MilliOnAir interactive Magazine April 2017 - Page 99


While brands have scrambled to launch Facebook Messenger chatbots since the social media behemoth opened up the channel for development last year, the early results haven’t been particularly promising. Facebook is seeing a 70% failure rate among those 35,000 or so bots when it comes to understanding user requests. To combat this poor performance, Facebook is making some changes to Messenger, including adding a persistent menu that will allow users to choose from a number of requests or statements instead of using natural language and risking stumping the bot entirely.

There’s no question that AI will play a huge role in the future of retail, but in these early days of chatbots and virtual assistants, how do you reap the benefits while avoiding the pitfalls of this emerging technology? We caught up with Linc engineer Alessandro Sanchez to talk about the potential weaknesses in current chatbots and how smart brands are creating a chatbot experience that beats the odds and delivers great service. He says brands that want to see chatbot success should focus on three best practices.

Define the use case

Facebook is struggling with its own conversational agent, M, Alessandro says because M is an all-purpose AI, without defined parameters. “Not all chatbots are created equal. The most successful chatbots are going to be ones that operate in specific domains, where it feels natural to interact with them,” he says. The post-purchase experience is an excellent example of where chatbots excel, Alessandro notes, because shoppers have a limited range of specific intentions (finding out when their order shipped, getting assistance with a return) that a bot can be programmed to identify and respond to. He points to order tracking as one area in which Linc’s clients have seen great success with bots:

“Tracking itself is an easy, step-by-step process, where the majority of shoppers simply ping the bot every once in a while for a status update. The most popular button is our ‘refresh’ button, where a shopper can have an incredibly easy and natural interaction to stay updated on their package outside of the push notifications they already receive. For example, 90% of an apparel customer’s shoppers don’t ever require human assistance because of this feature. When a shopper is not satisfied with just a status update, they can ask a range of natural language questions that we’ve anticipated and can readily handle due to our natural language understanding capabilities. Our bot can field questions like Where is my package?, When is it arriving?, How do I return my item?, etc.”

The message is clear — focus on having your bot do one thing (i.e., deliver post-purchase customer service) and do it exceptionally well. Specialization is a step towards success.


Talks Chatbots