My grandpa is a collector of vintage cameras. Like any old collector, he's quick to tell you about how much better the engineering of older equipment is.

"Things today are built to fall apart… It's planned obsolescence."

Unfortunately, he's right. And it's not just cameras. In many business models, the failure point is the business model.

From the auto manufacturers whose belts wear out at predictable cadences, to the batteries inside phones that will need to be replaced after so many charge cycles. These models are designed to extract revenue over long periods from the same customers.

But those failure points aren't advertised, and in the case of your vendors they most certainly don't come up on discovery calls. They're what you find out later. After the contracts are signed and the check clears on the other side of the sale.

The costs of "learning" on ad platforms and keeping up with the algorithm, or maintaining your cloud architecture, quickly balloon from an experiment to a daily operational expense. And often, once you get going, you can't stop.

That “can’t stop” fear is just one myth we're going to talk about today. By the end of this article, you will see how incentive structures create industry “best practices” designed to create hidden failure points to maximize revenue for vendors, while costing you your profits.

It probably will take you only 30 seconds of doom scrolling before you see an ad to "get the most out of your data" or a promise about how a new tool will "help you scale your business." If you have the discipline not to doom scroll, your LinkedIn inbox is flooded with the same types of messages.

We've seen that message on everything from analytics connectors to Salesforce. It's ambiguous. It doesn't offer a specific outcome. It feels vague, almost as if they hope you’ll imagine what that means on your own.

Unfortunately, the only thing most tools predictably scale is your costs, not your revenue. Not quite the promise we expected.

The sales team pitching you knows what their tool does. They don't know how your business makes money. And that means the tools they are pitching are designed as one-size-fits-all. You can imagine how that works out.

Most marketing agencies charge a percentage of your ad spend or a retainer that scales with the scope of work. The more you spend, the more they earn. That's the model. It means the agency is structurally incentivized to recommend increasing your budget, expanding into new channels, and launching more campaigns, regardless of whether the current ones drive profit. That is a perverse incentive: they make more money even when you lose money.

At a large insurance brand, a new agency reported decreasing Customer Acquisition Cost¹ (CAC). We were brought in as a second set of eyes on the initiative because some of the internal stakeholders were skeptical. In the interest of transparency, this agency sold and managed programmatic² ads, a service our firm did not offer and had no stake in, except to protect the interests of our client.

Their first test did show lower acquisition costs within the first three months, according to their reports. But, looking outside of the organization, we found a seasonal demand wave created by external triggering events, which neither the agency nor the business had been monitoring.

Because their test was global to all states and regions where this company could legally advertise, their data was noisy. They didn't know that. Or if they did, it wasn't in their best interest to admit it.

A seven-figure renewal was on the line for the agency.

For the insurance brand, on the other hand, that represented a 20% increase in ad spend.

The agency pressed for one more test, and this time we had full control over experiment design.

We deployed the attribution and measurement systems we've used to grow businesses from $3M to $100M in just a few years, and on turnarounds for brands like Potbelly.

Part of that system involves controlled experiments focusing on contribution margin.

The agency insisted they wanted recall to be the primary metric. We put our foot down on looking at net new acquisitions.

Acquisition costs went up in every treatment where the ads ran. When CAC did go down, it went down globally, across both the control and treatment areas, aligning with larger news events and seasonal waves that impacted demand. As we suspected, the agency had been riding a seasonal demand curve.

To be fair to the agency, we did note opportunities in their creative messaging and the structures they used to design their cohorts so they’d be armed with what they needed to succeed in the future. But the findings were clear: the ads hadn’t reduced CAC as the agency originally reported. So, the seven-figure renewal didn't happen.

When the firm doing the analysis also sells the media or the creative, there's no incentive to tell you what's not working, unless they are invested in your success. When they aren’t, their revenue depends on you continuing to spend and scale even when it doesn't work.

