Delivering a good customer experience requires human-like qualities such as empathy – understanding the intent of the users in order to deliver exactly what they want, and compassion – putting the customers above any limitation that may negatively influence the user experience.
These two qualities help build an emotional connection and trust, therefore, enhancing the customer experience. Understanding the user’s intent is important today because customers have higher expectations and are careful about where their money goes. The availability of many choices has also made it a key requirement to stand out in every aspect of user experience.
Conversational AI has many obvious benefits. Yet many users are hesitant about using chatbots even though it has replaced a majority of the front-line support today. One of the biggest difficulties people have with chatbots is the unclear expectations that most of the users have. Many chatbots do not deliver immediate resolutions to issues and instead suggest other generic questions that do not resolve the customer’s query.
Another prominent reason is the bad experiences customers have had or heard about automated conversations. A survey by James E. Katz showed over 90% of people prefer talking to a human correspondent, 35% find automated calls difficult to communicate with, and only about 10% were satisfied with the calls.
As humans, we enjoy interactions when the other person is listening and is willing to help. We like building trust with others as a means of connecting with other individuals. This domain is difficult for chatbots to imitate. Therefore, people feel a sense of security and trust hearing responses from a live agent compared to chatbots.
Some of the other issues people usually face with automated chatbot conversations include:
Humans are social creatures that thrive on empathy. We discover more about each other through empathetic dialogue. We build conversations and arrive at decisions with emotions at its base. From a marketing perspective, this can be seen along the buyer’s journey since emotions have an enormous impact on our behaviour. People are much likely to stay loyal to a brand when they have had positive emotional associations with the brand in the past. Therefore, if brands want to improve their customer experience, they need a system that is intuitive, learns from experience, senses intentions and understands both the cognitive and emotional pathway of human conversations. This is exactly what conversational AI aims at.
With the growing dependency of humans towards technology, there are studies that investigate how empathetic chatbots affect the users’ moods positively and on how AI assistants are even used to make people feel less alone. This proves that it has become critical to code empathy into machines as AI advances more and more towards imitating human conversations.
Conversational AI can cater to many use cases in businesses that require empathy. For example, a customer is trying to buy an insurance plan for his family and communicates with a customer service AI bot. The AI chatbot would be able to conversate with different tones and inflections depending on the conversation. Based on the customer’s responses, the empathetic chatbot can also sense complex emotions such as frustration, happiness or satisfaction and respond accordingly. In short, instead of feeling like you are talking to a robot, the experience of talking to an AI powered chatbot can be a lot more personal.
Apart from this, conversational AI offers a wide selection of options and applications compared to traditional chatbots. For starters, conversational AI can address both voice and text commands compared to chatbots that can respond to only text commands. This makes it easier for people to communicate with conversational AI fostering a better customer connection.
Regular chatbots are limited as they function exclusively in chat interfaces, whereas conversational AI systems are omnichannel, meaning they can be accessed on websites, and through calls, smart speakers, and voice assistants. In all of these applications, conversational AI has the same features and is often capable of continual learning and fast iteration to provide empathetic responses to customers.
Here’s an example of a mechanical response from a regular chatbot.
Whereas, Here’s how a Conversational Chatbot would respond
This is just a classic example of how conversational AI is here to be the holistic alternative for chatbots.
Check out some of these examples of conversational AI with individuals.
Someone is trying to cancel an order they placed.
Someone trying to decide which saucepan they should buy.
Understanding the user intent, remaining in the context of the discussion, and providing the users exactly what they need are some of the crucial components of Conversational AI and they need to be constantly optimized. Machine learning makes this easier for conversational AI by using valuable insights from past conversations to shape future conversations. Conversational AI solutions are advancing rapidly, and continue to become more humanistic – to the extent that someday chats between conversational AI and humans will become indistinguishable and that day is fast approaching.