Facebook Ad Performance Boost

Today, in Facebook ads, creatives serve as the strongest targeting lever, more so than any other targeting options. In performance marketing, it is famously called soft targeting. Hence, choosing the right type of communication is more important than targeting and bid/budget optimization in Facebook ads. You should have different ads that speak to people at different stages in a manner that best suits them. So, what are these stages and the corresponding ad types? 1. Unaware Ads These ads are for the audience who are unaware of the brand and the problem. These ads should turn an unaware audience into a problem-aware & brand-aware audience. 2. Problem-aware Ad These ads are for people who are well aware of their problems but not the solution or the brand. Ads for these users should talk about their problems and introduce the brand as a solution. 3. Solution-Aware Ads A certain set of audiences is well aware of their problems and of possible solutions, too. But they don't know which solution/product to choose. Solution-aware ads should help in determining which solution/product to choose while introducing the brand 4. Brand-Aware Ads These people know everything about their problems. They also know how to solve it and what product/brand to choose. They are just waiting for the right time and the right offer to buy it. In traditional campaign structuring of Top of the Funnel(ToFU), Middle of the funnel(MoFU), and bottom of the funnel(BoFU), - Type 1 and Type 2 ads should be used in TOFU campaigns - Type 3 ads should be used in MoFU campaigns and - Type 4 ads should be used in Bofu campaigns.

Meta just replaced interest stacking with a text box. What Meta Is Really Changing Meta is moving from manual interest stacking → to AI-driven audience interpretation. Before: Select interests manually Narrow by behavior Layer demographics Try to “hack” the perfect combination Now: You describe the audience in natural language Meta interprets intent using AI Algorithm finds matching behavior patterns This is similar to how Google Performance Max shifted targeting from keywords to signals. 🎯 Why This Is a Big Deal for Advertisers 1️⃣ Targeting Is Becoming “Signal-Based,” Not “Interest-Based” When you type: “Startup founders scaling with paid ads” Meta doesn’t just match the word “startup.” It analyzes: Engagement behavior Content interaction Pixel data Video watch patterns Purchase intent signals This means targeting becomes dynamic, not static. 2️⃣ Creative Is Now the REAL Targeting This is the most important shift. If your creative clearly speaks to: First-time home buyers Gym beginners SaaS founders Real estate investors Meta’s AI learns from: Who stops scrolling Who watches 50%+ Who clicks Who converts The system then expands toward similar behavior clusters. Broad audience + strong messaging = scale. 3️⃣ Interest Layering Will Lose Power Over Time Stacking 5–6 interests used to feel “smart.” But in reality: It limited scale It slowed learning It increased CPM It delayed optimization Now Meta wants: Clear audience intent Strong pixel data Strong creatives Broader targeting This shortens learning time. ⚠️ What This Means for Performance Marketers For someone like you (who already tests multiple campaigns and creatives), this is actually an advantage. Your focus should now shift toward: 🔹 1. Message Clarity Instead of hunting interests: Write precise audience descriptions Build ads that speak directly to one persona 🔹 2. Creative Testing Over Audience Testing Old testing: 5 audiences × 1 creative New testing: 1–2 broad audiences × 5–8 creatives Creative becomes the targeting filter. 🔹 3. Better Pixel Data Becomes Critical Meta relies more on: Website conversions Engagement signals High-quality events Poor tracking = poor optimization. 🚀 The Real Strategy Going Forward Here’s what will likely win in 2026 Meta Ads: Broad targeting Strong, persona-specific creatives Clear conversion events Faster creative iteration cycles AI-guided scaling Manual targeting is slowly becoming obsolete. The advertisers who understand messaging psychology + data interpretation will dominate.

🔥 I burned ₹10 lakhs testing campaign structures on Meta Ads—here’s what I learned (and what actually works). Over the past 3 months, I’ve tested multiple campaign structures on Meta Ads. Call it “testing” or “wasting” – but it taught me one game-changing lesson. Here’s What I Tested: 1️⃣ Broad Campaign with Advantage+ Audience Enabled ▪️ Ad sets by creative theme. ▪️Concerned that spend might favor engaged audiences (e.g., website visitors), I moved to the test#2. 2️⃣ Broad Campaign with Original Audience (No Advantage+) ▪️ Ad sets by creative theme. ▪️ Outcome: Spend distribution across engaged audiences was the same as in Point 1. ▪️Hence, no delta benefit. 3️⃣ Advantage+ Shopping Campaign (ASC) + Broad Campaign ▪️ Idea: Identify winners in the broad campaign and scale them in ASC. ▪️ Reality: Marginally better performance with ASC but lower scalability due to a lack of creative freshness in ASC at scale. 4️⃣ Broad Campaign (Interests + Lookalikes) ▪️Hypothesis: Interests and LAL might perform better. ▪️Outcome: No significant performance improvement, even at scale. 5️⃣ Broad Campaign (Excluding Website Visitors) + Dedicated Website Visitors Campaign ▪️Hypothesis: This structure would improve efficiency. ▪️Outcome: Performance tanked. Meta’s machine learning thrives on frequency for conversions. The result? Nothing. No significant difference. Here’s what actually matters: ✅ Your creatives. Instead of overcomplicating campaign structures: ✅ Keep it simple. ✅ Focus 80% of your energy on testing creatives (hooks, angles, messaging). ✅ Use ASC only for catalogs or specific setups. ✅ Launch creatives by theme in a single broad campaign with ad sets aligned. ✅ Avoid remarketing campaigns unless you have a specific offer/messaging to hammer home. Summary: Stop chasing the perfect campaign structure. Start obsessing over creative quality. Note: - This learning applies to direct purchase campaigns on the web. - I’ll share separate insights for app campaigns in another post. What’s your take on this? 👇 #performancemarketing #metaads #fbads #campaignstructure

