation rate, their payback period on current marketing dollars yielded a negative internal rate of return. In a situation like this, the CFO would be right to cap the budget given the performance. From there, agencies will turn over, and layoffs get discussed.

Their marketing teams were in denial at first, thinking they could solve the gap with promotions, giveaways, and sales. They wanted to increase spend temporarily, figuring the chaos in their metrics would calm down. Multiple internal teams intentionally worked in silos, hoping to figure the issue out themselves because they wanted the credit.

By the time they came to us for help, we had our work cut out for us. (And believe me, I love these scenarios because nothing is more fun than pulling everyone together with data and seeing what they’re capable of when they feel confident again).

Our analysis revealed that this marketing insolvency began two years prior, just as the company was recording its highest annual profits to date. Three seemingly minor factors spiraled simultaneously, all of which should have been measured and monitored together. But, silos made each of them feel like someone else’s responsibility to worry about, and if you’ve been an organization where political capital is earned by staying in your lane, this chain of events won’t surprise you:

They lost a lawsuit in one state. A customer submitted a claim and was denied on the opposite end of the country. The political climate positively impacted their sales for two consecutive years, just as their long-time customers started complaining on public forums.

Their agencies told them the algorithms weren't picking up their content. That it was creative fatigue.

Their other consultancies said it was the economy, and customers "couldn't afford them" anymore.

Here is how those factors connected:

When the lawsuit happened, compliance restricted certain phrases and sequences of the offer, effectively sanitizing their messaging to the point of ambiguity. They had to tread lightly on specifics of what the product could do for their customers, so they started to lead with promotional gifts.

This reduced audience engagement with the ads. But sales increased because category demand was up during a volatile political climate, bringing their ad inventory costs down while click-through rates went down. (Yes, that’s an odd phenomenon that looks positive and would have been difficult for anyone to track. Internal data sources would have shown all systems normal or better than ever when the increased ad platform attribution was a sign the ads weren’t effective.)

The denied customer claim would become a national story at the same time the political climate shifted back to its homeostasis. The perceived need for the product returned to baseline.

Their ads hadn't changed for two years, and everything the team assumed was working had zero impact on sales. But they'd increased their spend anyway.

As the environment shifted and demand for the product along with it, new entrants had come in with lower prices at the same time, and consumer trust in the brand had been all but destroyed by the national story.

All of this made acquiring a customer six times more expensive and cut LTV in half in what appeared to be a matter of months, and it started with small global changes and decisions that could have been contained years prior.

The S-curve wasn't flattening because of the economy. And the delay in acting on early warning signs made the recovery harder and more expensive than it needed to be.

Stories like that brand suddenly losing a significant amount of their customers are common. And as more teams attempt to adopt AI into their reporting, and to agentify processes in their organization, it shouldn’t surprise you why.

Data in most companies is a mixed bag. We’ve seen some orgs spend hundreds of thousands on dashboard build-outs, only to be outdone by analysis in spreadsheets and open source versions of SPSS. And the push for rapid AI adoption on top of that messy data landscape is a key culprit to why many of these initiatives fail.

Personally, I blame maturity models. Machine learning has been this far-distant, unreachable future for most organizations, but it’s been accessible for decades.

A maturity model says: here's where you are on a five-stage scale. Here's what you need to build at each stage, and you'll get there eventually, as long as you keep paying us to tell you where to go. That framing assumes you have the luxury of time and an unlimited budget. Most businesses hitting a plateau have neither, but they’ve been caught in a series of endless deployments with incremental movement by cloud platforms and vendors for years.

Think back on the maturity models you've seen. Are they all pointing in the same direction? No. They conveniently show you’re always somewhere in the middle and the next step just happens to be what the vendor wants to sell you.

Would waiting to have everything in your organization perfectly aligned before testing AI make sense in 2026 in a slow series of adoption?

If machine learning has been the gold standard for more than 30 years in leading organizations, but someone is telling you to get started with single-channel, descriptive analytics, how far behind will you be at the end of that first year on their maturity model?

Here’s how it goes when you’re led by a team that’s operated a turnaround:

Map where you are in one to two weeks. Dump all your data into a single view. Start tracking the leading indicators. Be willing to kill the sacred cows. (Yes, I know those "GTM Motions" and that “PLG Strategy” are sacred cows.)

