A couple of summers ago, one of our clients had just tested a new agency with a $3 million campaign. According to the agency's attribution model,¹ they'd improved acquisition costs by 25%. The agency did everything right on paper. They aligned with the marketing team on the metrics that mattered to them: lead costs, Return on Ad Spend² (ROAS), and Customer Acquisition Cost ³ (CAC). They even used machine learning to optimize their messages, proving their confidence "using the Euclidean distance."

But finance didn't see it in the numbers, and neither did we. As we dug into the data outside their platforms, where pixels can't track, we found the whole campaign was a net loss.

Agencies are incredibly good at concealing structural failures. When I first learned to bake cheesecakes from scratch, my very first cake cracked right down the middle. Without time to make a new one, I simply threw a can of cherries over the top so no one would notice. That is exactly what agencies do with ROAS: they pour vanity metrics over a broken data foundation to hide the structural cracks.

That's what we solve when executive teams lose trust in their reports. So what caused the data from marketing and the data in finance to drift so far apart?

By the end of this article, you'll know why most marketing attribution models mislead more than they inform and the best path toward fixing yours.

Here's something you need to consider. Even with industry best-practice tools, your marketing attribution can still assign credit to the wrong channels, because your marketing strategies weren't designed to be measured against financial impact.

Industry standard metrics track engagement and reward performance where advertising makes the least difference, such as when a prospect has already decided to purchase or already knows what they need.

On top of that, the industry ties performance to ROAS and blended CAC. ROAS conflates a multiple of ad spend with profitability. CAC requires looking at an entire marketing system with double-blind tests, but gets conflated with cost per sale. That's why, no matter how sophisticated a third-party tool or automation with AI, nothing can overcome flaws in experiment design.

You absolutely can (and should) automate attribution with in-house tools. But before you can do that, you need to prove it out in a spreadsheet first. And most importantly, prove what doesn't lift sales.

Most of you already know this. Google wants credit for the sale. Meta wants credit for that same sale. TikTok wants it too. And each of them has their own attribution reporting.

We've seen a lot of attribution models come and go. Last-click. First-click. Linear. Time-decay. Data-driven. Multi-touch. Marketing mix modeling. And now incremental Return on Ad Spend⁴ (iROAS), which promises to tell you what would have happened if you hadn't run the ad.

All of them attempt to answer the same question. And all of them suffer from the same structural problems:

Platforms rely on pixels.

Their reps all tell you the importance of paying for "brand awareness," and always want you to try their new "best practices" for growing your account.

Often, all they end up growing is your costs.

They want you focused on format. Short-form, vertical video, square, UGC. The real key to connecting with audiences and driving them toward purchase has nothing to do with the format. Even print and mail ads work today.

In the worst attribution setups we've seen, the total sales reported across all channels are two or three times your actual revenue. Every one of these platforms is incentivized to get you to spend more. Their reporting is designed to show you enough positive results that you keep spending in fear that if you pause, all hell will break loose on your CAC or CPMs. Your revenue will disappear, and when you do decide to turn your ads back on, you'll restart the "learning phase."

I know what you're thinking. "You should never trust platform analytics." Or, "That's just because you didn't set up your tags correctly."

You're right on both accounts, with an important caveat.

Unless you sell only online, with only one traffic source, in an area with no privacy laws, where no one uses ad blockers, and no one uses an iPhone, then sure: you can trust your tags.

For the rest of us who live in a reality where people use multiple devices, and tracking is actively blocked by legislation and company policy, correctly tagging your site won't mean accurate attribution.

And no, unfortunately, even if moving to Google Analytics, Segment, Hyros, Funnel.io or even Triple Whale or Northbeam improves your results at first, their data is directional. But it's not causal. It doesn't account for purchases that would have happened without advertising. And it doesn't consider what forces outside your control are impacting demand.

And none of them will get you the best possible results without moving beyond quantitative data analysis.

In fact, even when implemented correctly, any of these solutions will still leave your customer acquisition cost at double what it should be.

As of early 2026, AI agents are estimated to account for a growing share of all Internet searches, and that number is climbing every day.

As much as 50% of traffic comes from bots. So impressions, site visits, and clicks may not even be coming from real people.

Your attribution tool counts all of these sessions the same as a real visitor, even if you have tools like Cloudflare (a tool that helps prevent bot traffic) or Akamai in place. There are tons of GitHub repos dedicated to getting past anti-bot platforms and CAPTCHAs.

Then, when a real user does reach your site, if they decide not to accept your cookie policy, or have an ad blocker, none of your tags fire. In Europe and states with stricter privacy laws, you may not even see they visited your site before clicking decline.

And many already know that Apple's Intelligent Tracking Prevention not only removed access to their data from Meta, but often strips your UTMs and metadata, which makes direct or organic channels look like they're pulling in traffic you actually paid for. That's often the case anyway, since most users hear about you from friends or ads that drove them to search for your brand in the first place.

Last year, I had an article get 270k views on Reddit, with 18k readers on Medium in the first 72 hours. In my analytics, traffic sources were coming in everywhere, from direct to organic social, email, and internal Slack. A post on one channel, but the analytics show traffic from all the others. Which channel would you attribute those views to?

But what about enriching your data with tools like Apollo and Audience Labs?

We've used the cream of the crop in B2B and ABM strategies: Demandbase. We've also helped clients implement 6Sense, Apollo, and AudienceLabs directly into their CDP or CRM. They're all great for re-marketing and customer segmentation… when they're accurate.

In my old office, our IP address was categorized in Demandbase as The University of Utah, because they had a satellite campus in the same parking lot. (As a BYU alum, I might have cared more if we'd ever had a good football team)

All of this noisy data informs metrics across your conversion rate, channel referrals, split tests, and which ads get the credit. It doesn't matter which of these platforms you choose, or which marketing attribution model you deploy if you aren't accounting for limitations in data accuracy.

Some of you notice that after you implement any of these tools, your results improve. That initial uptick comes from having better tracking, which is always better than none at all.

It's just like the canned cherries I put on my first cheesecake. Some topping is better than no topping. Anything to cover the crack underneath.

The data you're seeing doesn't tell the full story. It's helpful directionally. You can see what content users engaged with. How long they engaged with it. And what they did in the same session as the one that drove the sale. But you can't see every user. And you can't see what content they would have engaged with, if you hadn't published it in the first place.

There's one more pitfall, and it matters just as much as the metrics you choose to measure success.

Some of you have gone down the path of setting up server-side analytics. Or a 1st party-cookie. You're already ahead of the game. But if you're still modeling your attribution based on channels, your IT team is about to receive a lot more tickets.

If you've ever baked a cake from scratch, you know the balance of the ingredients impacts everything from the taste to the texture. Too much flour, and it's dry. Too much salt, and it tastes like swallowing a mouthful of seawater at summer camp.

When you have the privilege of building a brand from scratch, you see marketing channels the same way.

Within days of launching our ad campaign for Transparent Labs on Facebook, searches for the brand spiked from almost nothing to nearly 30k that week. It was enough to start showing up in Google Keyword Tracker within a month.

But the purchase journey wasn't as simple as clicking on an ad, adding to cart, and checking out.

Prospects learned about the brand and a keystone product from the ad. They searched to find reviews and credibility on an affiliate site. They visited Transparent Labs for the first time as either an organic visitor to the blog or under branded PPC. So Google Analytics or any tool looking at first-visit referrals would mistakenly give credit to where the user first entered the site, not what got them to visit in the first place.

We knew this because there were 5x more visits to the site than clicks on any of our Facebook ads. And our controls showed that where we were running ads, there was also a lift on all channels in the regions where ads were active.

Most importantly, we knew this because we grew the brand from $2M to $10M in 12 months.

All of those touchpoints worked together as a system.

Without the social proof of 3rd party recommendations, one segment of the audience will feel wary of buying from a brand they've never heard of, let alone online. Without the initial ad they never hear about you to begin with. Without paying for the PPC ad, if you don't rank at the top SERP, you lose the sale to one of those "We're sooo much better than XYZ brand" ads that are sniping your sales.

If the entire customer journey were available all from a single channel, would your customer never have to hear from you outside of Meta or the place where they discover you?

That's just it. The entire customer journey was available on all platforms. We had full awareness journey coverage on Facebook and Instagram. The same on Google. And full email flows (but of course, you don't get emails until someone opts in anyway).

