What makes a great chatbot? Laser focus on customers

Chatbots are all the rage right now but they’re virtually useless unless enterprises nail the customer experience. Here’s how to ensure your chatbot initiative pays off.

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Fewer technologies have garnered more attention over the past year than chatbots, those virtual assistants that mimic human speech while facilitating tasks on behalf of humans, typically via a conversational messaging interface. At a time when software is driving unprecedented levels of automation , companies are using chatbots to help customers order anything from food to office supplies to additional computing capacity .

Chatbots are a big reason why corporate adoption of cognitive systems and AI will drive worldwide revenues from nearly $8 billion in 2016 to more than $47 billion in 2020, according to IDC. But what exactly makes a great chatbot? Perhaps more importantly given enterprises’ investments in such tools, what makes a bad one? What precautions should CIOs take in building them?

Perhaps no one is better equipped to answer these questions than Conversable CEO Ben Lamm, whose company has built chatbots for the likes of TGI Fridays , Whole Foods, Budweiser, and, most recently, Shake Shack .

Chatbots are all about the customers

A great chatbot is designed, implemented and deployed based on a deep understanding of a company’s customers, Lamm says. That means your chatbot initiative must have clear customer experience mandates, goals and key performance indicators that enable it to add demonstrable and measurable value for the user and the brands. “Whether that’s more convenient purchases, streamlined conversational support, or elevating your experience of a live event, the chatbot needs to take the customer experience to a new level of engagement,” Lamm says.

Lamm says the best chatbots can be continuously improved by line-of-business employees. Conversable, for example, has built an adaptive response system called Aqua that uses machine learning to identify broken queries, and allows employees to write and modify chatbot responses. “If your chatbot isn’t getting better, it’s definitely getting worse,” Lamm says.

Conversable has documented its chatbot-building process :

  • Design the conversation: Identify the best use cases in customers’ existing operations and write out the conversation flows. Generally there are multiple stakeholders involved, varying depending on the customer, but it spans business and IT.
  • Build the conversation: Think of this as the 1.0 of the final product. Test drive to make sure the experience as a whole is up to par. Revisions to the conversation flows and other facets of the experience happen at this stage.
  • System integration: This is classic integration work. Conversable enables webhooks to make sure the data needed for each conversation flow is available. For example, if someone wants to know the price of a product, or how many calories are in a menu item, we need to pull on that data on-demand during the conversation.
  • Learning: This is a human and machine collaboration to improve algorithms. Too many people assume you can just set an AI loose and it will figure it out.
  • Expansion: Think of this as enabling more sophisticated conversations, expanding to other important areas where customers have questions or needs. People often leap from one topic to the next during a conversation, and there’s a relationship somewhere in that leap. When you identify a relationship between one conversation and another, easily linking the flows saves customers a lot of time, and ensures the chatbot can stick with the user as an organic conversation unfolds.
  • Advanced AI: This is all about continuous improvement over time. You’re not done once you push the chatbot live, and our technology makes it easy to analyze what’s happening in bot-user interactions and identify areas to improve. With that data in hand, you can increase the sophistication of your chatbot using our AI.

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