Facebook Ad Performance Boost

CEO: Our campaigns aren’t scaling we need better ads. Me: Maybe! But the issue could come from any of these 4 layers too. Yes, it might be poor execution weak content, wrong format, bad targeting. Or maybe your funnel’s off. Or the offer just isn’t resonating. Or honestly… you never nailed your audience in the first place. Let’s be real: Performance issues usually start deeper than performance. Because here’s the thing: You can’t optimize your way out of a broken marketing foundation. - No A/B test will fix an irrelevant offer. - No ad creative will work without positioning clarity. - No automation will convert if your audience isn’t understood. CEO: So what should we actually do to grow? Me: Step back. Run your stack like a full-stack marketer: 1. Start with market insight: Who exactly are you marketing to? What do they struggle with? AI helps marketers listen smarter and synthesize faster. 2. Define the offer + funnel strategy: What’s your promise? Where are you leading them and why should they care? AI gives you faster, deeper strategic drafts and feedback loops. 3. Launch real assets that match the funnel: From SEO to reels to landing pages build what supports your journey. AI is your creative co-pilot here... fast, scalable, multi-format. 4. THEN optimize: Track, test, and scale what works. AI spots what’s working (or not) faster than manual dashboards. You don’t need a “5th layer” for AI. AI is the electricity that powers every layer. Because stacking tools ≠ marketing. Full-stack strategy is what drives real growth. Want to build like a full-stack marketer? → Start deep. Don’t just fix the copy. Fix the stack. ♻️ Repost it to share with your network. Follow me Madhav Mistry for insights on full stack marketing My full-stack tool stack: Research & Insight: Semrush, Brand24, SparkToro, Google Trends Strategy & Planning: Notion, Miro, Canva Whiteboard, Lately.AI Execution: Meta Ads, HubSpot, Pictory, OpusClip, Zapier, Lately.AI, Hootsuite, Figma, Canva Growth Ops: Google Looker Studio, Amplitude, Mixpanel, VWO, Airtable

“More content” is not the answer. In many cases, it is the reason the message is not landing. A few months ago, during a campaign review with a SaaS team, everything looked impressive on the surface. AI had helped them produce dozens of well-written assets in record time. But none of it connected. Each piece said something, just not together. No sequence. No structure. Just volume. That was the real issue. Not a content production problem, but an orchestration problem. AI excels at creation. Where it truly changes the game is in deciding how and when messages reach buyers. The strongest teams are not using AI to publish more. They are using it to be more intentional. To align messages across platforms. To respond to real buyer signals instead of internal calendars. When AI works quietly in the background, sequencing and adapting content at the right moment, marketing starts to create momentum instead of noise. This week’s newsletter breaks down why orchestration matters more than output, with real examples from teams getting it right.