🚩 Running interest targeting on Meta in 2024 is a complete losing strategy. This isn't a new phenomenon and has actually been the case since 2014. During my tenure at Facebook, one of my initial actions with each ad account I oversaw was to steer them away from interest targeting and leverage broad targeting instead. Every time we ran broad, we saw a 2X - 3X improvement in performance compared to the segmented interest targeting approach. I also just had a new account we helped move towards broad targeting and embracing Meta's Power 5 and are seeing 60%+ improvements on some campaigns. The reason why? Interest targeting narrows the pool of potential viewers for your ad, whereas broad targeting proved more effective due to Meta's conversion optimization capabilities (based on the infinite behavioral signal they have visibility to in their apps and across the web). In Meta's own studies, broad targeting has been found to outperform interest-based targeting, delivering +16% better CPAs. Broad targeting was so successful that Meta transitioned advertisers who used interest targeting to new programs that didn't restrict them to such narrow parameters (e.g. Advantage+ Detailed Targeting). Now when interest targeting is employed, the system automatically widens its reach to include more users. Therefore, advertisers who are still targeting multiple different interest groups are essentially just fragmenting their budget significantly and diminishing their overall performance. While running interest targeting in 2014 could be forgiven, there is no excuse to be running interest targeting in 2024.

Why is no one talking about this huge change? If your ad performance has been struggling since August, you're not alone. Meta just rolled out their biggest algorithm update since iOS 14.5  and most advertisers have no idea what hit them. 🚨 Meta's Andromeda Update is Quietly Revolutionizing Facebook Ads 🚨 What is Andromeda? Think of it as Meta completely rebuilding their advertising engine. To quote Meta: 'Meta Andromeda: Supercharging Advantage+ automation with the next-gen personalized ads retrieval engine.' Instead of scanning hundreds of ads, it now analyzes THOUSANDS in seconds using sequence learning to predict user behavior. The Game Has Changed: ❌ Old way: 3-6 ads per ad set ✅ New way: 30-50 ads with creative diversity ❌ Old way: Multiple campaigns with tight targeting ✅ New way: Consolidated campaigns with broad targeting ❌ Old way: Perfect individual ads ✅ New way: Creative portfolios (testimonials, founder stories, UGC, demos) Real Results: One of our clients saw CPMs drop 20% and CPAs decrease 35% after restructuring from 6 campaigns down to 2, loading every winning creative from the past year into one Andromeda-optimized campaign. ⚠️ Warning: Check your backend data. We've seen cases where Andromeda optimizes for existing customers rather than new acquisition.  Manual exclusions are a must. Action Steps: Consolidate campaigns with CBO + broad targeting Build creative portfolios with 10+ different ad concepts Test 20+ creatives per week (Meta's recommendation) Monitor backend metrics, not just Meta's reporting The advertisers who adapt now will dominate while others wonder why their 2023 playbook stopped working. Are you seeing similar changes in your accounts? Drop your experience in the comments 👇 #andromeda #Metaads

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.

Almost every startup founder I mentor asks the same question: How do we make influencer marketing actually work? It’s an evolving space, full of buzzwords, constant algorithm shifts, and formats that become stale overnight. Here’s my quick checklist from what’s worked across the brands I’ve led and the startups I’ve advised: 1) Start with the “why.” Are you using influencers to build brand awareness and relevance (long term objectives), amplify a campaign, or drive sales (short term objectives)? Your objective dictates everything from investment levels to creator selection, content strategy, to paid media amplification. 2) Measure what matters. Define which metrics should move as a result of influencer activity. For awareness, track site visits or a surge in brand searches; for relevance, focus on engagement and shift in sentiment; and for sales, look at add to cart or conversions. Brand lift studies are a good start, but don’t stop there. Build a full measurement framework. 3) Build social intelligence to fuel your creator strategy. Don’t just track brand mentions or sentiment on social. Analyze trending conversations, buzzwords, and creator themes. The Vaseline Verified campaign that won a Grand Prix this year is a great example of using social intelligence to spark creative ideas. 4) Avoid format fatigue with social fresh storytelling. GRWM (Get Ready With Me) videos owned beauty last year but quickly flatlined as more brands copied them. Experiment with episodic storytelling in social first series instead. Gen Z and Gen Alpha follow creators like they follow shows. Multiple exposures in the same content series with a loyal fan base earn brand recognition quicker than stand alone creator videos. 5) Go broad, not just big. Many nano and micro creators across different niches often outperform a few big names. Diversity drives discovery. 6) Frequency compounds. Working with the same creator across multiple drops builds trust faster than one off shoutouts. 7) Let creators lead. Campaigns that start from creators, not with them, scale better and feel more authentic. #ShotOniPhone is a great example, always fresh, always creator led. 8) 9) 10) Leaving the last three open, what would you add to this checklist?

