Integration with AI chatbots: When to automate & when to keep it personal

Unless you have been living under a rock, I am sure you have interacted with some form of AI chatbot. These conversational AI chatbots are just about everywhere, from Blinkit to Swiggy. If you have approached customer service over chat, it's probably been with an AI-powered virtual assistant. As we all know, the festive frenzy in India is around the corner, and data from a recent study suggests many of the customers expect customer service responses within 10 minutes. This is why chatbots are the most useful tool for businesses of all sizes. 

Oh sure, you must be wondering about the pain that comes along with using these chatbots. This prejudice is only because in the past these chatbots have not been used to their full potential. They have generally been impersonal, limited in capabilities, and very time-consuming to train. This led to most businesses shutting their doors on them even before the market could really exploit their usage. 

Thanks to LLM’s (large language models) and NLP's (natural language processing), we can now have very conversational AI-powered chatbots that can speak to customers without involving any human resources in the background. These conversational chatbots trained with one’s business data and intelligence can provide a human-level interaction to one's customers at any time they need. 

Today many brands have deployed these in various avatars; while some are still identifiable, the newer ones won’t let you believe you are talking to a machine-enabled representative. Even without any person-to-person interaction, there are many ways brands today deploy these chatbots to keep the customer engaged with the brand. 

Take Disney+ Hotstar that has a 24/7 WhatsApp chatbot that has helped them reduce their dependency on call center agents, reduce customers long wait times to talk to someone and improve resolution time for the customers. This is a great example for someone with a large customer base trying to tackle customer grievances by providing a quicker TAT to their problems helping improve customer experience extensively. 

On the other hand, you can see brands drive great customer engagement with the help of conversational bots. Let’s look at Sprite that enabled a great engagement bot with their Joke in a bottle campaign. This WhatsApp bot engaged customers by bringing them LOL’s whenever and wherever they needed them. Engaging customers with a busload of comics that would deliver jokes on demand and also engaging the customers to participate by adding their jokes to win some fantastic prizes.  

While these are great examples customer service chatbots will always top the lists with multiple use cases readily available for it. Take the finance industry for that matter, what stops my bank from providing financial advice to me via a conversational AI chatbot, not only can it help me have a more convenient banking experience but also improve financial management for me. Another great example can be ticketing for travel, a great conversational bot could act as a travel agent understanding your needs and giving the most apt solutions for them by providing the most suitable ticket options for your flights to recommended itineraries for planning your stay.

But what makes a great AI chatbot? 

Conversation design 

Planning how your chatbot will interact with your customers is extremely important. It's always advised to keep it conversational rather than scripted. Always make the customer feel you are there for them and understand their needs or their queries rather than offer stock responses and alienate them. With NLP today, it's possible to keep the language easy and away from misinterpretation,  and also the responses can be very detailed to achieve customer satisfaction. 

Understanding Intent 

It is important that your chatbot can read between the lines of what the customer is trying to say.  Training your bot to identify keywords to understand message intent is imperative to getting the right responses. 

Keeping up with the conversation 

A wise chatbot never forgets; it retains customer information from previous queries and offers new advice to keep the conversation going rather than looping in circles. 

Adding voice 

A human voice always helps with establishing the intonation of the message rather than reading it.  This can be done both ways by using customer voice as a voice-to-text input for the chatbot as well as the responses being completely in voice. 

Language capabilities 

Understanding the customer in their language can make them feel comfortable and confident that the support understands them. With multilanguage capabilities today, chatbots are capable of not only understanding intent in a multitude of languages but also providing comprehensive solutions free from misinterpretation. 

Constant training 

The more training data that keeps becoming available with time only helps better the responses from your AI Chatbot. One should constantly track performance and keep adding to the learning of the chatbot to help improve performance that impacts customer experience in the best possible way. 

The world today is already accustomed to conversing with bots, some with preset responses,  some more conversational. The future lies in personalizing these conversations to take the AI  chatbots one step closer to understanding the customer they chat with. Intent apart, I wait for the day when bots will understand the emotion behind the conversation and choose to respond more wisely!

This article is penned by Raunaq Sikka, Creative Director, Hogarth India 

Disclaimer: The article features the opinion of the author and does not necessarily reflect the stance of the publication.