11 days ago, I took over as interim CMO at Directive. Since then, form fills are DOWN 10%, but Strategy Calls held are UP 127%. And… we’re already out of sales capacity. Here are the 3 changes I made to our ad campaigns: 1. Asked the TWO Most Important Marketing Questions What I’ve learned from managing 100+ marketers is that media buyers can lose track of 2 core drivers of B2B SaaS performance: A. What % of our budget is going to what size companies? I found 81% of budget was going to orgs with <250 employees. I then analyzed our intro calls and found that SaaS companies with fewer than 200 employees did not have enough ad spend to hit our monthly minimums for our retainer. B. What % of budget is going to what titles? When you run a campaign it’s best to separate managers from Directors and up. This allows you to have large audiences + test buying authority in the vertical you target. It also lets you pause manager campaigns for enterprise, but keep live for mid-market, for example. 2. Reset the Employee Size of Tiers in our TAM I use employee size targeting instead of revenue when advertising. Revenue is a guess in data platforms because revenue is not reported by private companies. We have three core segments at Directive: - Startup: 51-199 - Mid-Market: 200-999 - Enterprise: 1000+ A few months ago we opened up our startups segment to 50 employees instead of 100. And, unfortunately, companies with under 100 employees were gobbling up ad spend due to their volume. The solution? Pause ads in the Startup sector, reset tiers so that startups was 100-199 (instead of 50-199), and reallocated budget into the Mid-Market and Enterprise segment. 3. Inspect, Inspect, Inspect I held a 1.5 hr marketing all hands where we reviewed every single active ad + copy we were running. Here were the takeaways we wrote up: Ad's moving forward need to have all the following included: - Hook - Directive (not Directive Consulting) - Exclusively for SaaS (not B2B SaaS) - Be clear about 2 things “Marketing Agency, Biggest in the space” - Using active and direct language, no more passive sentences Areas of improvement: - Test different subject lines - Capitalize and trademark Customer Generation™️ - Use customer logos and names in ad copy and imagery - Adjust the Ad copy to speak TO Enterprise, NOT startups - Run drastic Macro tests, not micro test (AZ tests not AB test) - Refresh convo ad copy + Sponsored Content on a monthly basis - Include numbers from benchmarks, NSMs goals achieved, etc in copywriting TAKEAWAYS: If you don’t add a layer of firmographics to your media buying you could be spending your budget on prospects who can’t actually buy from you. If you don’t tier out your TAM your budget can not be optimized for impact. Marketing is art, not science. Make sure you are in love with what your brand is communicating. Take a breath, follow this process for yourself, and reap the rewards of a sales team with a fully booked out calendar.

How I Actually Use AI (Every Day) to Run a Marketing Agency Everyone’s talking about “AI revolutionizing marketing”… But here’s what that actually looks like in practice inside my agency. I’m not building robots or coding custom GPTs. I’m using AI as a force multiplier - to help my team think faster, execute cleaner, and make better decisions. Here’s how we’re using it day to day: 💡 Creative direction: We use AI to brainstorm ad angles, headlines, and visual directions... not to replace our creative team, but to give them a running start. It’s like having a junior strategist who never sleeps. 📊 Data analysis: When we’re reviewing performance reports, AI helps us summarize insights and spot patterns across campaigns faster than any spreadsheet filter ever could. ✍️ Content and messaging: AI drafts the first 70% of a post, email, or ad. Humans do the final 30% that makes it actually sound like us. It’s faster, cheaper, and still personal. 🧠 Strategy support: I’ll often drop a client’s brief into ChatGPT and say: “Pretend you’re a strategist at a B2B agency - what would you test first?” Sometimes I agree, sometimes I don’t ...but it helps me challenge my own thinking. 👥 Internal ops: We use AI to help summarize meeting transcripts, generate ClickUp task descriptions, and even write SOPs. It keeps the machine moving without adding headcount. None of this is about replacing people. It’s about augmenting them and giving our best minds better tools. If you run a team or an agency and you’re not experimenting with this stuff yet, you’re leaving efficiency and creativity on the table.

We’ve reviewed dozens of Meta ad accounts over the past few months. While every brand has its nuances, these 5 actions come up in nearly every audit: 1. Streamline the Account Structure Consolidate campaigns, reduce overlap, and align budgets and bids with funnel stage and margins. A simpler structure = stronger signal and faster insights. 2. Refocus on Net-New Customer Acquisition Cap retargeting at under 20%. Tighten exclusions and shift spend more towards prospecting. 3. Optimize Based on 7-Day Click Attribution Use 7DC-only data for decision-making. It may show a lower ROAS, but it's a more accurate reflection of what’s actually driving purchases. 4. Use a Structured Creative Testing Framework Launch one concept per ad set. Name things consistently. Evaluate after a clear spend/impression threshold and scale winners into ASC or Cost Cap campaigns. 5. Invest in Upper Funnel and Creator Partnerships Dedicate 5–10% of spend to awareness. Run Partnership Ads with creators to expand reach, improve creative diversity, and bring down CPA over time.

