Retail

IBM Watson Trend: An app to outthink holiday shopping stress

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Not got the foggiest what to buy your fashionable loved ones for Christmas? Panic not, Watson Trend is here to help. If, like me, you’re a self-confessed nerd and about as ‘down with the kids’ as socks and sandals, this big-data-meets-shopping app can help you work out what’s hot and what’s not.

The IBM Watson Trend app is designed to help shoppers identify the trendiest products, understand the background behind those trends, and suggest the best time to buy, so you’re sure to bag a good deal before stocks run out.

Extracting meaning from millions of online conversations

The idea came about when the team behind Watson Trend realized how much purchase advice they got from social media. The beauty of the app is that it can understand tens of millions of similar conversations, including the tone, and use those insights to highlight what’s trendy to shoppers.

Beyond the obvious

Granted, some of the recommendations made by the app might be obvious to anyone with eyeballs and an imagination. But unless you have unlimited time, you’re still likely to miss things. What sets Watson Trend apart from other product recommendation resources is the variety and magnitude of the data it sources. The app pulls in product information from more than 10,000 sources, from major ecommerce sights, blogs, product reviews and social media.

It’s like having a very well-informed personal shopper at your beck and call – one who can predict when certain products are likely to sell out so that you’re not disappointed.

Check out the video below to see IBM Watson Trend in action. There’s some good eyebrow work and a very cute ‘Beauticorn’ too:

Learn more

IBM is doing lots of work in the world of retail to help bring cognitive smarts to the brick and mortar store. You might enjoy this post about Watson and XPS – creating a virtual personal shopper. Or if you’re still on the hunt for Christmas gifts, take a look at our list of top 10 IoT stocking fillers for under $35.

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