Happy Sunday, Everyone!

I hope you’re all enjoying playoff baseball and the first weekend of Q4! I just returned from Groceryshop (an absolutely wonderful show) and am off the road for a few weeks – then it’s back to Vegas, California & Boston for a series of events focused on Senior Living, Legal & Tech.

We’re back with the second half of the Meta Ads Audit Guide. Last week’s issue dove deep into the side of Meta most marketers skip: the foundation. We talked about understanding the business model/goals/objectives, researching your target audience, mapping the competitive landscape and fixing your data infrastructure. That split was intentional - because 80%+ of the results you see in Ads Manager are, directly or indirectly, influenced by the stuff outside the ad account.

For today’s issue, we’re going to open the hood on the account itself.

I’ve completed well over 100 Meta Ads Audits over the past few years. And every time I’m engaged to start a new one, the person/brand commissioning it asks a form of the same question: what do you look for? What separates the remarkable, high-performing accounts from the ones that consistently fall short?

In my experience, it comes down to 4 things, each executed uncommonly well:

1. An account architecture that aligns with the two things that matter most to the business: people (customers) and profit (how the business makes + retains money)
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2. Full-journey creative alignment - ads aligned to the audience; post-click experiences aligned to the ads; product/service experiences that keep the promises made from the outset of the relationship
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3. A legitimate, well-informed testing strategy that balances 10% (incremental) gains with 10x (revolutionary) swings
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4. An anti-fragile, future-proofing approach that maximizes the probability that the account will be able to thrive (not just survive) future disruptions and changes

This issue is a deep dive into those four levers. I’ll share the specific diagnostics tests and metrics I use to assess each one, along with the less obvious traps like attribution distortions, budgetary blind spots, creative stagnation and post-click experience issues that, left unchecked, will materially degrade performance.

Let’s get to it.

The Ad Account Structure

Last week’s issue was squarely focused on building a strong foundation under the account; this week’s begins with ensuring the structure itself isn’t warped.

The reality is that - for most media buyers - account structure is treated as busywork. It’s a thing that has to be done, but not something that should be done with obsessive care or careful thought. To be blunt: that’s a mistake.

Structure is a value statement. It is how you (the media buyer) communicate your goals and priorities to the machine (Meta). If your structure is completely flat OR hyper-fragmented, you’re saying everything is equally important, which means nothing is important.

A poor structure will destroy signal quality, which, in turn, will evaporate profitability. Meta’s machine learning thrives when it has clean, consistent feedback loops. Most accounts I audit make that harder than it needs to be.

In my experience, there are four major “structure” red flags:

1. Over-fragmentation: dozens of campaigns or ad sets, most (or all) starved of sufficient budget to exit the learning phase.
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2. Structural drift (or a shanty-town structure): a slew of legacy campaigns with mis-aligned goals, outdated audiences/creative, incorrect exclusions or no-longer-relevant targets still hoovering up spend because they’ve “worked in the past.”
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3. Conflated Goals: ad sets with different hero products/services, audience targets + different optimization actions, all jammed into the same overarching campaign (with either CBO or ABO). I have yet to see this actually perform…but it happens in >30% of accounts.
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4. Broad Bro: this one is particularly pervasive in B2B SaaS + B2C lead gen - campaign structures that default to “broad” - resulting in a significant amount of spend dedicated to people who are obviously DQ’d for the business.

A healthy account structure tends to look deceptively simple: campaigns focused around a single offer/angle, with an optimization action + attribution window aligned to the business objectives. Each campaign has 1-3 well-designed prospecting ad sets with tailored creative/messaging plus a smart retargeting ad set. The very best have a dedicated testing structure (either a testing campaign, or a method for integrating test concepts into the existing structure), plus exclusions that keep spend flowing toward incremental opportunity rather than back to existing buyers.

It’s not rocket science. It’s the basics executed with uncommon brilliance.

When I audit structure, I’m not just counting campaigns. I’m asking:

Is the account built around how this business actually generates profit?

For a multi-SKU ecommerce brand, that may mean segmenting by hero product line and evergreen bundles rather than by creative type. For a lead-gen service business, it may mean campaigns tied to service offering, geo or service tier. Structure is an operational map: it should mirror how profit (or contribution margin) is actually created.

Is budget flowing to the right places?

