Happy Sunday, Everyone!

I trust you’re enjoying the first real hints of fall - the cooler mornings, football weekends and the final few days of Q3 (hard to believe October begins this week). For most brands, the next 90 days will decide how 2025 ends. And for those brands, Meta remains the single most powerful lever available to them finishing what has been a volatile, unpredictable and all-around rough year strong — but only if the account is set up and managed the right way.

In the past few weeks, I’ve had a steady stream of brands reach out for guidance, consulting, and audits, almost all focused on their Meta Ads accounts. That spike in requests isn’t random; it happens every year. And it happens because brands know that when Meta underperforms, it’s rarely the algorithm’s fault; it’s almost always because something fundamental is missing, misaligned or broken.

There’s no two ways about it: Meta is the greatest demand-creation and demand-capture platform in the history of modern commerce. A strong product paired with a well-run Meta strategy can unlock extraordinary, profitable growth. And there’s no time when that matters more than right now - in the weeks leading into BFCM. For most B2C brands, this is the make-or-break window. Nailing Meta in the next 90 days is the simplest way to recover from the headaches that plagued you earlier in the year - the tariffs, soft demand, stockouts, whatever.

If you’re in that boat, my recommendation is simple: audit your account. Either have someone you trust do it or roll up your sleeves and do it yourself. Start by understanding where things have gone off-track, then move to incremental optimizations and finally look for the blue-ocean opportunities hiding behind the noise.

To make that easier (and to help you get started) I’m dedicating the next two issues to my six-part Meta Ads Audit framework.

This issue covers the foundation: business context, fundamentals, and data infrastructure — the places where most unseen problems live. Next week, I’ll build on that with architecture, creative and testing — the levers that turn the insights you uncovered this week into performance gains.

Let’s dive into the first 3 pillars:

Pillar #1: Get Clear On The Why

Every successful audit starts by knowing why it’s needed. Without that, even exceptional findings miss the mark. Over the last few years, I’ve reviewed hundreds of ad accounts, from small spenders to million-dollar-per-month-plus behemoths. Regardless of account size or industry, I can categorize the “why” behind every audit into one of five buckets:

1. Poor Performance: the most common (and the most obvious) - the account just wasn’t performing to the level expected.
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2. Stalled Growth: the second most common issue - the account was performing exceptionally well at a certain level of spend, but efforts to go beyond the current performance volume consistently failed.
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3. Right People / Right Seats: this is the classic “I’m not sure if the person who got us from 0 to 1 can take us from 1 to 100” audit. There’s nothing actively wrong (in fact, things are usually going well!)...but the brand wants to know if there’s more that could be done. This is largely a “right people in right seats” question - are the people running the account the right ones to unlock new levels of scale? Is the structure in place conducive to that scale? What else needs to happen/change for that scale to be possible + profitable?
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4. Data Discrepancies: typically, this breaks down into one of two flavors - either the platform data looks sublime but the bank account looks like a horror show, or the ad account looks horrific but the brand is printing cash. Neither is good, though the latter is certainly preferable to the former. Either way, something doesn’t add up.
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5. Second Opinions: These sound innocuous, but they’re (almost) always caused by something - a new CMO is brought on board, a CEO has a bad feeling, etc. No matter what tips that first domino, the last domino is always the same: someone wants a second opinion.

Before opening the ad account, it is essential that you understand the root cause behind the audit. This enables you to ensure that the output aligns to the rationale. There’s nothing more frustrating for a brand owner than commissioning an audit due to poor performance, only for the person to come back and tell you everything’s fine. Clearly, it’s not. Something is wrong (else, they would not have hired you!). That something may not be in the ad account, but it exists.

It’s very likely you’ll find other problems as you go through the audit. Some of those problems might be more severe than the one you were brought in to solve. But finding all of that doesn’t matter if you can’t connect what you found to why you’re here in the first place.

As I’ve done more of these audits, I’ve come to the conclusion that Jeff Bezos was absolutely correct when he said, “When the data and the anecdotes disagree, the anecdotes are usually right.” That may be infuriating to hear as a data-driven marketer, but it’s no less true. The most successful and effective audits are the ones that identify why the data + anecdotes appear to be in conflict, then provide a path that unifies them while moving the business forward.

