How AI Is Changing Customer Experience
Artificial intelligence (AI) has quickly moved from science fiction to the mainstream across many industries, including retail and commercial banking. AI is proving itself to be useful in a variety of ways in back-, middle- and front-office applications. Not all of these applications tangibly impact the customer experience (CX). Those that do are of significant and growing interest among institutions of all sizes across the globe. This article looks at the ways in which AI is changing the customer experience, how this is happening and the business case for doing so.
AI is influencing CX in two primary ways. The first is through personalised data insights and advice generation (also known as next-best-action or next-best-conversation)—both direct to the consumer via digital channels and/or via interactions with a business banker, contact centre or branch staff. More recently, conversational AI is increasingly influencing CX, taking on a variety of forms. Specific technologies include natural language understanding, generation and processing (NLU, NLG and NLP) and predictive or propensity modelling of various types. These technologies are not used singly but are combined to support a large and growing number of use cases.
Many banks have a general understanding of their customers and/or customer segments. Fewer have a deep understanding at the individual customer level. Some have built the capabilities to understand individual customers’ profitability, lifetime value and/or share of wallet. These are useful perspectives, but they are rarely used to inform how banks engage with individual customers. Operationalising the use of customer data to form actionable insights that inform the customer conversation in real-time, across all touchpoints, is a rarity. But, thanks to AI, it won’t be for long.
A small but growing number of banks are creating “decision hubs” powered by a portfolio of statistical models and fed by readily available customer data to create next-best-conversations that are customer, context and channel aware. Initially built to maximise sales effectiveness at many banks, these hubs are increasingly designed to support a diverse set of business goals such as acquisition, retention, cross-sell and compliance but are stringently prioritised against each other—to ensure banks engage customers with the most relevant next-best-conversation possible—at each point of interaction, from branch and contact-centre conversations to mobile apps and chatbots.
Chatbots (and other variations on the theme) have been a white-hot area of development over the past few years. All have two important elements in common:
Chatbots broadly refer to natural language text interfaces used as an alternative to navigate an app or browser to accomplish certain tasks. Celent refers to all types of automated conversational interfaces as chatbots and AI-powered bots as virtual assistants. First-generation chatbots were constructed using rules that encouraged linear interactions navigated by pre-defined flows. Chatbots were designed for a specific and narrowly defined number of tasks. If a given user’s need could be met using predetermined flows within the bot, then the CX was favourable. But if the user’s intent veered outside the bot’s predetermined-scenario portfolio, then the CX could quickly deteriorate. This limitation is why first-generation chatbots were generally a disappointment. The good news: more capable alternatives are now available, thanks to conversational AI.
Conversational AI utilises natural language processing (NLP) and natural language understanding (NLU) to enable dynamic dialogues between a customer and a machine; that is, one with multiple turns as opposed to a single-turn static conversation (for example, “What is my balance?” “What was my last credit-card charge?”). The AI model interprets context and sentiment, going beyond searching for an answer and delivering it. The dialogue can be by voice or text.