Want to scale your Meta Ads without wasting ad spend? Here’s the framework I use to turn chaos into performance: ✅ 𝟭. 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝗪𝗮𝘀𝘁𝗲𝗱 𝗔𝗱 𝗦𝗽𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗖𝗕𝗢 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 • Create a CBO (Campaign Budget Optimization) campaign for prospecting. • Launch ads in packs (4–6 creatives), each as a new ad set. • Facebook will automatically allocate spend to top performers. • If you need to force budget to an ad, use ad set spending limits—but go slow ($10/day max to start). • This creates a competitive testing environment that naturally filters top creatives. ✅ 𝟮. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗣𝗿𝗼𝗽𝗲𝗿 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 • Graduate winning creatives into a dedicated scaling campaign. • This campaign should be broad targeting only, minimal to no restrictions. • Do NOT pause the winning ads in the testing campaign - let them run in both places. • Scaling campaigns should eventually have 5–10 top creatives, with growing budgets over time. • Monitor performance and grow budgets methodically. ✅ 𝟯. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗬𝗼𝘂𝗿 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘄𝗶𝗺 𝗟𝗮𝗻𝗲𝘀 • Segment your campaigns into: • Prospecting (100% net-new customers) • Retargeting (site visitors / add to carts who haven't purchased) • Retention (existing customers / purchasers) • Use custom audience exclusions and CRM lists (e.g., from Klaviyo) to enforce clean segmentation. • Each lane should have distinct budgets, KPIs, and expectations. ✅ 𝟰. 𝗦𝗽𝗲𝗻𝗱 𝗠𝗼𝗻𝗲𝘆 𝗪𝗵𝗲𝗻 𝗬𝗼𝘂’𝗿𝗲 𝗠𝗼𝘀𝘁 𝗟𝗶𝗸𝗲𝗹𝘆 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗠𝗼𝗻𝗲𝘆 • Analyze performance data by day of week, platform, placement, age, and landing page. • Use data from Meta Ads, Google Ads, and Shopify together. • Increase weekend spend if data shows higher conversions (e.g., Fri–Sun). • Rebalance weekday budgets downward accordingly. • Re-assess performance every 4 weeks. 🔁 𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽𝘀 & 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿𝘀 • Never pause a working ad - always duplicate into new campaigns. • Data-led decision-making beats intuition. Let Meta do the heavy lifting. • Use Shopify data to validate ad platform insights. • Track graduation timing and only assess ad success from that time onward. #PerformanceMarketing #MetaAds #GrowthMarketing #EcommerceMarketing #CustomerAcquisition #ROAS #Meta

Here’s a silent growth killer we often uncover in Google Ads audits: Campaigns capped by budget, even though they’re hitting ROAS / CPA targets. In one account, 17% of potential conversions were missed because campaigns kept hitting their daily limits. The result? > Profitable campaigns switching off before the day is over > Competitors picking up the demand you’ve already paid to create > Growth stalling even though efficiency is strong The fix is simple (but often overlooked): 1. Monitor budget caps alongside ROAS / CPA  performance 2. If campaigns are profitable, increase budgets to capture more conversions. 3. Treat Google’s budget recommendations with caution. In high-spend campaigns, increasing budgets by more than 20% from one week to the next can disrupt learning and cause performance swings. 4. Reinvest into what’s already working before chasing new experiments If a campaign is hitting targets, a budget cap shouldn’t be a brake, it should be a signal to scale. 👉 Question: Are your budgets limiting wasted spend… or limiting profitable growth? AdSuccess - Hidden Profit Playbook Practical fix to stop profit leaks in Google Ads.

