Martin Perron

Old SEO was about beating algorithms. New SEO is about earning trust. And if you're still optimizing for 2019, you're already behind. Here's what changed: Old SEO = Visibility → Race to page one → Win with keywords and backlinks → Traffic was the victory metric New SEO = Credibility → Be cited across platforms (Google, ChatGPT, Perplexity) → Win with authority and structure → Trust is the victory metric The implications for growth teams: 1. Content isn't just for Google anymore → GEO (Generative Engine Optimization) - be the source AI cites → AEO (Answer Engine Optimization) - own the zero-click results → AIO (AI Integration Optimization) - get known by the model itself 2. Discovery happens everywhere now → Reddit, Quora, niche communities → AI summaries and recommendations → Search is just one touchpoint 3. The funnel changed → Old: Rank → Click → Convert → New: Get cited → Build trust → Get chosen This isn't a marketing problem. It's a growth systems problem. Because when discovery fragments across platforms, you need: → Unified measurement (not just Google Analytics) → Authority-first content (not just keyword-first) → Multi-platform presence (not just your website) The companies that win in New SEO won't just rank. They'll be the answer AI gives when someone asks. That's the shift. Big thanks to Chris Donnelly for the breakdown. * * * 📈 I talk about the mechanics of turning visibility into growth. Follow me, David Manela, for more.

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SEO is dead. Adobe has proved it. Adobe Analytics shows a 3,500% increase in traffic to U.S. retail sites from generative AI sources in May 2025 (from July 2024). Think about your last 10 searches. How many did you ask ChatGPT instead of Google? Most people haven't realized what just happened. We've shifted from optimizing for search engines to optimizing for Large Language Models. Adobe's new LLM Optimizer, announced at Cannes Lions, is the first enterprise tool designed specifically for this new reality. Here's what's brilliant about Adobe's move: 𝐓𝐡𝐞𝐲'𝐫𝐞 𝐧𝐨𝐭 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐭𝐫𝐞𝐧𝐝. 𝐓𝐡𝐞𝐲'𝐫𝐞 𝐝𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲. While everyone's still thinking "SEO," Adobe is thinking "GEO" - Generative Engine Optimization. The tool identifies which content gets cited by ChatGPT, Google AI Mode, and Perplexity, then provides one-click optimization recommendations. This is massive for three reasons: 1) 𝐓𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐩𝐞𝐫𝐦𝐚𝐧𝐞𝐧𝐭    People get answers directly from AI now     No more clicking through 10 blue links 2) 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐫𝐞 𝐛𝐥𝐢𝐧𝐝    Most platforms track human visitors, not AI agents accessing your content     You're optimizing blind 3) 𝐀𝐝𝐨𝐛𝐞 𝐬𝐚𝐰 𝐭𝐡𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐟𝐢𝐫𝐬𝐭     They're targeting SEO leads, content strategists, and digital marketers     The people already managing search strategy The companies that understand this shift now? They'll dominate the next decade of digital discovery. The ones still focused on Google rankings? They're optimizing for a world that no longer exists. LLM Optimizer connects AI visibility directly to business outcomes - tracking how LLM exposure converts to qualified traffic and revenue. This isn't theoretical anymore. It's measurable. It's actionable. It's here. Adobe just handed enterprise marketing teams the roadmap for the AI discovery era. LLM Optimizer is officially coming this fall. Date to be determined. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬: 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐬𝐭𝐨𝐩 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐩𝐚𝐬𝐭 𝐚𝐧𝐝 𝐬𝐭𝐚𝐫𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞?

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A better Meta strategy starts with smarter targeting. (Especially if you’re selling high-ticket offers) Because the wrong targeting choice decides your ROAS. Here’s how I break it down: Broad Targeting → Big reach, low control, wasted clicks. Smart Targeting → Filters for intent, books better calls, scales profitably. Both Together → Test wide, optimize narrow, balance scale + ROI. Clicks won’t build your business. But qualified calls and predictable revenue will. P.S. Which side do you rely on more right now: Broad or Smart? 👇 Follow me for more Meta Ad breakdowns. 🔁 Repost to help another coach scale smarter.

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Is Performance Max cannibalizing your Search campaigns? We analyzed over 500 Google Ads accounts and found that PMax often serves ads for the exact same keywords as Search, even when it shouldn’t. Key findings: -91% of accounts had keyword overlap between PMax and Search -In 56% of Search campaigns, PMax still triggered for the same terms -When there’s a big performance gap, Search usually outperforms PMax -The overlap is often caused by mismatched budgets, targeting, or schedule, not keyword setup In my latest blog, I break down what’s really happening, how to spot cannibalization, and what to do if PMax is quietly eating into your Search performance.  Read the full post: https://lnkd.in/gEBjnGNE #PPC #GoogleAds #PerformanceMax #SearchMarketing #DigitalAdvertising #Optmyzr #PaidSearch #PMaxInsights

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Most Shopify ad accounts don’t fail because of expensive traffic. They fail because of sloppy fundamentals. Not the obvious wrong platform mistakes. These are some important issues like: No attribution in place → inflated ROAS Creative testing random → no learnings to scale Every SKU pushed → budgets spread thin Product pages leaky → ads work, site doesn’t No AOV levers → bundles, upsells ignored Retargeting scattered → warm leads left cold Retention untracked → LTV is just a guess Weak margins don’t come from lack of spend. They come from lack of structure. The profit is already in your account but if it’s buried under wasted clicks and broken systems… it’ll never surface. That’s why, before scaling, we strip away the noise. Fix every issue and only then put budget behind growth. P.S. What’s one mistake you see Shopify brands repeat again and again?