Meta - done well - is both a demand creation AND demand capture machine. But - left to its own devices - the platform will default to the path of least resistance. For some accounts, that means over-indexing toward remarketing (demand capture) at the expense of prospecting, because that’s the easiest way make the ROAS number look good; for others, it’ll drop 95%+ of spend to net-new audiences, with virtually no follow up (because that makes the rCPM sparkle). Neither is optimal. A high-performing account tends to have a healthy balance between prospecting / demand creation (~80%) and demand capture (~20%). Assess this by pulling a 30 day spend report by audience – if you see that 50%+ of your budget is going to your WCA and existing customers, you’re likely way too heavy on remarketing.

A second, quick diagnostic that catches most of the big mistakes: if the top three campaigns don’t account for at least 60% of spend, or if more than a handful of campaigns each produce fewer than 25 optimization events (“conversions”) in 30 days, fragmentation is likely kneecapping performance.

The other question is whether the structure facilitates scale. Meta’s algorithm needs 25+ optimization actions per ad set per week to exit learning and stabilize delivery (their official documentation says 50, but if I have plenty of ad sets exit learning at 15-25). If you can’t reliably get to ~2 optimization actions per day, per ad set, then something must change. The simplest resolution is consolidating budgets into fewer, better-defined ad sets - which often lowers CPA 10–20% without touching creative or targets.

Budgets: Follow The Money

A budget analysis often reveals more about what’s holding an account back than any random setting or hidden report. Many brands assume their budget distribution is rational because they’ve been “optimizing” over time. In practice, spend often clings to legacy campaigns that once performed but no longer contribute incremental growth.

To diagnose this, compare three things: spend by campaign, new-customer revenue (or NC-ROAS) by campaign, and marketing efficiency ratio (MER) at each level of scale. Any ad set that consumes >10% of budget but contributes <5% of incremental revenue deserves a hard conversation. The same principle holds true for lead generation accounts - if an ad set takes 10%+ of your total budget, but is driving fewer than 5% of your MQLs/SQLs, there’s likely a problem.

A wonderful side effect of this exercise is that it identifies high-efficiency campaigns artificially capped by budget. Shifting even $10,000 a month from an inefficient campaign to a hyper-efficient-at-low-scale campaign will improve MER more than just about anything else you could do.

The real goal here is to understand the marginal return curve: if I add $1 to this campaign, how much incremental new-customer revenue do I get? If the curve is flat or declining, that’s your cue to shift dollars elsewhere. Where those dollars should go depends on the business or account – it might be to a more efficient campaign; it might be to a different geo or service line; it might be to a different platform (like Google or Pinterest or YouTube).

Just because the dollar is being spent on Meta today does not mean it should be tomorrow.

Daily Budgets Are A Silent Account Killer

One of the most common (and least discussed) drags on Meta performance is the over-use of rigid daily budgets. I see it all the time - budgets set based on neat spreadsheet rows (whatever the CFO allocated, divided by 30.4) - rather than informed by the market dynamics. On paper, setting daily caps like this seems smart and responsible - budgets are essentially guardrails that promise tighter control, more predictable capital deployment and (unless you’re bad at math) eliminate the possibility of blowing the budget.

In practice, they often do the opposite. The reality is that neither your audience nor Meta behave in a uniform manner. A massive segment of your target audience might be keen to buy your product on a weekend, but utterly exhausted and unwilling on a Thursday night. When that happens, even Meta’s ability to exceed the daily cap by up to 75% is insufficient – Meta might be able to spend 10x your daily budget at your desired efficiency target on Sunday, but not be able to deploy more than 25% of it on Thursday.

When this situation arises (and it does far more than most media buyers want to admit), the daily budget ceases to be a guardrail and starts functioning like an inhibitor. Instead of your account being able to spend $5,000 at a 5.0 ROAS (netting you $25k!), it can only spend $500 at that 5.0 ROAS - meaning you miss out on $18,000 in marginal revenue (less ad costs). In virtually every case, your overall performance would be better if you dropped the entire Wednesday + Thursday budget for the month on that single Sunday.

A related issue: strict daily caps create delivery volatility. It’s common to see a budget-capped campaign hit its limit mid-afternoon, pausing delivery right as Meta has found a converting pocket of traffic. The following morning, the pacing model starts cautiously to avoid overspend. This start-stop rhythm produces uneven impression distribution and inflated CPMs - not because the audience changed or the creative is bad, but because the budget guardrails throttled delivery at the wrong time.

A better approach for most evergreen or high-volume campaigns is to allocate sufficient budget such that either the target (Cost Cap/Bid Cap) or the campaign budget (lifetime budget) is the limiter - not the daily budget. Either change gives Meta’s pacing algorithm room to smooth spend across the periods when demand and opportunity are present, leaning in on high-conversion hours or days and pulling back when traffic quality dips. The result is more stable, predictable delivery, more consistent CPMs and a sufficiently high optimization event volume to keep the ad set out of the learning phase.

