What Retailers Need to Know About Conversational AI vs. Chatbots

Traditional chatbots have become commonplace on e-commerce sites, yet too many are becoming unreliable and are alienating consumers due to their limitations. This is because many retailers rely on what's called "rule-based" chatbots when instead they should be implementing conversational artificial intelligence (AI), two terms often used interchangeably, but which are actually very different.

Conversational AI vs. Intent-Based Chatbots

One of the key differences distinguishing conversational AI from chatbots is the use of back-end systems to provide customers with information. Rule-based chatbots are rigid in nature because they follow a pre-determined set of guidelines. If a shopper were to ask a chatbot for information that's not already programmed into its script, the chatbot becomes useless to the shopper and forces them to look for the information elsewhere. For example, a chatbot has no way of understanding a convoluted ask like, "I want to return the pink dress I bought from your Mulberry Street store on 5/27."

Conversational AI, however, has the capacity to learn the human language and continuously layer various use cases on top of that understanding engine so it can keep pace and adapt to ever-changing content, datasets and customer demands. This is because, unlike rule-based systems, conversational AI platforms have direct access to information sources such as websites, APIs and databases. This allows the platform to gather the appropriate information and context to maintain a flexible and carried conversation, allowing all customer interactions to be more fluid and dynamic.

Increased Insights Into Consumer Demands

For retailers, implementing a conversational AI platform across communication channels not only allows them to increase their customer service capabilities, but also gives them the tools to gather customer insights through conversational flows. With conversational AI, a retailer is able to see, for example, that 30 percent of its customers are looking for black sneakers at a certain price range, or 50 percent of its customers bought a brown crewneck sweater in the past month. By analyzing these insights, retailers are then able to stay on top of consumer demands and quickly adapt their marketing and merchandising plans to keep in line with current trends, which inevitably increases their overall sales.

If a retailer were using a rule-based system, however, it would be limited in the number of insights it could retrieve, as intents are confined to a small number of conversational flows that a retailer could deploy. Unlike conversational AI, retailers would have to pre-program specific insights they would be looking for, which limits their access to consumer demands and slows down the process of adapting to new consumer buying patterns.

Although chatbots have become a mainstay in the retail space, the adoption and use of conversational AI is skyrocketing, with some reports even indicating that by 2022, conversational AI will conduct 70 percent of all customer interactions. If retailers hope to stay competitive as the world becomes more digitized, implementing a conversational AI solution into their network will increase their efficiency, improve customer service, and boost overall sales.

Israel Krush is the CEO of Hyro, a conversational AI company.