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Google will keep spending on losing SKUs unless you tell it to stop → Filter: Cost > 2-3x your selling price, Conversions = 0 → Pause or isolate those products → Reinvest in proven winners Control the bleed before you scale

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I can predict which eCom SEO strategies will fail just by looking at how agencies structure their first 30 days. Agencies that will fail spend weeks on: → Technical audits that find 50+ "critical" issues → Keyword research with zero intent mapping → Content calendars for blog topics → Competitor analysis spreadsheets Agencies that will succeed spend the first week on: → Fixing only broken links and basic indexing → Mapping purchase-intent keywords to existing pages → Optimizing collection pages for target keywords → Setting up proper internal link structure The difference is focus. Failing agencies try to fix everything at once. Winning agencies fix revenue drivers first. When I see a 90-day plan that's 80% technical optimization and blog content, I know it'll fail. When I see a plan that optimizes money pages in week 1 and builds supporting content after, I know it'll work. I comes down to doing the right work in the right order. But most agencies are optimizing for their own convenience. → Technical audits are easy to template → Blog content is easy to outsource → Keyword research is easy to automate Meanwhile, optimizing collection pages and building strategic links requires actual thought... So they skip it entirely. Find an agency built to do the hard work first. The stuff that will actually scale your revenue.

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Not all SEO is created equal. Backend vs. Front-End vs. Content SEO… Which one is actually driving your ranking? Most sellers treat SEO as one big blob. Big mistake. → Some levers index faster → Some influence clicks, not ranking → Some are way more customer-facing That’s why I made this side-by-side SEO comparison cheat sheet. Simple. Visual. Tactical. ✅ Tools required ✅ Workload to implement ✅ Customer-facing impact ✅ Indexing power ✅ Best for (new vs. mature listings) ✅ Ranking time frame ✅ Common pitfalls ✅ Scaling potential Comment “SEO” and I’ll send you the PDF. If you want better organic ranking → start here.

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The most successful brands I work with don't even look at performance marketing KPIs. No GA4, no attribution, no ad engagement. Think about a typical performance marketing or agency dashboard: • Spend by channel and tactic • Engagement KPIs like CTR, VTR, Hook rate • Some kind of attributed ROAS or similar ROI metric • Drill-downs into the tactics and the sub-campaigns Occasionally you'll get some kind of smart "translation" or "coefficient" work that's happening, in which an MMM or incrementality test places a weigh against these different platform metrics. But in reality, for many brands, it just causes analysis paralysis. Why does it matter if your CTR is going up? Why does it matter if your CPC is going up? Because there's some kind of logical assumption that: • More efficient media = more efficient driving biz results • More engaging media = better targeting, better biz results • Directional "signals" correlate and correspond with the P&L In reality, this is rarely true once you get to a certain size. I think the critical flaw that most people have made in their career is at some point, they were incentivized to achieve some kind of silo metric as their focus area. "Get that ROAS up." "Get the contribution from Google up." "Target on making the most engaging ads." And if they worked at a smaller business (sub $20m/yr in revenue), these things often DO correlate with growth. You see that if you're driving a lot of attributed sales in Meta that you're ALSO driving a lot of sales in Shopify. Unfortunately at a certain point, that kind of simple linear correlation dissipates. And in fact, becomes contradictory. The brands that are winning and growing (our top cohort of brands over $50m/yr in revenue are growing more than 40% this year YOY) are committing to a simpler view: • Trended revenue, costs, and contribution margin • Trended relationship of new vs repeat revenue • The above by product category or relevant segment • A workflow of specific tasks designed at removing "blockers" --- Complete customer survey cohort for October --- Produce these 15 ad variations for this new product --- Secure relationships with 25 new influencers in our List --- Conclude the email send/segment incrementality test etc. They stay focused on specific ACTIVITIES and then closely monitor how those activities relate to their PROFITABILITY. There's none of these logical leaps or inferences of "if only our XYZ got better, then these next fifteen KPIs in the 'funnel' will also all get better!" In reality when a diagnostic metric becomes a goal, it stops being a good measure of health. The more you tell your agency or your team to go and get a certain KPI, the more they will over-rotate and manufacture "wins" against it. • It's human nature to focus on new information • We should have data and clear measurements • But we should make that data and those KPIs relevant to the P&L #mmm #pnl #marketing

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A lot of agencies make big claims about Amazon listing optimizations. But how many actually measure their changes against real sales data before setting expectations with clients? The challenge: Marketplace tools can give you a great ‘score’—but what does that number really mean for your bottom line? Many brands fall for shiny dashboards, but get disappointed when lifted scores don’t line up with actual conversion gains. Emplicit does things differently. We link optimization tool metrics directly with before-and-after sales data to calibrate what those scores mean for each client. This approach lets us promise gains we know we can deliver, not just hope for. Example: We ran a test where a 12,000-point listing score boost led to a 17% increase in conversions across three ASINs in one month. That’s trust built with data—not empty promises. Are you correlating your optimization efforts to real-world ROI, or just chasing higher tool scores? How do you prove value to new clients before the first sale even happens?

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There’s a lot of FUD right now about the fate of SEO. Is AI search going to collapse our industry? Kevin Indig doesn’t think so. He believes we’re simply entering the next exciting cycle of SEO — one where the industry and the profession continue to evolve, not disappear. Check it out:

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