The retail and eCommerce sectors were the first to adopt natural language processing (NLP) in the organization, most notably through chatbots and communication interfaces. In this article, we cover three ways retailers can use NLP to automate business processes and provide a better experience for the customer. We also give examples of AI vendors offering this technology and describing their products. NLP capabilities we discuss:

In-Store Robots: Programming in-store robots with NLP based voice recognition technology for customer service.

In-Store Virtual Assistants: Interacting with virtual avatar customers will increase brand loyalty.

Chat-based Product Recommendations: Using analytics to determine customer preferences and recommend products that customers want to buy in the chatbot window.

Also Read: Top 10 AI Trends Marketers Should Watch for In 2020.

In-Store Robots

One of the most common uses of NLP in the retail sector is customer-facing communication interfaces or chatbots. Retailers can apply chatbots to in-store touchscreen interfaces and robots to provide an interactive customer service experience. It automates some of the work that human employees do on top of their normal responsibilities.

CloudMinds is a vendor that provides communication interfaces to retailers. The company offers a customer service robot called Pepper, which can field customer service queries by voice. In addition, CloudMinds provides a virtual assistant called Cloudia, which can be found on various store-touchscreen interfaces.

Cloudminds’ robots and virtual assistant could assist retailers by:

Serving as a retail concierge and suggesting products based on customer profiles.

Discerning the sentiment behind a customer’s question or word choice and accommodating their mood with more careful language. This is an indicator that their NLP algorithm is capable of sentiment analysis.

Guiding users through technological interfaces they may not be familiar with. One example of this may be a self-checkout station at a grocery store.

In-Store Virtual Assistants

Some of the retail interfaces in retail are combined with a computer-generated avatar, creating a more personalized virtual shopping assistant than a chatbot on the device. This improves the customer’s relationship with the brand. This kind of virtual assistant uses NLP just like other chatbots like Apple’s Siri. However, we must also signal when and how to move the virtual avatar based on the customer’s responses.

One vendor offering this kind of virtual assistant is Twenty Bn. Their Virtual Assistant Milli can deliberately read the customer’s body language and gestures and use them to determine the moods behind the customer’s speech and their emotional state. TwentyWin says Millie is launching the following capabilities for retailers:

Customer service: Customers could ask Millie questions about individual products and upcoming sales. She could also explain product features to customers while they are examining them or trying them on.

Customers could ask Millie questions about individual products and upcoming sales. She could also explain product features to customers while they are examining them or trying them on. Personalized customer experience: Millie can purportedly discern the preferences of returning customers. The software may create customer profiles based on the customer’s name and appearance, which may necessarily involve machine vision.

Millie can purportedly discern the preferences of returning customers. The software may create customer profiles based on the customer’s name and appearance, which may necessarily involve machine vision. Inbound Marketing: For example, Millie can purportedly pose for selfies with potential customers and collect contact information in exchange.

Chat-based Product Recommendations

When combined with Analytics Analytics, users can also use NLP-enabled communication interfaces to recommend products based on their preferences. If a customer asks “Which brand of milk is for sale”, the chatbot can recommend milk brands that sell and match the customer’s preferences, for example, 1% recommend milk.

An example of the Satisfi Labs chatbot interface

Satisfi Labs provides a chatbot with this capability, although the company does not specify that its software uses predictive analytics.

Satisfy claims that its chatbot comes with the following capabilities: