How Campaign Structure Impacts Ad Performance

How to Audit a 7-Figure Meta Ads Account Like a Pro When I audit a 7-figure Meta ads account, the goal isn’t just to find inefficiencies—it’s to unlock scale. Most accounts I audit are messy: ✔️ Bloated structures with redundant campaigns ✔️ Poor alignment between ad spend, customer acquisition cost (CAC), and business profitability ✔️ No clear process for testing, scaling, and optimizing Here’s how I audit accounts... Step 1: Account-Level Financial Performance Before diving into the ads, I start at the business level. Scaling isn’t just about ROAS—it’s about profitable customer acquisition. Key metrics to analyze: 📊 MER (Marketing Efficiency Ratio) & AMER (Acquisition MER) – Is ad spend translating to profitable revenue? 📉 CAC vs. LTV – Is the cost to acquire a customer sustainable in the long run? 📈 Blended ROAS vs. Platform ROAS – Are platform-reported numbers misleading? 💰 Contribution Margin – Does scaling ad spend improve or erode profits? This step ensures Meta isn’t just driving revenue—it’s driving profitable revenue. Step 2: Campaign Structure & Organization Next, I review the campaign architecture. It should have a clean, hierarchical structure with clear objectives. 🚀 The Ideal Structure: 1️⃣ Testing Campaigns – New creatives & audiences (structured and controlled) 2️⃣ Scaling Campaigns – High-performing creatives & audiences (increased budgets, bid strategies applied) ❌ Red Flags in Structure: ⚠️ Randomly mixed testing & scaling in one campaign ⚠️ Poor naming conventions (hard to analyze performance) Step 3: Creative Performance & Messaging Meta ads succeed or fail based on creative. I analyze: 📊 Creative Performance Metrics: ✔️ CTR (Link Click-Through Rate) – Is the ad engaging? (Target: 1.5%+) ✔️ Thumb-Stop Ratio – How many people watch the first 3 seconds? ✔️ Engagement & Shareability – Do people interact, comment, and share? Step 4: Bid Strategy & Budget Allocation Scaling isn’t just about increasing budgets—it’s about doing it efficiently. 💰 Analyzing Bidding & Scaling Strategies: ✔️ Manual vs. Auto Bidding – Is the account using bid caps, cost caps, or lowest-cost bidding correctly? ✔️ Scaling Strategy – Are budgets scaling gradually, or are sudden jumps causing instability? ✔️ Budget Efficiency – Are some ad sets spending too much with poor results, while winners are capped? Step 4: Action Plan & Next Steps After identifying what’s broken, I create a clear, step-by-step plan to fix inefficiencies, optimize scaling, and increase profitability. Immediate Fixes (0-7 Days) ✅ Pause redundant campaigns & consolidate structure ✅ Cut high-spend, low-return ad sets ✅ Implement strict CPA-based budget controls Short-Term Strategy (7-30 Days) ✅ Launch systematic creative testing ✅ Introduce structured scaling campaigns ✅ Optimize bidding If your account lacks clarity, structure, and a clear path to scale, it’s time for a real audit. Drop “AUDIT” in the comments, and let’s take a look. 🚀