We ran the ad to problem-unaware segments to show them the harmful effects of proprietary blends in supplements. How specific dosing was required for clinical effectiveness. They learned about the problem and solution, all the way to being called to action to buy the product.

And they still needed to do their own research, then decide after visiting the website through other channels.

What about when the only ads you're running are to product-aware segments?

That only accelerates prospects bouncing across channels to get the information they need. You're forcing them to move from channel to channel by design. To get your channel mix right, your team needs to work on closing gaps in your experience, instead of thinking about channels as separate outlets for specific types of tactics.

That's why channel attribution is a myth.

Yes, some channels are better for certain stages of the customer journey than others. And yes, some aren't that effective at all. You need both eggs and flour to hold your cake together. Skip either, and you end up with a puddle dripping out of the sides of the pan (speaking from experience). But other ingredients you can skip altogether.

Building an attribution model only gets more complex when you start selling in multiple channels (website, partners, distributors, 3rd party retailers, etc.). And when sales competes with marketing for credit with your org, budget cuts lead to smaller and slower pipelines.

Let's look at two examples in completely different industries that all share the same issue.

You've probably heard "the customer journey starts and ends online" regurgitated all over LinkedIn. It's yet another myth that supports the first one about trusting your platform and attribution tool data. But even when everything from tablets to smartwatches is connected to the Internet, it's still not accurate to suggest customer journeys all start or end online.

Here are two real-world examples of how industry "best practice" on attribution falls apart at scale.

The SaaS customer journey for a CTO.

We've worked with both Digital Realty and Equinix, two of the world's largest data center providers who support all of your favorite brands from Amazon to Netflix.

We helped them go to market with new software and services aimed at decision-makers. Think about how a CTO buys a new data platform.⁵

Before they even begin their research, the journey starts with a need: increasing bandwidth and scale. But they don't immediately go online. They have friends in their space who have the answers already, so they call a friend to ask what they recommend. Or they learn about a tool at a trade show.

From here, they may go online to book a call on the website. Or they might continue to do research by going to Reddit and seeing what others are using.

They might skip all of that and go directly to AI. Then if their friend has a rep at their favorite data center, they may never even touch the website at all.

A DTC brand expanding into retail.

Because of the SaaS and e-commerce booms, a lot of marketers forget: over 83% of retail sales happen in physical stores. That means most customer journeys end offline.

Retail is a completely different game for attribution and go-to-market strategy. It's why Popcorners can win with a Super Bowl brand linking their chips to the blue-sky obsession of Breaking Bad, and why that strategy would tank brands that sell exclusively online.

As you might have read in our article on what you can learn from turnarounds, we met with a Senior Executive at a publicly traded mattress company to help them figure out why, even though they were spending 25x more on paid advertising than the category leader, they were near the bottom of all mattress sellers, losing market share every year… and losing nearly $90M in just 12 months.

The cycle started when their old marketing team had continued to use return on ad spend (ROAS) as their primary metric.

It started with their first successful campaign in 2016. By 2018, they're in stores and on the stock exchange.

They continue to expand into new retailers, but they're spending more and more on advertising. Eventually, their scale of costs is outpacing their revenue. The reason?

While people could learn about the mattress from the ads and the website, they still wanted to try it for themselves before ordering online. So they went to the mall, lay on the bed, and bought it in the store. The website showed a near-zero conversion rate, and ROAS looked miserable, even though these new channels were growing.

And consider this: a little over half of shoppers will visit a website before buying in the store. Even half of those customers will pull up the website while inside the store to shop.⁶ The customer doesn't experience your brand as separate channels: to them it's all a monolith.

But best practice tells you to track channels as if they operate in a vacuum.

Doing just that led to a series of changes in ads and their website that cost them their positioning. Instead of showcasing what made their technology superior and talking about how to know whether your current mattress is awful, they diluted their message down to "A better night's sleep," and talked about keeping cool at night. Surveys will show you that's a real customer concern, but not one you can position around for very long.

One of your agencies will tell you just to set up a geofence and track walk-ins tied to ad impressions. But it won't solve measurement issues. We used that measurement strategy for Potbelly and tied in POS data, but that data still only helps directionally. Nothing beats a double blind and tracking how all channels lift together.

