They weren’t alarmed. They weren’t even sure they believed me. They were just... quietly confused about why their ads never seemed to match what was happening in the business. They had taken it for granted that the numbers were never going to line up — so instead of digging into why, they just kept paying the “Google tax” and hoped that more spend would eventually move the needle.

It never did.

That confusion is exactly what a mirage looks like from the inside.

Here’s what I found when I got into the account.

The Mirage

The client had server-side conversion tracking set up through Elevar — which, on the surface, is actually the more sophisticated approach. Better data capture, harder to block. Their agency had done something right.

But there were two problems nobody had addressed.

The first: the setup couldn’t distinguish between new customers and returning ones. Every purchase looked the same to the algorithm — a conversion is a conversion. So when a loyal customer who had been buying from them for two years clicked an ad and purchased, Google took credit. When someone came in from email, clicked a retargeted ad on the way, and bought — Google took credit. When someone searched their brand name because they were already going to buy — Google took credit.

The second: they also had a physical retail store — and nobody had filtered out the POS sales channel in Shopify. That store was actually doing more revenue and transactions than the online store. Which means the majority of what Google Ads was claiming credit for had nothing to do with Google Ads.

The ROAS looked great. The algorithm was thrilled. It kept doing more of what it thought was working.

This cuts both ways. A 36x ROAS sounds incredible — but if it were real, why wasn't the business scaling immediately? The number itself is the red flag.

What it was actually doing was recycling warm audiences and claiming credit for in-store sales — and calling all of it performance.

When this becomes the agreed-upon measure of success — and the agency never speaks up — there are only two explanations: negligence or malice. Either way, they should be fired.

The Shift

The next step was clear. Three core changes to get this account back on track, understand what was really happening, and — more importantly — feed the algorithm the right data.

1. Filter the POS sales channel out of conversion tracking. We excluded in-store purchases from what was being reported back to Google Ads. The algorithm could now only see — and optimize toward — actual online revenue.

2. Implement OccamizeOS network tagging to separate new and returning customer conversions — and capture 100% of clicks. Returning customers are valuable. But they’re largely going to buy with or without your ads. Letting the algorithm take credit for them inflates your numbers and trains it to chase the wrong audience. With OccamizeOS in place, we had a clear, accurate signal of what the ads were actually driving in net-new acquisition. A simple rule of thumb: if more than 10% of your budget is going toward repeat buyers, you’re spending too much on them. Full stop.

3. Restructure the campaigns around clean data. Once the signal was clean, we rebuilt the campaign structure around it. The algorithm finally had an accurate picture of what a real new customer looked like — and it could start finding more of them.

None of this required a new tool the client had to learn. No new budget. No new strategy. It was diagnostic work first — understanding what the data was actually measuring before deciding what to do with it.

The Result

Within the first week, the account was operating on clean data for the first time.

To understand what new customer acquisition had actually been costing them, we had to back into it. We took their total ad spend over the date range, identified any Shopify orders that came from a Google Ads click, and divided cost by those first order conversions. That was the only way to reverse-engineer a real nCAC — because Google Ads is already a bottom-of-funnel, high-intent channel, so attributing those clicks to new customer acquisition was a reasonable proxy. It wasn’t perfect. But it was the closest thing to the truth the old setup would allow.

Here’s what that math looked like: over a 15-day period, they spent $6,250 on Google Ads. Elevar reported 1,024 conversions at $6.54 each. But when we looked at only the 23 net-new customers acquired during that same window, the real nCAC was $271.74.

They thought they were getting new customers for $6.54. They were actually paying $271.74.

That clarity changed everything, including the media mix. They had been spending $16,000 a month on Google Ads. We pulled that back to $10,000 — which was what I thought they needed to hit their goals on that channel — and shifted the remaining budget to Meta.

The reason: they’re a visual brand with products that stop the scroll. Meta is a cold audience machine when you’re set up correctly, and we knew that’s where their next wave of first-time customers was going to come from.

The reason: they’re a visual brand with products that stop the scroll. Meta is a cold audience machine when you’re set up correctly, and we knew that’s where their next wave of first-time customers was going to come from.

