Lorenzo Carreri

For years, B2B marketing revolved around one question: “How do we rank higher, get more backlinks, and publish more content?” But that game is disappearing. Buyers aren’t scrolling through #SERPs anymore. 🔺ChatGPT summarizes your entire category in seconds. 🔺Google AI often skips organic results completely. 🔺Perplexity cites your competitors without sending traffic your way. The buyer journey looks nothing like the funnel you mapped: 🔺They’re not reading 10 blogs. 🔺They’re seeing one AI-generated summary—and making a shortlist right there. Which means: ◼️ Funnels are compressed. ◼️ Content reach is shrinking. ◼️ Influence is shifting from clicks → citations. Yet most teams still play the old game: more blogs, more ads, more SEO tweaks. The best SaaS marketers aren’t just adapting tactics—they’re rebuilding for an AI-first buyer journey. ⚠️ Four Pillars of AI-First B2B Marketing 1️⃣ Market Intelligence - Track what matters now: - What prompts buyers use - What LLMs say about you vs. competitors - Where your brand gets cited Visibility is no longer impressions—it’s mentions, summaries, and citations. 2️⃣ AI-Aligned Strategy - Funnels are collapsing. Instead of TOFU → MOFU → BOFU, ask: - Where does my buyer ask questions? - How is my message reinforced across AI surfaces? 3️⃣ Quotable, Structured Content Ranking #1 won’t matter if you’re never cited. What works now: - Machine-readable content (headings, FAQs, schema) - Crisp insights worth quoting - Data and definitions that AI can lift directly 4️⃣ Trust-Building Campaigns Lifecycle marketing still works, but differently: - Reinforce what buyers already saw in ChatGPT or Google AI - Deliver credibility signals (case studies, social proof, expert mentions) - Consistency > volume In 2026, B2B marketing isn’t about pumping out content or buying more ads. It’s about: ✅ Tracking what buyers see in AI-first discovery ✅ Structuring GTM around compressed, summary-driven journeys ✅ Creating content that earns citations ✅ Running campaigns that build trust and reinforce decisions If you’re still measuring clicks, you’re playing yesterday’s game. The next edge in B2B marketing is visibility inside AI answers—and the trust that follows. Comment your thoughts below — and if you’d like more save-worthy SaaS growth frameworks, follow my profile to catch the next one. #SaaSMarketing #B2BMarketing #StartupGrowth #GoToMarket #SaaSFounders #MarketingStrategy #SaaSGrowth #growthcx

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Google is testing something inside PMax that advertisers have wanted for years. The ability to choose which partner networks your ads run on. Search Partner Network. Google Display Network. Both. Either. Neither. Your choice. Right now PMax decides this for you automatically. No control. No exclusions. This test changes that. Still in early testing. Not live everywhere yet. But the direction is clear. More control over PMax is coming. #googleads #pmax #ppc

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No business was successfull by scaling complexity. No ad account reached a 7-digit budget by beeing more complex. It's easy to get lost in complex systems, custom scripts, tool integrations, import- and export flows and 3rd party tools in #ppc, but remember that simplicity is key. Beeing consistend, doing the right things constantly over time is much more valuable then finding the next hack or using a 5th tool. There is no limit to make things more complex in PPC, but the real impact on scale is in having a good core foundation. Have a good offer or product. Have a data centric infrastructure. Scale sustainable. That's all it takes, everything on top is just a bonus. How to scale sustainable so you dont need to change budgets every other day 👉 https://lnkd.in/euky9_CW #googleads

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5 Ways to Top Google AI Overviews Right Now: SEO isn't dead. But the rules changed. Google's AI just changed the game. Being "#1 on Google" doesn’t mean what it used to. AI Overviews (SGE) now sit above the results, answering the query before anyone clicks. That means: - Less traffic to your site - Rankings ≠ visibility - If you’re not in the AI box, you’re invisible So no, SEO isn’t dead. But the playbook has shifted: Google’s AI now decides which sites to trust, quote, and feature. It's time for you to know how to be included... and not buried. → Save this post so you can come back to it later! ___ Want to make your site show up in AI Overviews... automatically? 👉 Run a Maverank audit in 60 seconds + fix what blocks your visibility in one click. Try it FREE for 7 days: https://maverank.com

