Campaign Performance Optimization

Most DV360 campaigns don’t fail because of strategy. They fail because of inefficiency. After managing large-scale programmatic spends, one pattern is clear. Brands are not losing money on bidding. They’re losing money on distribution. Here’s what typically goes wrong: • 30% of impressions go to users already overexposed • Low viewability inventory quietly eats budget • Audience segments are scaled without performance validation And yet, teams keep optimizing CPM. The real optimization framework looks very different: 1. Fix reach vs frequency imbalance If your frequency is above 6–8, you’re not scaling — you’re repeating. 2. Eliminate cost leakage Inventory source and viewability reports often reveal 20–30% wasted spend. 3. Rebuild audience strategy High CPM + low engagement segments should never scale. 4. Shift bidding logic Manual CPM is control. Automated bidding is efficiency. 5. Optimize distribution, not just delivery The goal is not more impressions — it’s better allocation of impressions. Because in programmatic: More spend ≠ more impact Better distribution = better performance The brands that win in 2026 are not spending more. They’re spending smarter. #programmatic #optimisation #adtech #marketing #leadership

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.

What does ongoing Amazon PPC optimization really look like on a weekly or monthly basis? Let me share how we do it at Scale Wave so you can get a clear picture. 🔷Daily Check-ins (especially for new campaigns) When launching new campaigns, we check them daily. We ask simple questions: • Are we getting impressions? • Are people clicking? • Is the data meaningful enough to make changes? Based on that, we might adjust bids, pause targets, or keep monitoring. 🔷Weekly Optimization Once a campaign is live, we look at a few key areas each week: • Inventory levels – Are we over-advertising a low-stock item? • Search term reports – What keywords are converting? What should we negate? • Budget allocation – Are our best-performing products getting enough budget? Sometimes one product eats up too much ad spend but brings low returns. That’s a sign to shift budget toward better-performing SKUs. 🔷Monthly Review This is where we zoom out. We ask: • Did we hit our goals? • What worked and what didn’t? • Which optimizations made the biggest impact? We often adjust broader strategy here. This could mean testing new campaigns, changing creative, or doubling down on proven tactics. 🔷What counts as “optimization”? • Bid adjustments (up or down) • Keyword targeting changes • Search term negations • Reallocating budget • Adjusting campaign types based on seasonality or goals Example: We once took over an account where 40% of ad spend was going to a low-margin product. After shifting the budget to a more profitable SKU, performance drastically improved. So ongoing optimization is not just about tweaking bids. It’s about making data-driven decisions consistently and knowing when to zoom in or out. If you’re managing your own Amazon PPC, try using this cadence: • Daily – Monitor new campaigns • Weekly – Optimize spend and review keywords • Monthly – Evaluate strategy and pivot when needed

5 things you can do today that cut campaign waste by 40%. None require new tools: After managing $20M+ in ad spend, I've learned that prevention matters more than any optimization we currently have. The data is clear on what works: 1/ Protect your audience quality ↳ Audience fatigue doubles your costs ↳ Use frequency caps on all campaigns   ↳ Address overlap issues early with exclusions ↳ Your budget works harder when targeting is precise 2/ Prioritize creative testing ↳ 3-5 creative variations minimum ↳ Dark, clean creative without cluttered text ↳ Address creative fatigue before it hits ↳ Poor creative prevents campaign scalability 3/ Monitor performance regularly ↳ Daily optimization checks (key is consistency) ↳ Budget reallocation counts if metrics shift ↳ Automated rules for basic adjustments ↳ Monitoring increases campaign growth factors 4/ Stay data connected ↳ Guessing increases campaign risk by 50% ↳ Regular performance analysis matters ↳ Use analytics, test hypotheses, maintain insights ↳ Quality of data beats quantity 5/ Manage your bidding strategy ↳ High bids without strategy damage campaign performance ↳ Target optimal CPC ranges when possible ↳ Testing, patience, and strategy when needed ↳ Smart bidding feeds performance centers Why these work: Each prevents a different path to budget waste. Which of these 5 areas do you want to focus on first? #MediaBuying

Performance is not always lost in the ad account. Often, it disappears in the seconds after the click. In one campaign, a team successfully scaled paid media. Click-through rates were strong. Targeting was precise. Creative was clean and compelling. On paper, everything signaled momentum. Yet conversions refused to rise. Copy was adjusted. Bids were optimized. Audiences were refined. Nothing changed. The real issue surfaced later: the landing page loaded in just over four seconds. That brief delay was quietly draining budget. Visitors clicked, waited, and left. Bounce rates increased. Quality scores dropped. Cost per click climbed. The algorithm interpreted the behavior as weak relevance. The team was not only losing conversions, they were signaling to the platform to charge more for future traffic. Website speed is not a minor technical metric. It is a performance multiplier. It influences CPC, conversion rates, data integrity, return on ad spend, and even brand perception in high-stakes B2B decisions. In paid acquisition, every second either compounds returns or compounds waste. For teams investing heavily in traffic without recently auditing load times, this may be the most overlooked growth lever available. The latest newsletter breaks down the economics, the algorithm implications, and a practical speed optimization playbook for protecting ROI.

Meta just rolled out major improvements to its value optimization models and the results are hard to ignore. Advertisers optimizing for conversion value instead of volume are seeing up to 29% higher ROAS. This update means Meta’s AI is now better at understanding what valuable users look like, not just those who install or convert, but those who spend, engage, and stick around. For performance marketers, this is a big deal. It gives us more control over how Meta defines “success,” allowing campaigns to focus on lifetime value instead of surface-level metrics. Meta has also deepened its integration with Mobile Measurement Partners (MMPs) like AppsFlyer, Adjust, and Singular. That alignment means attribution windows and user definitions now match more closely, improving data accuracy and campaign optimization. Bottom line: Meta’s AI is becoming more business-aware. Marketers who feed it the right data and define value clearly will see the biggest lift in efficiency and ROAS.

I recently ran a Growth Diagnostic on a Google Ads account with 1,800 campaigns and 150,000 ad groups. If you’re a completionist like me, you can lose days in the details. 2 principles keep me out of the weeds: - The 80/20 Pareto rule - A consistent 5-step diagnostic model Pareto comes first. Always. I sort by impact, most often by media spend, and focus on the small number of elements that actually drive outcomes. Every step below is filtered through that lens. ___ 1. Performance & operating model Once the high-impact areas are isolated, I look at performance through an operating lens: - How has performance evolved over time? - What are the root causes of underperformance? - What are the drivers of overperformance? - How often is the account actively optimized or experimented on? - How is spend distributed inside the channel? This creates a clear first read of where problems and opportunities sit. ___ 2. Structure Then I assess whether the account is built in a clean and logical way: - How are campaigns, audiences, and creatives organized? - Are platform features and formats fully leveraged? - Is there sufficient testing of audiences and creatives? Everything is benchmarked against best practices developed across thousands of accounts globally. ___ 3. Budgeting & bidding Here I look at allocation efficiency: - Is budget flowing to the campaigns with the best marginal ROI? - Are bidding strategies aligned with objectives and data maturity? - Is there a sufficient number of conversions per campaign for the algorithm to optimize? Misallocation usually becomes obvious very quickly at this stage. ___ 4. Tracking & measurement - Are key conversion signals missing? - Are success metrics helping the platforms learn? - Is value-based bidding implemented correctly? - Is server-to-server tracking enabled? Weak measurement quietly degrades performance across all other dimensions. ___ 5. Future of the channel & innovation This is the newest dimension we’ve added as a team. Examples: - In Search: are you preparing for LLMs and generative engine marketing? - In Social: are AI tools consistently used to produce or augment creatives and copy? This ensures the account is not only efficient today, but structurally ready for what’s coming next. ___ This is the structure that's allowed me to analyze large ad accounts without getting overwhelmed. It's also how we run diagnostics for our Growth Vision Partners clients: Pareto first. Structure second. Focus always.