By month one, nCAC had dropped from $271.74 to $8.90 (and this is just in-platform but remember, this is network-tagged so it’s directionally more accurate…that said, we always look at all ad spend / 1st click CAPI imported new customers to get that real number in a spreadsheet)

Before we started working with them, Meta was running a single DPA campaign — essentially a retargeting loop with no way to distinguish new from returning customers. Once we implemented network tagging, we could see the full picture for the first time: first-click, edge-tagged CAPI imports, new vs. returning split, and in-store traffic filtered out.

Here's what that revealed:

New customers were up 46% year-over-year by February — from 1,803 to 2,609:

Now, a fair question: how do we know it was us?

This business runs Google Ads, Meta, and Amazon. Buyers don’t purchase from a single channel — they touch multiple before they convert (like literally all businesses and consumers). That’s exactly why we don’t hang our hat on platform-specific metrics. We focus on business metrics: new customers acquired, nCAC, nMER, and contribution margin (just to name a few). These are the numbers that reflect what’s actually happening inside the business, not what any single platform is claiming credit for.

We are the only thing that changed for this business over the past year. And the business metrics moved. That’s the most honest attribution model there is.

The client’s question at the start of the engagement had been:

“How much do we have to spend?”

It became:

“How much can we spend?”

That’s the difference between an algorithm trained on a mirage and one trained on reality.

The Check

Here’s the question worth sitting with this week:

Do you actually know what your paid advertising is training the algorithm to do?

Not what whoever is running your media buying told you. Not what the platform dashboard implies. What signal is actually going into the algorithm — and whether that signal represents the customers you actually want more of.

Because this isn’t about reporting. We don’t care what the platform says. We care what the bank account says. Those are two very different numbers, and confusing them is exactly how you end up paying $271 for a customer your platform told you cost $6.54.

A few things to audit right now:

• What are you actually measuring? Most businesses are overreporting — both in-platform and through third-party attribution tools that claim to solve the problem. They don't. They're just a more expensive guess. All you need is an understanding of how the algorithms work, your e-commerce store's real revenue data, and a sheet tracking the right metrics weekly. We track 12 business metrics — not marketing metrics. There's a difference, and it's the difference between knowing what's actually happening in your business and chasing numbers that make your agency look good.

• Can you separate new customers from returning customers in your reporting? If the answer is no, your algorithm is being trained on the wrong audience — warm, returning buyers instead of cold, net-new customers you can actually scale from.

• If your in-platform results look too good to be true, they probably are. Google and Meta are taking credit for conversions that were going to happen anyway. I like to say it this way: if my in-platform ROAS looks bad, I know my bank account will look good. That’s not a bug — that’s the point.

That gap between what the platform is claiming and what the business is actually doing? That’s your mirage.

Think this might be happening in your account? Book a free Conversion Diagnostic at Occamize.com and find out exactly where your tracking is lying to your algorithm — and what to fix first.

Learn more about Occamize

The Performance Mirage is published by Ryan Levander - founder at Occamize. We help businesses running Meta and Google ads replace broken measurement, bad attribution, and ad-hoc "let's try this" testing with a simple, rigorous optimization process — so you always know what your ad dollars are really doing.

Scott Reid is the founder and CEO of E-commerce Optimizers. In this episode, Ryan and Scott dig into why most e-commerce brands are measuring the wrong things — and what to track instead.

What We Cover

→ Why platform ROAS is a mirage Meta and Google will always tell you things are working. That’s their job. The real question is what’s happening in your bank account — and most brands never look there.

→ The shift from marketing metrics to business metrics 75%+ of e-commerce businesses have no real view into what’s driving results. They’re measuring activity inside ad platforms, not performance inside the business. Scott breaks down why that gap is costing brands more than they realize.

→ The Marketing Contribution Tracker Scott walks through the exact spreadsheet he uses with every client — populated manually every Monday morning, taking roughly 10 minutes. Key inputs include net sales, COGS, new vs. returning customers, Meta spend, Google spend, and email/SMS revenue.

→ The metrics that actually matter The four guardrail metrics Scott tracks religiously:

• MER (Media Efficiency Ratio) — total revenue divided by total direct marketing spend. Not platform ROAS. Your whole business.

• nCAC (New Customer Acquisition Cost) — if he had to pick one metric, this is it. New customers are the growth engine. Returning customers are noise in your ad data.

• Contribution Margin — CM1, CM2, and CM3 strip out COGS, direct marketing costs, and variable overhead to show what’s actually profitable.

• Max nCAC with Buffer — a break-even guardrail that tells you exactly how much room you have to test, scale, or take risk without hurting the business.