As we mentioned in our article on what marketing audits and consultants miss, we know an agency owner who told us directly that their strategy was to "extract revenue from enterprises." Even when they knew internal teams were perfectly capable of doing what they were doing and could have pointed it out, they didn't mention it. The cruel irony is that this was a vendor hired specifically to identify growth opportunities within the business and to enable teams through skilling them up. Which implicitly means they should have had the goal of finding profit.

It got worse when they found out another vendor was doing something similar. They spent half their time and resources trying to figure out how to undermine that other vendor so they could win that contract too. Instead of finding profit, they were protecting their territory, afraid of being found out, all the while hurting the morale of the internal team. The right thing to do would have been to enable the internal team as much as possible, even if that means working yourself out of a job.

It's not just ad agencies of course. It's any vendor that sells you services where there's an exchange of time for money. The incentive to expand scope, extend timelines, and add deliverables is baked into many vendor models. First year is capital expenditure. Every year after that, it comes out of operating expense. And it never gets questioned because it's "the agency we've always used."

It's the reason we scope every engagement up front. Our fees are flat and not tied to your spend, and we don't bill by the hour, so you know exactly what you're getting. ROI is planned from the start.

Marketing agencies have their place and often can do things no internal team can. But why do some agencies insist on staying long past their welcome?

Let's zoom out for a minute. All of us have heard that "breakfast is the most important meal of the day." If you've ever visited a high-end home from the early 20th century, you've probably walked through a music room. Neither of these ideas showed up on their own. Both of them trace back to Edward Bernays, the father of propaganda.

It turns out bacon and eggs were mostly eaten at dinner. But the Beech-Nut Packing Company wanted to sell more bacon, so Bernays got 4,500 doctors to confirm that a hearty breakfast was healthier than a light one, then published the results in newspapers across the country.³ The American breakfast was born. Just the same, Bernays outlined in his 1928 book Propaganda⁴ how to sell pianos without ever asking someone to buy a piano. Instead, you convince architects to include music rooms in their home designs.

You stage exhibitions of music rooms by well-known decorators. You invite famous musicians and society leaders. Before long, having a music room is a status symbol, and no one really understands why.

Thought leadership from industries walks this same fine line between being genuinely helpful and creating unnecessary problems. The agencies we work with follow the same trend reports and industry narratives that all of us read on LinkedIn or Forbes. Most of it is genuinely helpful.

But it's hard to know when there's another motive at play. They've got to sell you on ideas that make the industry's offerings seem both irresistible and essential for the next stage of your business.

Let's look at how their incentives don't always align with yours.

B.F. Skinner proved you can make a pigeon superstitious.⁵ Put it in a cage and arrange for food to appear at regular intervals. Whatever the pigeon happens to be doing when the food arrives, spinning around, bobbing its head, it will keep doing over and over again, convinced that the dance caused the food to appear. Seth Godin wrote about this in Fast Company, and the parallel to business is uncomfortable: there's plenty we do, plenty we've always done, that has nothing to do with what actually works. But once we've made up our minds, we're like pigeons. We don't want to change our behavior, regardless of how much data we see to support a better alternative.

There are a few key superstitions that advertising platforms have created that impact what your agency does downstream.

The myth of awareness. The concept of awareness has been bastardized. It's been twisted inward to have businesses focus on the idea that we need to help customers become aware of who we are. "Here I am!" Look at us. Notice us. Remember our name.

The original intent of the customer awareness model comes from Eugene Schwartz, whose 1966 book Breakthrough Advertising is still considered one of the most influential works on advertising ever written. By his own account, 85% of his ads paid out, a hit ratio he described at a Rodale Press seminar and one of the highest anyone in direct response has claimed.⁶ He would know. He literally wrote the book on this. And his model was for businesses to be deeply aware of what customers cared about, in a specific sequence:

First, helping them organize their symptoms into a simple, easy-to-state diagnosis, moving them from unaware to problem-aware.

Second, helping them understand what solutions were available and comparing and contrasting yours.