This 10-minute change more than doubled the profit for one of my ecommerce clients. And it didn’t require new creative, audience testing, or a bigger budget. Here’s what actually happened: A DTC brand came to me running multiple Meta ad campaigns — each targeting slightly different stages of the buyer's journey. ROAS was stable — but just barely breaking even. Their main challenge? Structural inefficiency across the account: → Too many campaigns competing for the same users → Audience overlap driving up CPMs → Fragmented learning across campaigns, and ad sets → No clear segmentation between new, engaged, and existing audiences We made one change: Consolidated three separate campaigns into a single campaign to reduce audience overlap and improve learning. **Clearly define audience types (new, engaged, existing) for accurate reporting and deeper analysis.** Same creatives. Same product. Same total budget. Just one campaign — maximizing signal density, reducing audience overlap, and unlocking more efficient spend delivery. 📈 The result? → ROAS increased 31% → Revenue increased 31% → Net profit increased 1,750% (previous ROAS was breakeven — every lift went straight to profit) Meta was able to: → Centralize performance data — giving the algorithm a clearer feedback loop to optimize faster → Reduce fragmentation — so each ad set benefits from more data and quicker learning → Eliminate audience overlap — lowering CPMs and preventing budget cannibalization → Focus spend on the highest-intent users — improving efficiency without increasing complexity Takeaway: Big profit jumps don’t always come from big creative overhauls. Sometimes, it's one strategic restructure — done with intent — that unlocks sustainable scale. 💬 Running Meta ads and unsure if your campaign structure is built to scale efficiently? Shoot me a DM — happy to walk you through how I approach building lean, scalable account structures that drive real performance. – If you think someone in your network would benefit from this, like, comment or repost to share. Follow for more frameworks that tie ad structure to actual eCommerce profitability.

Your ad account structure matters, but not for the reasons people think. Everyone is shouting from the rooftops that creative is the only thing that matters and that having one broad campaign is the key to making $1M+ a day with Meta ads. They have the right idea, but they are missing a huge caveat. You should not consolidate at the expense of your business goals. If your business has three main product categories, you should probably have a campaign for each one. Structure your campaigns the way you would look at a P&L. This approach will create two major benefits (among others): 1. You can now analyze CAC and CPO by each product category. 2. You can control inventory and the velocity at which you sell through each product category. These benefits alone are worth any performance trade-offs you might encounter with a single campaign setup. Segmenting by product is not the only way to group your campaigns, but it is one of the most common and effective use cases. The end goal is to have a healthy P&L. Your marketing implementation should reflect that objective.

Companies spend thousands on Meta ads but often see high CPC, low CTR, and weak conversions. After analyzing five (sales) campaigns yesterday, I found common mistakes that kill performance. Here’s how to fix them 👇 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗶𝗻 𝗠𝗲𝘁𝗮 𝗔𝗱 𝗦𝗲𝘁𝘂𝗽 🔹 High Frequency ≠ Success If frequency is 3.8+ in cold audience campaigns, your audience is too narrow—the same people see the ad repeatedly and stop engaging. Retargeting campaigns? High frequency (3-5+) is fine because those users already showed intent. 🔹 Budget Misallocation High-performing campaigns aren’t scaled despite low CPC and high CTR. Poorly performing ads keep running without creative refreshes, wasting spend. 🔹 Wrong Ad Formats & Placements • Static images overused instead of video formats. • Carousel ads lack storytelling - each slide should highlight a different benefit. • Meta Reels & Stories underutilized, despite having the lowest cost per interaction.    🔹 Lack of testing & iteration • No A/B testing on creatives, headlines, or CTAs. • No experimentation with visuals or audiences. 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗶𝗻 𝗔𝗱 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲𝘀 🚫 Weak Call-to-Action (CTA) “Shop Now” is too generic. Better CTAs: • Sign Up Now • Claim Your Discount • Reserve Your Spot 🚫 Generic Headlines that don’t drive action Bad Example: Big Sale! 60% Off! Better: • Last Chance! 60% Off – Don’t Miss Out! • Only Today – 60% Off! 🚫 Poor visual hierarchy • CTA buttons blend in - use high-contrast colors. • No countdowns (e.g., "Only X Hours Left!"). • No badges/stickers like “LIMITED OFFER” or “50% DISCOUNT.” 🚫 Too similar images in Carousel Ads Each slide should emphasize different benefits, like: • 50% Off Everything • Mentor Support • 24/7 Access • 10,000+ Happy Users 🚫 Story Ads look like regular Feed posts • No motion elements (GIFs, animations, swipe-up prompts). • No stickers like “Only X Hours Left!” • No engaging hooks - start with a question: Have you used your 50% discount yet? 𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵 𝗮𝗯𝗼𝘂𝘁 𝗟𝗼𝗼𝗸𝗮𝗹𝗶𝗸𝗲 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲𝘀 𝘃𝘀. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗲 Lookalike audiences aren’t the magic bullet anymore. Before (pre-iOS 14): Lookalikes worked well - Meta had more user data. Now: Meta favors broad targeting, meaning strong creatives outperform segmentation. Why? • Limited tracking (iOS 14 impact) - Meta relies more on creative performance. • Creative is the number 1 success driver, not audience targeting. • Broad audiences often outperform lookalikes - Meta auto-optimizes based on engagement. How to fix your Meta Ads? ✔ Segment cold vs. warm audiences properly. ✔ A/B test creatives, headlines, and CTAs. ✔ Use more video & dynamic content. ✔ Prioritize Meta Stories & Reels. ✔ Make CTA buttons & key elements pop. Small changes = big impact. But don't change things every day! You'll never get out of the learning phase. A campaign that isn’t working today might explode tomorrow - with the right optimization.