But their marketing ROI issues went deeper than tracking store sales back to ad spend.

While they were losing the positioning they had earned in the first 5 years since launch, new entrants came in with the same generic messaging at less than half the cost of their flagship mattress.

Throw in the same Apple privacy and platform changes we've all seen, and you have a recipe for financial disaster. Quarter after quarter of negative EBITDA left analysts doubting they could climb out of the hole.

Using ROAS as a North Star long after it stopped reflecting the business led them to offer steep discounts online to make that channel "win."

This is exactly why we talk extensively about the importance of incentives both in and outside of your business. And just as important as aligning incentives, you've got to align metrics.

You probably already came to the conclusion based on the mattress case study that ROAS is a volatile metric. It not only can't tell you whether your channel mix works together; it sometimes points you in the opposite direction of successful messaging.

For example, when you are running ads to an audience who has never heard of you, then retarget them within a day or two with a coupon, but the decision window is six months, that first ad gets almost no credit while your margin erodes on multiple fronts.

That first ad is a critical touchpoint. It's where you have the opportunity to interrupt belief patterns. Helping the prospect to have language for how to discuss their problems.

ROAS itself is a proxy. It tells you revenue per dollar of ad spend, but it doesn't account for overhead, fulfillment costs, returns, or lifetime value. A 5:1 ROAS can be a loss if your margins are thin and the customer never comes back. Not to mention, it's a single point metric, which we've shown is one of the first missteps in strategic planning: Why Strategic Initiatives Fail. And How to Make Yours a Success.

At Old School Labs, where I joined as the CMO and lifted revenue 87% in 10 months, our most profitable cohort came in on a .6 ROAS. But because margin was so high on that product, it outpaced our second most profitable cohort by 5x.

Agencies would have cut that campaign out of fear because they only know how to look at platform data, not the business as a whole. When they aren't high-fiving over views, comments, and shares, they're falling for the ad industry psyop: more awareness means growth.

Awareness doesn't drive action. I know exactly where Chuck E. Cheese is out here, but it's not my go-to place for wings and beer with my friends.

Other proxy measurements like iROAS are supposed to measure incrementality, isolating the revenue your ad created above what would have happened without it.

Just like we talked about at the start of the article, that data is dirty. The platforms are incentivized to take credit. And it's often measuring the performance of content targeting your full audience. There are no control variables.

In practice, incrementality estimated outside of data you can audit and control is still an estimate built on assumptions the platform controls. The platform decides what the baseline would have been. The platform decides how much credit to give itself.

As my news media professor at BYU reminded us at the end of every class, "If your mom tells you she loves you, get a second source."

Take a moment to think about the strategy decks you've seen up to this point in your career. Did any of them make the mistakes that we've discussed so far today? Did they base a strategy on channels or increasing ROAS? Did they fail to demonstrate how channels should work together?

If you answered yes to any of those, then you'll know this is a very common problem.

These issues show up even in multi-million dollar strategy decks from reputable agencies. We've seen that firsthand from firms you wouldn't expect. Steve Jobs was right to criticize thinkers (consultants) who don't do the work, leading to mediocrity.

It's an industry issue in the making for the past 60 years that got worse with TV, and has only decayed with "digital marketing."

This decay is best seen in how we've bastardized the term marketing mix, which you've been told is the same thing as what channels you use for advertising and communication.

That's one-twelfth of what Neil Borden illustrated when he built the marketing mix in the 1940s. It included everything from the product to distribution channels, personal selling, advertising, servicing, fact-finding and analysis, or in other words, research.

When research gets diluted to a simple S.W.O.T. analysis, a few bullets of competitive comparisons, ads get tracked with ROAS, and discounts replace selling on the merits of the product, it's the fastest way to commoditize a brand. That's exactly what happened to the mattress company.

Their newer agencies made content they thought would be funny, without understanding the market. Digital marketing got treated as separate from marketing, leading them to focus inward, prioritizing results on digital channels, instead of anticipating consumer needs. Market intelligence got replaced with dashboards. Then company-wide messaging changed so often that they lost their positioning.

But their internal teams kept approving the changes and pushing IT to update the website, all to their detriment. They missed what the entirety of the market was doing because they were too focused on visitors, followers, and customer lists instead of the population at large.