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. 🚀

We manage over $500k+ / mo in Amazon Ad spend. ��   I've noticed that Amazon Advertising is very simple.   At the end of the day you just need to analyze a few variables, tweak them to achieve goal, and you're running optimally.   ROAS is X, I need Y, I decrease/negate/pause terms to achieve that goal.   BUT, when you get into the weeds...   I've noticed that Amazon Advertising is incredibly complex. 😅   Finding the balance between increasing sales & optimizing for ROAS is difficult, & never stops.   Tweaking one variable might change the entire console. - Cut back too far and sales decline overnight. - Push one variable too high and ACOS is spiking. - Launch a winning campaign and you're a hero. - Launch the wrong campaign and you're digging yourself a hole.   Here are four things I think about to work through the complexity of Amazon Advertising.   1️⃣ What is the goal?   Are we in a period of pushing sales volume, or in a period of profit?   Once you have decided what your main goal is, build a strategy to achieve it.   If sales is the primary goal, focus on spend increases within a ROAS/ACOS/TACOS parameter.   If profit, focus on efficiency within ads & CRO.   However, do not set both goals of sales growth and profit increases.   As sales naturally rise, profit will increase down the line once you become more efficient at your new sales level, however, in the short term profit might decrease.   2️⃣ Branded vs non-branded.   Branded terms always have the best ROAS, but they won't grow a business.   Non-branded terms will always have a lower ROAS, but they drive new-to-brand shoppers.   A % of ad spend should be set aside for branded terms, a % of ad spend should be set aside for non-branded terms.   If you just optimize by throwing more money into branded terms, you will see better ROAS numbers but you won't grow sales.   Find the balance.   3️⃣ Campaign performance relative to console performance   You must view how your ad campaigns are performing relative to how your ad console is performing.   If an ad campaign is taking up 39% of all ad spend within your console, tweaks to this campaign will have a large impact.   If a campaign is taking up 1% of all ad spend, tweaks to this campaign will have a little impact.   If a campaign is at 100% ACOS, spend more time refining.   If a campaign is at goal, spend less time within it.   4️⃣ Am I in a stage of doing more? Or doing better?   You must do more, before you can do it better.   More in this context means more ad spend.   If you do not have a statistically significant sample size of data, you cannot do things better.   A statistically significant sample size doesn't mean 10 clicks.   It means spend relative to price.   If 10 clicks is $30 spent, but the product is $997, you have need A LOT more clicks to make a decision.   Once you have a statistically significant sample size then you can optimize.   #Amazon #PPC #Digitaladvertising #digitalmarketing  

Outcome-based optimization in programmatic advertising is rarely used at a large scale, but from my experience, it’s highly effective. Over time, this approach improves key platform metrics like CPA as well. We’ve integrated this methodology into out dashboards, and it’s now a primary driver for my team’s optimizations. In this framework, channels and tactics serve as independent variables, while conversion pixel fires are the dependent variable. As you allocate spend across different channels and tactics over time, outcome analysis identifies which channels are truly driving an increase in daily conversion pixel fires. This approach focuses solely on the correlation between spend and the rise or fall in conversions, eliminating reliance on vanity metrics like CTR, CPA, Viewability, VCR, etc. Moreover, this methodology allows you to measure channels in ID-free environments like Safari, CTV, audio, and other tactics with a higher likelihood of signal loss. Our dashboard provides insights into current spend distribution per channel or tactic, the optimal spend distribution to enhance performance, and even a budget recommendation for increased spend along with a forecasted conversion lift. This is a unique approach to optimizing programmatic campaigns, and as far as I know, no one else is using it—but it works incredibly well.