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Business News/ Opinion / First Person/  How Conversational Analytics Can Help Banks Better Understand Their Customers
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How Conversational Analytics Can Help Banks Better Understand Their Customers

Banks can turn to conversational AI analytics to improve customer service and gain insights from customer interactions.

How Conversational Analytics Can Help Banks Better Understand Their Customers istockphotoPremium
How Conversational Analytics Can Help Banks Better Understand Their Customers istockphoto

For decades, companies have been documenting customer conversations as they recognise the significance of these dialogues. This is not only for improved understanding of customer intent but also to gauge and enhance customer service efficacy. Nonetheless, the copious unstructured information stored within call logs of customer contact centres presents a challenge in extracting meaningful insights.

The banking sector today is under pressure to innovate and remain pertinent in a swiftly changing environment marked by augmented competition from fintech startups, digital transformation and an emphasis on customer experience. In this scenario, banks are striving to adapt and remain competitive. They have endeavoured to integrate artificial intelligence into their operations, particularly evident in the utilisation of chatbot assistants to manage customer inquiries. Although this has yielded cost savings, these chatbots frequently fail to offer effective solutions, causing unfavourable user experiences that can detrimentally affect customer contentment and allegiance over time.

Due to outdated technology and ineffective utilisation of static, unstructured data, these chatbots persist in utilising an IVR 2.0 format that frustrates callers and restricts banks from adequately serving customers. Advancements in Conversational AI in recent times have altered the way industries manage their interactions with customers. Contemporary conversational AI analytics, exploiting state-of-the-art machine learning, generative AI and natural language processing, now possess the capability to provide substantial aid and contextualised, personalised encounters. Such encounters lead to heightened levels of satisfaction among bank customers as they rely on tailored solutions.

Progress in natural language processing (NLP) has amplified AI's capacity to comprehend, interpret and reply to human speech. This evolution enables next-gen conversational AI analytics to not only comprehend customers' issues but also grasp intricate contextual nuances. This facilitates tailored responses that cultivate empathy, signifying that customer concerns are acknowledged and prioritised. Consequently, banks can substantially enhance chatbot experiences, fostering a sense of empathy and humanness in automated interactions, consequently boosting customer satisfaction and prolonged engagement.

Apart from refining chatbot-customer interactions, advanced conversational AI analytics can also produce automated summaries of customer dialogues. Banks can mine these summaries for actionable insights into customer requirements and sentiments. This invaluable data can be harnessed by financial institutions or banks to enhance service quality, address prevalent issues and fortify customer relationships.

Analysing a customer's interaction patterns with their bank, encompassing service requests, unveils significant insights into the customer's financial circumstances, such as the intention to purchase a home. Recognising these "micro-moments" empowers banks to offer pertinent products and services adjusted to the customer's distinct needs at that precise juncture. AI can streamline this process via intelligent automation. If, based on a few interactions, a bank identifies a customer's readiness for a particular life event, it can then devise a tailored set of offerings. Achieving this at scale necessitates conversational AI analytics for enhanced efficiency and engagement rates.

Banks and financial institutions may benefit from a wide range of conversational AI technologies that go far beyond simple cost reductions. As the technological landscape continues to evolve as a result of digital breakthroughs and increased competition, banks have, in Conversational AI analytics, the one-of-a-kind opportunity to truly understand the needs, sentiments and aspirations of their customers.

Conversational AI is the embodiment of empathy and a model of efficiency during customer encounters due to its expertise at decoding complex discussions, as well as its ability to provide personalised responses and produce useful summaries. In this age of perpetual change, Conversational AI appears not just as a tool but also as a reliable partner in the quest for excellence based on the consumer.

The writer is Chief Growth Officer and Co-Founder, Rezo.ai

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Published: 29 Aug 2023, 03:04 PM IST
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