Not All AI Is Created Equal: Five Things to Consider When Building, Deploying and Testing Your Conversational AI Chatbot

Whether or not you realize it, AI has taken a powerful hold on the way people communicate with businesses. 85 percent of customers already prefer to interact with a chatbot for customer service — in other words, consumers already want conversational AI. These AI-powered chatbots provide a superior customer experience through their ability to utilize Natural Language Understanding and Processing (NLU/P), which allows chatbots to accurately understand humans, decipher what they’re saying, and generate a human-like response, regardless of channel and in both voice and text. In fact, this technology has become so accurate that 27 percent of consumers aren’t sure if their customer service was provided by human or a chatbot.

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But the key to doing this well lies in the ability to do it accurately. The first generation of AI chatbots lacked the sophistication to truly understand and respond to their users, resulting in frustration and hesitancy on the part of consumers. That’s why it’s especially crucial for customer-facing bots to now produce near-perfect accuracy.

So what can you do to make sure you build, deploy, and test your chatbot to ensure you’re putting your best bot forward? Keep these five considerations in mind to deliver an accurate, personalized customer experience:

Your chatbot should utilize NLU/NLP for user utterances and intents

Ideally, an intelligent chatbot should understand the semantic meaning of a user’s statements, as well as detect the user’s sentiment throughout a conversation. A successful interaction would include understanding the nuances in the customer’s request, showing empathy for the customer’s need, and delivering a personalized conversation in resolving the issue. Accurate NLU/NLP will also identify when the bot has not correctly identified the intent so it can hand off to a human agent to resolve. In order to do that well, the NLU/NLP should not only interpret the user’s intent, but also have the ability to overlay contexts, such as past purchases and customer satisfaction history, and identify possible areas of support.

Bots should support different languages, natively

AI-powered chatbots are capable of enabling 24/7 support in all major languages to connect with wider, global customer bases. It is imperative in this competitive global market that chatbots deliver a seamless customer experience for consumers regardless of the language they speak or where they are located.

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Use Machine Learning to continually improve the conversational experience

Though you have countless conversations every day, language is complex. A simple statement can be expressed with slightly different wording, intonations, and punctuation to have vastly different meanings. Due to this, AI chatbots have to master several different types of observation in order to have a successful conversation with a user. Thankfully, intelligent chatbots leverage machine learning to continually understand and adapt to these subtle nuances and variations in speech pattern. This ultimately provides more context and allows a chatbot to hold a context-based conversation that better identifies an individual’s intent. The more context it can capture, the better the AI’s back and forth dialogue with a customer will be.

Enable integration between the chatbot and your CRM system

For a complete understanding of a customer’s background and needs, a chatbot must work hand in hand with your CRM system. In the omnichannel world, traditional marketing funnel is less relevant. The customer journey occurs on any number of channels, voice or text, in-store or online. A customer may have placed an online order to pick up a purchase at a physical store location and calls the customer support line for help. To enable the most accurate conversation, it’s crucial for the chatbot to have access to the complete customer context across the different touch points.

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These days, a personalized greeting with the customer’s name and proactive order detail recall is expected as a basic requirement for a satisfactory customer experience. If the bot is able to anticipate possible issues with known orders, it can quickly navigate the customer towards a personalized set of choices or automatically trigger a workflow. Accuracy, here, will help build the customer’s confidence— in both in the bot and the business.

Establish a testing method for your chatbot

Because it has become crucial for customer-facing bots to produce near-perfect accuracy, the testing phase is a key factor in the success of utilizing an AI chatbot for your business. Testing a chatbot is more complicated than testing standard software because advanced capabilities like NLU/P and voice recognition require constant training and improvement — making it a continuous process that should be designed to include the continuous changes and advancements made in the industry.

To test for a bot’s ability to accurately interpret slight nuances while still initiating the correct sequence of interactions, first capture an ideal interaction – in other words, a conversation between a user and a bot with zero errors. Once captured, replace each user message with an equivalent message from a different user. The bot’s responses should remain the same, regardless of which equivalent statement has been inserted. The bot should also detect when a non-equivalent statement has been inserted and should change the conversational sequence accordingly.

Ultimately, it’s the interaction between the customer and the business and the perception of how the business treats them is what leads to customer retention and loyalty— but you can’t do this well without the accuracy you get from continually testing your bot.

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