How Brands Can Future-proof Their Customer Support Strategy With Conversational AI

The clock starts ticking the moment a customer contacts a company for assistance. It will not stop until the customer has received a satisfactory response

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Building delightful and personalised customer engagement at scale, is the Holy Grail for any customer support team. The goal is straightforward: the more positive a customer journey is, the more invested a customer is in a brand, the less likely they are to shop around for a better deal. This customer engagement chain is long and twisted, starting with the first interaction and continuing past the point of purchase.

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The clock starts ticking the moment a customer contacts a company for assistance. It will not stop until the customer has received a satisfactory response. In a nutshell, that clock tracks ‘time to resolution’, a popular customer service metric. Customers are most aware of this personal connection when something goes wrong and they seek assistance. Until recently, customer service was regarded as a cost that had to be borne, which frequently resulted in unsatisfactory interactions between the parties. The adoption of digital services by both users and businesses has elevated customer service to an entirely new level.

As the world moves to digital experiences, companies expect customer support to engage customers and drive meaningful interactions. Chat conversations are becoming more popular. In 2021, there was an increase of 80 per cent in the number of new businesses (both enterprise and small businesses) that used conversational AI. However, this is still insufficient for a customer. According to PwC's "Global Consumer Insights Survey," consumer behaviour is rapidly changing. Customers are now focusing more on digital, health and sustainability trends, which have been accelerated by the COVID-19 outbreak. Customers' purchase journeys must be reinvented by consumer-facing companies and retailers.

Today, improving customer relationships entails more than just providing excellent customer service. Through customer engagement, organizations must improve the customer experience in order to strengthen their loyalty to the brand. It must be clear that buyers' relationship is valued and not just about the money they spend. New technologies that focus on improving the customer experience and, as a result, driving business success are critical. According to HubSpot Research, 90 per cent of customers want an immediate response to customer support queries, and 60 per cent define "immediate" as 10 minutes or less. Customers want the brand to be omnipresent so that no matter when your customer needs your help, your team is there. Modern support tools, such as conversational AI, are available 24 hours a day, seven days a week, and provide instant answers to frequently asked questions. This not only shortens the resolution time but also the time the customer must wait for a response.

Conversational platforms have improved the speed with which they respond to customers. The bot's first response time (FRT) increased to 1.16s, which is 68.81% faster than in 2020. Companies in India experienced the fastest response times, with 0.96s FRT, a 74.19 percent improvement. For complex questions, AI can assist by locating an agent who is capable of answering the customer's questions and seamlessly connecting them. Simultaneously, the agent obtains the context of the query as well as all of the customer information required to answer the question. Customers will receive a quick response from the agent and will not have to repeat their questions multiple times.

To win the hearts and minds of today's customers, brands must build a foundation of empathy and genuine connection with them. This means using machine learning and automation for transforming big data into empathetic customer experiences. AI technology functions as a scalable empathy engine, attempting to comprehend the context and intent of each individual customer interaction. Empathy occurs when a brand taps into the wants and motivations of each individual customer and connects with them on a deeper level than just a one-time exchange of value.

The next step in achieving empathy at scale is to combine data intelligence with prescriptive AI and predictive machine learning techniques to get a view of customers that goes beyond just one touchpoint and includes all of their previous interactions with the brand, whether that was on the website, browsing in-person, or leaving a review on social media. Only after a brand has created this 360-degree view of the customer will they be able to work on improving each subsequent interaction.

Customers today have more sophisticated expectations for digital experiences. The pandemic just exacerbated this need.  It is not enough to simply have information about what the customer has done; businesses must also put themselves in their customers' shoes and anticipate their future needs or desires. Accomplishing this means analysing the existing data to detect wider patterns and changes in preferences. Doing this will enable brands to build trust with their customers. When a person believes that a brand is using its data thoughtfully and intentionally, rather than simply to drive a sale, they are more likely to provide that brand with additional data to inform future experiences. Each meaningful experience creates more value for both the organisation and the customer. This is why AI chatbots are the catalyst for establishing a large-scale empathy engine that will foster deeper connections and long-term customer loyalty.