This year, I reviewed 80+ ad accounts spending between $50K–$2M/month. Same patterns. Same problems. And 90% of them were leaving serious money on the table. Here are 5 critical issues I spotted over and over again: 1. No line between testing and scaling 🧯 Same campaigns used for both → changes break what's working 🧪 New ads dumped into scaling campaigns with no structure 📉 Inability to isolate what's driving performance ✅ Fix: Structured test vs. scale setup. Defined promotion criteria. Weekly creative test cycles. 2. Zero creative diversity 🖼️ Repeating the same formats, hooks, and messages 🔁 Every ad sounds like the last 😐 No placement-specific variation ✅ Fix: Format variety mapped to funnel stage. Multiple angles per product. Native-to-platform creative. 3. Comments being completely ignored 👎 Negative feedback left untouched 🤔 Customer questions go unanswered 💬 Missed goldmine of insights and UGC ✅ Fix: Monitor sentiment. Mine feedback for copy. Use social proof in ads and landing pages. 4. Weak landing experiences 🧩 PDPs used as catch-all destinations 🧠 Disconnect between ad angle and what users land on 💸 Wasted attention and poor conversion ✅ Fix: Tailored landing pages for each angle. Comparison pages. Problem-solution flows. 5. No AOV game plan 🛒 No bundles. No upsells. No structured offers. 🚫 Leaving LTV and AOV flat 💰 Relying on discounts as the only lever ✅ Fix: Smart bundles. Upsell flows. Subscription incentives. One example? We implemented these changes for a home goods brand over 6 weeks: → Rebuilt testing and scaling structure → Rolled out angle-specific landers → Introduced bundles + upsells → Lifted AOV by 22%, with more stable CAC Here’s the real issue: Most brands don’t fail because they aren’t trying. They fail because they skip the fundamentals. If you're scaling with leaks like these, you're paying for growth - but not keeping it. Which of these are you still letting slide? 🚨 If any of this hit a little too close to home... it might be time for a proper audit. We’ve just opened up 3 Meta Audit slots for next week. Each one is a deep dive into your creative, funnel, and account structure - built to surface blind spots and scale blockers. Drop “Audit” in the comments or DM me directly to grab one. Let’s fix what’s holding you back.