That last bit is vitally important - an audit is only as good as the go-forward auctions it enables. No one cares if you can tell them what’s wrong IF you can’t tell them how to fix it. Lead with solutions. Prioritize next steps. Make it easy (or, at least, intuitive) for them to fix the problem that led to where you are today.

Pillar #2: The Business Itself

You can not produce a high-quality audit of a Meta Ads Account (or a Google Ads Account, or any other ads account) if you do not understand the underlying business. Every ad account (even the great ones) is an imperfect reflection of the business it promotes, with the level of dissonance between the account + business proportional to the overall health of the ad account. That’s a fancy way of saying: the tighter the connection between the ad account and the underlying business, the more likely it is that the ad account is performing well.

Before diving into the ad account, you should be asking four main types of questions:

Type 1: Business Goals + Constraints

• What is your ICP / primary target audience?
• Who are your most + least valuable customers/clients? Why?
• What is your primary goal for the account?
• What’s your target CAC by Service/SKU
• What’s your LTV by Service/SKY
• What is your target payback period?
• Are there other constraints or considerations (inventory, location, etc) that can inhibit scaling or should be factored into a review?
• What are your current budgets? Are these fixed or flexible? If so, how flexible?

Type 2: Historical Performance + Trends

• What is the history of your account (basically - how did we get here?)
• What are your primary concerns about the account?
• Does seasonality influence your business? If so, how? Are certain SKUs/Service Lines more prone to seasonality than others?
• What are your promo + product cycles (if any)?
• Are there any other externalities that have impacted the performance of your account (interest rate changes, tariffs, port shut downs, recalls, etc.)?

Type 3: Infrastructure + Data

• What is your current tech + data stack?
• Do you use a landing page builder? TPA tool? Incrementality tool?
• How often is your tech stack or data stack changing?
• What other tools do you use to support your marketing?
• What data do you collect? Where is it stored?
• How often is your CRM/CDP updated/cleaned?
• What other sources feed data into that CRM/CDP (do you buy lists? Do you receive lists from partners - such as conference organizers, co-marketers, etc)?
• Is there a current ads + landing page repository?

Type 4: Customer + Offers

• Who are your direct + indirect competitors?
• What are your ICP’s current alternatives to your solution (and one of these might be “do nothing”)
• How is your company/brand different from your competitors?
• What are your UVPs/USPs?
• What is your brand DNA/core values / ethos?
• How do most of your customers hear about you?
• How does your sales/intake process work (if applicable)? Can we test it?

To be very candid, this should feel like the business equivalent of a proctology examination, because that’s exactly what it is. Just as a doctor will commission an ungodly number of tests, or a great lawyer will ask you highly personal, borderline offensive questions, or an accountant will ask to see every receipt, bank account statement, credit card statement, invoice and document….a great marketer will want to know as much as possible about your business before they begin.

Once you have that information, the next step is getting crystal clear on the industry/space. The real leverage comes from knowing the market you’re auditing. Before I ever open Meta ads manager, I spend hours conducting company + competitor research. The goal of this is to create a picture of the demand, constraints and economics that shape what “good” looks like for the brand in question. Here’s what I gather up-front:

Keyword & Audience Intelligence

Even if we’re not running search, keyword research tells us how customers describe their own needs - and which phrases signal buying intent versus curiosity. The more you understand about the target audience, the better you’ll be able to evaluate creative, offers, messaging + landers. Pair this with audience-level research (affinities, purchase triggers, proxies for income or lifestyle) so that when we review Meta targeting, we know whether those segments make sense.

Customer / Client Feedback

I’m continually shocked at how often customer feedback, reviews, ratings and other third-party validation/credibility is overlooked in ad account audits. This often manifests itself in the form of actual customer testimonials being ignored in ad and landing page copy. If your actual, real-life customers are telling you, “We went with your company for X and Y reasons,” or, “Your brand is the best in the world at Z,” – that’s absolutely incredible information.