A simple way to test the impact:

1. Identify one or two of your best-performing prospecting campaigns that already meet your efficiency goals
2. Shift them from daily to a lifetime or 7-day rolling budget using the same total allocation
3. Monitor delivery, CPM, CPA, and MER over a two-week period.

Most advertisers find that this single change reduces volatility and improves cost-per-result - all without touching creative or bids.

The takeaway: daily budgets often starve Meta of the efficiency, signal density and pacing flexibility it needs to spend optimally. Loosening those constraints is often one of the lowest-effort, highest-impact steps you can take to stabilize performance. You will need to actually look at your account when you do this (and intervene sometimes!) - but the rewards (improved efficiency + more stable performance) are often worth the risk.

Incrementality: Don’t Trust Meta Blindly

One of the most counter-intuitive traps in Meta audits is letting in-platform ROAS dictate all decisions. A campaign can look spectacular in Ads Manager while doing very little for the actual business.

I’ve seen this most often in two scenarios:

• When a large share of conversions are actually existing prospects or customers making repeat purchases. Meta happily claims the credit, but incremental revenue barely budges when the campaign is paused – all that happened was conversions that would have gone to email or direct get attributed to Meta.
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• When accounts over-index on 1-day view attribution, which can make a campaign look like a hero while adding little genuine lift.

There’s also the inverse (which happens more than most performance marketers or Meta Ads X Gurus want to admit): Meta looks like garbage on a last-click attribution basis, but is quietly driving top-of-funnel traffic that closes later through branded search, affiliate, organic, or retail/in-person/on-call.

That’s why I always compare in-platform ROAS to MER (marketing efficiency ratio) and NC-ROAS (new-customer ROAS). If platform performance is climbing while MER and NC-ROAS remain flat (or worse, decline) - you’re buying the same customers twice.

When possible, I look at 28-day click vs. 7-day click vs. 1-day click vs. 1-day view data (Ads Manager → compare attribution settings) to understand if/where Meta is having an impact. If a significant portion of Meta’s claimed conversions are 1DV, that’s a strong signal the true, incremental impact might be lower than claimed. If you’re seeing a large chunk of optimization actions in 1DC, you’re (more than likely) overindexing on remarketing.

My preference (in almost all cases) is to look at 7DC – that tends to be a good balance of immediacy, impact (click-based attribution actually forces Meta to send you traffic, not just claim eyeballs) and true incrementality.

A second, and related, mistake: brands making decisions based on a disconnected third-party attribution tool (like Triple Whale or Northbeam). The results in a nefarious issue: disintermediating the optimization actions (changing targets or budgets) from the data in Meta’s view. Translation: Meta has no idea why you’re doing what you’re doing. Think of it like a teacher (the TPA platform) telling a parent (the media buyer) that their son/daughter (Meta) was behaving badly in class - then the parent, with no explanation, sends the child to bed without supper when they arrive home. The child (Meta) has absolutely no idea why this is happening - and is just as likely to act out in the future as s/he is to figure out why this terrible thing happened. The better solution is for the parent to communicate the issue to the child, and provide the concrete details leading to the consequences. Fortunately, Meta allows you to do this by uploading TPA data via the conversion API (assuming you’re using a compatible TPA tool). If you are using TPA, please ensure it is integrated into Meta Ads, so you aren’t (inadvertently) sending Meta to bed without supper.

Creative: The Real Growth Engine

If architecture is the skeleton, creative is the muscle - it’s what actually moves the algorithm. No other factor has a larger impact on sustained scaling than creative diversity, velocity and alignment.

Meta’s single-greatest advantage is its ability to match the right message to the right person at the right time. But if your ads don’t provide it with compelling stories to work with (or worse, if your ads promise one thing and your post-click experience delivers another), even Meta’s world-class machine learning can’t save you.

I think of creative performance in terms of three alignments that must click like gears:

1. Creative–Audience Alignment: Does the ad open with a hook that actually matters to the audience seeing it? Can the ad earn the attention of your desired audience with consistency and regularity?
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2. Creative–Lander Alignment: Does the landing page reinforce the exact promise or pain point the ad led with, above the fold and without friction?
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3. Audience–Lander Alignment: Are we sending the right segment to the right destination, or dumping everyone onto the same generic page?

