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Chatbots & Conversational Commerce Impact CX

 Customers want to connect with their favorite brands, and smart brands understand that customer engagement is key to building long-term customer satisfaction and loyalty. Good customer engagement builds an emotional connection between a customer and a brand, leading to increased frequency of purchases, higher average spending, and a much higher propensity to choose that brand in the future.

There are many avenues of customer engagement, but at the heart of it is personalization. Customers want to feel like their voice is being heard, that they’re important and valuable to the company. We see this every day in the social media world, where customers can speak directly to brands on Twitter and Facebook and get personal attention that may be lacking in other channels. But even as fast as social media moves, it’s still a non-real time interaction and doesn’t guarantee a response.


With the meteoric rise of messaging applications over the last few years, we’re entering into the era of “conversational commerce”. According to Chris Messina, “conversational commerce (as I see it) largely pertains to utilizing chat, messaging, or other natural language interfaces (i.e. voice) to interact with people, brands, or services and bots that heretofore have had no real place in the bidirectional, asynchronous messaging context. The net result is that you and I will be talking to brands and companies over Facebook Messenger, WhatsApp, Telegram, Slack, and elsewhere before year’s end, and will find it normal.” And much of these conversations will not be with a human. Thanks to a corresponding explosion in bot frameworks and NLP (natural language processing), chatbots are enabling a new wave of customer engagement that is both self-service and personalized.


The ubiquity of voice chatbots from some of the largest brands in the world like Apple (Siri), Google (Assistant), Amazon (Alexa), Microsoft (Cortana), and Samsung (Bixby) have smoothed the way for consumer acceptance of human-machine interaction, taking it from science fiction to something so maintstream that it’s table stakes for any mobile device. And some of those tech giants are positioning themselves to do the same thing for messaging bots, rolling out APIs and frameworks that combine machine learning and NLP for building chatbots on a plethora of messaging platforms. Facebook (Bots for Messenger), Google (Parsey McParseface, TensorFlow), Amazon (Lex), Microsoft (Microsoft Bot Framework), and IBM (Watson) have all jumped into the scene, along with dozens of smaller companies eager to power the chatbot revolution.

With all of the excitement around chatbots and their potential for customer engagement, we shouldn’t lose sight of the goal: a positive customer experience. These tools allow for rapid experimentation with very little overhead, but that can also lead to trouble. Which is why a well thought out strategy that is responsive to analytics data is critical to the success of any chatbot endeavor. With the right strategy driving them, chatbots enable fantastic new opportunities for customers to communicate with brands, with good interactions that deliver a great customer experience.