You can’t solve these problems by looking at quant data alone, so get your hands on every bit of qualitative data you have at your disposal and start structuring it.

It takes six weeks maximum to begin restructuring your offer and delivery, and only slightly longer to address product issues, unless it requires software development and new market research. Product teams might hate that they need to pause features it turns out only a small segment of the market wants, but gutting everyone’s tasks and backlogs into a 90 day concerted sprint has worked for our clients with few exceptions.

If you treat your survival like a five-year maturity journey, you will likely be out of business in two.

Clayton Christensen got the Innovator's Dilemma right.⁵ Disruptors don’t often have the budgets to launch with a loud ad campaign or a fully baked product. They start slow, with a slightly inferior product that speaks to a niche market segment that data teams dismiss as outliers, while your customers start to trial their products. By the time they're competitive with your offering, it's too late. The S-curve has already shifted.

Schumpeter called this creative destruction.⁶ But where both he and Christensen stopped short is leaving with the assumption that you figure this out only once. That you identify the disruption, respond, and you're safe.

Apple’s strategy for decades was being second-to-market, waiting for MP3 players like the Rio and the Creative Nomad to gain product-market fit before launching the superior iPod. Or the Pebble and Galaxy Watches before they launched the long-awaited Apple Watch. Other times, they’ve set new standards such as with their Retina Displays, Gorilla Glass partnership with Corning, and their chips. They have learned when to take those bets, and know they must keep taking them.

The S-curve repeats in perpetual cycles. The forces that bend it are always moving, but none of the popular business theories are designed to keep up with the pace of the market.

Sinek's Start With Why⁷ is a powerful concept for teams at the very start of this awareness, but in that model, your why may not need to change, while the market demands that your product mix does. Where Sinek’s model helps you answer Why, it misses how, what and when.

Alexander Osterwalder's Business Model Canvas⁸ is an excellent diagnostic tool, but it's designed as a point-in-time exercise. Your strengths today become your weakness tomorrow, like the ability to rent DVDs at a store.

Van der Kooij's Revenue Architecture⁹ is sophisticated, but it assumes people are either product-aware or they're not, with no bridge between stages, along with some assumptions about siloed channels that don’t map to reality. This is a common thing on LinkedIn: more teams want to see more traffic coming from email than their ads but need to answer the question, how do you capture emails to begin with? And, why should you need to capture an email before you begin nurturing a cold audience?

None of these frameworks tells you what to do when the curve starts flattening. They tell you how it's happening and stop there. They don't tell you how to monitor or react, let alone everything to plan for or how to mitigate it.

Those answers aren’t found in white papers from SaaS platforms or cloud providers.

They’re found in monitoring the market in real-time and testing against assumptions, and treating anomalies as insights rather than outliers to quash. As we often say here, data that flows together goes together. And that’s why it’s critical to make sure everyone in your company is playing on the same team.

As we touched on earlier, Porter's Five Forces⁴ point you to external disruptors to your business: rivalry, new entrants, supplier power, buyer power, and substitutes. Most businesses receive this analysis once from a consulting firm and file them away in a deck somewhere on their desktop or downloads folder.

Others stalk their competitors and mimic their offers or messaging verbatim to the point of homogenization. AI is accelerating this trend because internet marketers have been led to believe that competitive copying is a strategy. While it works in the short term for quick wins, eventually everyone loses as positioning erodes.

One extreme of this is Rebel Creamery filing bankruptcy because of losing a $24M lawsuit for copying a competitor’s brand. The mattress company we cover in our article on marketing measurement shows what happens when positioning softens into a bland middle.

Then here’s something we came across recently when auditing Dell for one of our clients. Their website shows very little of their real business model. Read their 10-K. Their top business category is now AI servers. Their SEO shows the words PlayStation 5 and Kindle in their top 1000 ranked keywords. If you’re competing with DELL in AI data centers build outs, you don’t want to copy their ads and website strategy unless you also have a marketplace and sell laptops.

But here’s the good news: the S-curve bends much less from the competitor activities you're aware of such as their ads or their offers. Your real threats come from outside these five forces, from the friction neither Porter's nor Osterwalder’s frameworks were designed to capture.

