Original article published in Information Age

Imagine it as, ‘You bought this item today and this last week; you’ve also looked at all these items, but today you are looking at these – so how about this?’ level of assistance.

Such intelligent, context-sensitive and informed recommendation prompts are the core of what some observers are starting to call ‘Conversational Commerce’, which is about embedding intelligence and data-fuelled capability into your web store.

Such rich personalisation, at scale, is going to be unlocked by AI (artificial intelligence) and data-driven, real-time-capable smart software. And the glue to make it work together and mine the growing number of data connections and data sources the personal shopper of tomorrow will call upon is native graph technology.

A conversation with customers

Early examples bear this out. A standout example is eBay’s AI-based US-only (so far) ShopBot, which uses this intelligent recommendation technology. ShopBot asks qualifying questions, then quickly serves up relevant product examples customers can choose from.

The functionality is impressive. You can send the app a photo with a direction like, ‘I like this watch, can you find similar?’ and it will figure out similar products to display, and really fast, too. You can also use speech or text to engage in conversation with the bot.

The key to ShopBot’s cleverness is the conversation element: just as for us humans, speech allows the software to establish far more contextual information than a typical search box would garner on its own. And it’s this context that empowers this personal shopper app to make an extremely well-informed guess at what the buyer’s intention is.

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