The Role of Artificial Intelligence in Customer Experience

Banking CIO Outlook | Monday, December 14, 2020

AI affects CX through customized data insights and advice generation directly to the customer through digital platforms or through interactions with the company banker, contact center, or branch staff.

Fremont, CA: Artificial Intelligence (AI) has gradually changed from science fiction to mainstream across various sectors, including retail and commercial banking. AI has proved to be useful in many ways in back-, middle-and front-office applications. Not all of these applications have a tangible effect on customer experience (CX). Those who do are of considerable and increasing interest among organizations of all sizes around the globe.

More recently, conversational AI has increasingly affected CX, taking on a variety of forms. Relevant technologies include natural language comprehension, generation, processing and predictive or inclination modeling of various forms. These technologies are not used individually but used together to support a large and growing number of use cases.

Here is how AI is Enhancing customer experiences:Top Customer Experience Solution Companies

Chatbots, Conversational AI, and Virtual Assistants

Chatbots have been a hot-white field of growth over the last few years. All of them have two essential elements in common:

Conversational Interface: Instead of a user scrolling through an app or website interface to meet their needs, conversational interfaces may provide a convenient and sometimes faster user experience. The text chat is now leading this realm, but the voice is rapidly gaining ground for a subset of use cases.

Automation: Conversational interfaces are not new to this. Firms have been providing live chat for years and inviting people-to-people conversations in contact centers and branches. However, doing so requires significant human capital. The primary advantage of automation is its ability to satisfy routine consumer needs efficiently for consumers and at a low cost to the institution, freeing up resources for more challenging tasks.

Conversational AI uses natural language processing (NLP) and natural language understanding (NLU) to allow interactive dialogs between the customer and the system, i.e., one with multiple turns instead of a single-turn static conversation. The AI model interprets meaning and emotion, goes beyond finding a response and presenting it. The dialog may be by voice or text. Text is more advanced than speech technology. Both involve a smooth transition to a human when the conversation exceeds the technology's capacity to produce a satisfactory outcome.

Data Insight and Advice Generation

A small but growing number of banks build "decision hubs" driven by a portfolio of statistical models and fed by readily accessible customer data to create the next-best customer, background, and channel-conscious conversations. Originally designed to optimize sales productivity in many banks, these hubs are increasingly designed to support diverse business goals, such as acquisition, retention, cross-selling, and enforcement. Still, they are narrowly prioritized against each other to ensure that banks engage customers with the most appropriate next-best-conversation possible at each point of interaction, from branch to contact center.

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