Finally, helping them by educating them on what your product can do for them, promising them a transformation.

That last step is where most marketing funnels begin. Even sophisticated models like the bowtie funnel introduced in Revenue Architecture⁷ by Jacco van der Kooij make this same mistake: assuming that awareness starts with telling people "here I am" and trying to capture a lead where you then have the opportunity to educate them. Why would anyone want to give you their email address before they even understand they have a problem?

It all goes back to how advertisers have made money since the newspaper days. They make money when you buy an ad. The more ads you buy, the more budget you put behind them, the more money they make. But what if you don't get sales right away? They want you to think that direct response is something only tacky marketers do, and that you're doing something more sophisticated by "raising awareness through branding."

Yes, there are brands that win with a brand play. But those brands have massive budgets and years of market presence.

If you're BMW, you can afford high acquisition costs and have a huge moat as a luxury vehicle.

If you're Popcorners betting on a Super Bowl commercial, you don't do that before you're in every grocery store to capture the moment someone wanders down the chip aisle looking for snacks.

Distribution models rely on a sequence of logistics. Successful advertising relies on a sequence of needs and wants, not just the ability to state you exist.

For businesses where there's no organic rediscovery, or where the problem is far disconnected in the customer's mind from the available solution, awareness doesn't translate into sales. It wastes money.

Think about Spirit Airlines. You knew they were around when they existed. But only as the butt of a joke. Being aware of them doesn't mean you're going to book them for your 10th anniversary trip. Awareness does not equal action.

Keep everything running no matter what. You've probably heard this from your reps. It doesn't matter whether you're advertising on Meta, Google, or TikTok. Every one of them will tell you "don't turn this off, it will reboot the learning phase" or that one ad is helping these other ads get credit. They want you afraid to turn off what's not working, thinking it will hurt everything else that is working.

One of my friends owns several successful chiropractic clinics and was spending nearly 15% of his revenue each month on advertising heading into the pandemic. When he thought he wouldn't be able to continue operating, he shut off all his ads. Revenue stayed the same. It turned out everyone was finding him organically through Google My Business and local SEO. When he was ready to open a few new locations and actually grow, we were able to get him better results for a tenth of what he had been spending before, helping him open two more clinics.

It turns out a century later, this quote hits just as hard as it used to: "Half the money I spend on advertising is wasted. The trouble is, I don't know which half." In a world of bots, ad blockers, and third-party data becoming less and less available as privacy regulations expand, even the attribution tools you're paying top dollar for can't do much for that problem. It really doesn't help when every platform wants its credit.

So how do you know what's actually working? It's common practice to "raise awareness" on Meta, driving to a vague home page, leaving prospects to Google to figure out what it is you actually do. They end up reading about you on an affiliate site or a reviews blog, only to come back in organically, if they come back at all.

You've forced them to jump from channel to channel. In this case, all channels were necessary, but that's not always the case. It happens in organizations of all sizes, from the Fortune 15 down to brand-new businesses. And the more fragmented channel management gets, the more they start cannibalizing each other between competition for budget and credit.

These strategies can look like a win because revenue goes up. But 15-30% of your margin gets lost in the inefficiency of having to leave everything on to protect a fragile system.

Between gaps in the customer journey and fragments of information spread across multiple channels, most customer experiences are an obstacle course when they should be an amusement park.

Don't let your agencies substitute research with "industry best practices" designed to benefit the platforms that created them. It's the reason a certain platform keeps telling you that shorter is better when it comes to ads. That "attention spans are only 8 seconds."

How many times have you heard people share that attention spans are only 8 seconds? It's still shared on LinkedIn by executives. To their credit, the reports shared by their teams likely corroborate this, but not for reasons they'd expect.

The latest data shows that the most popular creators on YouTube are long-form. MrBeast averages about 30 minutes per video. Kill Tony, one of the biggest comedy podcasts, runs for three hours. Alex Hormozi goes for an hour. Cody Sanchez averages about 20 minutes. Even Ninja Kids runs 15 to 20 minutes for content directed at children.