From an audit perspective, understanding how the audience perceives the underlying brand/product/service is an invaluable data point when assessing creative and landers.

The same is true for credibility/trust factors: if you’re a challenger or upstart brand, every customer/prospect subconsciously risk-adjusts your offering, because buying your widget or going with your company poses a risk relative to using the known and /trusted brand. Reviews, Ratings & Third-Party Validation (awards, press, etc.) can reduce the perceived risk. If a finding from your audit is that conversion rates are lower than you’d expect, and you find that trust factors aren’t prominently featured in ads and/or landers, you can make an informed observation.

Competitor Landscape

No brand advertises in a vacuum. There’s ALWAYS competitors - the question is whether or not you recognize them and do your homework on them. Before I open the ad account, I want to know which competitors are advertising, how they’re positioning, the angles they lean on in creative and where they’re active. This isn’t about trying to mimic their strategy or copy their creative; it’s about spotting gaps and differentiators that my client can exploit and identifying areas where we are simply not competitive (every client will ALWAYS tell you they’re the best at everything; that’s (almost) never the case).

Seasonality, Promotions & Product Drops

An audit that compares a non-promo month to a big sale month (or Q1 to Q4) will mislead you every time. Before you jump into the ad account, collect the brand’s promo calendar, product-drop schedule and any historic sales patterns. This allows you to normalize performance swings before assessing the account.

Unit Economics & Margin

It is absolutely imperative that you know the actual thresholds for profitable growth - product/service -level margins, shipping and returns impact, contribution margin, rework rates, etc. Fundamentally, this is what defines acceptable CAC or ROAS. Having these numbers up front allows for an unbiased, clear-eyed evaluation of the account performance.

Sales-Cycle & Lead-Quality Data

For higher-consideration products and services, it’s critical to know the initial qualification rate (lead to MQL/SQL), the expected time between each stage (lead to MQL, MQL to SQL, SQL to resolution) and what percentage of qualified inbound ends up closing. If there’s a 30-day lag between lead and closed/won, that must be factored into the evaluation of a campaign AND the measurement setup used in the account.

Geo-Market Economics

One of the more counterintuitive things in advertising is dealing with situations where the smart move is to spend “inefficiently” on advertising due to geo or market economics. As an example: an ad account I recently reviewed showed sky-high customer acquisition costs in certain geos. The initial reaction (and what most marketers would have said) was to cut spending in those areas and re-allocate to others. What they would have missed is that this business had exceptionally high fixed costs per location AND an inability to re-allocate personnel to other areas.

This business had about $120,000 per month in per-location fixed costs (rent, salaries, trucks, insurance, regulatory permits, etc.). The CAC in one particular market was $3,000/customer (vs. $1,250 elsewhere). Each customer’s net revenue (gross revenue - COGS) was ~$10,000. As counter-intuitive as this sounds, the optimal solution for this brand was to spend about $30,000 in this market.

Why?

Because this acquires 9-12 customers per month, which off-sets about 60% of the location’s fixed costs and keeps the team there busy but not overwhelmed.

And, you’re probably wondering, why not just spend ~$54k to have the location break even?

Answer: because re-allocating the final $24k ($54k to break even - $30k spent) to other markets with much lower CACs AND capacity is optimal from an enterprise perspective. In those other markets, the $24k will drive ~19 customers - about the number those other locations can serve and contribute ~ $166,250 in net revenue to the enterprise. The end result is a net gain of $116,250 ($166,250 in gain from the other markets + ($50,000) loss from the expensive market).

Great media allocation (exactly what ever audit should do) isn’t just about channel metrics (like CAC or ROAS) – it is about how ad dollars interact with the underlying business (fixed + variable cost structures, capacity limits, marginal returns, organizational priorities) across markets + segments.

Historic Pricing, Offer & Promotion Strategy

I want to understand how the brand has used bundles, discounting, financing or shipping thresholds in the past. What promos were run? How do those compare to what competitors have (or are) running now? Changes in offer structure often explain sudden CVR swings that get wrongly blamed on creative or algorithm changes.

