Chatbot Development: Your Guide to Getting It Right

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Another consideration, according to Schneeman, is whether the company will use several chatbots, each developed for specific services, or fewer, more generic chatbots that will handle a wider range of subjects. Some chatbots are extremely focused, designed to answer only a handful of questions, though natural language understanding enables these chatbots to answer these questions asked in different ways (e.g., “What is my balance?”, “How much is my balance?”, etc.). Natural language understanding is critical for chatbot success, Snell agrees. The chatbot needs to be able to understand the various ways questions are posed, using clarifying questions when necessary. The answers to the clarifying questions can be used to further train the chatbot to recognize the initial question for future similar queries.

The advantage to subject-specific chatbots is that they can be trained and ready to go live sooner than a chatbot handling a larger range of issues, says Cheryl Martin, chief data scientist for Alegion. However, these “point” chatbots also must be able to recognize when the customer is asking about a non-core (for that chatbot) subject, then quickly and seamlessly hand off the call to the correct chatbot for the customer’s new query.

“For each one, the overall umbrella is: ‘Is the current state of the conversation something that you know about?’ If the inference is that the conversation should go to another chatbot, then it is routed to that one,” Schneeman says.

But keep in mind that if a chatbot’s capabilities are too narrowly defined, the complexity of managing multiple chatbots can hinder the efficiency of the automation, according to Snell.

“Organizations can overcome this by establishing a long-term business and IT vision,” Snell says. “This vision should consider how chatbots and associated technologies can support business goals far beyond the first deployment. Additionally, businesses should prioritize the curation of a collaborative culture among their employees. At the end of the day, bringing technology out of its silo will only work if the organization isn’t broken into isolated divisions.”

When designing the chatbot, the company has to determine the logic of the message flow—how the message moves from one step to the next, says Jeremy Pollock, principal product manager for PubNub. “It’s important to have a logic layer separate from the chatbot layer. As you move to a conversational approach, you have a bridge from the chatbot to all of the data.”

APIs provide the necessary bridge to the data, Pollock explains. “You need to look at how you want to route the messages—the different topics and the different channels that you are going to want to interact with. Topology is very important. You need to understand the usage. You need to identify the most active chat customers.”

It’s also important to understand who will be the most active users of the chatbot and develop a topology that serves them best, Pollock explains.

Integrating Your Chatbot

Many chatbot projects fail because they lack the proper integration with ERP, CRM, or other systems where the underlying data lies, so they can’t provide the requested data, or can handle only very limited requests before they need intervention from a human agent, says Simha Sadasiva, founder and CEO of Ushur. “Most are meant to deflect phone calls. Every time you need human intervention, it is expensive.”

It also impacts customer satisfaction when chatbots fail to do their job or to find someone who can. “You need intelligent automation so that the chatbot can do more than just a simple task,” Sadasiva says. “It needs to do more than just understand what the question is about, but also needs to understand workflow and what the customer is interested in.”

A good chatbot will orchestrate conversations without a lot of IT infrastructure, Sadasiva says. “You need to have intelligence embedded within the chatbot.”

Consider too, that a great conversational self-service solution needs to be integrated with multiple systems of record in order to self-serve across your entire site. By including stakeholders from across the organization, you can better ensure the integrity of each department’s role within the overall customer experience.

Go Live, Collect Data

Once a company goes live with its chatbot, the next step is to continue to collect data from interactions to attempt to improve the chatbot, experts agree.

“Modern companies are always monitoring and measuring,” Schneeman says. “They look to optimize, optimize, optimize and test, test, test. They never stop trying to improve. They are always monitoring and expanding what they can do with their chatbots.”

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