Someone has finally had enough of waiting in grocery lines and wants their food to show up on their porch. Another imagines a faster way to get home than hailing a taxi. Or in the case of Transparent Labs, which we helped grow to an 8-figure exit, they ask a simple question about what they're putting in their body. That's how new categories are born.

What might surprise you is Netflix didn't compete with Blockbuster on the same curve. They weren’t directly competing in the brick and mortar rental space. From the very start, they planned to displace it. They created a substitute that made the previous curve irrelevant.

They eliminated all of the frustrations of driving to a store we talked about, but they also elevated the experience. They revived films that were difficult to find and would have made no financial sense to keep at a single store location. They made it easy to discover new films because they started tracking what you liked. And they didn’t stop at the genres you prefer. They looked at when you paused, whether you came back to watch, who was in the film, what it was about, and thousands of other data points to build sub-genres around your preferences. Those are the endorphin loops we mentioned being constructed by carefully watching what you do and don’t do.

When things were shifting with that SaaS team's metrics, they looked at their ads and email performance. They gave the product away when they heard people thought it was expensive. And they didn't realize they were churning sales reps because their competitor was able to reduce their own CAC through affiliate programs and by paying their sales teams double what was typical in the industry.

And they weren't just facing a new competitor. Their new rival coalesced latent sentiment from the customer bases at every existing player in the category into a product that shifted the definition of the category entirely. It was a shift like Ford mass-producing the automobile, when everyone who could afford one had a horse.

But the curve doesn't just bend from the outside. Internal follies weigh on the curve one drifting metric and OKR at a time.

Internal signals move even earlier than external ones. They can warn you five to ten years in advance.

The biggest culprit is often marketing. Not the people in it, but the structure. Someone on LinkedIn recently mentioned that marketing feels like a catch all for everything that goes wrong from product to sales. And it’s ironic, given that the original marketing mix was designed to operate the full business from developing the product, to pricing it, finding where to sell it and how to sell it. So if your organization conflates advertising or web evangelism (for the veterans) with marketing, the problem is far past simmering. Marketing was always meant to pull the whole business into a singular focus: to create a market and serve it in exchange for a profit.

When the organization starts with just a founder or two, there is no distance between understanding what the customer wants, what they value, and how they use the product. The founder even knows their best customers by name.

But as businesses scale, the marketing function that originally operated as a unified stack across a team of five gets distributed into silos. Product team. Marketing team. Sales team. Analytics. The creatives. Each silo staffs independently, measures independently, and optimizes for its own KPIs instead of contribution margin. They pick proxies like ROAS, or the number of assets deployed in a given week, and when nobody owns revenue accountability, the proxies get vaguer: engagement, intentionality, share of voice. Incentives fracture or break down entirely.

And as we’ve talked about before, those proxies don’t connect to real operator metrics like contribution margin, LTV and cost of revenue.

According to CMO Council and Deloitte research, only 17% of senior marketers say their firm tracks lifetime value well.¹⁰ The CMO Survey consistently finds that strategic metrics like LTV and brand equity are among the least measured, while tactical short-term numbers like click-through rates and platform ROAS are tracked religiously.¹¹ The advertising platforms want you to believe that awareness will convert into sales. The agencies want you to believe it's time to scale because ROAS is high. The SaaS vendors are billing you for seats instead of proving ROI, and your OpEx climbs every year.

One of the products we sold where I was CMO cost just $6 to manufacture, but had a $100 CAC. Another cost $17, but had a $60 CAC. So the decision is simple right? Kill that first product. I promise to break this down in a video, but the long and short of this is that first product out-earned the second by orders of magnitude. And you’d never know it if you were focusing on ROAS.

And yet, ROAS and iROAS are the top metrics marketing teams use. Malicious intent is rare in these discussions.

People are stuck within their paradigms, and they get measured on their own function's reports. Some of that is dictated by senior leaders that inherited those metrics when they entered the department. But most of them originate with the industry players who benefit most from ambiguity when they sell you ad space.

