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Happy Sunday, Everyone!
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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:
* 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)
* 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
* A legitimate, well-informed testing strategy that balances 10%
(incremental) gains with 10x (revolutionary) swings
* 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.
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The Ad Account Structure
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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:
* Over-fragmentation: dozens of campaigns or ad sets, most (or
all) starved of sufficient budget to exit the learning phase.
* 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.”
* 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.
* 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.
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Budgets: Follow The Money
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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 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
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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:
* Identify one or two of your best-performing prospecting
campaigns that already meet your efficiency goals
* Shift them from daily to a lifetime or 7-day rolling budget
using the same total allocation
* 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.
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Incrementality: Don’t Trust Meta Blindly
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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.
• 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.
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Creative: The Real Growth Engine
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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:
* 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?
* Creative–Lander Alignment: Does the landing page reinforce the
exact promise or pain point the ad led with, above the fold and
without friction?
* 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.
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Testing Discipline: Stop Guessing, Start Learning
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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 channe (
)l 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.
* 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.
* 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.
* Treat a promising early signal as an invitation to run a
confirmation test against a fresh audience before scaling.
* 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.
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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.
* 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.
* 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:
* What is this?
* Is it for me?
* Can I trust it?
* 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.
* 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.
* 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.
* Watch for long time on site with low conversion: often a sign
of confusion, not engagement.
* A/B test micro changes like anchor links, CTA progression
("Curious? Explore → Ready? Let’s go → Act now"), and narrative
reordering.
* 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.
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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.
• NC-ROAS (New-Customer ROAS): especially critical for
growth-oriented DTC brands
• Thumb-Stop Rate & Hook-to-Hold Rate: to diagnose whether video
ads earn attention.
• CTR-Link & CPM-New: reveal if creatives are breaking through
to fresh audiences or spinning on retargeting pools.
• Post-Click CVR & Bounce Rate: to flag lander or offer
friction.
• Cost per Add-to-Cart / Initiate Checkout: strong mid-funnel
signal even before purchases.
• 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.
This week’s issue is sponsored by Optmyzr.
------------------------------------------
Most performance marketers think of Optmyzr as a PPC platform
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Inevitably, Q4 always breaks things. Pixels drop, feeds misfire,
CPMs spike in one geo while falling in another, Advantage+
suddenly over-indexes to retargeting… all while your team is
buried in promo launches. By the time someone notices, you’ve
already burned through thousands of dollars in wasted spend.
Optmyzr’s Smart Alerts act like a 24/7 analyst who never sleeps.
It learns your campaigns’ normal baseline hour-by-hour and flags
anything that looks off, before the little fire turns into a
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product-feed sync error within hours on Black Friday morning,
saving a client an entire day of wasted spend.
You set the thresholds that matter: get a Slack ping if
thumb-stop rate on your best-performing creative drops 20%, or if
CPMs climb 15% in your highest-volume geo. That kind of early
signal is the difference between a quick tweak and a five-figure
problem.
If you still think of Optmyzr only as a PPC optimizer, Q4 is the
perfect time to rethink that. Their Smart Alerts for social ads
give you a real-time safety net, critical during the most
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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
----------------------------
* Confirm ICP and primary target segments.
* Identify most valuable vs. least valuable customers.
* Document core business objectives (growth, CAC, payback
period).
* Map revenue and margin by SKU/service line.
* Note seasonality, promo cycles, inventory or staffing
constraints.
* Capture geographic or channel priorities (markets, stores,
service areas).
---------------------------------
2. Data Infrastructure & Tracking
---------------------------------
* Validate pixel and/or CAPI implementation across all
properties.
* Test for duplicate events (e.g., form submits counted twice).
* Confirm deduplication logic for browser + server events.
* Check that all optimization events fire as intended (View → ATC
→ IC → Purchase/MQL/SQL).
* Verify conversion value accuracy and currency.
* Ensure event volume ≥ 25/week/ad set for stability.
* Reconcile Meta-reported conversions with backend orders/CRM.
* Confirm offline/qualified-lead passback via CAPI or partner
integrations.
* Audit feed freshness, price accuracy, inventory sync for
catalog/Adv+.
* Review privacy banners, GTM tags, or blockers that might
suppress signals.
-----------------------
3. Account Architecture
-----------------------
* Count active campaigns/ad sets and flag over-fragmentation (
* Identify legacy or “shanty-town” campaigns still capturing
spend.
* Check naming conventions—offer/product/audience should be
clear.
