Ecom: The Future. AI, Innovation
رفتن به کانال در Telegram
A channel about the future of marketplaces: innovations and artificial intelligence. Аdvertising placement @SellerDenPR https://telega.io/channels/ecommerce_AI/card
نمایش بیشتر
شبکه:📈 تحلیل کانال تلگرام Ecom: The Future. AI, Innovation
کانال Ecom: The Future. AI, Innovation (@ecommerce_ai) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 56 415 مشترک است و جایگاه 848 را در دسته تجارت و رتبه 485 را در منطقه الولايات المتحدة الأمريكية دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 56 415 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 15 سپتامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -3 238 و در ۲۴ ساعت گذشته برابر -92 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 0.63% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.38% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 354 بازدید دریافت میکند. در اولین روز معمولاً 216 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 2 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند insidead, mining, 1win, seller, shein تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“A channel about the future of marketplaces: innovations and artificial intelligence.
Аdvertising placement @SellerDenPR
https://telega.io/channels/ecommerce_AI/card”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 16 سپتامبر, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته تجارت تبدیل کردهاند.
56 415
مشترکین
-9224 ساعت
-7157 روز
-3 23830 روز
در حال بارگیری داده...
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پستهای کانال
Best Buy connects curated gift discovery with conversational checkout
Best Buy opened its holiday gifting center earlier than usual, combining curated gift guides and product highlights with a second discovery path: shoppers can buy Best Buy products through ChatGPT. The retailer is also using shop-in-shops with Google and Meta to let customers explore devices before choosing online or in store.
For commerce teams, the actionable workflow is catalog selection → conversational recommendation → cart → fulfillment. Test whether the same SKU, price and availability survive each handoff, then compare assisted-cart conversion with the retailer’s own gift-guide traffic.
The limitation is visibility. The current announcement confirms purchasing through ChatGPT but does not disclose assortment coverage, attribution rules or conversion results, so teams should treat this as a measured channel pilot rather than a proven replacement for onsite discovery.
| 2 |  Instacart and Shipt turn conversations and food photos into grocery carts
Instacart’s Clementine and Shipt’s Ask Shipt have launched new AI shopping workflows for online grocery. Clementine can turn a meal request into a cart using household preferences, purchase history, current store inventory and available promotions. Ask Shipt can identify ingredients from a dish photo, plan for a party and stay within a stated budget.
Both workflows end with an editable basket rather than an autonomous purchase. That matters operationally: the AI compresses discovery, recipe planning and product selection into one session, while the shopper still reviews substitutions, quantities and checkout.
For commerce teams, the hard requirement is live catalog quality. A conversational promise fails quickly when inventory, dietary attributes or promotion data are stale—even if the model understood the request perfectly. | 97 |
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| 5 |  Harps centralizes AI adoption as its store network reaches 177 locations
Harps Food Stores created a director of AI strategy and innovation role and promoted former category manager Will McDoniel to lead it. His mandate is to identify retail processes where AI can improve efficiency and oversee faster, more disciplined adoption of new capabilities.
The operating context matters: Harps has nearly doubled its footprint to 177 stores across eight states. A tool that works in one category or location therefore needs a controlled path into a larger network rather than a collection of disconnected pilots.
The concrete commerce workflow is process selection → limited pilot → measured result → governed rollout. Putting one owner across those stages creates a place to compare use cases, stop weak experiments and carry store-level learning into the next deployment without claiming that every process needs AI. | 241 |
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| 8 |  Target connects a shopper’s photo to product discovery and cart decisions
Target has added an AI-supported Photo Search workflow to its mobile app. A shopper can photograph or upload an item, receive visually related products, then use AI-generated Review Insights to scan recurring details from customer feedback before adding an option to the cart.
The retailer is connecting that discovery step with Buy Again, which surfaces frequently purchased items, past purchases and relevant offers, plus Continue Shopping for recently viewed products. Target says Review Insights has already increased conversions and cart additions, while Buy Again has supported strong year-over-year purchase growth.
Seller takeaway: visual similarity gets an item into consideration, but structured attributes and review evidence decide whether it survives comparison. Measure photo-search impressions, product-detail visits, cart additions and completed orders as one funnel rather than four separate features. | 305 |
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| 11 |  Amazon DSP turns product feeds into ads inside ChatGPT conversations
Amazon advertisers can now extend campaigns into ChatGPT through a managed U.S. pilot. The workflow starts in Amazon DSP: a brand supplies its campaign setup or product feed, Amazon helps optimize delivery, and text-and-image units appear beneath relevant organic answers with a sponsored label. OpenAI retains control over ad delivery.
The measurement loop is concrete. Advertisers receive aggregated impressions, clicks, cost per result, CPM, and CPC data, while product-feed ads can generate creative assets directly from the catalog. Delta Vacations is among the first pilot brands.
For commerce teams, this creates a new operating chain: clean product data, conversational placement, click-level measurement, then landing-page conversion. The useful test is not reach alone; compare which catalog attributes produce relevant placements and which queries lead to qualified sessions and completed purchases. | 353 |
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| 14 |  Maven connects store demand directly to mixed-pallet assembly
Maven Robotics has emerged from stealth with a concrete retail workflow: receive a store-specific order from the warehouse management system, select cartons arriving from different factories, and assemble a mixed pallet for outbound transport. The company says up to eight robots already operate 16 hours a day with uptime of 99% or higher.
The wheeled robots have two arms and can lift up to 30 kilograms. The operational value is not one faster pick. Maven aims to automate the full handoff between the digital order and the loaded truck while retailers adjust each store’s product mix using near-real-time demand.
Commerce teams should measure the pilot end to end: WMS order accuracy, carton damage, pallet stability, exception recovery, and completed outbound loads per shift. High robot uptime matters only if the correct mixed pallet reaches the correct store without manual rework downstream. | 386 |
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| 16 |  Ulta Beauty uses AI to source omnichannel inventory against customer demand
Ulta Beauty is applying AI-powered sourcing to decide how inventory should serve demand across ecommerce and stores. The workflow combines prior investments in automation and network optimization with product allocation, helping the retailer position beauty items where they can reach shoppers faster.
The same operating loop connects enriched product information and content creation for discovery on AI platforms such as ChatGPT, while Ulta AI handles on-site shopping assistance. The practical sequence is demand signal, inventory-sourcing decision, channel allocation, then product discovery and fulfillment.
Seller takeaway: AI discovery is only useful when the inventory layer can allocate the recommended item to the right channel; catalog enrichment and stock placement should be measured as one workflow. | 434 |
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| 18 |  Instacart turns meal intent into an in-stock grocery cart
Instacart has launched Clementine across most of its U.S. and Canadian marketplace. A shopper can enter a recipe, paste a grocery list, ask for budget-friendly lunches, specify dietary preferences, or request usual items; the assistant converts that intent into a personalized, ready-to-buy cart in seconds.
The workflow is grounded in real-time availability at the selected store, so recommendations, substitutions, and quantities are tied to products that can actually be ordered. The same technology also powers Cart Assistant on retailer-owned storefronts, using each retailer’s catalog and customer data.
Seller takeaway: structure product attributes around the decisions inside a meal plan—diet, serving size, budget, pack quantity, and availability—because the winning item must now fit a complete basket, not merely match a keyword. | 495 |
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