Tom Benattar

this one JSON file makes your ad copy feel psychic it’s called a context profile and it’s the missing piece in 99% of AI-powered marketing here’s how to build one (and why it matters)   most brands dump raw Shopify exports into GPT and expect personalized marketing. what they get is generic garbage. because without structure or context, your AI is guessing. the output sounds generic because the input is generic a context profile fixes that. it’s a structured snapshot of a customer’s behavior: - order history - site activity - promo engagement - avg order value - product preferences - churn signals and it’s stupid simple to build: 1. pull the data that matters use n8n, Triple Whale, or Shopify’s API to grab: – last 180 days of orders – email/promo activity – on-site behavior (viewed collections, abandoned carts, quiz data) – customer tags or notes 2. structure it as JSON (example below): {  "name": "Emily",  "email": "[email protected]",  "customerType": "Returning",  "lifetimeSpend": 742,  "averageOrderValue": 62,  "lastOrder": "2024-06-01",  "orders": [   {    "date": "2024-06-01",    "products": ["Night Gummies", "Hydration Tabs"],    "orderValue": 69   },   {    "date": "2024-03-14",    "products": ["Calm Focus Tabs"],    "orderValue": 55   }  ],  "productsPurchased": ["Night Gummies", "Hydration Tabs", "Calm Focus Tabs"],  "topCategory": "Sleep & Recovery",  "quizResult": "Struggles to fall asleep, wants non-groggy solution",  "preferredFlavors": ["Lavender", "Citrus"],  "pagesViewed": [   "Sleep Collection",   "Calm + Focus Bundle",   "Subscription Page",   "FAQ: Shipping"  ],  "clickedPromos": [   "Mother’s Day BOGO",   "Calm + Focus Bundle Launch"  ],  "abandonedCart": {   "date": "2024-05-27",   "items": ["Lavender Sleep Spray"],   "cartValue": 38  },  "emailEngagement": {   "openRate": 74,   "clickedEmails": [    "Productivity Tips for Night Owls",    "Calm Bundle Launch",    "Hydration How-To"   ]  },  "subscriptionStatus": "Not Subscribed",  "churnRiskScore": 5.8,  "lastEngaged": "2024-06-02",  "device": "Mobile",  "utmSource": "Instagram Ad" } 3. plug it into Claude or GPT: this is where the real leverage kicks in because now you’re feeding the AI a full psychological profile it can think with try prompting it like this: “you’re a performance creative strategist. based on the context profile below, write a UGC script and a 30-second VSL intro that speaks directly to this customer’s needs, preferences, and purchase behavior.” you can use this for: – segmentation-aware email flows – better upsells and winbacks – product recommendations that convert – banger ad copy, hooks, angles and anything else you want your AI is only as smart as the context you give it. this profile turns a generalist model into a laser-targeted strategist. and it works for every part of the funnel.

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According to iBusiness, 84% of the top 100 SEO agencies in Germany offer GEO services. For 30% GEO is already a top focus area. For 7% GEO is the number 1 priority!

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I’ve scaled 2 dozen eCommerce brands in 2025 — by 5–15x. How? By mastering demand capture with Google Ads. Here’s the campaign set I use: 1. Standard Shopping (performance-based grouping) → Superstars | High priority → A Players | Medium priority → Reserves | Low priority → Benched | Low priority Bid strategy: tROAS 2. Branded Search → Brand name as exact & phrase → Manual CPC 3. Branded Shopping → Low priority → Exclude non-branded keywords as they appear → tROAS 4. Non-Branded Search → For Superstars & A Players only → Manual CPC / Max Conv. Value → Upgrade to tROAS within 30–60 days 5. Dynamic Product Remarketing → Audience: Abandoned cart (7–14 days) → Frequency: ~7 impressions per day per user → Manual CPC / Max Conv. Value → tROAS within 30–60 days 6. YouTube Remarketing → Audiences: Abandoned cart & product view (7–14 days, no purchase) → UGC or product demo with CTA works best → Manual CPC / Max Conv. Value → tROAS within 30–60 days Timeline: ↳ Weeks 1–2: Learning & testing ↳ Weeks 2–4: Optimising ↳ 4+ Weeks: Scaling Is it easy? No. Is it worth it? Absolutely. Done right, this playbook can skyrocket your eCommerce brand. And the best time to get started? Right now — Q4 is around the corner. Not sure where to begin (and tired of not making the revenue you deserve)? Reach out.

