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From Cacophony to Choir: How AI Helps Banks Hear the Voice of the Customer

According to Forrester Research, companies with high CX performance “grow revenue faster than CX laggards, drive higher brand preference, and can charge more for their products.” However, as Alison Clarke, Principal Analyst, has noted, banking CX has stagnated in 2018.

This problem extends to other enterprises. Commenting on the findings of Forrester’s 2018 US Customer Experience Index, Harley Manning, Vice President and Research Director, said: “The results show that many companies don’t fully understand what matters to their customers. Rather than delivering experiences of critical value, companies have focused on the low-hanging fruit.”

What can banks do to shift the CX needle? 

“Many companies don’t fully understand what matters to their customers. Rather than delivering experiences of critical value, companies have focused on the low-hanging fruit.”
– Harley Manning, Forrester Research

CX professionals that have addressed problems and opportunities that are easy to spot – those low-hanging fruit – need to dig deeper and explore what customers are saying about their experiences.

Some banks have developed voice of the customer (VoC) programs, analyzing various streams of data in order to find these insights. Structured data, such as transactions, can provide useful proxies that help to determine next best actions and to monitor the progress of improvements. The challenge is getting beyond proxies and listening to what customers really think.

While the trained voices of a choir are distinct and clear, the voices and words of customers – unstructured data captured in myriad electronic channels – are rarely so easy to interpret. Forrester advocates using text analytics technologies to deal with the scale of the data, but this presents its own challenges.

The meaning of a complaint

Setting a text analytics tool to raise a flag whenever a customer uses the word “complaint” is relatively straightforward, but consider what brought the customer to this point. If you’ve experienced problematic service from a bank, most likely you expressed various words of dissatisfaction before you were frustrated enough to make a specific complaint. A complaint is not low-hanging fruit: it’s a sign of a customer at high risk of churn.

So how can text analytics be programmed to spot the language of dissatisfaction, ahead of a complaint? It only takes a moment to appreciate that dissatisfaction is expressed with almost infinite variety. “You’ve still not got back to me” or “I’m not seeing the expected return” and “I’m considering my alternatives” are all indicators of an unhappy customer. Banks need to find a way to identify these customers before problems become compounded and a customer is lost.

Experts think AI offers a solution

Fortunately for banking’s CX pros, the latest and most advanced communications analytics technology is able to adapt to natural language. Using a combination of machine learning and natural language understanding, it can also be trained by a bank’s own experts to produce a custom communications analytics engine. Industry leaders and analysts both cite AI as the way forward.

In an article for Digital Reasoning, fintech expert and former capital markets leader, Christhi Theiss, comments: “AI is enabling all divisions within banks to accurately and speedily turn communications data – emails, chat messages, even phone conversations – into understandable and actionable insights. AI helps banks capture what’s important, accurately and objectively, and automatically updates CRM systems and management dashboards.” He also argues that capital markets businesses should explore how to use AI to manage customer satisfaction and, as a result, triple their business.

“AI is enabling all divisions within banks to accurately and speedily turn communications data – emails, chat messages, even phone conversations – into understandable and actionable insights.”
– Christhi Theiss, fintech expert

In an era of industry disruption, it may be reassuring to banking incumbents that they can draw on years of both structured and unstructured customer data. “This uniquely rich source of customer insight is not available to Google, Amazon and other such tech giants,” say Isabella Fonseca and Meghna Mukerjee, both senior members of the Wealth Management team at Aite Group, an analyst firm. They add: “In an environment where competition and regulations are pushing wealth managers to be constantly appealing and transparent, it is easy for wealthy clients to take their money elsewhere if they are not satisfied. This makes it imperative that wealth managers identify the true reasons behind any client dissatisfaction – ideally much before the client attrition takes place.”

It is still early days for AI-enabled communications analytics in financial services, but there is a growing awareness that the industry’s winners of the future will be those willing to innovate. At the moment, customer communications data is often little more than noise, but like a skilled choirmaster the technology is helping banks to tune into what matters.

 


Digital Reasoning offers Customer Insights, an AI-enabled communications analytics solution that is used by global banks to analyze communications between their employees and customers. Find out more about Customer Insights on the solution page.

Written By
James Ollerenshaw

James Ollerenshaw is Director of Content Marketing at Digital Reasoning. He has worked in technology marketing for almost 20 years. His experience includes running marketing agencies, representing high growth and established tech brands, and leading the Forrester Research Executive Leadership Board for technology marketers in EMEA for over 3 years.

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