Product/Service & Inventory Roadmap

Upcoming launches, seasonal SKUs, or known stock-outs influence demand curves and CTR/CVR patterns. This context helps us distinguish between true performance changes and shifts caused by merchandising. The same holds true in service-based accounts – a just-introduced service will likely have lower demand than a core service, which can skew an evaluation of the account.

Channel-Mix & Halo Effects

This is difficult to do, but it is essential to understand how Meta interacts with other traffic sources like paid search, email, SMS, influencer, retail/wholesale. A stable MER with rising Meta spend can signal a healthy halo effect on branded search and direct traffic even if Ads Manager under-credits it.

Customer Sentiment & Category Perception

One of the major drivers of ad account performance is customer sentiment + category perception. Every brand will tell you things are great - but that often fails to fully map onto reality.

My solution: use customer sentiment and category perception as a market-intelligence scan rather than just a source of creative hooks. Before I ever open Meta ads manager, I want to know how the audience perceives the entire category, how they talk about each competitor, what they believe is table stakes versus what feels truly differentiating, and which frustrations or myths dominate the conversation.

That context is essential when evaluating whether the account’s current messaging is swimming with the current or against it, whether the offer addresses the real objections buyers have and how much of the performance gap is likely due to creative misalignment versus structural or budget issues.

By gathering this context first - through reviews, Reddit, forums, earned media and competitor chatter, I can start the audit knowing the real purchase drivers, perceived barriers and trust factors in the market. This shapes how I interpret metrics later: a low CTR may be a creative/market mismatch, not an algorithm issue; persistently low conversion rates might reflect a credibility gap the ad account data simply won’t show. The sentiment review grounds the audit in the real market forces shaping outcomes, not just what the dashboards record.

Regulatory & Compliance Factors (as relevant)

In categories like health, finance, alcohol, or legal, note any ad-policy or disclosure constraints up-front so recommendations stay realistic. There are few things more annoying to a brand than getting audit recommendations they are legally prohibited from implementing.

There’s no two ways about it - this is a TON of work up-front. It is not easy. But the benefit of having both the business understanding AND the competitive/market analysis is the frame it provides.

Pillar #3: Data Collection + Management

For all the talk about creative angles, bid strategies, and funnel hacks, the simple reality of Meta advertising today is that data is the single most impactful performance lever you can control.

Meta’s machine-learning algorithm thrives on rich, accurate and timely conversion signals. If those signals are missing, late or wrong, the platform is optimizing blind. No amount of clever audience stacks, scroll-stopping UGC or brilliant landers will overcome that at any level of scale.

A remarkable Meta audit must begin with the data infrastructure that powers the entire system. As the old saying goes, “Garbage in, garbage out.”

Meta’s algorithm is designed around optimization event feedback loops: the system learns which impressions led to meaningful outcomes and analyzes billions of user + system-level data points to determine what factor(s) are most likely to contribute (or detract) from positive events going forward. It then uses all that data to inform its probabilistic model of the expected value of each subsequent impression. That determination, in turn, controls bids + delivery in near-real-time.

As wildly impressive as all that is, it is absolutely worthless if:

• Conversions aren’t tracked at all (or are tracked twice)
• The event fired doesn’t match the business outcome
• Value parameters are missing or incorrect
• Post-click sales, subscriptions or lead-qualification outcomes never get passed back

When any of those things happen, the algorithm gets the wrong reinforcement signal and starts rewarding the wrong users or behaviors. The end result of that is higher CPAs, volatile ROAS and scaling stalls - not because the creative is bad, the audience is wrong or the market changed, but because we decided to run a Ferrari (Meta) on used cooking oil instead of 93 octane.

Step 1: Review Conversion Tracking

Your optimization events are the single-most-impactful data you pass to Meta. The first thing you should validate is that every conversion signal sent to Meta is intentional, unique and accurate.

