Ecom: The Future. AI, Innovation
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A channel about the future of marketplaces: innovations and artificial intelligence. Аdvertising placement @sellerdenai_support https://telega.io/channels/ecommerce_AI/card
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Red:📈 Análisis del canal de Telegram Ecom: The Future. AI, Innovation
El canal Ecom: The Future. AI, Innovation (@ecommerce_ai) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 55 407 suscriptores, ocupando la posición 882 en la categoría Negocios y el puesto 495 en la región EEUU.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 55 407 suscriptores.
Según los últimos datos del 26 septiembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -3 023, y en las últimas 24 horas de -68, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.61%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.40% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 337 visualizaciones. En el primer día suele acumular 223 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como insidead, mining, 1win, seller, shein.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“A channel about the future of marketplaces: innovations and artificial intelligence.
Аdvertising placement @sellerdenai_support
https://telega.io/channels/ecommerce_AI/card”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 septiembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Negocios.
55 407
Suscriptores
-6824 horas
-6157 días
-3 02330 días
Carga de datos en curso...
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Publicaciones del Canal
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| 2 |  Tapestry moves handbag checkout into Google’s AI shopping flow
Tapestry has made Coach and Kate Spade products purchasable inside Google Search AI Mode and the Gemini app in the US. A shopper can discover an item in the AI conversation, approve the transaction and pay with Google Pay without being redirected to the brand’s website.
The workflow uses the Universal Commerce Protocol to pass the product, cart and checkout steps between the AI surface and the merchant. Tapestry says it receives the same customer data as it does from orders on its own sites because the brands remain the sellers. The rollout is live in the US; expansion to other markets is planned, with no date given. | 181 |
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| 4 |  Amazon opens seller data to outside AI tools
Amazon has added Selling Partner plugins for Claude and Quick, letting sellers connect external AI tools to their Amazon account. Once linked, a tool can work with the seller’s catalog, current performance metrics and Amazon recommendations instead of relying on files copied out of Seller Central.
The same update expands Seller Assistant across sales, inventory, advertising and compliance. Amazon says it can coordinate workflows containing hundreds of steps, but sellers choose whether it only recommends an action or carries it out.
The practical test is permission control: teams need to define which tool may see account data and which actions still require human approval. Amazon announced the plugins on September 23; availability can vary by tool and seller account. | 225 |
| 5 |  Revieve connects beauty consultations to assortment planning
BeautyCatalyst, unveiled by Revieve, is designed to carry information from customer-facing beauty tools into commercial decisions. Instead of keeping skin analysis, virtual try-on, recommendations and product data separate, it links consumer needs with ingredients, routines and catalog attributes.
Signals from consultations and product interactions are structured into audience and market patterns. Merchandising teams can use them to examine demand, product fit and gaps in an assortment. Revieve says the same foundation can support guidance across retailer sites and AI shopping environments.
This is a vendor launch, not an independently measured result. Its test will be whether teams can trace an assortment change back to the underlying customer and product signals. | 224 |
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| 8 |  Fashion shoppers want AI to compare, not confirm
An ACI Worldwide survey of more than 3,300 consumers in the US and UK draws a clear line through the shopping workflow. People value AI when it gathers products and surfaces deals, but only 7% of fashion shoppers would let an assistant complete a purchase without their approval.
That makes assisted discovery the safer near-term design: the system can collect options, compare relevant attributes and prepare a cart, then hand the decision back to the shopper before payment. Retailers testing purchasing agents should therefore measure more than completed checkouts. Useful signals include whether recommendations fit the request, how often shoppers edit the proposed cart and where they decline approval. The survey describes stated preferences, not observed conversion, so it cannot prove that this workflow increases sales. | 329 |
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| 11 |  Crocs puts sales and service in one AI workflow
Crocs has deployed Rivet on its website to handle product discovery, sizing, order tracking, returns and checkout in one customer conversation. The brand’s previous chatbot handed 80% of conversations to people while its service team managed 178,000 inquiries a month. Rivet appears as one assistant, but a coordinator routes each task to specialized agents behind it.
That design removes the handoff between catalog search, help pages and a support queue. The published figures describe the old workload, however, not sales growth after deployment. A useful evaluation still needs completed-purchase rates, repeat contacts, sizing errors and cases escalated to staff. | 338 |
| 12 |  Amazon blocks Meta’s shopping agent from its store
Amazon has asked Meta to remove Amazon.com from Muse, Meta’s new AI agent that can buy products for users. Amazon told Retail Dive that it had received no advance notice and had not authorized the agent to access its store.
The blocked workflow is broader than product search. Muse can choose an item and complete a purchase through Link by Stripe, so it acts as an outside buyer inside another retailer’s checkout. Amazon’s position is that such applications should identify themselves and let the service provider decide whether to participate.
This exposes a practical limit for agent-led commerce: a technically possible checkout is not automatically an approved integration. Retailers evaluating buying agents need explicit access terms, agent identification, payment liability and an opt-out path before measuring conversion. | 342 |
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| 14 |  Instacart turns repeated shelf scans into a replenishment signal
Instacart has announced two tools built from shelf images captured by shoppers and Caper carts. Inventory Intelligence gives store teams a virtual aisle view; the Store Excellence Portal gives brands a daily store-and-product view of availability, substitution and estimated lost sales.
Scans identify a shelf gap, link it to a product and location, and let a team prioritize replenishment. Instacart says roughly 600,000 shoppers generate more than 10 million data points a day, but the tools are initially rolling out with selected grocers and brands.
That limit matters. A useful deployment should measure scan coverage, recognition errors, update delay and the time between an alert and a restored shelf—not only the number of signals produced. | 382 |
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| 17 |  A controlled workflow for AI-generated product scenes
Shopify Magic can replace a product-image background, generate a new setting from a text prompt, or extend the frame inside the media editor. The workflow creates one scene at a time, saves edits in the original format, and requires PNG when transparency must be preserved.
Start with one clean, high-contrast product image near 1 MP. Generate two or three background variants for a single placement, save each as a new file, then check product shape, color, shadows and crop against the source before publishing. Keep the original image as the control and compare product-page conversion and returns by variant.
The limits matter: larger files are scaled down, smaller files are scaled up, and AI-generated media receives an invisible watermark. Human review remains the release gate.
Seller takeaway: treat background generation as a bounded catalog test, not permission to alter the product itself. | 425 |
| 18 |  Tulip uses AI to map retail data into one associate workflow
Tulip has launched Integration Builder, a visual tool that connects customer, inventory, order, service and product data without forcing retailers to replace their existing schemas. It can work with APIs, flat files, Snowflake, Databricks and BigQuery, while targeted AI assists with schema discovery and field mapping.
The company also expanded its Shopify Connector for multiple regions and Shopify Markets. Regional storefronts can feed one Tulip environment while preserving local catalogs, prices and languages. Tulip says its combined platform supports about 100 enterprise retailers and more than 100,000 sales associates across 70+ countries.
Seller takeaway: map one bounded flow first—such as regional inventory into clienteling—then validate field matches, update latency and write-back behavior before connecting customer outreach or orders. | 372 |
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