Some readers may remember the TV commercial for the UK’s Midland Bank in the 1980’s which characterised itself as ‘The Listening Bank’. The commercial
seems quaint (at best) now, but banks across EMEA are now finding that tuning in more intently to what customers say, and don’t say, is more critical than ever. The data-driven, digital-first era has multiplied the complexity of customer conversations – but it has also provided the means to generate and act on real insight.
Rush to digital
COVID has seen digital and mobile banking take off in many countries. The steady decline of cash has picked up momentum, with consumers switching not just to card payments, but to mobile and instant payments. According to McKinsey
, interest in making mobile payments grew between 9 and 13% across US, UK, France and Germany between March and June 2020. Open Banking provider, Trustly, found that around a half of 16-35 year-olds
across Europe preferred fingerprint or face recognition on their phones to using cards to make payments.
When lockdowns and other restrictions are finally lifted, it seems unlikely that these users will switch back to old habits of in-branch banking. One clear reason is the ongoing closure of branches underway even before the pandemic. As banks rush to reduce costs the real-estate portfolio and the staffing of hundreds of branches are an obvious target. Approximately 10,000 bank branches
were closed for good across the EU in 2020 and almost 75,000 since the crisis of 2008. Cost savings and the shift online are the underlining trend here. By 2019 (before the pandemic) 58% of Europe’s
population were using internet banking – recent figures
suggest significant growth even since then. However, it is also clear that not all banks will survive and differentiation in a crowded market is at least as important as cost-cutting. To date, services have been automated but not reimagined. The banks that win will be those that capture attention and put themselves at the heart of customers’ digital lives.
Vying for attention
This digital-first era of banking has profound effects on the way banks listen to customers and try to understand their requirements and preferences. For the time being, there will be customers who require person-to-person interaction in branches – either for more complex product and service delivery, or for more advice and reassurance at key moments. But the danger is that without daily or weekly face to face contact, issues are not picked up and customers feel distanced and unable to complain about niggles or problems which then develop into deeper dissatisfaction over time. At the same time regulators are mandating easier account switching, and open banking is disaggregating services. The obvious outcome is more customers choosing to leave their bank, or regarding another provider as their ‘front screen’ destination.
More worrying, customers may not even leave you for another bank, but for the sexy apps that dominate their attention and the first screen of their devices. Today’s customer, transacting and interacting from their mobile phone, is one swipe away from data driven consumer apps that are razor-focused on anticipating and satisfying their every whim. These are not banking apps, or even payment apps, but sticky lifestyle applications that increasingly integrate financial services as part of the platform. To win, banks need to compete with these highly personalised apps. Where do they start, what should banks do first?
Elements of digital listening
It is not all doom and gloom. Leading banks are quickly learning to gather and leverage vast amounts of data through their digital channels. Last year, working with Teradata, a major European bank analysed over 650 million online sessions to generate over 600 million personalisation decisions every day. This is what listening to customers looks like in the digital world.
It is not easy. It takes commitment and a coherent data strategy driven by the CEO down. To engage in ‘Intelligent Conversations’ with customers, banks must be able to integrate relevant data
from every touch point as well as transactional and operational systems and build a decision-making capability that can respond to customers in the moment to be relevant and grab that attention. Holistic, up-to-date and accurate data is the pre-requisite for knowing exactly where each customer is on their customer journey.
Scaling to enterprise listening
Listening to customers and engaging them in intelligence conversations cannot be done through ad-hoc vanity AI projects run by individual teams. Having a distributed approach to innovation does not have to mean it is centralised, but the imperative to operationalise findings requires a clear-eyed strategy within which these innovators operate - and the need to integrate them with the ‘rest of the bank’.
Understanding does not come from just one source of data or a single channel. Interactions need to be managed across channels, driven by customer choice and preference, without being repetitive or disjointed. AI and automation are crucial – handling the vast quantities of data in near-real-time scenarios is impossible any other way. But it must be sensible AI, and banks need to work hard to understand when to involve humans and how to manage the hand-offs between AI and person-to-person interaction.
Relevance, usefulness and timeliness are the drivers of customer engagement in the digital world. They are what will keep customers on your app, happy to receive notifications and marketing communications. With the right elements in place banks can confidently make the next best offer at every point on each customer’s journey. To do so they must put strategies and investments in place now to leverage analytics and data science at the heart of decision-making. To keep up with the pace of change in customer behaviour they need to create and deploy thousands, if not tens of thousands of analytical models that can score data from millions of customers interacting with hundreds of products across multiple channels. At this point, understanding your customer becomes an exercise not only in building the best models, but in operationalising them at enterprise scale. I’ll consider this topic in the next blog.