How I increased a brand’s ROAS by 44% in 75 days (while scaling spend 248%) A 7-figure watch brand was stuck at a 1.49 ROAS on Meta ads. They were testing random campaigns, lacked structured creative testing, and weren’t optimizing their ads effectively. In just 75 days, we turned it around. Here’s the 3-step framework I used: 1. Simplified Campaign Structure They had 7 scattered campaigns—one was only hitting a 0.7 ROAS. I consolidated them into: ✔️ Advantage+ for broad scaling ✔️ DPA for retargeting ✔️ DABA for new customer prospecting 2. Strategic Creative Testing We launched 15+ UGC assets with influencers, testing: ✔️ Hooks that grabbed attention ✔️ Product demos & unboxings ✔️ Mashable-style viral videos 3. Ad Creative Optimization We tracked performance using the following metrics and made changes to the ad creatives: ✔️ Hook Rate = Scroll-stopping power ✔️ Interest = Avg. Watch Time ✔️ Desire = Click-Through Rate ✔️ Action = ROAS The result? 🔥 ROAS up 44% (1.49 → 2.14) 🔥 Ad spend up 248%—profitably The takeaway: → Keep campaigns simple → Test diverse UGC → Measure creative performance If you’re a $1M-$10M DTC brand struggling with Meta ads, I’ll analyze your account for free and show you where to improve. 🔗 Get a Free Analysis Here: https://lnkd.in/gqrtTC3U

Quick and dirty optimization guide for incrementality. In general, the higher a platform attributes itself credit, the less incremental it is. (Not always but it's a good rule of thumb to drill into your brain). Here are some facts: 1. Every platform over or under credits itself 2. Incrementality measurement "fixes" this 3. It's hard to optimize off incrementality experiments So how do you tell an ad buyer what to do after an experiment? 1. You can make media mix decisions off the test(s) 2. You need to translate incrementality to platform attribution 3. Intra-tactic optimizations (ad A vs B) are fine to use attribution Let's focus on number 2, as that's the one that trips most people up. An example focused on Meta. Here is our campaign structure: • Awareness (reach + frequency) • Prospecting (new customers, convs) • Retargeting (site visitors, convs) • Retention (past customers, r+f) We run an incrementality test on all four tactics within Meta. We come back with results that look like this: • Awareness: iROAS of $5 • Prospecting: iROAS of $4 • Retargeting: iROAS of $2 • Retention: iROAS of $3.50 But their platform attribution results look very different: • Awareness: pROAS of $0.5 • Prospecting: pROAS of $2 • Retargeting: pROAS of $4 • Retention: pROAS of $7 If I just optimized off of the platform results, I'm probably tempted to put less money into Awareness and more money into Retention. But, due to our incrementality test, we know that this is the opposite of what we should do. Instead, I want to set up a coefficient to translate that tactic's self-reported attribution to ACTUAL incrementality. This is problematic for a number of reasons I won't get into on this post, but, suffice to say it is "good enough" for a media buying team and is a heck of a lot better than just using platform results. So that translates to a Platform to Incrementality coefficient of: • Awareness: 10x ($5 vs 0.5) • Prospecting: 2x ($4 vs $2) • Retargeting: 0.5x ($2 vs $4) • Retention: 0.5x ($3.5 vs $7) Let's say our "target" ROAS for any tactic is a $4. That means that we can push up and scale on those above $4 and pull back on those that are below $4 (modified from platform to incrementality). That would mean: • Awareness: scale up • Prospecting: hold steady • Retargeting: pull back • Retention: hold steady Conveniently in this example we can just reallocate some of our budget from Retargeting and put it into Awareness. Presto, you've just optimized for incrementality AND given your team something to "shoot for" in the account. #ecommerce #attribution #incrememtality

