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Welcome back to AIM!
This week, we're diving into Google's push for simpler, AI-optimized campaign structures and why fighting it might be costing you performance. We're also spotlighting HubSpot's new AI Engine Optimization audit framework, plus a quick win for turning your best-performing content into a month of social posts.
Let’s make this week count! 🤝
More than 50,000 AI marketers are reading this newsletter this Tuesday!
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📰 Main Story
Google Says Your Granular Campaign Structure Is Holding You Back
The Challenge: For years, PPC managers built their reputations on granular campaign structures: separate campaigns by device, meticulously organized ad groups by match type, and manual bid adjustments for every variable. That expertise is becoming a liability.
The Data: Google's latest guidance explicitly recommends advertisers abandon highly segmented campaign structures in favor of consolidated accounts that give AI room to optimize. The logic is straightforward: when you over-segment, you starve each campaign of the data AI needs to learn. Google's algorithms perform better with larger data pools, more conversion signals, and fewer artificial constraints.
This isn't subtle. Google is telling advertisers that the manual control they've prized for a decade is now actively hurting performance.
The Results:
Advertisers who consolidated campaigns report faster optimization cycles and improved ROAS.
AI-powered tools like Performance Max and broad match keywords perform significantly better with simplified structures.
Manual bid adjustments by device, location, and time of day are increasingly counterproductive when AI handles these variables dynamically.
The Broader Context: This shift reflects a fundamental change in how search advertising works. Google's AI can now process signals you can't see: user intent, cross-device behavior, and real-time auction dynamics. Your job is shifting from tactician to strategist: set clear goals, feed the system quality creative, and let automation handle execution.
Your Takeaway: Audit your Google Ads account this week. Identify campaigns artificially split by device, match type, or minor geographic differences. Test consolidating one campaign pair and measure performance over 30 days. The data will tell you whether Google's advice holds for your account, but early adopters are already seeing results.
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🛠️ Tool Spotlight
HubSpot's AEO Audit Framework: Measure How AI Search Engines See Your Brand
What it does: HubSpot has released a comprehensive guide and toolset for conducting AI Engine Optimization audits. The framework evaluates how your brand appears in AI-powered search engines like ChatGPT and Gemini, measuring entity clarity, factual accuracy, and citation frequency to ensure AI-generated summaries correctly represent your brand.
Best for: Marketing teams, SEO managers, and brand strategists who need to understand and improve their visibility in AI search results. Essential for any brand that relies on search-driven discovery.
Why it matters: Traditional SEO tells you how Google's algorithm ranks your pages. AEO tells you how large language models understand and represent your entire brand. As more users turn to AI assistants for answers, controlling your brand narrative in these systems becomes critical.
Quick tip: Start by asking ChatGPT and Gemini basic questions about your brand: "What does [Brand] do?" and "What are [Brand's] main products?" Document inaccuracies, then use HubSpot's framework to identify the source content feeding those errors.
⚡ Quick Win of the Week
Turn One Blog Post Into 30 Days of Social Content in 12 Minutes
Stop staring at a blank content calendar. Use AI to extract a month of posts from content you've already created.
Step 1: Find your top-performing blog post from the last 90 days. Pull the full text and paste it into ChatGPT, Gemini or Claude.
Step 2: Use this prompt:
"You are a social media strategist. Analyze this blog post and create 30 unique social media posts for LinkedIn. Include: 10 key insight posts (single takeaway with context), 10 question posts (spark discussion), 5 contrarian takes (challenge conventional thinking), and 5 data/stat highlights. Each post should be 50-150 words, use a conversational tone, and end with a hook or CTA. Format as a numbered list."
Step 3: Review the output and edit for your brand voice. Flag any posts that need fact-checking or feel off-brand.
Step 4: Schedule the posts across your content calendar, spacing similar formats at least 3 days apart.
Expected result: 30 ready-to-schedule social posts that drive traffic back to your existing content, no new writing required.
Time investment: 5 minutes for generation, plus 20-30 minutes for editing and scheduling.
Why it works: Your best content already resonates with your audience. AI helps you atomize it into platform-native formats without starting from scratch.
📈 This Week in AI Marketing
Why it matters: Target is partnering with OpenAI to test contextual ads within ChatGPT using its Roundel retail media network. This is the first major test of retail media within AI chat interfaces: if it works, expect Walmart, Amazon, and every other retail media network to follow. Brands should start conversations with retail media partners about AI placement opportunities now.
Why it matters: Jasper's State of AI Marketing report shows ROI confidence fell from 49% to 41% year-over-year. The silver lining: 60% of those who successfully measure ROI report at least 2x returns, rising to 79% for enterprises. The gap isn't AI performance, it's measurement infrastructure. Prioritize attribution systems before scaling AI spend.
Why it matters: Airbnb CEO Brian Chesky announced 33% of U.S. and Canadian customer service now runs through AI. This validates enterprise-scale AI customer service deployment. If you're still piloting chatbots, study Airbnb's rollout strategy; they've proven the model works at scale.
Why it matters: Google's new WebMCP protocol standardizes how AI agents interact with websites. This is infrastructure for an AI-first internet. Web developers and technical SEO teams should familiarize themselves with the protocol now; early adopters will have an advantage as AI agents become primary traffic sources.
📚 Learn Something New
This Week: Data Consolidation for AI Optimization
The basics: Data consolidation is the practice of combining fragmented campaign data into unified pools that give AI algorithms enough signal volume to learn effectively. Instead of spreading 1,000 conversions across 20 micro-campaigns, you feed 1,000 conversions into a single campaign, giving the AI 20x more learning data per optimization cycle.
Why it matters: Google's AI needs roughly 30-50 conversions per week to exit learning mode and optimize effectively. Over-segmented accounts often leave campaigns starving at 5-10 conversions each, forcing AI to make decisions on statistically insignificant data. Consolidation accelerates learning and improves bid accuracy.
Common mistakes:
Splitting campaigns by device type when AI already optimizes cross-device.
Creating separate campaigns for minor geographic variations.
Maintaining legacy match type segmentation from pre-AI account structures.
Fragmenting budgets so thin that no single campaign gets meaningful data.
Best practices:
Aim for a minimum of 50 conversions per campaign per week before considering segmentation.
Use audience signals and asset groups within campaigns instead of campaign-level splits.
Consolidate geographic targeting unless performance varies dramatically by region.
Let Smart Bidding handle device and time-of-day adjustments automatically.
Questions? Just write a comment below. I read all the comments and respond to them.