Lead volume goes down, cool, run a promo. Ad costs are getting expensive, pause them or refresh the content with a square video, since landscape is out. The same message from last week with a small tweak here or there to “game the algorithm.” It keeps things moving at status quo, until it can’t.

We all remember the lesson about precision versus accuracy with the darts clustering together, but not on the bullseye. That’s what marketing has become. It’s the same activities recycled, with the same message, missing the mark on business performance and tracking what the market wants versus what customers did.

But when someone does try to raise the alarm, something predictable happens.

I’m a bad news first kind of person. Bad news always means opportunity after we get through the discomfort.

When we first started working with Potbelly, we shot a whole campaign geared toward getting people to come into the restaurant to enjoy a lunch break. Then the pandemic happened. That strategy we’d tested beating the control by 2x was out. So I got to open Premiere and start recutting, lay in some voice over, shoot some new photos and completely rebuild the campaign from scratch in two weeks so we could hit our launch deadline. And because we were plugged into the market, the campaign performed better and better as it ran over the next year and a half.

But, while I like getting the bad news first, I’ve learned how much others hate it. I’m still quick to give it and have also learned to package it as an opportunity. Framing is everything.

Maybe you’ll relate to this, like one of my former teammates.

Doesn't it always feel like the days when things are supposed to be light and easy, so you can duck out early for 9 holes after lunch, are when everything goes wrong at once? How do you feel when that happens? Who do you feel those feelings at?

Research from Harvard Business School, published in the Journal of Experimental Psychology, confirmed across eleven experiments what most operators already know: people shoot the messenger.¹²

When someone delivers bad news, even when they had no control over the outcome, the people receiving that news rate the messenger as less likable and subconsciously attribute malicious motives to them. The researchers found that this is driven by the human desire to make sense of unexpected or negative outcomes. In the absence of a clear cause, people assign blame to the person closest to the information.

In organizations, this plays out exactly how you'd expect. The marketer who surfaces that customer acquisition cost is doubling gets questioned on their competency. In a rush, or with a vacation a few days away, it's not uncommon to hear any of the following:

"Did you verify the data with BI?"

"When was the last time you changed the ads?"

"I thought things were going well... didn't you tell me last week we'd had this figured out?"

"Let’s not overreact. We hit our numbers last quarter. Let’s see how this month closes before we change anything."

The same thing happens to the analyst who flags that customer churn is going up.

"That doesn't seem right... We haven't changed our product or pricing. Did you ask them why they're leaving?"

"Aren't we offering them a discount if they stay?"

"You're just being negative."

"Let’s give the new offer some time to work before we sound the alarm."

The same pattern reaches outside advisors: the diagnosis of structural waste gets received as the problem, instead of the waste itself:

"We weren't paying you to look at that area of the business quite yet..."

"Just don't share this with the VP while we take a few days to look over the report."

Some of those are blatant dismissal. Some are sugar-coated. Either way, this is exactly the moment to pause and ask: am I the one saying this, or is someone saying it to me? And what important information might be missing?

When dismissal or worse happens, the best people either stop talking or start leaving.

This is why employee experience is inseparable from customer experience. Your best talent knows the product is falling behind. They know the metrics are drifting. They know the offer is misaligned. If they don't believe leadership will act on what they see, they disengage.

We’ve seen this in experience surveys and exit interviews when walking into turnarounds: people kept raising issues, but started to believe management didn’t care or were afraid to say anything until their last day.

When they do stay, sometimes they quiet quit instead of an actual resignation. Either way, the institutional knowledge of what's actually happening stops hitting your Slack channels and white boards, eventually walking out the door.

The cumulative effect is that your internal forces are bending the S-curve just as hard as the external ones. And the people responsible for watching the curve are the same people whose incentives make that same curve invisible to them.

One of the signs that a company has severe silos actually cannibalizing growth is when managers start referring to other teams in the third person. "Yeah, I talked with IT. They don't want to give us XYZ." They start complaining about those teams.