These are all good people doing what makes sense, according to their agencies, the ad reps that advise them, and different execs cycling in and out. It's what happens when businesses scale: they get siloed, and every silo does what it can to survive. And sometimes that means quick wins in the form of tactics that break away from a strategy.

The first question I ask executives when a business is off track: "Who do you serve, with what product, for what purpose?" If they can't answer that in a sentence or two, every plan, deck, and fire drill will only cost them more time.

That's when we roll up our sleeves and get what I like to call "a good look inside the house."

Your vendors see traffic or leads go in the front door.

Your team sees sales come out the back.

Most reports show visits, views, reads, what pages were read, but they don't give you enough to know what's going on in the house.

Week-over-week reports miss what prospects were thinking when they got to the site. What outside information they consumed. The conversations they had with their stakeholders or when they passed the baton from a junior researcher to a decision-maker.

There are a lot of stats in dashboards, but rarely any insights.

It's hard work. Even with AI. You've got to know how to structure data and think like an anthropologist to discern between a generic persona and a genuine customer cohort. You've got to completely change the paradigm from advertising is marketing to marketing is a study of human behavior responsible for driving sales.

It's a big shift. But if you can pull it off, you might just be the next Nvidia or Apple.

It's one thing to know that there's a 2% conversion rate on your site. But another thing to know what the other 98% cared about that's missing from the site.

Last year, we helped a brand with a full campaign that dropped their CAC from $52 to $13.50 in just a few weeks. But before we created any content, we filled in gaps by answering questions visitors had before visiting their site. And we did that through feature engineering and machine learning. And if that sounds intimidating, it doesn't have to be. Equivalent tools are free and work on your laptop.

But that discipline showed us the prospects didn't care what the product did. They didn't care about the temperature or volume of this product. But once they understood how it impacted their pet's quality of life, we nearly ran out of stock.

20 years since cracking my first cheesecake, I've long since mastered the key to preventing it from cracking. It took dozens of attempts, all with controlled variables: sometimes I took things away, changed the bake time, the temperature, added more flour.

Your marketing works the same way. The people, the product, the positioning, the message, the experience, and the measurement all have to work together. The biggest mistake you could make after hearing this is going back to reporting on channel performance using single-point metrics like ROAS, instead of getting to the heart of why your customers buy and why the prospects aren't.

The good news is, you don't have to rebuild anything right now. If anything, what you have is the right scaffolding. There are just gaps in the experience you can start closing today.

To get your team pointed in the right direction, consider these questions:

Are you measuring contribution margin, or ROAS?

Are you monitoring market shifts in real time, or relying on a strategy deck an agency made two years ago?

Do you have visibility into what information your ICP has or wishes they did?

Can you describe how your ideal customer sees themselves, not just their age, gender or income?

No platform can replace that level of understanding. People intelligence is the strategy that matters most when it comes to marketing. Everything else is boxed cake.

You don't have to figure it out alone. We've been through this hundreds of times and are happy to help while you enjoy your weekend or head out on vacation. You'll come back to a full plan of attack to cut waste and turn things around in weeks, not months.

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. Run the Diagnostic →

¹ Attribution model: A set of rules or algorithms that assign conversion credit to marketing touchpoints along the customer journey.

² ROAS (Return on Ad Spend): Revenue generated per dollar spent on advertising. Typically reported by ad platforms using their own tracking data.

³ CAC (Customer Acquisition Cost): The total cost of acquiring a new customer, including advertising, sales, and onboarding expenses.

⁴ iROAS (Incremental Return on Ad Spend): A measurement approach that attempts to isolate the additional revenue generated by an ad above what would have occurred without it.

⁵ The enterprise buying cycle for technology platforms typically involves 6 to 18 months of evaluation across multiple stakeholders, during which prospects move across dozens of channels and touchpoints that no single attribution model can fully track.

⁶ Published research puts these figures higher: Bazaarvoice's ROBO (research online, buy offline) study found 82% of smartphone users consult their phones on purchases they are about to make in a store, and multiple retail studies place the share of shoppers who research online before an in-store purchase above 80%. The in-text figures are stated conservatively.

Related:

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.

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