But creators like Jack Neel, Joe Rogan, and the Diary of a CEO average 3 hours for long-form conversations.

The most watched creators? The wealthiest creators? The most trusted? Same creators. All long-form content.

It’s a hell of a lot longer than 8 seconds. This is yet another "breakfast is the most important meal of the day," undermining your ability to connect meaningfully with your customers.

Maybe you're thinking, "But Omar, we're not content creators. I don't want to sit in front of a camera all day." All content has to be designed to persuade, and importantly, hold attention.

Someone in your network has shared an infographic or a quote from the great Robert Cialdini's book, Influence.⁸ This principle is proven time and again: what we pay attention to, we deem more important. If you can hold attention long enough, you will be considered more important in the customer's mind.

One of our longest-running ads was over 3 minutes with a watch retention of 80-90% for almost 3 years. We only had to relaunch it after an algorithm update had us remove the word "ass." And it kept running for years after that. 3 minutes.

In fact, most of our ads have been long-form for the past decade, holding attention to broad audiences for 90 seconds or longer. And the most important thing: they drove tens of millions of sales. That ad, three years untouched and a few more after that single edit, took a business from $300k per year to $20M per year and a healthy exit.

If watch retention is 8 seconds, the content is aimed at the wrong person, says the wrong thing to them, or misses on both. That's what makes this next myth so brutal.

This one is ironic. Any ad platform is a content platform first. They are designed to keep users inside their ecosystems by showing engaging content. Whether that's blogs, videos, social media, or music, people came to pay attention.

So the idea that your Return on Ad Spend⁹ (ROAS) is the most important metric when it comes to promotion embeds an assumption: you have to spend money to be seen.

For most brands, this is true, and there's nothing wrong with advertising. When you're unknown, it's the fastest way to get noticed. But advertising doesn't compensate for not having the best product in the market. As Robert Stephens, the founder of Geek Squad, once said, "Advertising is the tax you pay for being unremarkable." The less remarkable you are, the more you pay.

Think about this. Tesla reached $81.4B in revenue and a trillion-dollar valuation with an ad budget of under $200k. About $0.15 per vehicle sold.

Then there's Apple. Every campaign had a goal to reshape a conversation and redefine the culture around its products. And it paid off.

From their 1984 campaign that drove $155M in sales, to the iPod ads with colorful backgrounds, and the iconic "I'm a Mac, and I'm a PC" campaigns, Apple has achieved more profitable ad efficiency than any other company of their scale.

They became the first trillion-dollar publicly traded company. And they did it, spending 1 to 2% of their revenue on ads.

Their secret weapon was never the media buy. It was knowing their customer. The better they knew that customer, the less they had to spend to acquire customers.

Apple advertising investment relative to revenue. Source: Form 10-K filings, 2006–2015.¹⁰

In Steve Jobs' own words: "You've got to start with the customer experience and work backwards to the technology. You can't start with the technology and try to figure out where you're going to try and sell it."

The 1984 campaign held the subtext, "you're worth more than just being a corporate drone," leveraging motifs from one of the most famous works of dystopian literature.

The iPod commercials doubled down on standing out from the crowd with white headphones instead of the black ones everyone else had. And they never had to say you can dance around without your CDs skipping. They showed it.

And the "I'm a Mac" campaign solidified Apple as the go-to brand for creatives and a status symbol for those who could afford them.

Over the decade, Apple's ratio of marketing spend to revenue improved because its target wasn't restricted to ROAS or whether it outspent the competition.

Goodhart's Law puts it plainly: once a metric becomes a target, it stops being a good metric. And teams focusing on short-term ROAS wins to keep the status quo are missing top-line growth while squeezing margin every second they ignore insights about their customers.

