Tagged: Ecommerce support

Every guide tagged "Ecommerce support", newest first.

Store chat recommending a waterproof cycling jacket under a budget, with a product card showing price and stock

Ecommerce support

30 Sep 2026 3 views

Conversational product recommendations: how a store chatbot suggests the right item

Conversational product recommendations are answers, not adverts: a shopper describes what they need in their own words and the chatbot suggests the items from your catalogue that match, ranked by what actually sells, shown as cards with live price and stock. This guide explains how that works, the catalogue data it depends on, three real exchanges, what it is not (no cross-sell engine, no browsing profiles), and how to measure it.

Chat conversation looking up an order by number and email and returning the status with a tracking link

Ecommerce support

08 Sep 2026 254 views

Order tracking in chat: why WISMO should not reach a person

Where-is-my-order is one of the most common store support questions and the easiest to automate safely: order number plus email or phone, read live from the store. What the bot should say at each status, which platforms support it, and how to measure deflection.

Five steps to writing a chatbot knowledge base, from reading real conversations to keeping one copy of each fact

Ecommerce support

07 Sep 2026 158 views

How to write a chatbot knowledge base that answers 90% of customer questions

A practical guide to building a store chatbot knowledge base: audit 100 conversations, load eight source types in a format retrieval can use, write quotable answers, and test with a fixed 50-question set. The 90% figure is a goal to work toward.

Checklist of the questions shoppers most often ask a store chatbot, from order status to payment methods

Ecommerce support

05 Sep 2026 128 views

The 12 questions shoppers ask a store chatbot

Shoppers ask a store chatbot the same twelve questions in rotation, from order status to talking to a human. For each one: how it is phrased, the data the bot needs, a good answer pattern and the failure to avoid.

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