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Is AI SEO really delivering results? We tested it with a construction SaaS brand. The outcome: ✔️ +74% conversions ✔️ +50% sessions ✔️ +36% top 3 rankings Here’s how strategy (not hype) turned AI into profit:

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Most B2B ad accounts don’t fail because of bad creatives. They fail because the structure is wrong. A scalable B2B ads account needs: • Clear campaign roles • Separate testing and scaling • Controlled budgets and bidding • Strong remarketing loops This framework shows how to move from awareness → experimentation → scaling → cost control, without burning budget. If you’re running Meta ads for B2B brands, this structure is non-negotiable. #B2BMarketing #PerformanceMarketing #MetaAds #PaidMedia #GrowthStrategy #LeadGeneration

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Why Fast-Growing Companies Fix SEO Early https://lnkd.in/gtQD2J7Z The fastest-growing companies don’t wait for SEO to “break.” They fix it before scale. Once traffic, content, and products expand, structural issues become expensive to undo. Poor architecture, cannibalized pages, and weak internal linking slow everything down right when speed matters most. Early SEO investment enables: Clean site architecture that scales Clear topic ownership before competitors crowd the space Faster content compounding Lower marginal acquisition costs as growth accelerates Retrofitting SEO after growth is slow and costly. Building it early creates leverage. At Preo Communications, we see the same pattern repeatedly: companies that treat SEO as infrastructure early move faster later — with fewer constraints. SEO done early isn’t conservative. It’s strategic acceleration.

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THE 7 EDGES YOU NEED TO STACK: Edge #1: Direct Response High-AOV Funnels Post-purchase upsell sequences (4 upsells + 4 downsells) Maximize AOV from every customer acquired Advertising is competitive – if you don't optimize, competitors will Example: Civilians = $60 AOV | Swiss Knives = $120+ AOV Higher AOV = can outspend competition in auctions Edge #2: AI Processes to Pump More Ads Don't just use ChatGPT manually – build AI workflows Combine Claude + Gemini + 11 Labs + GumLoop Automate script creation → voice-over → ad production Pump 3-5x more content than competitors Result: Higher chance of finding winners, scale faster Edge #3: In-House Creative Team + Agencies Don't choose one – use both and make them compete $100-300k/month: Creative strategist + media buyer + video editor $300-600k/month: Add 2-3 more video editors + creative coordinator $600k-$1M+/month: Multiple pods, agencies competing with in-house Key: Keep it lean, scale team as revenue grows Edge #4: Backend Dialed In (Email/SMS/Phone) Email should generate 10-30% of total revenue Flows: Automated sequences (abandoned cart, post-purchase) Campaigns: Broadcasts 3-7x per week to list Phone sales: Upsell after purchase, recover abandoned carts (for $700k+/month brands) Tools: Klaviyo/Omnisend for email, Koncio for phone sales Edge #5: Product Innovation (Incremental Improvements) Collect refund feedback from every customer Identify 80/20: What are 80% complaining about? Fix those issues with supplier, don't rebuild from scratch Example: iPhone releases new version yearly with small upgrades Result: Better product over time, less refunds, more repeat customers Edge #6: Subscriptions (Even for Gadgets) If you sell one-time products, add consumable accessories Example: Manscaped = gadget + consumable blades/creams on subscription Build recurring revenue that stacks over time Can break even on front-end, make profit on backend Tool: Loop Subscriptions (better than Recharge) Edge #7: Amazon (Capture 20-30% You're Missing) 20-30% of consumers only buy on Amazon They see your ad → Google your product → buy on Amazon If you're not there, you lose the sale Profit margins: 50%+ on Amazon vs 10-20% on Shopify Example: Primal Queen makes $2M/month on Amazon while breaking even on Shopify

