Key behind building personal relationships with clients

Emergency or uncertain situations cause call volumes to spike and reduce customers’ appetites for “your call is extremely important to us. Please wait while we connect you to a representative’s voicemail messages. This is where AI will be a game-changer for businesses around the world. According to Gartner, around 70% of interactions with customers will see the involvement of technologies such as machine learning, chatbots and mobile messaging, etc.

In the normal post-pandemic period, the need to humanize and accelerate customer engagement increases exponentially. Today, customers expect brands to be accessible anytime, anywhere, and to have a well-equipped support system to quickly resolve issues or answer questions. It’s a need conversational AI is supposed to meet. It looks like a human and understands the client’s speech with almost human precision. As a result, customer conversations become more personalized and engaging even though callers are actually talking to a computer. Conversational AI assistants respond with the same efficiency and precision to text and voice commands and give users the freedom to choose whether they want to talk or chat. Business leaders take notice of development and quickly embrace technology. With a CAGR of 21.9%, the conversational AI market is expected to reach $ 13.9 billion by 2025.

AI is now moving beyond the simple power of verbally responding to commands and acquiring the ability to hold out in deeper, multi-level conversations. The emergence of natural language processing has made it easier for AI customer service systems to understand what is being said faster and better. It’s not uncommon for the AI ​​to independently handle the entire conversation without needing to redirect the call to a human agent.

The best part is, the higher the frequency of conversations the AI ​​system has with humans, the better it is at understanding and engaging them. Machine learning algorithms developed by leading voice AI brands ensure that every conversation is used as data allowing the AI ​​system to understand nuances and predict the correct response. Whether it’s monitoring a customer’s words to understand intent or using past data to deliver personalized content and suggestions, AI keeps getting better and better. By continuously processing more data, AI algorithms self-learn and improvise to proactively make suggestions and display contextual awareness. When he gets the correct rating and receives consumer satisfaction as a response, he tries to repeat the same approach with other callers, and if a response does not thrill the customer, then it is avoided with the next caller.

One of the biggest challenges in the universal adoption of voice AI to drive personalized conversations was the reliance on English as the language of communication. Moreover, even the use of spoken English has proven to be complicated for AI due to the diversity of accents, manners and styles. An American would speak English differently from a Briton. Likewise, an Indian from the south of the country would have a different accent compared to an Indian from the north. All of these accents are valid and should be treated the same. However, this never really happened because AI assistants were trained in specific accents. Today, natural language processing capability removes these accent or language barriers and takes into account the characteristics of the spoken language such as intonation, vocal energy, silence, pauses, and usage. words, etc.

Equipped with this power to respond to the diversity of languages ​​and dialects, the AI ​​voice assistant is ready to be the best friend, an impartial advisor and even a personal shopper depending on the use case. This is the future we are heading towards.

The article was written by Tapan Barman, Co-Founder and CEO, Mihup

Joseph P. Harris