As we cover in our article on the S-curve of growth, market fit goes deeper than just the product, and it's not a one-time event. And neither is scaling your ads to a specific monthly budget.

That's what makes this next myth a gut-punch.

As we just looked at, the platforms exist to capture your attention for as long as possible. They do this with algorithms.

If knowing your customer better directly translates into better profits, it's no surprise the platforms with the largest data stores have the best algorithms and the margins. Their decision engines aren't built on just age, income, and demographics.

They're built on what people do, how they react, and what they pay attention to. It's why two people on opposite ends of the world, in different age groups, with different career paths, can both be shown the same shows on YouTube, or even the same ad for a SaaS product. Relevance is about behavior cohorts more than simple personas.

So the good news: algorithms are not designed to steal money from you or to hurt your advertising. They're designed to connect better with customers and provide users with a better experience.

Nobody has more data on what each of us cares about, how we feel about brands and products, and social issues than social media platforms. AI is quickly catching up. As of 2026, millions of people are using AI as a therapist, a confidant, and a sounding board for their deepest concerns. It's no wonder OpenAI just started selling ads.¹¹

These qualitative data stores are massive and growing larger every day.

The bad news: They can't share it with you. Privacy laws don't allow them to. And it's their competitive advantage against the next platform trying to sell you ads.

Instead, they aggregate their insights to the lowest common denominators:

"Others in your niche are seeing the best results from short-form content."

"You've got decent hooks, but you really want to be using UGC or testimonials."

"Have you looked at your landing page? Maybe there's a better place to drive traffic."

Meanwhile, everyone's content breaks when there's an algorithm update.

This is where all of that bad industry advice across domains compounds from seeming genuinely helpful to financially dangerous.

Let's think about how that advice sits against the real world.

So, UGC and testimonials are all that matter? Because they have the highest ROAS?

Discounts often have the highest ROAS, too. Should we be giving away the product to increase ROAS?

It's no surprise that the ads that perform the best using the ROAS metric capture people at the end of their customer journey and are reported on a metric built for and tracked by the pixels placed on your site by the people who make money by having you pay for ads.

Last click attribution is like saying Apple is only successful because Tim Cook was the CEO.

It's a clever frame. They know you won't like the ROAS metrics of the content that does the real persuasion and changes public opinion, because it doesn't sell right away. They know you won't like the truth that not every click or impression can be directly tracked. But that indirect measurement is critical for growth.

And what they most don't want you putting together: dollars out have to multiply dollars in. Not just in revenue, but in profit. And that's the real problem with ROAS. It reports ad costs relative to revenue, not profit.

That's why with Potbelly, we made spend five times more efficient at level budget, and grew average order size, before we scaled.

This approach helped swing Potbelly's net income from a $23.8 million loss to $4.3 million positive.¹² That efficiency supported a 30% lift in revenue in 2021 and 19% the following year, with same-store sales up 18.5% and full-year shop-level margins expanding from 7.4% to 10.5%. Their own earnings release credited "strength in digital marketing and traffic."

And we did that by focusing on the feeling our ads created, built on hundreds of insights we gathered from the market in real time, only treating ROAS as a leading indicator, not the destination.

The incentives at play keep you from the industry insider strategies that dwarf "best practices" because they're designed to gate data and to serve reps who just want to see you spend more on their platforms.

Remember earlier how we talked about how it's not just ad agencies, but all vendors whose incentives are not aligned to your business? Unfortunately, it's not just the ad platforms either.

From SaaS companies that want to sell you more seats to justify their sales teams' quotas, to cloud platforms that make more money the more you store on their systems, all of these businesses survive on the vague promise that you'll get better insights and make your team move faster. It is the same perverse incentive at work: these vendors earn more when your costs go up. None of them are willing to be accountable to your actual top and bottom lines.

They know it. You have to wonder who created the term "middleware" to begin with.

