Machine Learning is Bringing Shopping Experiences to Life

What is machine learning? According to Arthur Samuel, pioneer of computer gaming and artificial intelligence, “Machine learning gives computers the ability to learn without being explicitly programmed.” Samuel coined the term “machine learning” in 1959 during his time at IBM, and it has been widely used ever since.

This kind of machine learning and artificial intelligence is currently taking the technology space by storm. When most people think of AI or machine learning, they think of human-like robots in sci-fi movies interacting with people in different ways. While this idea may appear ahead of our time, it’s actually not as far-fetched as it seems. According to Gartner, “By 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human.” What many people don’t realize is that machine learning isn’t limited to just physical robots, but also includes technologies with the capacity to learn about the end-user, including Amazon Alexa, chatbots, visual search, and product recommendations. Companies are gaining a newfound understanding of their customers’ behaviors without the hassles of programming new rules in order to get additional information. And machine learning is continuously improving the ways in which all of that new information gets interpreted and utilized.

What does machine learning mean for commerce? Despite the latest technological advancements, roughly 90% of retail sales still comes from physical stores, suggesting that brick and mortar stores are not disappearing any time soon. However, retailers are still struggling to provide more engaging in-store experiences for their customers. Customers aren’t getting the service they need from sales associates, associates aren’t given adequate support to do their jobs, and business users aren’t gathering enough relevant information to promote their products as effectively as they could. This is where machine learning can help, as it goes beyond simple product recommendations and can serve as a driving force for enhancing the in-store customer experience as well as the business user experience.

Imagine if you could walk into a store, instantly find what you needed, and have a store associate give you personalized product recommendations along with a special discount based on your shopping history. Now imagine if retailers could record their customers’ activity history—including but not limited to online and in-store purchasing history, online cart additions, return information, and in-store visits—all in one view. By empowering store associates with relevant customer activity history, machine learning can allow them to work more effectively with the customers in the store.

What role will chatbots play in retail? Chatbots have been designed to inform the customer and answer questions quickly and effectively. For instance, customers can now get 24/7 support when asking standard questions about about store hours, return policies, order statuses, and item availability. However, it’s important to recognize that chatbots can’t serve as a replacement for live shopping assistants. Nevertheless, they do allow companies to focus less on answering basic questions and more on how to increase their bottom line.

Visual search is also gaining momentum. For example, ASOS rolled out a visual search feature in their app that customers can use to upload a picture of an item they want to buy. The app will then display products similar to the one shown in the photo. Richard Jones, the head of UX and product at ASOS, explains how visual search is meant to speed up product discovery, though the process is still far from ideal: “The tech isn’t yet perfect, if anything the examples out in the market to date have been a ‘bit clunky,’, but with machine learning and big data, it’s only going to improve.” 

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Source: Forbes

 

As machine learning continues to evolve, it will also be able to increase efficiency around fulfillment, returns, and restocking. For example, a company called Pepper is experimenting with various robotic capabilities and how they might work in retail stores. According to a recent CNBC article, “Pepper, a humanoid robot created by Softbank that uses software from JDA Labs, can sense when a shopper walks up. Pepper asks the customer what they need help with, can notify them if a store has an item in their size, and if not, refer them to the nearest location that does. Pepper is still in the research and development phase.” 

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Source: CNBC

 

Dave Barrowman, Skava’s VP of Innovation, will soon be talking at Shop.org about how machine learning will impact retail and bring experiences to life. He notes that we’re already using machine learning today with technologies such as Alexa and image recognition. “These latest innovations will be huge for retail,” he continues. “Better recommendations are just the beginning. Machine learning will impact retail from end to end, and across all touch points. Machine learning is about making the shopping experience more efficient, more engaging, both online and in-stores, and providing a simplified user experience. The customer journey will become more meaningful for the individual as it’s infused with machine learning.”

In that same interview, Barrowman listed some predictions for the future of retail:

  • Product design assortments will be determined by predictive models
  • Product and inventory allocation will be supported by intelligent systems
  • Discounts and markdowns will be automatically suggested
  • Customer visits will be driven by truly personalized marketing
  • Online experience and recommendations will be augmented with tailored UI
  • High online return rates will be mitigated by identifying likely returns and offering alternatives
  • Store associates will be supported by smart tools to drive sales


By harnessing the power of machine learning, businesses will continue to discover new and interesting ways of interacting with their customers. While having more information about customers is highly beneficial, simply having more data isn’t enough—it’s what you do with the data that matters, along with how well you define your goals. This is where machine learning will be able to make a significant impact, as retailers work to develop clearly defined goals and take this next big step toward an exciting future with AI and machine learning.

 

Skava is hosting a webinar with PYMNTS.com: Machine Learning is Much More Than Product Recommendations, on September 6. Reserve your spot today.

 

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