We’ve all been there, pouring money into Google Ads with little to no ROI. Your immediate reaction is, "F*** this channel." The reality is, it was more likely that way you were executing or a bigger problem down funnel. Let me tell you a quick story. 👇 A Series B B2B SaaS brand approached me around six months ago to help improve the performance of their paid spend (primarily Google Ads) After my team and I spent hours going through their account, we noticed a few troubling things: ❌ Targeting broad terms and giving Google Ads too much leeway ❌ Campaign structure had all keywords combined as one ❌ Landing pages didn't allow for exploration and didn't match the main website ❌ Offline conversions weren't being tracked in-channel ❌ Most budget was spent on low-intent terms ❌ Desktop performed best, yet there were tens of thousands of $$$ spent on mobile I then looked at the keywords that converted over the last 90 days and noticed more than 70% were marked not a fit by sales, another 20% weren't followed up with, and then 10% or so did happen to move forward into opportunities. Sidebar: This is why sales gets frustrated with marketing. Same way I felt when I was on the other side. So then we made a shift after in the first 30 days. ✅ Narrowed keywords & match type: We focused on specific, high-intent keywords aligned directly with their services. Instead of broad terms, we used phrases like “B2B SaaS solutions for healthcare” and “custom CRM development.” ✅ Revamped landing pages: We got the client to eliminate the old “one-size-fits-all” approach and allowed visitors to explore other areas of the website to self-educate. ✅ Increase impressions on Desktop: We diverted more traffic to desktop vs. mobile In just 60 days, they saw a ~30% increase in qualified leads and a ~40% drop in cost-per-opportunity 🎯 All because we abandoned the old ways and adopted a targeted approach on Google Ads. 💡 The lesson? The way you execute matters. P.S. Remember that the success of a channel is a team sport. You don't win by making strategic changes on Google Ads in isolation. You need a strong product, a good story, and a strong sales team to help convert that demand.

The 4 Core Levels of Google Ads Structure (and why each matters)👇 1️⃣ Account Level → Top-level container. → Controls billing, user permissions, and shared assets (audiences, conversion actions, scripts). → Houses MCC linking for multi-account oversight. ↳ Key tip: Centralize conversion actions to avoid duplicate tracking and strengthen bidding signals. 2️⃣ Campaign Level → Core strategic control point. → Defines budget allocation, bid strategy (tROAS, tCPA, Maximize Conversions), geo-targeting, language, networks (Search/Display), and schedule. → Sets segmentation logic for spend and optimization. ↳ Key tip: Avoid over-segmentation — too many campaigns fragment data and hurt automated bidding performance. 3️⃣ Ad Group Level → Controls keyword or audience segmentation within a campaign. → Groups closely related keywords or intents. → Directly houses ads tied to that specific theme. ↳ Key tip: One intent per ad group. Group by meaning, not just by match type — crucial for maximizing Quality Score and relevance. 4️⃣ Ads & Keywords → Execution layer. → Ads: multiple variants per ad group, using RSAs and all available extensions to dominate SERP real estate. → Keywords: manage match types (exact, phrase, broad), bids (if manual), and negatives to refine traffic quality. ↳ Key tip: Audit search terms weekly — essential for eliminating waste and uncovering new high-intent terms. Strong structure improves signal quality and speeds up machine learning. Cleaner setup = clearer insights, easier optimizations, better scaling. ___________________ ♻ Repost if you found this helpful! ♻ Follow Marc Jordan Waldeck and Bounce Marketing for more! Need expert Google Ads management? DM me! 🤓

Spending LESS got us MORE revenue. Here’s how it happened on a $1M campaign: Most e-commerce brands try to scale with brute force: “Raise budgets. Widen targeting. Go bigger.” We did the opposite, and got better results. A DTC skincare brand was spending $80K/month on Meta ads. Results? Plateaued. → CPA was climbing → ROAS slipped under 2 → Margins were thinning after $1M+ spent Here’s what we changed: ⇢ Dropped spend by 40% ⇢ Cut 70% of ad sets—kept only the top performers ⇢ Tightened audience to high-LTV segments ⇢ Focused on 2 best-performing creatives ⇢ Shifted budget toward warm retargeting, not cold The result? ✅ ROAS jumped from 1.9 to 3.2 ✅ CAC dropped 43% ✅ Revenue increased, despite lower ad spend Takeaway: More spend doesn’t always mean more revenue. Smarter structure + sharper targeting = clean growth. ↳ Be honest: If I give you a roadmap to grow profitably while spending less, would you: A) Use it to clean up your ads? B) Keep winging it and hope? Drop your letter (A/B) below.