Mobile Machine Learning in E-commerce and Retail: Visual Search [Ebook Sample]

Mobile ML, Retail, and E-Commerce

When it comes to retail and e-commerce, the AI revolution is already here. From AI-enhanced search and at-home product demos to clothing/accessory try-on applications, machine learning is increasingly baked into shopping experiences, both in-store and online.

In fact, Gartner has predicted that up to 85% of all customer interactions are in some way managed by AI and/or machine learning (ML).

Combine this with projections that suggest the total e-commerce marketplace will reach more than $4.8 trillion USD in sales by 2021, and the implications become clear: e-commerce, retail, and machine learning are becoming inextricably linked.

But how does AI actually help drive this booming industry sector, and how can on-device AI help retailers delight their customers while offering them more personalized, responsive, and convenient shopping experiences?

We’ve identified a number of key opportunities retailers have to leverage mobile machine learning to build experiences that will increase sales, decrease product returns, and build brand loyalty and trust. We’ll cover each of the following primary use cases in-depth:

  • Visual Search
  • Virtual Try-On
  • AR/VR Product Demos

We’ll also briefly explore a few other potential ways on-device machine learning can create more streamlined shopping experiences and more efficient supply chains.

Visual search allows users to quickly explore an entire product catalog by using their device’s camera. More specifically, visual search allows users to take real-world images (screenshots, their own photos, etc) to initiate online searches.

Using this real-world input data, AI-powered visual search applications work to recognize patterns — and then find items within a product catalog that match those patterns.

For instance, imagine you’re riding the subway to work, and you see a fellow passenger with a pair of shoes that you really like.

An effective visual search application would allow you to snap a photo of the shoes with your phone’s camera (and their permission, of course), and then quickly compare it to a retailer’s entire catalogue, showing you the products that most closely match the product in the original photo.

The convenience for the user here is clear, and as more and more companies experiment with visual search, more and more users expect it to be a part of their mobile e-commerce experiences.

Recent research from ViSenze found that 62% of generation Z and millennial consumers want their favorite brands to implement visual search features more than any other technology.

And if that statistic above isn’t enough to convince retailers that this tech will be key for the future of e-commerce more broadly, an online report from Wise Guys Reports predicts that the market for visual search software will rise from roughly $6.7 billion USD in 2018 to nearly $28.5 billion USD in 2027.

Currently, major brands like Pinterest, Ebay, Amazon, Nordstrom, Disney, Etsy, and more employ visual search engines as part of their e-commerce platforms. And major AI research efforts — like this one at Facebook AI — are being made towards building more powerful and efficient ML models designed to enable unique visual search experiences.

If you liked this sample, you might also enjoy our other free ebooks:

Fritz

Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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