In the age of AI, a lot of these tools are poised for disruption. Our team has been able to cancel several subscriptions by replacing them with on-demand APIs and Claude Code, completing the same work at a fraction of the cost with more flexibility and no recurring contracts. But there's a catch: we are starting to see token costs increase. It's what made us pause to focus on building deterministic software and minimizing context into each LLM call, avoiding the new industry trap: agentification.

The people telling you to use more tokens are the ones selling the tokens. This builds on the trend we've already seen with cloud providers.

They perpetuate myths like "data gravity," the idea that the more data you have, the more tools, data, and talent it attracts. Of course they want you to think you've got to spend more money storing every single data point you've ever tracked, even when you're measuring proxies like ROAS or views or shares. Things that don't track back to revenue.

Most of the data that lives inside these clouds can be archived without any impact to business operations. Once the actual insights of what happened and when are extracted, they go from raw data into real intelligence.

In fact, against most enterprise systems we've encountered that cost millions to build, we've seen teams relying on raw data for decision making in dashboards, while the actionable intelligence was extracted with simple tools like spreadsheets and local statistical analysis. Free tools.

This extends the point we made in our article on what marketing audits miss: half of purchased martech tools go unused by the teams that bought them, which means the cost of implementation, maintenance, and roll-outs was wasted. That waste is only accelerating as quicker, better, and cheaper options become available that your teams may not even be aware of, yet.

With all of these channels running and conflicting narratives bombarding us all day, it's no wonder that when we identified gaps in the go-to-market and data infrastructure plans with the CIO of a half-billion-dollar insurance company, his word to describe our findings was "dizzying." He recognized the truth in them right away.

Others in his position have asked, "With everything I have running, how do I even know what works and what I'm wasting money on?" That question is the origin of our most successful frameworks based on subtraction. We've relied on it for attribution modeling, causality, and tracking incremental revenue lift in every engagement since 2016.

If there's any wisdom to take away from this analysis of external incentives, it's not to trust the advice of those who don't actually do the work or take accountability for the results at face value.

Your ad platform reps, your cloud vendors, your SaaS sellers all have their own agendas because they've got families to feed, bosses to impress, and team members to support. You making more money or hearing the full truth doesn't always align with their incentives.

But if that's what's happening outside your organization, what's happening on the inside?

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 Customer Acquisition Cost (CAC): total marketing and sales spend divided by the number of new customers acquired.

² Programmatic: automated ad placements across websites and video platforms, purchased through bidding algorithms.

³ Tye, L., The Father of Spin: Edward L. Bernays and the Birth of Public Relations, Crown Publishers, 1998. Documents the Beech-Nut Packing Company "hearty breakfast" campaign and the 4,500-doctor survey.

⁴ Edward Bernays, Propaganda, Horace Liveright, 1928.

⁵ B.F. Skinner, "'Superstition' in the Pigeon," Journal of Experimental Psychology, 1948.

⁶ Eugene Schwartz, Breakthrough Advertising, 1966. Hit ratio as described by Schwartz at a Rodale Press seminar and widely reported in direct-response literature. Self-reported, not independently audited.

⁷ Jacco van der Kooij, Revenue Architecture, Winning by Design, 2023.

⁸ Robert Cialdini, Influence: The Psychology of Persuasion, 1984.

⁹ Return on Ad Spend (ROAS): revenue divided by advertising cost.

¹⁰ Apple Inc. Form 10-K filings, fiscal years 2006–2015. Advertising expense disclosed under Summary of Significant Accounting Policies; net sales from segment reporting tables.

¹¹ OpenAI confirmed advertising in ChatGPT January 2026, with rollout to Free and ChatGPT Go tiers in February and a self-serve Ads Manager in July 2026.

¹² Potbelly Corporation, fourth quarter and full year 2022 results. Full-year revenue of $452.0 million, up 19%; same-store sales up 18.5%; shop-level margin 10.5% versus 7.4%; net income of $4.3 million versus a net loss of $23.8 million.

Related:

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.

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