When Agencies Go Head to Head, the Data Is the Advantage 

Jason Smith asked me something on The Truth About Social Ads that I hear from agency owners a lot: "I want third-party tracking, but I want it for all my clients. How does that work?"

How an Agency Owner Gets Attribution Answers From Claude 

When I sat down with Jason Smith on The Truth About Social Ads, the part I was most curious about was how he actually uses the product day to day. His answer was Claude.

Why This Agency Runs Top of Funnel at Breakeven on Purpose 

On The Truth About Social Ads, I turned the tables and asked Jason Smith a question. If you know you can attribute recurring revenue back to the original ad, does that change what you're willing to pay to acquire a customer?

A Brand Thought Customers Took 30 Days to Buy. Most Bought in 7. 

One of my favorite moments with Jason Smith on The Truth About Social Ads was a story he told almost in passing. It's the kind of thing that changes how a brand runs its ads, and most brands never find out.

How One Agency Taught Meta to Find New Customers 

If you want to know whether a feature works, give it to an agency owner who's skeptical by trade. On The Truth About Social Ads, Jason Smith told me he did exactly that with Advanced Signal.

I've been on Jason Smith's show, The Truth About Social Ads, three times now. Jason runs Spotlight Social Advertising and he runs his client accounts on Wicked Reports, so he doesn't ask soft questions. He asked what changed in the platform. The honest answer starts with an AI that gave terrible advice.

Why Your Ad Platforms Shouldn't Be Your Source of Truth

I sat down for a conversation about one of the most important and most overlooked decisions in marketing: what you treat as your source of truth for attribution.

Most brands, without really deciding to, let the ad platforms be that source of truth. You look at what Meta or Google reports, and you make budget calls based on it. The problem is that every platform reports on its own performance and is incentivized to claim credit. So the numbers you are trusting are not neutral, and acting on them tends to over-reward whichever platform is best at taking credit rather than whichever is actually driving new customers. An independent, first-party view, measured against your real orders, is what turns reporting you hope is right into a scoreboard you can actually trust.

The other thing I dug into is how to think about touchpoints when you measure ROI with multi-touch attribution. Not every touch deserves equal weight, and the right way to judge them depends on what a campaign was actually meant to do. A cold-traffic prospecting campaign and a bottom-of-funnel retargeting campaign are doing completely different jobs, so measuring them the same way guarantees you misread one of them. The skill is matching how you weigh the touches to the intention behind the campaign, so a channel that starts valuable journeys gets credit for that work instead of losing it all to the last click.

Put those two ideas together, an independent source of truth plus touchpoint weighting that matches intention, and you stop flying blind. You can see which marketing genuinely creates customers, and fund it with confidence.

Watch the full episode below. 

Building Wicked Reports : Scott on the Practical Founders Podcast

I joined Greg Head on the Practical Founders Podcast for a wide-ranging conversation that covered both sides of Wicked Reports: the attribution problem we solve, and the practical way we built a company around it.

The heart of it is the thing I talk about everywhere. Most brands running paid ads are trusting the ad platforms to grade their own homework, and they have no reliable, independent way to see which marketing actually drives revenue. Wicked Reports exists to give them that: multi-touch, first-party attribution that ties real orders back to every touch along the customer journey, so you can tell the difference between a channel that is genuinely creating customers and one that is just claiming credit for demand that already existed.

I told Greg who this is really for. Our wheelhouse is direct-to-consumer and ecommerce brands spending real money on ads, where the cost of measuring wrong is high, though we support plenty of businesses smaller and larger than that too. The common thread is that they have gotten past guessing and want a scoreboard they can actually trust to guide budget decisions. That is the job we are hired to do, provide clarity on what to scale, chill, or kill.

The origin story is the part Greg's founder audience latched onto, and it is also the clearest way to explain what we do. The company started because a friend running a lobster business was about to write off a marketing channel that was actually working, because the platform reports made it look like a failure. Tracking the full journey showed the opposite. That one problem, proving what really drives revenue, is what turned a data side-project into a software company.

We also got into the business itself: why I chose to bootstrap rather than take the VC money on the table, and why the growth rate that funding would have demanded did not fit the company we wanted to build. It is a candid look at building a practical, profitable software business without big funding, and at the attribution problem that made it necessary in the first place.

Whether you are a founder or a marketer trying to measure what actually works, this one covers a lot of ground. Watch the full conversation below.

The Muddled Attribution Puzzle Solved : Scott's Five Forces 

I came back on Perpetual Traffic with Ralph Burns and Lauren to dig into the frustration that started it all: accurate attribution, and why most marketers are still flying blind despite having more data than ever.

This is the episode where I first walked through the Five Forces framework properly. It did not come from a whiteboard session. It came from years of frustration and testing, from realizing that even though I could solve these data puzzles myself, I was asking customers to wade through hundreds of columns across a dozen attribution models to figure out what to do. That is not a system. So I built one, designed to distill all of that down to the smallest set of inputs that let you always measure correctly, diagnose what is happening, and get to a clear call.

We spent a good chunk of the episode on why most attribution models fail. The core mistake is treating attribution as one fixed model instead of matching your measurement to what a campaign is actually trying to do. When those two things do not line up, the numbers lie to you, and you end up killing winners and scaling losers.

Ralph, Lauren, and I also got into some of the parts that actually change how you run a business day to day. How long you should really wait before judging a campaign, because new-customer conversions take time. Why not all customers are created equal, and what happens to your ROAS when you forget to factor that in. And one I care about a lot: Decision Logs, which are a simple but powerful way to keep a record of the budget moves you make and why, so you are building a compounding understanding of your account instead of reacting to whatever number looks good this week.

If you are running serious ad spend, or you just want to scale smarter with the data you already have, this is the conversation where the whole Five Forces idea gets laid out from the ground up.

Give it a listen here.

How AI Is Making Marketing Attribution Easy

I joined the Ecommerce Coffee Break podcast to talk through something that trips up a huge number of brands: why the attribution data they rely on is quietly leading them astray, and how to fix it.

The root problem is that most brands lean on ad platform reporting to tell them what is working. The trouble is that every platform has an incentive to make itself look good, so it claims credit generously and reports on its own performance. Act on those numbers and you end up over-investing in whatever channel is best at taking credit, rather than whatever is actually driving new customers.

A big part of the conversation was about lookback windows. A default 30-day window sounds reasonable, but it can create false attribution signals, crediting a channel for a conversion it did not really cause, or missing the genuine first touch entirely. The window you use has to match the buying cycle you are actually measuring, not a platform default. Get that wrong and your scoreboard is telling you a story that is not true.

From there I got into the fix, which is having a real measurement strategy instead of trusting the platforms. That means prioritizing new customer acquisition in your paid media, and being able to segment new customers from repeat ones, because if you cannot tell them apart, you cannot tell whether your ads are growing the business or just re-converting people who were already coming back. It is also the reasoning behind the Five Forces system, which is designed to reveal whether your marketing is actually aligned, and to distill all that data down to a clear call: scale, chill, or kill.

We also talked about where AI fits. Used the right way, with strict guardrails, AI can take a huge amount of the manual work out of attribution and make accurate measurement far more accessible. Used carelessly, it invents confident answers that are wrong. The difference is entirely in how tightly it is governed.

If you run an ecommerce brand and you have ever suspected your attribution is not telling you the truth, this one is a quick, practical listen.