Sam Nouri
Founder, ADSRUNNER
Sam Nouri is the founder of ADSRUNNER and a growth operator working at the intersection of paid media, customer behavior, measurement, and business economics.
Across more than 12 years in paid growth, Sam has worked in over 50 industries and managed more than $200 million in ad spend. That experience taught him that stronger platform metrics do not necessarily mean a stronger business. The useful question is what paid media contributes after attribution assumptions, customer value, margins, and payback are taken into account.
Sam founded ADSRUNNER in 2021 to bring strategy and execution closer together. He remains close to the work while building an agency where experienced judgment is supported by better systems, proprietary technology, and AI-assisted analysis. The aim is to make growth decisions clearer and more accountable, without turning them into a black box.
His work focuses on the choices that determine whether paid media creates durable value: who to reach, what makes an offer relevant, where the customer journey breaks down, how performance should be measured, and when additional spend has earned the right to scale.
Sam writes about paid growth, attribution, customer acquisition economics, and the role of human judgment in an increasingly automated industry.
ADSRUNNER is a performance marketing agency for established Shopify and DTC ecommerce brands and consumer subscription software, where senior operators run paid media on a platform built in-house that judges every channel against what the business actually banked.
From $2M to $3M a month, the lever is what each order is worth.
Somewhere past $2M a month, a well-run account stops finding cheaper customers. The room to keep spending comes from the order side: AOV, offer design, gross margin, and returns. Here is the arithmetic, and a rule for which lever to pull first.
Subscribe and save is an acquisition decision, not a checkout feature.
Subscribe and save looks like free retention. It is a permanent price cut paid in exchange for orders you hope to get. This post works the arithmetic: what a subscriber is worth at different churn rates, how much more acquisition cost that supports, and the point where the discount eats the margin.
The November ad budget, week by week, with a stop rule.
How to split November spend by week from your own prior-year demand curve, how to estimate marginal ROAS from daily spend and store revenue during the ramp, and the one rule that tells you when to stop raising the budget.
From $1M to $2M a month, you need a new-customer engine.
Between $1M and $2M a month, blended ROAS is increasingly a measure of your customer base, not your acquisition. Here is how to split new from returning against your own customer list, set a new-customer CAC ceiling, and use the Google and Meta settings that point spend at people who have never bought.
From $500k to $1M a month, you need a second channel and a number you can trust.
Between $500k and $1M a month, a Meta-only brand carries real dependency risk, and the second channel is the obvious fix. It only works if you can tell whether the new channel created demand or just claimed it. Here is the readiness test, a worked MER example, and a 60-day scorecard.
Q4 offers that protect margin.
The volume lift a Black Friday discount needs just to break even, as one formula and one table. Then five offers that cost far less margin, and a rule for choosing between them by contribution margin.
From $250k to $500k a month, the constraint is creative supply.
Between $250k and $500k a month, most Shopify brands are limited by one thing: how many distinct creative concepts they ship. Here is the throughput math that tells you how many you need, a lean system a founder-led team can run, and the projects that can wait.
How to double ecommerce revenue: the arithmetic.
Revenue is returning customers plus new customers times first-order AOV. Returning revenue grows slowly, so doubling the total usually needs more than double the new customers, at a cost that rises with every extra one. Here is the sum, and the ceiling that decides whether the plan makes money.
Fourteen questions your agency should be able to answer.
Most agency reporting answers the question the platform is built to answer: did the ads get credit? These fourteen questions ask what the business needs to know instead, grouped into money, truth, and growth, with what a real answer requires and what an evasive one sounds like.
Why Meta, Google, GA4, and Shopify report different revenue.
Meta says one number, Google another, GA4 a third, and Shopify books something smaller than all of them combined. The reasons are mechanical, and once you know them, the gap between the four stops being a problem and starts being a diagnostic.
What a new customer really costs.
Blended ROAS makes every established brand look efficient, because it credits the ads for customers the business already won. Separating new from returning, against your own customer list, is how acquisition cost becomes a number instead of a guess.
Why we left automatic execution switched off.
The ability to carry out changes on ad accounts automatically is built and tested in our platform. It is switched off. Switching it on is a decision about client money, and a system being capable of something is not a reason to let it.
Paid acquisition for subscription apps and digital products.
Subscription businesses inherit an ecommerce playbook that does not fit them. The platforms optimize toward an event on day one, while the money arrives over months. This guide covers the metric order, trial design, the signal to send the platforms, cohort reading, and payback.