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There are 3 layers to e-commerce marketing. Most brands ignore the first two. Great infographic by my friend Francesco Gatti! This is a very common problem with e-commerce brands of all sizes. They test creative, optimize landing pages, and launch campaigns. But their results are inconsistent. My diagnosis is almost always the same: you've skipped the fundamentals. Here are the 3 layers of marketing every e-commerce brand should cover: ➡️ Layer 1: Strategy Before you spend a dollar on ads, you need clarity on: - Who your best customers are - What makes your offer different - How you're positioned in the market - What your pricing and retention model looks like Without this, you're guessing. And guessing gets expensive. ➡️ Layer 2: Marketing Plan Once strategy is clear, you build a system that holds together. - Which channels you're prioritizing and why.  - How budget is split across acquisition and retention.  - How you're segmenting your audience.  - What attribution looks like so you know what's working. This layer turns strategy into a repeatable system. ➡️ Layer 3: Execution Creative, landing pages, A/B tests, influencers, checkout optimization. All of it matters. But none of it works if the first two layers are weak. Great execution of a bad strategy will only lead you to fail faster. Each layer will ensure a different outcome. ✅ Acquisition gets cheaper when positioning is clear.  ✅ LTV grows when segmentation and offers are aligned. ✅ Retention improves when the customer journey is mapped.  If your results have been inconsistent, don't blame your creative. Just ensure that you're not executing without a foundation underneath. At Ad Pros, this is the sequence we follow with every client: Start with strategy -> Build the plan -> Then execute. Remember: execution without strategy is just expensive trial and error. Ready to add $1m/month to your eCommerce business? Join the waitlist: https://lnkd.in/e-Av-tdY What's your biggest marketing challenge right now?  Comment below. ♻️ Repost to share these marketing fundamentals with your network.  Follow Nehal Kazim for more marketing insights. 

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Don’t buy on buzz! 🐝 The biggest problem in digital advertising today? There are thousands of “experts”… but very few genuine specialists. A good example of this expert sales strategy is the hype around Google Ads Performance Max (PMax). When PMax launched, it quickly became one of the industry’s biggest buzzwords, mentioned at every networking event where agency representatives were selling their services. Suddenly many agencies that wanted to project themselves as experts were presenting it as the future of PPC, building entire pitches around automation, AI-driven campaigns, and “fully managed” advertising. And many unwitting businesses bought into the sales buzz. But here’s the reality. PMax is not a strategy. It’s a tool within a strategy. Strategic PPC is still built on fundamentals that automation cannot replace: 👉 Understanding search intent 👉 Structuring campaigns properly 👉 Separating brand and non-brand traffic 👉 Managing different stages of the funnel 👉 Analysing performance data to drive decisions Platforms like Google Ads Performance Max can absolutely play an important role. Used correctly, they can unlock scale across Search, Display, YouTube, Discover and Shopping. But they are only one component of a much larger strategic framework. The challenge is that many businesses understandably don’t live inside these platforms every day. They rely on agencies to guide them and that’s where the difference between an agency and a specialist agency becomes clear. At Thrive Online Group UK, we’ve always taken a different approach. As a boutique specialist digital agency, our focus has never been chasing the latest industry buzzword. It’s about building structured, strategic digital ecosystems that deliver measurable long-term growth. Because real performance doesn’t come from tools alone. It comes from specialist thinking, experience, and strategy. #PPC #DigitalSpecialists #ThriveOnlineGroupUK #Nottingham #Agency

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Most LI Ads fail for one simple reason: They ask for the demo too early. Imagine meeting someone for the first time and immediately asking them to commit. That’s what most B2B campaigns do. They show a demo ad to a cold audience …and then wonder why CPL is high. LI Ads work best when you respect the funnel. I like to think of it in three stages. Stage 1: Awareness. You’re introducing yourself. Targeting based on job titles, functions, seniority, skills. This is where thought leadership shines. Insights. POVs. Useful ideas. Not “Book a demo.” Just, “Here’s something valuable.” Stage 2: Consideration. Now you retarget. People who: • watched your videos • engaged with your posts • downloaded content • visited your website • followed your page This is where you offer depth. Guides. Checklists. Whitepapers. Case studies. You’re earning trust. (And importantly - exclude these people from Stage 1 so you’re not talking to them like strangers again.) Stage 3: Decision. Now it makes sense to ask. Demo. Trial. Talk to sales. By this point, they’ve already seen you twice. The conversation is warmer. Conversion rates improve. Cost per conversion drops. Yes - there will always be a small percentage of in-market buyers. You can still run demo ads broadly. But the real efficiency comes from sequencing. LI isn’t just a targeting platform. It’s a nurturing engine. When you structure your campaigns like a funnel instead of a single ask, performance stops feeling random.