AI for marketing: from hype to how I’ve witnessed firsthand how AI has transformed from a futuristic buzzword to an essential tool in our daily marketing efforts. Early on, AI seemed like an exciting possibility, but now, it’s a game-changer. 1. Personalization at Scale: A Dream Come True Personalization used to be a challenge. We tried to manually segment customers, but it was time-consuming and often inaccurate. Then we integrated AI tools like Segment and Dynamic Yield, which analyze customer data in real time, enabling us to deliver personalized experiences automatically. These tools track behavior, preferences, and interactions, helping us target the right customers with the right message, whether through email campaigns or product recommendations. Thanks to AI, we can now personalize at scale, delivering relevant content to each customer without the manual effort. The result? Increased engagement and higher conversions, all while saving time. 2. Content Overload, Solved The demand for fresh content was overwhelming, and keeping up while maintaining quality was difficult. Enter AI tools like Jasper and Copy.ai. These platforms use AI to generate blog posts, social media content, and email copy. They can create content drafts based on simple prompts, significantly speeding up the creation process. AI also helps us optimize content. Tools like Headline Analyzer and Convert.com assist with A/B testing, ensuring we’re using the best headlines, calls to action, and tone. This allows us to produce more content faster, without sacrificing quality, and improve its effectiveness over time. 3. Smarter Decisions with Predictive Analytics In the past, we’d react to past campaigns, but with AI-powered predictive analytics tools like HubSpot and Pardot, we now predict future customer behavior. These tools analyze past data to forecast which leads are likely to convert, enabling us to focus our efforts on the most promising opportunities. AI provides us with actionable insights that help us prioritize leads, tailor messaging, and increase conversions. It’s like having a roadmap for what’s coming next, allowing us to make smarter decisions and improve our marketing ROI. 4. Real-Time Customer Insights – No More Waiting Traditionally, gathering insights involved waiting for surveys or reports to come in. Now, with Google Analytics 4 and Crimson Hexagon, AI tracks customer behavior in real time, providing immediate feedback on how campaigns are performing. These tools help us monitor customer sentiment, identify trends, and adapt campaigns quickly. Real-time data allows us to be agile and responsive, adjusting our strategies as needed to meet customer expectations and improve satisfaction.

Five months ago, we realized we were using AI the wrong way. So we rebuilt our internal workflow. Today, our internal AI system collects data from: • Client calls • Brand books and positioning documents • The Shopify store and product catalog • Klaviyo engagement data across every touchpoint • Financial analysis tools • Customer behavior and purchase patterns All of this feeds into a single knowledge base. And based on that data, the AI creates the first draft of the email campaign. But that draft is never the final version. Think of an email like a skyscraper. AI builds the foundation. Then we build everything on top of it.   We adjust wording, restructure sentences, shorten some sections, expand others, and choose the exact phrasing we know the audience will respond to. I know that some agencies have employees responsible for 10-15 clients at the same time. If you're managing that many accounts, there simply isn't enough time to deeply review every single email. So naturally, the workflow becomes: Prompt → copy → paste → send. We approach it differently. AI gives us speed and structure. But the performance still comes from human iteration on top of it. That combination is what turns a good email into one that actually drives revenue.

Just wrapped a call with a fashion brand struggling to scale past 20L monthly revenue. Their agency kept pushing for higher budgets, but ROAS tanked every time. Sound familiar? Here's the exact audit process I used to fix this: 1. Creative Analysis Map every ad by: ↳ Theme (offer/UGC/lifestyle) ↳ Spend levels ↳ Purchase data ↳ AOV impact ↳ Revenue Per Click ↳ Product < > Content creation mapping 2. Product Performance Check which SKUs are: ↳ Eating budget with no returns ↳ Converting but underutilized ↳ Driving repeat purchases 3. Landing Page Experience Review ↳ Navigation clarity ↳ Product visibility ↳ Price transparency ↳ Mobile experience ↳ Checkout blockers 4. Campaign Structure Look for: ↳ Traffic campaigns (Not entirely red flag) ↳ Broad vs. Targeted performance ↳ Creative consolidation ↳ Creative testing velocity (High Velocity ≠ Good performance always) 5. Catalog Management Analyze: ↳ Low-spend SKUs (<2% of budget) ↳ High-CAC products ↳ Return rates by product ↳ Revenue contribution 6. Price Point Testing Track: ↳ Conversion rates by price tier ↳ Bundle performance ↳ Collection page results ↳ AOV impact Key insight: Most brands find 70% of their problems in steps 1 & 5. What's killing your ROAS right now?