Voice Bots 101: A Crash Course in Designing Bots that Speak Customers’ Language

Companies across sectors are leveraging artificial intelligence (AI) and machine learning technology to introduce chatbots, help bots, and digital assistants to improve customer satisfaction and operational efficiency, and users are increasingly opting for bot assistance over human help. Earlier this year, Insider Intelligence found that 40 percent of users prefer chatbots to live support when given the option, and the global chatbot market size is expected to expand at a compound annual growth rate of 26 percent until 2030 (according to a Grand View Research report).

Even so, users know that interacting with bots can pose myriad challenges and lead to frustrating miscommunications—which comes down to engineering. Although chatting seems straightforward on the surface, humans have a tough time defining the “rules” of interaction. Getting bots and humans to speak the same language is an incredibly complex task—and it’s one that many customer experience (CX) designers underestimate.

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The tricks of translation

To people, conversations seem straightforward. Language feels concrete, and interactions flow in a natural cadence. That’s not the case.

The “natural” flow of human interactions results from years of implicit training through interactions with others. The meaning and connotations of words are similarly fluid as language changes to keep up with modern sensibilities. Human conversation is defined by empathy, context, and experience. Bot logic, on the other hand, is primarily defined by the analysis of structured data and the associated interpretation of the query. They only understand the context of words as far as their programming allows.

Even so, keeping a few key features of human communication in mind can ease the process, so companies design bots that help rather than hinder customer interactions. Here are three things to keep in mind when structuring bot customer service conversation:

1. Conversations are balanced:

Conversations are an active give and take between two entities. A good conversationalist won’t deliver a long-winded monologue without interjections from others. One of the critical mistakes many screenwriters make when scripting dialogue is letting a character say too much at once. The same is true when designing conversational AI.

Users respond well to balanced conversations, so turn-taking is key. People are quickly turned off by bots that speak for too long without offering an opportunity for the user to respond or digest the information. Keeping responses concise helps users feel more connected to the interaction. If they feel disconnected from the experiences, they may walk away without the information they needed in the first place.

2. Conversations are active:

Prioritizing balance is also an excellent way to ensure that conversations remain an active experience for the user. One way to do this is by eliminating dead-end responses. In improv, participants are encouraged to “yes, and” during scenes. In other words, you’re supposed to accept your partner’s world-building while bringing a new element to the table. The most effective bots operate similarly: They phrase responses in an open way that encourages additional inquiries. Offering options and additional, relevant choices can help ensure all customer needs are met.

That said, designers should consider user limitations when designing menu dialogues or lists of options. Many people have trouble remembering long strings of options, so keeping brevity top of mind can help keep the conversation balanced and active. Breaking lists into shorter chunks can help mitigate the risk of overwhelming users or wasting their time by making them listen to options that are irrelevant to them. Offering targeted “barge-in” programming (chances for users to interrupt the bot) can do the same.

Finally, tapering the bot’s responses—editing out recurring information in repeated dialogue—can help keep conversations active, balanced, and satisfying to customers who may get fatigued by repeated strings of longer explanations.

3. Conversations are built on rapport:

It’s unlikely that a voice bot will trick users into thinking it’s a person, but that doesn’t make personal touches fruitless. When possible, bots should begin interactions by greeting the customer in a familiar and friendly way—and they should introduce themselves when they first “meet” a user. Introductions form the basis for familiarity and rapport throughout prolonged interaction.

Of course, developing familiarity takes more than an introduction or learning someone’s name. We discussed how humans come to learn and expect specific cues during communication, and bots can do the same. The data they use may differ, but the need for practice, experience, and learning is the same.

Companies with holistic, comprehensive data collection programs can go above and beyond in this endeavor. Leveraging information from other channels—like chat, live phone calls, account history, and more—to personalize conversations to customers’ needs. Some even succeed in using this information to better anticipate requests and frame responses in ways that will resolve the customer’s query more efficiently.

Putting it together

Although these points seem obvious in hindsight, evaluating the factors that support effective communication is a monumental task. Human interaction is nuanced and building bot conversational design that can navigate its complexity is an ongoing challenge. However, once achieved, well-structured conversational design can lighten the load on customer service teams, improve customer experiences, and help customers connect with your brand.

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