MAX()Largest value 2️⃣ Examples SELECT COUNT(*) AS TotalCustomers FROM Sales.Customer; ✅ Counts customers SELECT AVG(TotalDue) AS AverageOrderValue FROM Sales.SalesOrderHeader; ✅ Average order value

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": "emily@example.com", "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.

حالا مدل پایه برای محاسبه CLTV این فرموله: CLTV= (AverageOrderValue) × (PurchaseFrequency) × (CustomerLifespan) فرمول: میانگین ارزش هر سفارش X میانگین تعداد خرید X طول عمر مشتری اما یه مدل پیچیده هم داره... برو بعدی ↓

برای محاسبه CLTV اول نیاز به AOV دارید. AverageOrderValue * این میانگین ​​پولیه که هر مشتری در هر تراکنش خرج می‌کنه. فرمول: کل درآمد در یک بازه زمانی باید تقسیم بر تعداد سفارش بشه. (دقت کنید تعداد سفارش نه تعداد کالا یا ...) برو بعدی↓

دو - AverageOrderValue یا AOV به ما میانگین مبلغ خرج شده توسط مشتریان در هر سفارش رو می‌گه که نشان‌دهنده رفتار خرید مشتری و ارزش کلی ایجاد شده در هر تراکنشه. AOV = Total Revenue / Number of Orders برو بعدی↓

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