2020: the year of the customer service chatbot

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Image by Mohamed Hassan from Pixabay

Turn back the clock a few years, and Gartner made a few bold predictions about the year 2020. It started in 2011, when they foresaw 85 percent of all customer service interactions would be handled by chatbots. Seven years later, they forecasted that over 50 percent of medium to large enterprises would have chatbots deployed by this year.

2020 is here. In the years leading up, the results of the use of chatbots have varied in terms of perceptions and success. And while response may be speedy with them, different customer segments have diverse expectations. Cutting-edge and rapidly-evolving technology is going to have both growing pains and naysayers, and this has led to some understandable adoption paralysis.

With months still ahead, 2020 may still be the year of the chatbot. For those companies still coming onboard, the following best practices help maximize results.

Identify problems and goals

New technology is adopted to solve a problem, so that’s where to begin. Identify the customer problems best solved by a chatbot. The higher volume, more common, and less complex issues that consume customer service’s time are the best starting point.

Don’t neglect to set the success metrics for the chatbot when the types of issues to solve have been selected. Is the goal to have a certain percentage of the overall service volume addressed by the chatbot? What percentage of the selected issues are expected to be handled by the chatbot vs. other channels? What are the expectations for metrics like mean time to resolution with the chatbot?

Performance metrics are important, but don’t stop there: measure customer satisfaction. Get customers’ feedback as they use the chatbot. Some common questions to ask include: was the issue resolved efficiently? Did they enjoy using the chatbot? Would they use it again?

Sprinkle in personality

A fast, efficient chatbot interaction for the customer doesn’t mean the experience must be, well, robotic. It’s more likely customers would consider using it again if it has some personality.

Simply providing the chatbot with a name is a start. That name could be playful or more formal, depending on your company or brand style. With a name, there’s a degree of personification that eliminates some of the robotic nature of the interaction.

Next thing to consider: tone. Should the chatbot converse in a polite, professional manner or is it more relaxed? Create and maintain an interaction style guide to ensure the chatbot’s conversations stay consistent and on-brand while avoiding anything that might be confusing or off-putting to customers.

Powered by solutions

Personality is important, but at the end of the day, customers want solutions as quickly as possible. Much of customers’ frustrations with chatbots lies in their inability to get to the right answer efficiently. For that reason, successful chatbots are those that focus on resolving a defined set of problems with proven solutions.

Solutions offered by chatbots can tap into other available self-service options. When many steps are involved, having the chatbot refer customers to a knowledge base article makes it easier for customers to perform them. Directing to automated solutions–forms to submit information or to perform other automation–is also ideal.

Teamed with humans

Implementing the chatbot to solve a defined set of problems is critical. That also means it should “know” its limitations. It should be able to identify the point at which it has no solution and cannot assist the customer.

This is important because today’s chatbots lack the ability to reason and troubleshoot as a human being can. For the scenarios the chatbot is unfamiliar with, live customer service agents should be standing by and ready to step in. Asking the customer unrelated or redundant questions does not value their time and creates frustration.

But simply transferring the customer conversation from chatbot to human is not enough. All of the customer details and the context collected so far–name or identifying information and a transcript of the interaction including any possible solutions offered–must be included in the hand-off. When this happens, it isn’t necessary for the customer to repeat already-shared information. (Remember: value the customer’s time!) When agents aren’t available due to high volumes or it’s outside business hours, the chatbot should offer to create a case for follow-up by a customer service agent at a later time.

Understanding language

Chatbots match the keywords and phrases used by customers with the solutions they can provide. Humans can express themselves in a myriad of ways, and that can create challenges for chatbots–they struggle with nuance that humans easily adapt to.

Technology continues to improve in this area, and modern chatbots use Natural Language Understanding (NLU) to provide a better conversational experience. With NLU, a chatbot understands customer statements by using models to determine what the customer wants to do and to extract relevant values from their input. NLU also means the chatbot can offer a more natural and engaging conversation.

Even with NLU, not every chatbot conversation will succeed or may take longer than necessary. Regularly review chat transcripts of both successful and unsuccessful interactions. What words and phrases did the customer use? Apply that information to improve the chatbot, both the solutions it currently addresses as well as insights into what might be future additions to the chatbot’s solution library.

A chatbot is in your future

The number of customer service chatbots continues to rise, and companies are seeing the benefits. They are providing fast, efficient answers to the most common problems while agents focus on the more complex.

2020 has been called the year of the chatbot, and more and more successful examples will continue to come to light. It’s clear that with the right technology and by taking a measured and thoughtful approach, a chatbot can be a productive and valuable member of the customer service team.

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