Often, a call center is conditioned to receive calls, find solutions, note the reason, and pass it on to the next customer in line quickly. But with contacts of just a few minutes, obtaining relevant information is difficult. What if there was a technology that turned these interactions into learnings for the future?
This technology already exists and goes beyond telephone calls; any conversation between a customer and a company can be analyzed. In fact, it’s not just the tone of voice that defines whether an experience was positive or negative, but rather the overall context of communication. Factors such as regionalisms and cultural expressions play a fundamental role in this interpretation, as a person can sound agitated when commenting on an event without necessarily being dissatisfied or can use colloquial expressions without negative connotations.
With Generative Artificial Intelligence — one that not only automates tasks but also analyzes data and generates insights — companies can, in addition to resolving specific issues, examine thousands of files and data, identify patterns of dissatisfaction in conversations, anticipate needs, and help companies improve the consumer journey.
“AI performs a detailed analysis with every interaction, something a human analyst, due to the data volume, could not do with the same scope and in the same time. By identifying opportunities, even in the smallest conversations, the tool turns these insights into actionable intelligence for the company,” explains Carlos Sena, founder of AIDA, a platform specialized in the use of Generative AI to turn interactions into actionable intelligence.
Brazil is already emerging as one of the global leaders in the adoption of this “arm” of AI: the country is among those that most use generative artificial intelligence in the world, according to a study commissioned by Google — 54% of respondents stated they used the technology last year, while the global average was 48%.
Applied to customer service, generative AI can go beyond its more traditional use, which involves chatbots and virtual assistants to automate contact. Even in automated interactions, user experience is not always satisfactory. That’s why more complex customer service interactions — or even the customer themselves — still require human presence.
In these cases, the not-so-obvious use of AI can be valuable: generative AI analyzes customer behavior in conversations with agents, identifies dissatisfaction patterns, and maps friction points, allowing continuous adjustments to make the journey more efficient. The data analysis performed by the tool helps brands understand bottlenecks and areas of greatest dissatisfaction in service, without having to “guess” anything. Therefore, improvement decisions are more well-founded and, consequently, have a better chance of producing positive effects.
“More than just responding to user requests, Artificial Intelligence allows companies to turn each interaction into an opportunity to improve their services, creating in the end a true source of information and going to the ‘root of the problem’ to solve it. Listening well, reflecting, analyzing, and organizing calls can be the difference between losing a customer or gaining them forever. It seems contradictory, but technology ends up being a great ally in making service more humanized,” concludes Sena.
