Chatbots

There is often confusion between the differences between virtual assistants and chatbots. Keep in mind that there is much overlap between the two. Both use NLP to interpret language and perform tasks.

But there are still critical distinctions. For the most part, chatbots are focused primarily for businesses, such as for customer support or sales functions. Virtual assistants, on the other hand, are geared for essentially everyone to help with their daily activities.

As we saw in Chapter 1, the origins of chatbots go back to the 1960s with the development of ELIZA. But it was not until the past decade or so that this technology became useable at scale.

Here’s a sampling of interesting chatbots:

• Ushur: This is integrated in the enterprise systems for insurance companies, allowing for the automation of claims/bill processing and sales enablement. The software has shown, on average, a reduction of 30% in service center call volumes and a 90% customer response rate.²⁵ The company built its own state-of-the-art linguistics engine called LISA (this stands for Language Intelligence Services Architecture). LISA includes NLP, NLU, sentiment analysis, sarcasm detection, topic detection, data extraction, and language translations. The technology currently supports 60 languages, making it a useful platform for global organizations.
• Mya: This is a chatbot that can engage in conversations in the recruiting process. Like Ushur, this is also based on a home-grown NLP technology. Some of the reasons for this include having better communications but also handling specific topics for hiring.²⁶ Mya greatly reduces time to interview and time to hire by eliminating major bottlenecks.
• Jane.ai: This is a platform that mines data across an organization’s applications and databases—say Salesforce.com, Office, Slack, and Gmail—in order to make it much easier to get answers, which are personalized. Note that about 35% of an employee’s time is spent just trying to find information! For example, a use case of Jane.ai is USA Mortgage. The company used the technology, which was integrated into Slack, to help brokers to look up information for mortgage processing. The result is that USA Mortgage has saved about 1,000 human labor hours per month.²⁷

Despite all this, chatbots have still had mixed results. For example, just one of the problems is that it is difficult to program systems for specialized domains.

Take a look at a study from UserTesting, which was based on the responses from 500 consumers of healthcare chatbots. Some of the main takeaways included: there remains lots of anxiety with chatbots, especially when handling personal information, and the technology has problems with understanding complex topics.²⁸

So before deploying a chatbot, there are some factors to consider:

• Set Expectations: Do not overpromise with the capabilities with chatbots. This will only set up your organization for disappointment. For example, you should not pretend that the chatbot is a human. This is a surefire way to create bad experiences. As a result, you might want to start off a chatbot conversation with “Hi, I’m a chatbot here to help you with…”
• Automation: In some cases, a chatbot can handle the whole process with a customer. But you should still have people in the loop. “The goal for chatbots is not to replace humans entirely, but to be the first line of defense, so to speak,” said Antonio Cangiano, who is an AI evangelist at IBM. “This can mean not only saving companies money but also freeing up human agents who’ll be able to spend more time on complex inquiries that are escalated to them.”²⁹
• Friction: As much as possible, try to find ways for the chatbot to solve problems as quickly as possible. And this may not necessarily be using a conversation. Instead, providing a simple form to fill out could be a better alternative, say to schedule a demo.
• Repetitive Processes: These are often ideal for chatbots. Examples include authentication, order status, scheduling, and simple change requests.
• Centralization: Make sure you integrate the data with your chatbots. This will allow for more seamless experiences. No doubt, customers quickly get annoyed if they have to repeat information.
• Personalize the Experience: This is not easy but can yield major benefits. Jonathan Taylor, who is the CTO of Zoovu, has this example: “Purchasing a camera lens will be different for every shopper. There are many variations of lenses that perhaps a slightly informed shopper understands—but the average consumer may not be as informed. Providing an assistive chatbot to guide a customer to the right lens can help provide the same level of customer service as an in-store employee. The assistive chatbot can ask the right questions, understanding the goal of the customer to provide a personalized product recommendation including ‘what kind of camera do you already have,’ ‘why are you buying a new camera,’ and ‘what are you primarily trying to capture in your photographs?’”³⁰
• Data Analytics: It’s critical to monitor the feedback with a chatbot. What’s the satisfaction? What’s the accuracy rate?
• Conversational Design and User Experience (UX): It’s different than creating a web site or even a mobile app. With a chatbot, you need to think about the user’s personality, gender, and even cultural context. Moreover, you must consider the “voice” of your company. “Rather than creating mockups of a visual interface, think about writing scripts and playing them out before to build it,” said Gillian McCann, who is head of Cloud Engineering and Artificial Intelligence at Workgrid Software.³¹

Even with the issues with chatbots, the technology is continuing to improve. More importantly, these systems are likely to become an increasingly important part of the AI industry. According to IDC, about $4.5 billion will be spent on chatbots in 2019—which compares to a total of $35.8 billion estimated for AI systems.³²

Something else: A study from Juniper Research indicates that the cost savings from chatbots are likely to be substantial. The firm predicts they will reach $7.3 billion by 2023, up from a mere $209 million in 2019.³³