* Verify exclusions to avoid remarketing cannibalization of
prospecting.
* Segment campaigns around profit centers (hero SKUs, service
tiers, geos).
* Validate optimization goal alignment (e.g., purchases vs.
traffic).
* Compare prospecting vs. retargeting spend (aim ~80/20 for most
e-com).
* Ensure top 3 campaigns control ≥ 60% of spend.
* Confirm that test structures exist—either a dedicated testing
campaign or documented process.
-------------------------------
4. Budget Distribution & Pacing
-------------------------------
* Chart spend vs. NC-ROAS or incremental revenue by campaign.
* Flag any campaign using > 10% of spend but
* Identify high-efficiency campaigns budget-capped below
potential.
* Review daily vs. lifetime/rolling-7 pacing—test loosening daily
caps.
* Examine marginal return curves for each major campaign/geo.
* Check automated rules and bid strategies for alignment with
business goals.
-------------------------------
5. Attribution & Incrementality
-------------------------------
* Compare 1-day view vs. 7-day click vs. 28-day click results.
* Benchmark MER against in-platform ROAS for reality-check.
* Calculate NC-ROAS to detect double-paying for existing
customers.
* Identify campaigns skewing heavily to retargeting pools.
* Ensure any third-party attribution tool (e.g., Triple Whale,
Northbeam) pass data back into Meta.
-----------------------------------
6. Creative Alignment & Performance
-----------------------------------
* Review active creative concepts (goal: ≥ 6-10 live in
prospecting).
* Flag over-reliance on ≤ 5 ads capturing > 70% of spend in L90.
* Audit hook diversity: testimonial / demo / explainer / UGC /
lifestyle / static / carousel / VSL.
* Measure Thumb-Stop Rate (goal: ≥ 25% for most categories).
* Measure Hook-to-Hold (≥ 35-40% from 3-sec → 15-sec view).
* Track CTR-Link on prospecting (e-com goal: ≥ 0.8–1.0%).
* Track CPM-New to understand cost of fresh reach vs.
retargeting.
* Review ad-to-audience fit: is the opening hook relevant to that
segment?
* Check alignment of creative promise vs. lander above-the-fold.
* Verify ad copy and CTAs match specific offers/bundles shown.
* Check frequency decay - flag concepts fatiguing
* Review creative refresh cadence (ideally every 7–14 days).
* Evaluate testing pipeline - documented hypotheses per
angle/theme.
* Confirm creative tracker tags angles/themes so learnings are
actionable.
---------------------
7. Landing-Page & CRO
---------------------
* Test page-load speed (
* Confirm the headline mirrors the ad hook exactly.
* Ensure immediate clarity on: What is it? Is it for me? Can I
trust it? What next?
* Check CTA visibility above-the-fold on both desktop & mobile.
* Map scroll-depth vs. CTA placement; test anchor-link CTAs.
* Audit form UX: fields, validation errors, mobile friendliness.
* Examine friction types—cognitive, emotional, mechanical.
* Validate trust elements (reviews, guarantees, UGC, 3rd-party
badges) near CTAs.
* Check that promo messaging on lander matches the live ad
flight.
* Track Post-Click CVR (e-com: ≥ 1-2%; lead gen: ≥ 2-3%).
* Monitor bounce rates—identify > 60% as a friction flag.
* Use scrollmaps/heatmaps/session recordings to locate drop-off
zones.
---------------------
8. Testing Discipline
---------------------
* Confirm a written testing calendar or pipeline exists.
* Check budget allocation to testing (e-com 10–20% of total).
* Ensure mix of 10% incremental vs 10x moonshot experiments.
* Limit concurrent test variants to 2-3 for stat power.
* Verify minimum sample sizes (~2k impressions & ~15
conversions/variant) and 7-day run.
* Confirm documentation of learnings & confirmation-test process
for promising winners.
-------------------------------
9. Competitive & Market Context
-------------------------------
* Review top-spend competitor ads via Meta Ad Library.
* Note common hooks/offers competitors emphasize vs. ignore.
* Identify whitespace angles (value, durability, VSLs, gifting,
etc.).
* Check brand positioning relative to competitor promo patterns
(discount vs. bundle vs. value-add).
--------------------------------
10. Future-Proofing & Resilience
--------------------------------
* Confirm CAPI deduplication & event-parameter passback is
healthy.
* Verify product-feed sync frequency and error monitoring.
* Set anomaly-detection alerts (Meta rules or Optmyzr) for
CPA/CTR/CPM spikes & feed breaks.
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