→ Why you should still populate this manually Automation is tempting, but entering the numbers yourself forces critical thinking. Scott puts a recurring ClickUp task on Monday morning for every client. If it’s not on the calendar, it doesn’t happen.

→ Weekly vs. monthly tracking Weekly catches anomalies fast — a new customer count drop will show up here before Meta ever flags it. Monthly gives you the trend lines and year-over-year comparisons that actually tell the business story.

Get the Google Sheets metrics template HERE.

Connect with Scott: ecommerceoptimizers.com

Email: scott@ecommerceoptimizers.com

LinkedIn: https://www.linkedin.com/in/scott-s-reid/

About The Performance Mirage The Performance Mirage is a show about the gap between what your dashboards say and what your business is actually experiencing. Every episode, we bring in someone doing the actual work to dig into a real story — what looked good on paper, what was actually happening underneath, and what it took to fix it.

About Occamize Occamize works with e-commerce and lead gen companies running Meta and Google ads to replace broken measurement and attribution theater with a simple optimization system tied to real revenue — not platform ROAS. If your numbers look great but your growth feels off, that’s exactly who we built this for.

→ Learn more or work with us: occamize.com

It's been a minute since I've published here - sorry to keep you waiting!

A brief update:

Since I've last written is that a software has engaged me to write content for them, so part of my work/bandwidth will be seen on their platform. I'm very happy about it (I've used their tool for years) and really enjoy teaming up with awesome people/companies...stay tuned!

I do plan to keep this going weekly. And maybe even share more often (quick "bit-size" editions) as inspiration strikes. Today's topic will be more on the "bit-size" length :)

I was thinking the other day it's ironic how little numbers matter in Measurement. They might mean a bit more in "Analytics" but with Measurement (trends + patterns > accuracy) numbers are almost never important.

Here was an actual example interaction I had with another consultant working with a client of mine. She is largely in charge of email for them (and does a great job).

I proposed we add in a "bot link" to the email to see how many clicks it would get from email bots (something a human would never click). The trick is to add a common to something like the intro of "Hi, {first_name}}".

You can add a "ref=bot" link to URL param so you can figure out # of clicks in GA4 (I like this approach vs UTM'ing it). Also you should totally do this to get a better idea of true "opens"

At a certain point she mentioned there was "92" bot clicks on day one of the send, and then here are the subsequent questions I asked:

But just think if 100,000 people opened that thing...0.09% would have been a click. Not a concern at all.

Drop a couple zeros off and 9.2% click, starts to seem like a "problem" again...

...context with data is everything. Don't let the #s alone tempt you into forming an opinion without some contextual questions.

The top 3 contextual questions to be asking:

1. Compared to last week/last year, how are we doing?

2. Compared to other channels, how are we doing?

3. Compared to what we thought it was going to be (gold standard)

Examples and visuals of each

Comparison of time (previous period):

This one is the most obvious and the best place to start.

Tip: Hold own shift while you use the arrow keys for granular control over moving things to line up perfectly.

Comparison of channels:

Actually what I find most useful is the source, medium, and campaign. Or, the big three as I like to call them 💪🏻

• In range: Working as expected, look for opportunities to run tests on offers that will influence results greater.

• Below range: Maybe look at pausing campaign(s) that aren't performing well. Look at more page-level data to see where the user is falling off.

And notice the colors of the scorecards in Looker Studio - those are conditionally formatted to show stoplight simple actions:

• Green = above range

• Yellow = in range

• Red = below range

You can set that up to have rules at the chart level in Looker Studio:

Real world analogies to drive my point home

Health: The doctor would never just weigh you and call it a day. This is why body mass index as a proportion to your height makes sense.

Sports: You made 8 three-pointers in the game and almost set a new record, nice! But wait, you took 35 shots to make those 8..? Not so nice! 23% field goal percentage is nothing to brag about.

Work performance: Consider this conversation between a Consultant and a CMO:

CMO: "You only worked for 2 hours? You billed me $10k this month?"

Consultant: "I got done what you wanted, right?"

CMO: "Well, yes. But it only took you 2 hours."

Consultant: "Yes, and it took me 10 years to be able to do what I do in 2 hours. Should I be penalized for it?" 😉

Someone else could work 60 hours in a week, and that might seem like a lot (it is), but that isn't a measure of productivity.

Next time someone gives you a number, ask for more context before sharing a reaction to that!