Key checks include:

• Duplicates or irrelevant events: the most common issue I see. Example: “newsletter sign-ups” or “career submissions” or "partnership inquiries” firing the same “Lead” event Meta optimizes for. This misguides the algorithm to chase cheap, low-value actions instead of the leads you actually want.
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• Event-to-Journey Alignment: each stage of the buying journey (view content, add to cart, initiate checkout, purchase) should have its own discrete event. The same is true for leads - fire an event for form start, form submit, MQL, SQL, Closed/Won, Closed/Lost. Having this data in Meta ads manager allows you to understand the full-funnel, full-lifecycle impact of your marketing. It also prevents disintermediating the account management from the data in-view. I’ve reviewed far too many accounts where optimizations + budgetary decisions are made based on data not in Meta’s view (i.e. pausing an ad that appears to be driving fantastic results, because the leads driven from that ad are all DQ’d in the CRM). When that happens, Meta has NO IDEA why you’ve made that decision - so it can’t improve.
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• Validate Conversion Values: if purchase or deal values are passed, test that they match order totals (including or excluding tax/shipping as intended). Meta’s value-based bidding needs clean numbers.
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• Technical blockers: outdated pixel code (should be resolved if you use GTM or the native Shopify integration), cookie-consent banners or GTM errors often prevent events from firing. Test-event tools and order-to-event reconciliation uncover these silent failures.

In my experience, roughly 1-in-3 accounts have at least one major conversion tracking or passback mistake. Even if you think everything is set up correctly, it’s often worth double checking.

Step 2: Verify Conversions API (CAPI)

CAPI is Meta’s server-side bridge that supplements or replaces browser-pixel events. It boosts match rates for users on iOS or using ad-blocking browsers, stabilizes reporting and makes bidding models more resilient to fuzzy data situations (i.e. cross-device conversions, ad blockers, dropped FBCLID parameters, etc.).

Almost every business will benefit from a proper CAPI implementation. Here’s what to look for:

• Confirm that CAPI is implemented - many smaller accounts still haven’t done it. Meta has a native integration with Shopify, and for lead gen accounts, Zapier is absolutely wonderful.
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• Validate deduplication logic so browser and server events for the same optimization event aren’t double-counted. Meta’s system will do this by default IF you pass both the event name AND event ID parameters from both the browser + server (CAPI) events.
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• Make sure all critical parameters (event value, currency, content IDs, product SKU, service, location, etc.) are being passed.

Accounts with a proper CAPI implementation often see 5-15% more conversions in the ad account – these aren’t net-new (it’s not like you weren’t getting them before - you were! You just didn’t know where they came from), BUT they do provide Meta with significantly more data for optimization.

Step 3: Pass Back Post-Conversion Outcomes

In lead-gen, subscription and other high-consideration categories, counting every form submission as a conversion is misleading. Most accounts should optimize for qualified leads or closed customers, not for every raw lead that comes through the site.

Here’s what I look for in each post-conversion review:

• Map CRM/CDP data to identify which leads became MQLs/SQLS/paying customers.
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• Confirm with your sales/customer success team that data in the CRM is updated as quickly as is humanly reasonable – I’ve worked with far too many companies where sales didn’t bother to update the CRM until the end of the month (commission time)...which royally screwed over the marketing team because by the time that update happened, the attribution window had closed.
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• Ensure that those outcomes are pushed back to Meta as custom conversions (with values where possible).
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• Confirm time stamps and primary keys (email, phone, click ID (FBCLID) and/or event ID) so Meta can connect the downstream event to the original click.

This step usually drives the largest ROI increase for service-based businesses because it aligns spend with high-LTV buyers rather than raw lead volume.

Step 4: Review Feeds and Linked Data Sources

For ecommerce brands running catalog or Advantage+ Shopping campaigns:

• Check the freshness of product feeds. There’s no excuse to not have real-time (or, at worst, daily) syncs. One of the biggest culprits behind wasted spend in accounts is delayed product updates, which results in spend continuing to flow to out-of-stock or inaccurate SKUs. The same is true for service-based businesses (esp. ones with capacity constraints) – if you have no appointments available for a month in a particular geo, why are you spending money advertising there?
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• Review field mapping for errors such as SKUs with out-of-stock popular variants (nothing quite like advertising a SKU where you only have XS and XXL in-stock) or missing GTIN/price data that limits delivery.
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• Verify that inventory and price changes flow through automatically and quickly.