A classic failure pattern is a brilliant UGC video for a limited-time bundle that drives high CTR, but the click leads to a generic category page that doesn’t mention the bundle. CTR looks great; CVR is terribad as users feel misled or don’t feel like working for it; Meta optimizes in the wrong direction. When these three alignments lock in, you often see CTR jump 30–50% and CVR climb 20–40% with zero change to budget or bids.

Unlike structure and budget, where there are (pretty solid) quantitative tests you can use, creative is a true fusion of art and science. The solution is to evaluate it using a combination of unbiased qualitative assessment and quantitative metrics:

For video, I start with thumb-stop rate - the percentage of impressions that hold a viewer for at least three seconds. Under 25% is a red flag in most categories. I also look at hook-to-hold rate: of those who watched three seconds, how many continued watching for at 15 seconds? If that’s below 35–40%, there is likely a disconnect in the “body” of the ad.

Across all formats, I monitor CTR-Link (prospecting should generally clear 0.8–1.0% in ecommerce) and Cost per 1,000 new accounts reached to catch saturation or weak hooks masked by remarketing efficiency.

Finally, there’s LP CVR. If CTR is healthy but CVR dips below 1–2% for ecommerce or below 2-3% for lead-gen, that’s almost always a lander misalignment: either the page is too slow (mobile load >3 seconds), too confusing (sending a specific bundle audience to a generic shop page), too cluttered or simply not aligned to the ad (resulting in your audience feeling like it’s a bait-and-switch).

A creative audit also means looking at diversity and velocity. A good prospecting campaign typically needs at least 6-10 active concepts (note: a “concept” is a unique creative - not a different color font or a slightly different image) running at any given time, preferably a mix of UGC, static, carousel, demos, testimonials, and benefit-driven explainer formats. The majority of these will fail, at which time, pause them out and introduce new ones. Creative follows a power law (more on that here) - which means your account must continually introduce new creatives to find new winners.

My first quick check: If 70% of spend in the L90 days was directed to fewer than five ads, that’s usually a sign that the account/campaign is over-reliant on a handful of winners…and if one of those stops performing, there’s a world of hurt on the horizon.

The next check: ask for the creative tracker and implementation plan. Is there a cadence for refreshing hooks and angles every 7–14 days, or is the account content to run the same “winner” for months until it burns out? Are headlines and CTAs being tested deliberately, or swapped haphazardly? Are creative concepts tagged by theme so that we know whether “problem/solution” videos outperform “testimonial” carousels, or are we just guessing?

Most accounts fail every one of those tests, which is why they are asking for an audit in the first place.

Testing Discipline: Stop Guessing, Start Learning

The second most common creative mistake after stagnation is chaotic testing.

A lot of brands think they’re testing because they launch 10–20 ad variations at once. In reality, they’re throwing a bunch of coins in the air and calling whichever one lands heads a winner.

Statistically, every ad that gets early delivery is just as likely to be a loser as a winner — you’re just observing noise masquerading as signal. That’s actually not hyperbole. This happens because most of those ads never reach the sample size needed to prove anything. Meta’s algorithm is designed to favor whichever ad racks up the first few conversions at an acceptable-or-better efficiency, regardless of whether that concept is actually superior and sustainable over the long-run. That early bias creates a Type I error (a false positive, where a weak ad looks strong); at the same time, other ads that might have performed better if given fair delivery never get enough impressions to prove themselves - a Type II error (a false negative that leaves potential winners lying dormant, with no spend). If you’re curious about the math, check out this video on my YouTube channel where I break it down.

The result is a statistical illusion: a few random spikes presented as insight, budgets flowing to the wrong ads and no legitimate learning to show for any of it.

A disciplined testing framework feels slower at first because you fund each variation long enough to gather meaningful evidence. But that rigor makes scaling dramatically faster, because you’re backing ads that are legitimately viable over a mid-to-long horizon - not those that merely got lucky in the opening round.

The alternative is to balance quick hits with long-term bets - what I’ve termed the 10% or 10x Approach To Testing (there’s a full article on it here).

Why the Mix Matters

Incremental tests - the 10% tests - optimize what already works. They optimize the creative + post-click experience, push down CPA and cumulatively result in meaningful lifts over months. But by themselves, they trap you on a local maxima; you get a little higher on what might be a smaller mountain.

Big swings - the 10x bets - are where breakthroughs happen. They test entirely new offers, new hooks or new audience approaches that have the potential to double or triple your ad account’s output. Most of them will fail or be flat. That’s fine. The goal isn’t to hit 1.000; it’s to uncover the one or two bets a year that make every prior incremental win feel small.