That pattern is predictable, too: cannibalization leads to conflict, conflict leads to criticism, and criticism leads to contempt. This is everybody competing for resources because they're stuck in over-analysis mode. This is Daniel Kahneman’s System 2 thinking at the organizational level, and it's one of the most destructive forces on the S-curve. Kahneman describes System 2 as slow, effortful, and easily depleted.¹³ The collaboration research explains the rest: under perceived threat, organizations restrict information flow and tighten control, a pattern documented as the threat-rigidity effect,¹⁴ and Google's Project Aristotle found that teams where people don't feel safe taking interpersonal risks share less and perform worse, making psychological safety the strongest predictor of team effectiveness they measured.¹⁵

We saw this at a mid-market company where the marketing operations team had 56 planned initiatives. When we evaluated them, about 12 were duplicates proposed by different team members using different language to address the same problems.

Twenty were static retrospectives that could have been replaced by a single report if all teams were aligned to financial metrics instead of their channel’s proxies. Only 4 of the 56 were actually investigating what was going wrong with acquisition and retention. And the people working on those 4 didn't yet have the language to communicate how their numbers connected to the other teams' numbers. If those 56 initiatives were launched in isolation, each time they’d fixed one thing, they might break another. That's exactly the trajectory they were heading in.

The quickest way to start solving this is to get your teams on the same page about the business model.

If the people in your company don't understand how the business actually makes money, not "we sell X" but "we sell X which yields Y which enables Z," they're going to propose initiatives that don't move the company forward. More decks that get skimmed once and archived. More reports that die in the inbox. More dashboards whose last login was launch day. The S-curve flattens while the market is still there to be won, because the organization is consuming its own resources instead of investing them.

As we said before, if finance sees the marketing budget growing faster than revenue, they do what feels logical: they cap the spend. That saves money today, but then leads stop coming in. If the sales cycle is 6 to 12 months, that’s how long it might take for Sales to feel the impact, while the email team is trying to solve open rates and content strategy. Appointments slow down. Renewals disappear. The chain of events obscures the reality that acquisition and retention are inextricably related.

If existing customers are happy, they sell for you. If they aren’t, they keep new customers at bay.

Cutting marketing during a plateau is borrowing from future demand. It’s important to find the root cause of the change and get the team aligned on what’s going on in the market.

If a customer or market insight isn’t applicable to every channel in your business, it’s just a trivial proxy, not business intelligence.

You’ve now learned that maturity models will keep you stuck where you are on the S-Curve. That competitive research is the tip of the iceberg. And there’s a lot of things your business isn’t tracking, but should be.

If it sounds daunting, it is. When you’ve been spending 80-100 hours a week on this type of work for the past decade, it’s easy to make some of these solutions sound simple. That’s my job: to make it so easy it feels like a no-brainer for your business.

That’s how it should be. You don’t need to wait 5 stages from where you are now to take advantage of these approaches. You can get a quick nod in the right direction right now:

If your CAC is stable and LTV is growing, you're in the growth phase. Concentrate. Don't diversify outside of your category. We saw a brand that held the number one pre-workout on the market expand into a biography, a women's supplement line, spicy supplements, and a mobile game while the proven product went underfunded. All their customers wanted was more flavors of the same tried-and-true product. Their competitor doubled down on its own competing pre-workout and took it into GNC, where it did $36 million the following year, while the diversifying brand dropped 25%. Each expansion had a business case on its own, but they weren't all equally grounded in customer research.

If your CAC is rising while revenue is still growing, you're approaching the plateau. The growth is masking the structural shift underneath. Start investigating the weak points in your Continuous Market Fit (the product, offer and delivery as a system). Check the product against newer competitors. Check the offer against how people actually buy. Check the delivery against what customers are saying in the places they're honest, and learn how to tell when people are only telling you what you want to hear.

The CMO of a major seasonal ecommerce brand asked me this directly in 2021: "I've got more things running than we've ever had. More channels. More campaigns. More vendors. How do we know what's actually working?" If that’s what you’re wondering, the answer is simple: isolate variables. You don't need to wait for the plateau to become a crisis. You can start testing right now.

And when your best customers or employees start leaving, they're telling you something much louder than any survey or dashboard can.

If you catch the plateau early, within the first 3 to 6 months of the leading indicators shifting, you can save millions in top-line loss for a nominal investment.

If you catch it late, after 12 to 18 months of deterioration, you're restructuring the business. No way around it.