Six attribution models, one set of orders: how to read the disagreement.
Every attribution model is a rule for splitting credit among the touches you observed. Run several on the same orders and the disagreement between them tells you more than any one of them can: which channels open journeys, which close them, and which credit is solid.
How to scale an ecommerce business: the revenue ladder.
Most scaling advice is a list of tactics with no diagnosis attached. This guide is the diagnosis: the five rungs from $250k to $5M a month, the constraint that binds on each, the arithmetic that decides how a store doubles, and a test for which rung you are really on.
Freelancer, expert, or agency: who should actually run your ads?
"Google Ads expert" and "Google Ads agency" are usually typed by the same person on the same afternoon, and they lead to very different places. Here is what each option is genuinely good at, the spend range where each stops making sense, and the failure mode specific to each.
Performance dropped: a systematic method for finding out why.
Performance drops trigger a predictable panic: bids get changed, campaigns get restructured, budgets get shuffled, all before anyone has established what actually happened. We work a fixed diagnostic order instead: measurement, market, platform mechanics, then the account. In that order, for a reason.
Server-side tracking and the Conversions API: a practical 2026 setup.
Server-side tracking is no longer optional. The browser pixel misses too much, and the gap is widening every year. This is a practical look at how Conversions API and Enhanced Conversions work, what a trustworthy setup looks like, and where implementations go wrong.
The first 90 days: how we approach a foundation rebuild.
The interesting work in paid media, the bid strategy and creative testing and audience expansion, only compounds once the foundations are right. So our first 90 days on any account are deliberately spent on the unglamorous work first. Here is the sequence.
Incrementality testing: work out what your test can detect first.
Every attribution model is an allocation of credit among ads; none can tell you what would have happened without the ad. Only a controlled test can, but an underpowered test is worse than no test, because its nulls all point the same direction: toward "nothing is incremental". Here are the three designs a marketing team can run, and the arithmetic that tells you in advance which questions your data is capable of answering.
Why most Google Ads audits miss the real issue.
Most accounts we audit have great-looking dashboards and broken foundations. The interesting issues are never bidding strategy or keyword choice. They are upstream of those: in measurement, attribution, and architecture decisions made years ago and never revisited.
Attribution after iOS 14: what actually works.
Pixel attribution is not coming back, and the difficulty has moved on from missing data to something subtler: platforms now fill the gaps with modeled estimates displayed alongside observed conversions. Platform claim, observed correlation, modeled contribution, measured incrementality. Four numbers, four meanings, and most budget mistakes come from confusing them. Here is the stack that separates them.
Profit on ad spend: bidding to margin, not revenue.
A 4x ROAS on a 15% margin product loses money. A 2.5x ROAS on a 70% margin product prints it. Revenue-based bidding cannot tell the difference, which means most accounts are systematically over-buying their worst economics. The fix is to bid to margin.
MER vs ROAS: the argument is a decoy.
Every version of this article tells you to stop trusting ROAS and start trusting MER. That advice is not wrong so much as beside the point: MER and blended ROAS are the same economics inverted, and neither is safer than the revenue figure you feed it. Here is the ladder that ranks your revenue sources by trustworthiness, the arithmetic that turns one week of data into three contradictory decisions, and how to set the floor from your own P&L.
Brand vs non-brand: the error that grows as you succeed.
Counting branded search as paid acquisition is the most common measurement error in paid search, and the usual account of it (that it inflates ROAS) understates the problem twice over. It makes every other figure in the account uninterpretable, and it is self-concealing: the error grows precisely as your non-paid marketing succeeds. Here is the full decomposition, why separation always leaks, and the cases where the split is honestly ambiguous.
The paid-media account standard, v1.0.
Every "healthy account checklist" is a list of things no competent operator would dispute, which is exactly why accounts fail them: agreement is free and thresholds are not. This is the same discipline rebuilt as a real standard: 22 numbered criteria each with a pass condition you can fail, weighted scoring that gates on the layers which corrupt everything downstream, an explicit list of what we left out and why, and a version number so an account scored today stays comparable next year.
From Sam— Growth checkWhat's actually holding your growth back?
About 4 minutes, no account access. For Ecommerce stores, SaaS, and digital products. A score across five areas and the one constraint to fix first.
Want this applied to your account?
Run a free AI audit of your Google or Meta account, or talk to the team about what scaling profitably would take.