A sound testing strategy fuses the two. If your last 10 tests were all micro-tweaks (headline phrasing, button color, minor copy swaps), you’re due for a radical bet. If your last 13 tests were massive swings, you should probably introduce a few incremental tests to stabilize the gains.

The exact balance will vary based on any number of factors - the level of maturity of your business (start ups and early stage businesses are almost always better of placing the majority of their effort on 10x tests; mature/late-stage businesses should focus more on the 10% bets that unlock added efficiency on already-massive spend), your level of product-market fit (if you don’t have PMF, spend more on 10x tests), and your businesses’ adaptability (if you can’t quickly change bundles or service offerings, then those 10x tests aren’t likely to be viable).

Designing a Testing Mix

Think of a quarter’s testing calendar as a portfolio. Mature brands should bias about 60–80% of testing toward incremental lifts (headline hooks, CTA copy, hero image changes, creative concept refinements). The remaining 20-40% should be set aside for transformational bets: new value propositions, long-form storytelling, bundle or subscription shifts, landing-page architecture changes, or audience expansion into entirely new or untapped audiences.

Younger brands with less to lose can skew the other way — more moonshots early on, because a single breakthrough often matters more than marginal efficiency gains.

Guardrails for Both Types of Tests

Whether it’s a 10% tweak or a 10x swing, a test is still a test. It needs enough runway to prove or disprove itself. Too many accounts declare winners after two days or with fewer than a couple dozen conversions per variant. That’s not testing; it’s glorified gambling.

For incremental tests:

• Run only two to three variations at once so each gets meaningful delivery.
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• Aim for at least 2,000 impressions and ~15 conversions per variant - you don’t need statistical significance (we’re trying to make money, not publish a paper), but you do need something more than first day vibes. There’s plenty of room for a happy medium between the two extremes.
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• Let tests run at least a full week to capture weekday/weekend behavior swings

For big swings:

• Accept that sample sizes will be similar, but be ready to invest a larger budget to give them a fair shot.
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• Treat a promising early signal as an invitation to run a confirmation test against a fresh audience before scaling.
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• When multiple variables shift at once (as often happens with 10x bets) document exactly what changed so you can isolate the lever if it works.

Most brands I audit have no idea how to think about a 10% vs a 10x test, so here’s one example I often share: Imagine a bedding brand (something we all know because we all sleep).

A 10% test might be as simple as swapping sterile product shots for lifestyle imagery showing the duvet in a real bedroom, expecting an 8 percent bump in CTR and a small CPA drop. A 10x test could be bundling a duvet with pillows and bamboo sheets, along with a “bedding for life” subscription (sending a new set of tailored-to-the-season sheets every 90 days). That’s a fundamental shift in both the offer and funnel. If the re-worked idea hits, it would more than triple AOV and reduce new customer payback period by 65% – something that no sequence of small image or PDP tweaks would ever accomplish.

I recommend earmarking a fixed slice of the account’s monthly spend specifically for testing. For many e-commerce brands that’s 10–20% of total budget; for high-consideration services it might be slightly lower (10% to 15%). Within that slice, reserve anywhere from 33% to 67% for big swings. The discipline of allocating budget this way keeps testing from cannibalizing evergreen performance campaigns, while still giving radical bets the resources necessary to see if they have legs.

An effective Meta Ads audit should flag not just whether testing exists but whether it’s balanced, funded and properly instrumented to produce real learnings. Look for evidence that the account runs on a testing calendar, that it has guardrails for sample size and duration and that there’s a clear pipeline of both marginal and breakthrough ideas. A brand that hasn’t tested anything beyond incremental creative tweaks in 6 months is sitting on hidden upside. A brand running only moonshots with no steady 10% wins is either still in search of PMF or incinerating money with little regard for progressively improving efficiency (both bad).

Testing is how you find tomorrow’s growth engine before your current one stalls out. Treat it like a portfolio: steady compounding bets that keep you efficient, punctuated by the bold explorations that can rewrite what your business/funnel is capable of. The audit should leave no doubt about which side you’ve been leaning on - and where you need to rebalance.

Landing-Page and CRO Alignment

No matter how brilliant your ad is, how perfectly you target it or how sharp your offer seems, in my experience, 80%+ of the impact happens after someone reaches your post-click experience. I’ve sat with brands where every upstream component was pristine: creative, targeting, infrastructure…and performance still tanked.

The culprit was always the same: the post-click experience.

A persuasive ad only wins the right to continue the conversation. The landing page is the digital salesperson that must pick up that conversation, translate the interest into attention, then convert it into your desired action. If your lander is disconnected, generic, slow, or confusing, you aren’t losing potential sales/leads; you’re throwing away the resources you spent to earn that attention + buy the click.