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🚀 𝗣𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁'𝘀 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 @𝗕𝗮𝗶𝗿𝗲𝘀𝗗𝗲𝘃 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝘀𝘁𝗼𝗿𝘆 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 𝘁𝗵𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔-𝗽𝗹𝗮𝘆𝗲𝗿𝘀 When Pinterest needed to rebuild their Ads Manager for 482M users, they faced a choice: hire frantically or scale strategically. They chose the latter. 𝗧𝗵𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: ✅ Scaled from 5 to 233 A-level developers ✅ Pinterest Ads Manager launched on time ✅ Grew from 1 project to 37 across the entire company ✅ Built mobile ads tools, next-gen algorithms, and automated testing 𝙋𝙞𝙣𝙩𝙚𝙧𝙚𝙨𝙩'𝙨 𝙀𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙞𝙣𝙜 𝙈𝙖𝙣𝙖𝙜𝙚𝙧 𝙧𝙚𝙫𝙚𝙖𝙡𝙨 𝙩𝙝𝙚 𝙨𝙚𝙘𝙧𝙚𝙩: "Of the 15 engineers on my team, a third are from BairesDev. We've had quite a few superstars who resolve our problems without our full-time engineers having to step in. It's been amazing seeing them take ownership." 𝗪𝗵𝘆 𝗔-𝗽𝗹𝗮𝘆𝗲𝗿𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗵𝗲𝗮𝗱𝗰𝗼𝘂𝗻𝘁: 🎯 Quality over Quantity: BairesDev's A-teams integrated in under 2 weeks, immediately contributing at senior levels 🎯 Ownership Mentality: These aren't just contractors – they're A-players who take full ownership of complex problems 🎯 Strategic Scaling: What started as ads architecture became a comprehensive tech partnership spanning mobile, infrastructure, and AI 𝗧𝗵𝗲 𝗔-𝘁𝗲𝗮𝗺 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: 𝗧𝗼𝗽 𝟭% 𝘁𝗮𝗹𝗲𝗻𝘁: Rigorous vetting ensures only elite developers join your team 𝗖𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: A-players adapt quickly to your processes and standards 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽: They don't just code – they solve business problems 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲: Proven ability to grow from 5 to 233 while maintaining quality 𝗧𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻 𝗳𝗼𝗿 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀: Don't just add bodies to your engineering team. Partner with organizations that deliver A-teams – developers who think like founders, code like architects, and scale like your core team. When you're ready to 10x your development capacity without sacrificing quality, the right partner makes all the difference. Pinterest found their A-team. Have you found yours? 𝗥𝗲𝗮𝗱 𝗵𝗼𝘄 𝗣𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁 𝘀𝗰𝗮𝗹𝗲𝗱 𝘄𝗶𝘁𝗵 𝗔-𝗽𝗹𝗮𝘆𝗲𝗿𝘀: https://lnkd.in/gVjvBBBP #APlayers #TechScaling #SoftwareDevelopment #EngineeringExcellence #Pinterest #BairesDev #TechPartnership #RemoteTeams #TalentAcquisition #DigitalTransformation #StartupGrowth #TechLeadership #AgileTeams #FullStackDevelopment #MobileApps #CloudArchitecture #AI #Innovation #TechRecruitment #OutsourcingDoneRight #ScaleUp #TechStrategy #CTO #VPEngineering #TechTalent

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For all the #DTC #marketers out there—if you’ve already built: Conversion_LC → super easy with a solid clickstream product Conversion_FC → also super easy with a solid clickstream product And platforms like Meta, Google, TikTok, etc., are already providing click + view attribution data, isn’t that good enough for most merchants to understand overlaps? For example: I create Purchase_Paid_Facebook I attribute it to Google—Google claims 12% of those AND I create Purchase_Paid_Google I also create Purchase_Organic_Google I attribute it against Facebook’s reports and get a 33% overlap These results are much cleaner than any MTA (multi-touch attribution) model, because most MTAs are just modeling (i.e., guessing) clickstream behavior. But you can already get cleaner, deterministic data directly in-platform—no modeling required. As #MCP servers enable campaign data and clickstream systems like Blotout generate ID-stitched, UTM-stitched datasets, #AI + #MCP systems will soon make legacy MTA tools obsolete. Here’s what’s coming: Simple overlap creation tools—measured directly in-platform Data pulled directly from each ad campaign platform #AI-powered visualizations of attribution and overlap Next week, we’re launching #overlap tools—i.e., how many Applovin users drove the first touch vs. the first purchase. Who brought the user to your site initially, and who actually closed the sale? You may find Applovin is a better post-click performer than expected. Holdout testing and MTA don’t help with these questions in real time. Your own 1st-party data can—and it’s unclear why #merchants and #leadgen advertisers are still relying so heavily on outdated tools. #SaaSisDead #LongLiveSaaS #EdgeComputing

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The myth of aggressive CTAs is killing your conversion rates. Most CTAs scream “BUY NOW” or “GET STARTED” without addressing the     real friction behind user hesitation.  → People don’t want to feel trapped → They hate immediate commitment → They fear making the wrong choice And yet... most CTAs feel like pressure, not permission.   Here’s what actually works: ➡️ LOW-PRESSURE LANGUAGE ❌ “Buy Now” → ✅ “See Options” ❌ “Get Started” → ✅ “Explore Features” These subtle shifts ease anxiety and can lift clicks by 47%. ➡️ CLEAR NEXT STEPS ✅ “View Pricing (2-minute overview)” ✅ “Schedule Demo (15 minutes)” Clarity removes hesitation. ➡️ VALUE-FOCUSED MESSAGING ❌ “Sign Up Now” → ✅ “Access Free Templates” ❌ “Join Today” → ✅ “View Success Stories” Focus on what they get, not what they must do. When these principles are applied, conversion rates often jump by 60% or   more. The best part? Better leads More trust No pressure Stop pushing. Start connecting. Because the best CTAs don’t yell. They invite. #ConversionOptimization #MarketingStrategy #CopywritingTips  #UXWriting #GrowthMarketing

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