👉 Wondering how AI Max for Search can level up your Google Ads performance? I’ve answered the top 7 questions advertisers are asking—covering who benefits most, how to customize toggles, A/B testing tips, asset controls, negative-keyword safeguards, and more. 👉Mastering AI Max for Search: 7 FAQs Explained Key insights to supercharge your Google Ads performance 1. Who Benefits Most from AI Max for Search? • High-volume advertisers running hundreds of keywords • E-commerce brands with diverse product catalogs • Agencies managing multiple client accounts Leveraging AI Max helps you scale bidding and creative optimization without losing control. 2. How Do You Customize AI Max Toggles? • Budget allocation: Shift spend toward top-performing segments • Audience signals: Prioritize in-market or affinity groups • Geo and device adjustments: Fine-tune by location and device type These toggles let you blend algorithmic efficiency with your own strategic insights. 3. Best Practices for A/B Testing AI Max Variations • Test small changes: Try a single toggle or asset swap at a time • Define clear goals: CTR vs. conversion rate vs. ROAS • Run tests for at least two weeks to account for seasonality That way, you’ll know which AI-driven shifts truly move the needle. 4. Managing Asset Controls & Creative Inputs • Feed high-quality assets—headlines, descriptions, images, videos • Use asset reporting to identify underperformers • Rotate new creatives every 4–6 weeks to avoid fatigue AI thrives on variety: give it the best possible raw materials. 5. Negative-Keyword Safeguards • Auto-suggested negatives based on search term insights • Frequency caps to prevent costly mismatches • Regular audits—export and review search terms weekly Protect your budget by keeping irrelevant traffic out. 6. Interpreting Performance Insights • Monitor detail-level metrics: impression share, search lost (budget) • Segment by day/time to spot trends or anomalies • Use custom alerts for sudden dips in efficiency Stay proactive—AI Max signals issues early so you can intervene. 7. Optimization Tips Beyond the Defaults • Layer audience targeting on top of AI signals • Experiment with landing page variants for highest relevancy • Combine Performance Max with Search for a holistic approach AI Max is powerful, but your expertise completes the picture. 👍 Like this post if you’re ready to test AI Max in your next campaign. 💬 Comment below: which question should I unpack in my next deep-dive? & don't forget to follow Kautilya Roshan . #googleads #ppc #advertisement #seachads #aiinads

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

If I was a consumer brand looking to grow my ad spend with Meta to $10+ million a year profitably, here's exactly what I would do: 1. First, go all-in on GenAI creative and Creators - There's a reason Zuckerberg mentioned AI-powered creative tools repeatedly in Meta's recent earnings calls. Meta's algorithms now heavily favor content that keeps users engaged, and their internal data shows GenAI creative + Creator partnerships deliver significantly higher ROAS than traditional approaches. 2. Build your GenAI creative engine: - Implement Meta's Advantage+ creative testing to automatically optimize variations - Use Meta's AI-powered image expansion tool to repurpose existing assets - Apply a dynamic optimization framework (test 5-7 new concepts weekly) - Check out Pencil ✏️ for creative at scale with Meta-optimized templates 3. Develop your Creator strategy (where Meta is incentivizing spend): - Identify 20-30 creators in your vertical with 50K-500K followers - Allocate 15-20% of your budget to Branded Content ads - Utilize Meta's Creator Marketplace for matchmaking - Set up an always-on testing framework with 3-5 new creators monthly 4. Maximize Meta's AI-powered targeting: - Transition 70% of campaigns to Advantage+ Shopping or App campaigns - Implement broad targeting with detailed exclusions rather than narrow targeting - Upload first-party data monthly for custom Advantage+ audiences - Use the Meta Audience Overlap Tool to identify new segments 5. Scale strategically with Meta's financial incentives: - Join Meta's Scaled Solutions program (requires $5M+ quarterly spend) - Apply for Meta's Creative Acceleration Program for subsidized asset creation - Request your Meta rep connect you with their "Media Partnerships" team for co-marketing funds - Leverage Meta's Commerce Partner Program for additional rebates 6. Optimize your measurement approach: - Implement Meta's Conversion API alongside pixel for 20-30% more attributed conversions - Set up incrementality testing using Meta's Lift Test framework - Create custom dashboards in Meta's Business Suite comparing performance - Try Haus for advanced attribution beyond Meta's native tool The reality is that Meta is making massive investments because they see internal data showing these newer approaches dramatically outperforming traditional ones. Their algorithmic changes now favor these formats, and they're directly incentivizing brands who lean into their strategic priorities. Questions? Fire away in the comments.