Here’s how I audit that experience:

1. Narrative & Expectation Alignment

• The lander must mirror exactly what the ad promised - same hook, same problem language, same emotional tone. If your ad says “creative fatigue is killing your ROAS,” but the landing page opens with “scaling e-commerce brands,” you’ve effectively reset the narrative.
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• Use narrative-specific landing variants: each ad angle (pain, identity, urgency) should route to a slightly tailored lander that nurtures the same story, not a catch-all generic page.
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• Recognize that attention is fluid: users click, they skim, they drift. If you don’t reinvest attention immediately with clarity, trust signals, relevance and micro-convictions, you lose them.

2. Clarity Velocity: Move Users Through Four Questions Quickly

Your user’s journey on your lander should flow as naturally as this sequence:

1. What is this?
2. Is it for me?
3. Can I trust it?
4. What should I do now?

Delay or confusion at any step kills momentum. The faster you can move someone through those questions, the more sales (or leads) you’ll earn. Pages that linger on “time on site” as a success metric are often masking confusion, not engagement.

3. Friction: The Difference Between Taxing and Earning

Not all friction is bad. The trick is to remove unjustified friction (surprise load times, broken forms, hidden terms, unnecessary fees, whatever) while injecting framed friction that creates value or weeds out non-serious visitors.

• Cognitive friction: confusion created by mismatched messaging, complex layouts, or unclear hierarchy
• Emotional friction: uncertainty, skepticism, or fear of making a mistake
• Mechanical friction: slow load times, janky forms, incompatible mobile layouts

Great post-click experiences remove the first and manage the next two. Offer gated quizzes, multi-step flows or micro-explainers not to punish the user, but to earn their attention, commitment and clarity.

4. Proof, Recognition & Trust Signals

The page must reflect “this is for you.”

• Show vertical-specific social proof and case studies near CTAs, not scattered in footers. Proof + trust points often act as the “final push” that gets your potential customer over the hump - so concentrate their impact where it will be most powerful.
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• Mirror the problem language from the ad. If your ad spoke to “creative burnout,” the lander should use that same phrase, not a diluted synonym.
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• Use recognition, not shallow personalization. Don’t greet users by name; show them you know their world. Talk to their pain state. That’s what builds belief.

5. Behavior Diagnostics & Optimization

• Use scrollmaps, heatmaps, session recordings to see where attention decays.
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• Watch for long time on site with low conversion: often a sign of confusion, not engagement.
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• A/B test micro changes like anchor links, CTA progression ("Curious? Explore → Ready? Let’s go → Act now"), and narrative reordering.
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• Measure not just conversion, but “speed-to-understanding”: how quickly does someone land, read a headline, see a value promise, and know what to do next?

In short: in your audit, don’t just benchmark headline match and load time. Probe whether the lander earns the attention your ad bought. The performance delta rarely lies in brighter images or button colors - it lives in how well the post-click experience converts casual interest into real intent.

Seasonality, Promotions & Contextual Normalization

A frequent audit mistake is blaming creative or audience settings for swings that were actually caused by promo calendars, seasonality or inventory shocks.

Before diagnosing performance changes, I gather the brand’s promo calendar, product-drop schedule, inventory notes, and any external events that might have influenced buying patterns (tariffs, port delays, recalls, etc.).

A prospecting CPA spike in October may be entirely predictable if the brand historically spends light in late summer and ramps heavily into BFCM promos. An evergreen lander A/B test during a 30% off sale tells you little about how that page will perform when prices return to normal.

The audit’s job is to normalize for those factors so we don’t over-correct for noise.

Competitive Landscape & White-Space Angles

Another under-used audit step is scanning what the competitive set is actually saying and showing. Not to copy them, but rather to identify the gaps they leave open.

I’ll spend time in Meta’s Ad Library pulling the top-spend competitors’ active ads, analyzing how they position offers, which creative angles dominate (UGC vs. polished lifestyle vs. demos), and which incentives they lean on (financing, bundles, shipping thresholds, proof points, third party credibility, VSLs, etc.)

Done well, patterns emerge fast. Maybe every competitor leads with discounts and nobody leads with value (or durability, or how it actually works); maybe all of your competitors show the product in static photos but none bother to show it in action. Maybe every competitor uses % off or $ off discounts - leaving you free to shift to a free gift with purchase or a charitable giveaway that defies easy comparisons (and gives you an advantage AND more margin).

