Insights for training AI chatbots
This post is written by DJ Moody, Client Strategy Analyst at Robots & Pencils, and inspired by a recent experience helping one of our clients with creating and training an AI chatbot.
“Alright listen, Sparky…”
My father tends to be laid-back. However, there are clear indicators when he’s getting frustrated. These are so predictable that the whole family knows when a customer experience goes awry. First, he refers to the person as “Buddy”. If the situation doesn’t improve, they soon become “Sport”. With each step down this path of names, the chances the agent will keep the business drop precipitously. The final moniker is “Sparky”. No one comes back from Sparky.
It’s a familiar experience. A customer service discussion goes poorly. We become frustrated. Eventually, if it’s bad enough, we decide to spend our money elsewhere. After those conversations, our opinion of the company changes. We blame the business.
In comparison, the same isn’t true with static content. If we can’t find an answer in an instruction manual, we get a little annoyed, but our view of the company rarely shifts. Yet when we talk with a person who doesn’t have the answer, frustration bleeds over into our impression of the company in a much different way. A human failing tells us the company failed. The company doesn’t care. Basically, the company is bad.
More rare, but more powerful, is the successful customer service conversation. The one where you feel heard afterward, where the other person takes time to understand your need and works to get it addressed. Even if you don’t get the answer you want, you gain a positive perspective on the company. The experience tells us the company is good; they put resources into their customers. They care about us. Reading helpful documentation doesn’t offer that same gut-level reaction.
It takes a person for something to feel personal.
Chat-based AI attempts to mimic a conversation with a person. This brings with it many of the benefits of having a human involved. Markedly, it is an opportunity to show the customer you care and to develop a personal connection. This also opens up a lot of the dangers. It takes a person for something to feel personal, and we are creating a digital facsimile of a person. So the question becomes, how do we make our fake person seem genuinely caring?
Artificial intelligence, real heart
Experiences can be designed, whether on a website, in an app, or with a healthcare provider, sales professional, or customer service agent. The major difference between a digital product experience and a human experience is flexibility. The former we expect to be relatively static. It’s primarily up to us to find the right page or tool. The latter we expect to change based on our actions. If we’re angry we expect a person to show empathy and change their approach. We don’t expect an app to understand us; we do expect that of a person.
As with all programming, what you get from a chat experience depends on what you put into it. We tend to focus on training AI chatbots with the right data. Undoubtedly, we want them to have the correct answer to any question they might be asked. That might be enough if users interacted with them as they do a static resource. However, we want the benefits of AI accuracy and efficiency with the personal connection of human interaction. Just as the best human communicators have training in both information and interpersonal skills, we need to include the same when training AI.
3 takeaways for training AI chatbots from the intersection of science and the humanities
1. Legitimize the need.
Above all, we want customers to know we care. Like compassionate humans, our chatbots can show this care by simply acknowledging there is a need and a desire to help. We don’t have to promise we can solve the problem. At this point, we just need to make it clear we know there is one. If sentiment analysis is available, adding that detail is even better.
| Standard – All business | Better – legitimize the need | Best – legitimize with sentiment analysis |
| What solution do you want? | I understand that this is a problem for you and would like to help. | I can see that you are frustrated and would like to help. |
2. Confirm and clarify.
Great human communicators reflect back to the customer what they understand about a request and ask questions to clarify. Communication is messy and questions clean it up. Our bots need to do the same.
| Standard – problem statement | Better – clarifying question | Best – confirm and clarify |
| I don’t understand. | Can you clarify your question for me? | I understand you want information about your widget. Can you tell me more about what you are trying to do? |
3. Set and reinforce next steps.
One of the more frustrating parts of customer service, for both the consumer and agent, is when a solution is offered but missed steps cause it to fail. We can reduce this likelihood by summarizing the discussion, including all steps, before signing off.
| Standard – problem statement | Better – restate the solution | Best – reinforce and confirm understanding |
| Thank you for your question. | Thank you for your question. Remember, your next steps are to turn off the router, wait at least 10 seconds, and turn it back on again. | I appreciate your inquiry. To recap, your next steps are to turn off the router, wait at least 10 seconds, and turn it back on again. This will reset the system and help you get back online. Do you have any questions about those steps, or is there anything else I can help you with today? |
Developing and training AI solutions for your customers
These are just a few of the many ways your AI solutions can reflect the care and compassion you have for your customers. In order to fully accomplish our goal of more human interactions between AI and customers, we must carefully consider each solution from all sides. Poor communication can make even the most well-informed agent–human or bot–seem cold and uncaring. Conversely, bad data means even the best-behaved agents will be useless. For truly effective agents, those who can increase both customer knowledge and sentiment, we need to live at the intersection of science and humanity.
Every team developing a chatbot should include specialists in data science and AI as well as experts in communication, user experience, and customer care. In the case of Robots & Pencils, our Robots handle the code, our Pencils ensure an excellent experience, and our Ampersands bring it all together. To learn more about how Robots & Pencils helps organizations approach AI technology with a human-first approach, visit our AI & data science page or drop us a line at hello@robotsandpencils.com.