The bottom line: a delayed feed or broken catalog sync will obliterate account performance…even when campaign + ad set settings look fine. It will make otherwise exceptional creatives look terrible. It will trick Meta into serving the wrong ads to the wrong people.

Step 5: Data Management + Governance

Data infrastructure is often referred to as plumbing - and for good reason: that’s basically what it is. Unsexy, boring, plumbing. But getting that plumbing right is only half the battle - a flawlessly designed data infrastructure is worthless if the data moving through it is corrupted or wrong.

In my experience, this is the most nefarious issue that impacts Meta ads accounts, and 9/10 times, it is missed. Just this year, I’ve seen:

• Sales reps retroactively re-qualify leads in order to hit quotas (pro tip: don’t bonus your salespeople based on how many leads they turn into SQLs, and DEFINITELY don’t make it a competition so they all do it at the end of the month to try to win).
• Old or duplicate records remaining in the system (bonus points if you send the same product twice for one order b/c you didn’t deduplicate)
• Different business units using different CRMs…but not linking them so cross-sells were never counted (yay!) and LTV numbers were WILDLY off for certain user segments.

Identifying these issues is NOT easy. Here’s where I start:

• Review CRM audit logs for frequent retro-edits (that’s how I found the salespeople thing)
• Compare Meta-reported purchases to fulfilled orders to detect gaps
• Speak directly with business unit leaders to learn how leads are logged and updated day to day
• If different CRMs are used by different segments/business units (yes, it happens), actually compare the two!
• Actually do the stuff - purchase things. Submit lead forms. Schedule meetings. Subscribe. With real money. Then track how your data flows through the system.

The particularly persnickety thing about data management / governance issues is that a technical fix alone is usually insufficient – you also need to have the client (or people on the client’s team) change their behavior. Depending on what needs changed, that can be both difficult to do AND make you persona non grata with some of your client’s employees (like the salespeople who suddenly don’t get their bonuses just for changing Lifecycle Stage in HubSpot to SQL). It’s not fun, but it’s essential.

Recognizing Data-Driven Performance Problems

In almost all of the “poor performance” or “unable to scale audits”, clients complain of some (or all) of these issues – and are convinced they are a product of something in the ad account:

• Sudden spikes or drops in CPA/ROAS with no creative or budget changes
• Spend skewing toward low-value campaigns after new events were added
• Campaigns stuck in “Learning Limited” despite adequate budgets
• Inconsistent revenue numbers between Meta and Shopify/GA4/CRM
• Strong CTR, CVR + AOV but inability to scale

The reality? Almost all of them have data gaps or mismanaged data infrastructure as their root cause, not campaign settings, ad set targeting or creative. If you don’t check data first, you’ll end up chasing ghosts around the ad account for hours (or days, or weeks) – and be no closer to an actual resolution.

Here’s the exact checklist I use when reviewing Meta Ads / CAPI:

1. Gather documentation for pixels, APIs, feeds and CRM integrations
2. Review the current Google Tag Manager (or other Tag Manager)
3. Test event firing using Meta’s Test Events, GTM’s Preview and server logs
4. Reconcile a sample of orders or leads against Meta’s logged events
5. Validate values and parameters (currency, SKUs, order totals, event ID)
6. Inspect CRM change logs
7. Interview ops/sales teams
8. Deploy fixes and set up automated QA checks to prevent regression

For most audits, my deep-dive into the brand, the audience/competitor research + data infrastructure review comprises >50% of the total time. That’s intentional. Those three things account for a majority of the results observed in the ad account AND they’re often the least-reviewed. The net-net: these seemingly minor, annoying, boring things have the highest probability of uncovering things that were missed by everyone else - which are (likely) leading to the results the client is seeing/feeling.

Again, when the data and the anecdotes disagree, the anecdotes are usually right – because they’re picking up on something that your data is not. All we’re doing here is removing the blockers and allowing the data + anecdotes to tell a similar story.

Next week, we’ll get into the ad account – focusing on the account structure, audiences, creative + testing strategy.

Until then, have a great week!

Cheers,

Sam

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