If your audit ignores the competitive landscape, it’s likely incomplete. No brand operates in a vacuum, and every brand has competition (especially the ones that say they have none). When you understand where your competitors are, you implicitly learn where they are not – and that’s the area for the taking.

Future-Proofing the Account

Meta evolves faster than most businesses adapt. Privacy updates cut off data streams. Advantage+ formats reshape campaign structure. Compliance rules tighten with little warning.

A future-ready audit flags where the account is fragile and shores it up before the next shift.

That means ensuring Conversion API is live, properly configured and deduplicating browser + server events, not just installed. It means passing back offline conversions for high-consideration businesses so Meta isn’t optimizing for raw leads instead of revenue.

For ecommerce, it means catalog and product feeds syncing in near-real-time so you’re never paying to promote out-of-stock SKUs or wrong prices.

And it means budget agility- the ability to shift spend quickly for seasonal surges or inventory shortages without blowing up historical learning.

I also recommend setting up anomaly alerts - whether in Meta’s automated rules or third-party tools like Optmyzr - so you catch sudden CPA spikes or broken pixel events before they drain a week’s worth of budget.

The Metrics That Matter Most

Across all these sections, a few metrics rise above the rest for audits focused on growth and efficiency:

• MER (Marketing Efficiency Ratio): total revenue ÷ total spend; tells you if the business is healthier at higher spend.
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• NC-ROAS (New-Customer ROAS): especially critical for growth-oriented DTC brands
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• Thumb-Stop Rate & Hook-to-Hold Rate: to diagnose whether video ads earn attention.
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• CTR-Link & CPM-New: reveal if creatives are breaking through to fresh audiences or spinning on retargeting pools.
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• Post-Click CVR & Bounce Rate: to flag lander or offer friction.
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• Cost per Add-to-Cart / Initiate Checkout: strong mid-funnel signal even before purchases.
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• Reach vs. Frequency Decay Curves: to watch for prospecting saturation and creative fatigue.

I use these as early indicators before obsessing over last-click ROAS, which can be distorted by attribution quirks.

Auditing a Meta account isn’t about finding a magic setting buried three clicks deep in Ads Manager. It’s about surfacing the quiet misalignments - structural, creative, budgetary, post-click - that silently drag performance down and keep the algorithm from doing what it’s built to do.

If the first issue laid the foundation (the business, data, and market context), this one focused on the engine itself: the architecture that prioritizes spend, the creative that feeds the machine, the testing discipline that uncovers the next growth engine and the resilience required to weather the next shift/storm.

What I’ve seen again and again is that performance rarely improves because of a single clever tweak. It improves because all these gears start meshing: budget flowing to the right places, creative matching the right buyer with the right promise, landing experiences carrying that promise through, and testing that teaches the account how to get better month after month.

In the next issue, I’ll dig even deeper into creative strategy and testing at scale—the place where most brands either stall or break through. For now, if you take nothing else from today’s audit checklist, take this: strong Meta performance is rarely a mystery—it’s the by-product of well-aligned fundamentals executed consistently over time.

Here’s to removing friction, unlocking headroom, and making Q4 your best quarter yet.

Cheers,

Sam

BONUS: If you liked this audit, here’s a 75-point checklist you can use as you apply this framework to your own account (and yes, I absolutely used Gemini to create this):

1. Business & Goal Alignment

1. Confirm ICP and primary target segments.
2. Identify most valuable vs. least valuable customers.
3. Document core business objectives (growth, CAC, payback period).
4. Map revenue and margin by SKU/service line.
5. Note seasonality, promo cycles, inventory or staffing constraints.
6. Capture geographic or channel priorities (markets, stores, service areas).

2. Data Infrastructure & Tracking

1. Validate pixel and/or CAPI implementation across all properties.
2. Test for duplicate events (e.g., form submits counted twice).
3. Confirm deduplication logic for browser + server events.
4. Check that all optimization events fire as intended (View → ATC → IC → Purchase/MQL/SQL).
5. Verify conversion value accuracy and currency.
6. Ensure event volume ≥ 25/week/ad set for stability.
7. Reconcile Meta-reported conversions with backend orders/CRM.
8. Confirm offline/qualified-lead passback via CAPI or partner integrations.
9. Audit feed freshness, price accuracy, inventory sync for catalog/Adv+.
10. Review privacy banners, GTM tags, or blockers that might suppress signals.