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Is Google pushing to hard on broad match and no-keyword strategies on Google Ads? This is a really good article showing how search behavior and SEO optimization is evolving: https://lnkd.in/duWN7Y2Z. However, this is highly relevant for paid search as well. The way people search changes with more prompting, voice search, LLM advancements, AI overviews etc. Google is pushing for more broad match and topic/audience related campaigns that are goal oriented rather than rigid keyword focused. This makes absolute sense! Let’s push and automate more search campaigns! #google #sea #sem #marketing Thanks Kevin Indig for the article

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I just built an open-source Marketing Mix Modeling (MMM) platform, and it's free for everyone to use. As marketers, we spend millions on campaigns but rarely have the tools to truly understand what's driving results beyond last-click attribution. So I built one. Introducing MMM Campaign Analyzer, a full-stack web app that lets growth teams and marketing analysts run Marketing Mix Modeling without needing a data science team or expensive software. What it does: 📊 Upload your campaign data, supports Excel and CSV files from any ad platform 🗺️ Map your columns, flexible column mapping with auto-detection, even if your data isn't perfect ✅ Validate data quality, flags missing values, outliers, inconsistencies, and insufficient history before modeling 🧠 Run MMM analysis, time-series regression with adstock, lag, and saturation transformations to estimate the true incremental contribution of each campaign and channel 📈 Decompose performance, separates baseline demand from media-driven growth, with waterfall charts and ROI/ROAS/CPA breakdowns 🔮 Scenario forecasting, select active campaigns, adjust spend assumptions, and simulate "what if" scenarios: increase budget, pause a campaign, reallocate across channels 🛡️ Handles messy data, built-in fallback logic guides you when ideal inputs aren't available (no impressions? no problem. no revenue? use a proxy KPI) Why I built it: Most MMM tools are either locked behind expensive SaaS contracts or require Python expertise to run. I wanted something that a marketer could pick up, upload their data, and get directional answers within minutes. This entire project, backend, frontend, modeling engine, forecasting logic, and sample data, was perfected using Claude Code by Anthropic As someone who isn't a full-time engineer, Claude Code let me go from idea to a fully working full-stack application in a fraction of the time it would have taken otherwise. It's genuinely changed how I think about what's possible to build solo. Tech stack: - Frontend: React + TypeScript + Tailwind CSS - Backend: Python FastAPI - Modeling: statsmodels + scikit-learn - Charts: Recharts - AI coding assistant: Claude Code (Anthropic) It's fully open source. Clone it, use it, improve it. 👉 GitHub: https://lnkd.in/gggaA_Sy A sample dataset is included so you can test it immediately. Would love feedback from anyone working in marketing analytics, growth, or data science. What features would make this more useful for your team? #MarketingAnalytics #MMM #MarketingMixModeling #OpenSource #GrowthMarketing #DataScience #PerformanceMarketing #MarTech

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Not all the best metrics in Google Ads are visible... Sure, the platform gives you plenty of numbers to work with. But some of the most powerful insights can come from reading between the lines. Take days to conversion (from the attribution report)... Google can’t tell you where a user is in the funnel. But this metric gives you a pretty strong clue. The shorter the time to conversion, likely the more 'purchase ready' the users you're hitting. Use it right, and you can adapt your ad text and/or landing page to best 'meet those users where they are'. 💡 Two more 'proxy metrics' coming soon - or read the full blog now: 👉 https://lnkd.in/g_nWnGxj

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Your site doesn’t need more traffic. It needs to stop leaking conversions from the traffic you already have. Every funnel has hidden leaks: ❌ Weak offers that don’t match intent ❌ UX friction that breaks trust ❌ Copy that confuses instead of converts Most brands try to plug leaks by adding more ads. But scaling a leaky funnel just wastes money faster. Here’s how to find (and fix) every major leak before you scale 👇

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