It's not possible to track everything that impacts business performance. Over the years we have found hundreds of factors that shape buying decisions: the ability to buy, the capability to buy, the pain thresholds a purchase has to clear. The only way to monitor them at scale is with machine learning and a qualitative data practice. Without those, even when profit looks good and revenue is climbing, other forces are acting on your future.

That is exactly what happened with one of our clients. Revenue had never been better, and profits were increasing. Nothing in their reporting could have predicted that they were about to pay six times more for a customer and lose a third of the ones they already had.

Before we got there, they had been told to monitor retention and build a qualitative data practice. They dismissed both as maturity work, something to come later, after the descriptive analytics were finished.

Once the issues surfaced, they reacted. They fired their agency. They laid off part of their team. But the real problem had started almost two years earlier, and we found it in places their reporting never looked: niche community forums, a few big swings taken by their competitors, and a cascade of news events that was changing how the market thought about their problem, the products to solve it, and who to trust.

We have seen the same pattern in almost every turnaround we have run. What impacts a business most isn't in the dashboards, and it isn't in the $1.5M consulting decks from big-name agencies. It's also not as simple as signing up for an app and having it work.

Fortunately, you don't need to listen to a dozen books this month at 3x speed on Audible, or watch a hundred hours of how to build a consumer-insights practice, to get there. You can give us a call, and we're happy to help. We love getting everyone on the same page about data, aligning incentives, and putting the right answers in front of leadership with the evidence they need to make calculated decisions. It's a normal Tuesday for us. And it means not having to stay late on Friday, for you.

Fourth Order Intel diagnoses where your business sits on the S-curve and identifies which forces are acting on it. The average engagement identifies 15 to 20 percent of operating expenses as recoverable within the first 30 days.

S-Curve of Growth: A logistic growth function describing the trajectory of a product or company: a slow start, a steep middle (scaling), and a flat top (maturity/plateau), which repeats in cycles for long-term survivors.

Key terms: Continuous Market Fit, Leading Indicators, Marketing Insolvency, and Structural Waste.

¹ Fortune, "Meet the '70-Year Club'", 2024. 49 companies have appeared on the Fortune 500 every year since the list began in 1955.

² Anthony, S. D., Viguerie, S. P., Schwartz, E. I., & Van Landeghem, J., 2018 Corporate Longevity Forecast: Creative Destruction is Accelerating, Innosight, 2018.

³ Apple Q2 2026 earnings call, reported by MacDailyNews and MacRumors, and Bloomberg Businessweek, "Why Claude AI Agents Are Driving Record Mac Mini Demand," June 1, 2026.

⁴ Michael Porter, Competitive Strategy, Free Press, 1980. Five forces: rivalry, new entrants, supplier power, buyer power, and threat of substitutes.

⁵ Clayton M. Christensen, The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business Review Press, 1997.

⁶ Joseph A. Schumpeter, Capitalism, Socialism and Democracy, Harper & Brothers, 1942.

⁷ Simon Sinek, Start with Why: How Great Leaders Inspire Everyone to Take Action, Portfolio, 2009.

⁸ Alexander Osterwalder & Yves Pigneur, Business Model Generation, Wiley, 2010.

⁹ Jacco van der Kooij, Revenue Architecture, Winning by Design, 2023.

¹⁰ CMO Council & Deloitte Digital, "Humanizing + Analyzing Relationships To Drive Revenue, Retention And Returns," 2021. Survey of 150 global marketing leaders.

¹¹ The CMO Survey, Duke University Fuqua School of Business, Deloitte, and the American Marketing Association.

¹² John, L. K., Blunden, H., & Liu, H., "Shooting the messenger," Journal of Experimental Psychology: General, 148(4), 2019. Eleven experiments confirming the effect.

¹³ Daniel Kahneman, Thinking, Fast and Slow, Farrar, Straus and Giroux, 2011.

¹⁴ Staw, B. M., Sandelands, L. E., & Dutton, J. E., "Threat-Rigidity Effects in Organizational Behavior: A Multilevel Analysis," Administrative Science Quarterly, 1981.

¹⁵ Edmondson, A., "Psychological Safety and Learning Behavior in Work Teams," Administrative Science Quarterly, 1999; Google re:Work, Project Aristotle team-effectiveness study.

Related:

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.