3. Account Architecture

1. Count active campaigns/ad sets and flag over-fragmentation (< 25 events in 30 days).
2. Identify legacy or “shanty-town” campaigns still capturing spend.
3. Check naming conventions—offer/product/audience should be clear.
4. Verify exclusions to avoid remarketing cannibalization of prospecting.
5. Segment campaigns around profit centers (hero SKUs, service tiers, geos).
6. Validate optimization goal alignment (e.g., purchases vs. traffic).
7. Compare prospecting vs. retargeting spend (aim ~80/20 for most e-com).
8. Ensure top 3 campaigns control ≥ 60% of spend.
9. Confirm that test structures exist—either a dedicated testing campaign or documented process.

4. Budget Distribution & Pacing

1. Chart spend vs. NC-ROAS or incremental revenue by campaign.
2. Flag any campaign using > 10% of spend but < 5% of incremental revenue/MQL.
3. Identify high-efficiency campaigns budget-capped below potential.
4. Review daily vs. lifetime/rolling-7 pacing—test loosening daily caps.
5. Examine marginal return curves for each major campaign/geo.
6. Check automated rules and bid strategies for alignment with business goals.

5. Attribution & Incrementality

1. Compare 1-day view vs. 7-day click vs. 28-day click results.
2. Benchmark MER against in-platform ROAS for reality-check.
3. Calculate NC-ROAS to detect double-paying for existing customers.
4. Identify campaigns skewing heavily to retargeting pools.
5. Ensure any third-party attribution tool (e.g., Triple Whale, Northbeam) pass data back into Meta.

6. Creative Alignment & Performance

1. Review active creative concepts (goal: ≥ 6-10 live in prospecting).
2. Flag over-reliance on ≤ 5 ads capturing > 70% of spend in L90.
3. Audit hook diversity: testimonial / demo / explainer / UGC / lifestyle / static / carousel / VSL.
4. Measure Thumb-Stop Rate (goal: ≥ 25% for most categories).
5. Measure Hook-to-Hold (≥ 35-40% from 3-sec → 15-sec view).
6. Track CTR-Link on prospecting (e-com goal: ≥ 0.8–1.0%).
7. Track CPM-New to understand cost of fresh reach vs. retargeting.
8. Review ad-to-audience fit: is the opening hook relevant to that segment?
9. Check alignment of creative promise vs. lander above-the-fold.
10. Verify ad copy and CTAs match specific offers/bundles shown.
11. Check frequency decay - flag concepts fatiguing < 3 freq.
12. Review creative refresh cadence (ideally every 7–14 days).
13. Evaluate testing pipeline - documented hypotheses per angle/theme.
14. Confirm creative tracker tags angles/themes so learnings are actionable.

7. Landing-Page & CRO

1. Test page-load speed (< 3 sec mobile).
2. Confirm the headline mirrors the ad hook exactly.
3. Ensure immediate clarity on: What is it? Is it for me? Can I trust it? What next?
4. Check CTA visibility above-the-fold on both desktop & mobile.
5. Map scroll-depth vs. CTA placement; test anchor-link CTAs.
6. Audit form UX: fields, validation errors, mobile friendliness.
7. Examine friction types—cognitive, emotional, mechanical.
8. Validate trust elements (reviews, guarantees, UGC, 3rd-party badges) near CTAs.
9. Check that promo messaging on lander matches the live ad flight.
10. Track Post-Click CVR (e-com: ≥ 1-2%; lead gen: ≥ 2-3%).
11. Monitor bounce rates—identify > 60% as a friction flag.
12. Use scrollmaps/heatmaps/session recordings to locate drop-off zones.

8. Testing Discipline

1. Confirm a written testing calendar or pipeline exists.
2. Check budget allocation to testing (e-com 10–20% of total).
3. Ensure mix of 10% incremental vs 10x moonshot experiments.
4. Limit concurrent test variants to 2-3 for stat power.
5. Verify minimum sample sizes (~2k impressions & ~15 conversions/variant) and 7-day run.
6. Confirm documentation of learnings & confirmation-test process for promising winners.

9. Competitive & Market Context

1. Review top-spend competitor ads via Meta Ad Library.
2. Note common hooks/offers competitors emphasize vs. ignore.
3. Identify whitespace angles (value, durability, VSLs, gifting, etc.).
4. Check brand positioning relative to competitor promo patterns (discount vs. bundle vs. value-add).

10. Future-Proofing & Resilience

1. Confirm CAPI deduplication & event-parameter passback is healthy.
2. Verify product-feed sync frequency and error monitoring.
3. Set anomaly-detection alerts (Meta rules or Optmyzr) for CPA/CTR/CPM spikes & feed breaks.
   ​

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