Smarter Bots, Smarter Humans: The Virtuous Cycle of Human-Bot Collaboration
As technologists, we’re always looking for ways to make our technology better, smarter, faster.
As AI evolves, the tech industry is learning how bots can fail humans; whether it’s based on humans’ inherent distrust and fear of robots like the uncanny valley or the problems presented by artificial ignorance, which reinforce harmful tropes like gender bias.
So, when developing chatbots at my company, Mezi, the team looked at human psychology and the personality traits that facilitate better communication. Then, we studied how we could improve everyday interactions between our customers and our chatbots.
We found out that 52% of consumers believe that AI has a positive effect on their lives and we wanted to make that experience even better. From that jumping off point, we started focusing on what elements of machine learning make collaboration possible and researching how people interact with customer service professionals, using our human agents as our test subjects to help us refine our product.
Below are some guiding principles on how to develop bots that facilitate, not prevent, better communication with humans.
When Teachers Become the Students
The Zen teacher and monk Shunryu Suzuki said “In the beginner’s mind there are many possibilities, but in the expert’s there are few.” Suzuki was a thinker with a huge influence on Steve Jobs, and like Jobs, tech leaders like Marc Benioff and Jeff Bezos believe that the road to innovation is paved by keeping an open mind and not letting your presumptions guide you. I do my best to challenge my own assumptions, and like any good student, I know that any team can only develop a better bot by doing their research.
So how can you start researching building a better bot? First, have your engineers and data scientists analyze popular words and phrases that your users would typically look-up with your technology. Then use that as the basis for creating your “virtual persona.”
When creating your virtual persona, that persona should feel human — not like it’s spitting out words that sound programmed. The more human your bot sounds, the more of a personalized of an experience you create for your users.
Which leads us to…..
Model the Bot After Yourselves
I’ve mentioned before that in order to create a bot that increases customer engagement, you should design a bot that is more human. It’s not just the element of humanity that matters, but recreating an element of surprise and joy to delight your customers to inspire loyalty and lead to repeated transactions.
Usage of emojis on Instagram increases engagement, but that phenomenon is not limited to social media, it applies to all forms of communication. Think about it, aren’t you more willing to engage with texts from friends and family when sharing humorous anecdotes that are personalized by emojis? That is a compelling reason for any company to start using emojis in chatbot communication.
But don’t just let anecdotal evidence inform your decisions, make sure you have the research to back it up. Once you do your research, you’ll discover that people react to emojis like they do to real smiling faces and that emojis break the barrier of “understanding tone through text.” In fact, many psychologists believe that emoji use allows us to communicate better, and gives text a human element to it. By using emojis, you can develop AI to have a familiar and friendly voice.
So when developing your own bot, identify elements of speech that you responded well to, and start asking your customer service team to replicate that type of speech and communication with your customers. For us, after implementing emojis with our bots, we immediately noticed an uptick in engagement and customer satisfaction, and we’ve been using the same model to inform how our chatbots speak to customers ever since.
Avoid Your Impulse for Instant Communication
Tension between human and bot interactions stems from the feeling that a bot seems too smart, which makes users think, “How do they know that about me?” The “big brother” element of human and bot interactions can make people ask themselves: “Are you watching me?” and “What are you doing with my data?”
For most people, over-communication or rapid-fire communication makes you feel like you’re being harassed or you’re not being listened to. Although this might sound counter-intuitive, when a customer has a question they don’t want an immediate response to it, or else they feel like the service is too intrusive. This generates distrust around that interaction.
If you allow some time before your chatbots responds to your user’s request, you’ll find that your customers are more likely to ask more follow-up questions related to the other customer services that you provide. Instead of one transaction, customers can get all of their needs met at the same time from your technology.
It’s important to take IRL learnings and apply them to machine learning. By researching and testing how your customers prefer to communicate, you’ll be able to develop a better way for your bots to anticipate and respond to customers’ future requests and collaborate with users.
The more you model your bots and AI after your own communication style, the more naturally your bot will communicate and successfully deliver on the promise of how bots and people can work together for the best customer experience.
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