The physical retail shopping experience has remained pretty consistent over the past few decades, other than cosmetic changes to store layouts and signage. Some argue that the experience has actually declined since department stores and category killers have dominated foot traffic, and products like electronics and appliances have gotten more complex. Finding a store associate — and one who is knowledgeable — is consistently among the top dissatisfiers of retail shoppers.
Meanwhile, online shopping has readily adopted new technologies and shopping aids, such as reviews, recommendations, site navigation, fast checkout and more. Moreover, online retailers can easily gather highly detailed data on customer preferences and behavior throughout their shopping journey, providing them with better opportunities for loyalty programs, cross-selling and upselling and retargeting.
When — and how — will physical stores gather and make use of data to a comparable degree? It turns out a few tweaks to the shopping experience could do it, driving massive opportunities for engagement and loyalty, as well as the potential to richly map a shopper’s journey from store arrival to departure.
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Imagine this: A shopper enters a retail store and instead of wasting time hunting for the desired department/product, a personal concierge attentively guides her through the shopping experience. The concierge is not a human; it is artificial intelligence (AI) “living” inside of the brand’s smartphone app. Whether the shopper wants to know where women’s shoes are located, if a dress is available in a different size, or where the fitting rooms are, this concierge is eager to help.
The process of selecting and trying on items is made much smoother. Merchandise — whether displayed on mannequins, signage, or via holograms — can be quickly scanned via the smartphone camera, then display all the sizing and color options, as well as similar items and recommended merchandise to pair with it. If the shopper wants to try something on, she can simply tap a button and a store associate will place the item in a fitting room for her. Inside the room, a smart mirror shows how the outfit would look in a different color, how similar items might look on, or how the dress would pair with different shoes.
When the shopper wants to buy, she can scan the tags and tap a purchase button in the app, readying the contents of her virtual cart for pickup at the register. Alternatively, she could opt for home delivery instead. This would allow her to switch to an e-Commerce mode, except with the ability to try things on before making a purchase.
All of this may sound like sci-fi, but this is the direction retail shopping is already heading, thanks to the fast spread of AI-based technologies. Numerous retail brands are reworking their in-store shopping experience — from big box retailers to home improvement chains, department stores, grocery chains, restaurants and designer boutiques.
In less than a decade, machines that understand human language and are capable of continued learning went from fiction, to technology siloed in companies like Google and Facebook, to a solution available for all major consumer-facing retail brands.
This is a boon to both shoppers and businesses. On the consumer end, a shopper can receive quick and satisfactory answers to all their brand-related inquiries — color, size, availability, recommended accessories, etc. It’s a pleasant experience; conversations are generated dynamically (like a dialogue between humans) instead of being restricted by pre-programmed scripts.
Better yet, the AI-based assistant continues to learn more about language, humans and each individual shopper upon repeated exposure. In other words, the more often the conversations take place, the more sophisticated those conversations get. The assistant tracks each customer’s location, measurements, prior purchase and browsing history, and generates spot-on recommendations and useful tips.
Meanwhile, retail brands can better respond to customer demands both individually and in aggregate, tracking all the steps in a customer journey from start to finish (unsatisfied requests, clothing that was tried on but not purchased, etc.). A lot of data is captured in a long-tail natural language query; brands will learn whether there are products they should stock that they are not, where good opportunities for cross- and upselling lie, how to better manage their allocation of existing products across stores, and create a cohesive experience that unifies online channels with the offline in-store experience.
Sam Vasisht is CMO at MindMeld, a leading Silicon Valley AI company powering conversational interfaces for some of the world’s largest retailers, media companies, government agencies and automotive manufacturers. As a hi-tech product and marketing executive for the past 15 years, Vasisht has led marketing and product development for various large brands, including Motorola, Bose, Real Networks, TiVo and On2 Technologies (Google). He has been instrumental in the launch, growth, turnarounds and exits of various venture backed startups. Vasisht is a frequent industry speaker and has appeared in the Boston Globe, USA Today, Fox Radio and NPR Marketplace, among others.
