How banks can use Big Data to enhance the customer experience

As banking becomes increasingly commoditised, ‘Big Data’ offers banks an opportunity to differentiate themselves from the competition. Using advanced data science techniques to collect, process and analyse Big Data could help to deliver significant enhancements across all areas of retail banking and ultimately make banks more customer-centric.

Using customer data effectively means banks can deliver more targeted and cost-effective marketing campaigns, design products and offers that are specifically tailored to customer needs, and even develop more accurate models for assessing creditworthiness and detecting transaction fraud. Even better, combining data sets in creative ways can really surprise and delight customers, leading to retention, loyalty and a higher customer lifetime value. In reality, the potential for Big Data in retail banking is endless.

It is worth remembering that more data does not necessarily mean better data, although selectively and smartly applying new data science techniques can open up brand new opportunities. Indeed, as we highlighted recently, businesses should avoid asking people for information they will never use (as it leads to overly long forms and complicated application processes). As such, banks will need to identify the components of the Big Data trend that are right for advancing their business.

Customer Centricity: The Big Data Mismatch

Banks are data businesses, and they have long had access to more consumer data than other businesses. The volume and variety of data that banks hold about their customers has steadily increased, with frequent use of web and mobile banking channels driving an increase in the number of customer interactions. Holding detailed customer profiles, rich information on spending and income, and a pretty clear idea of where geographically people spend their time, banks are in a unique position to paint a pretty clear picture of each of their customers.

However, research carried out by Cap Gemini found that only 37% of customers believe that banks adequately understand their needs and preferences, with banks only using a small portion of the data available to generate insights that enhance the customer experience. Given that research from Bloomberg Businessweek showed that 70% of banking executives worldwide say customer centricity is important to them and over 90% of financial institutions in North America believe that successful Big Data initiatives will determine the winners of the future, why are we not seeing an accelerated drive towards implementing advanced Big Data infrastructure?

Why Aren’t Banks Making the Most of Customer Data?

The good news is that banks are investing in big data analytics and are looking for innovative ways to collect and analyse data to better understand their customer base – the chairman of BBVA, Francisco Gonzales, has even gone so far as to say that BBVA will become a technology company in the future.

However, while the promise of big data analytics is beyond dispute, the problem banks are having is that the data they hold often sits in disparate legacy systems. Making data science tools work with legacy platforms and databases sitting in silos represents a major challenge for banks. Organisational silos constitute the top barrier to success in using Big Data analytics because they prevent banks from generating a single view of the customer.

As banks embrace Big Data, they will also need skill sets and technologies that go beyond what is required for traditional analytics applications – there is simply too much information to visualise effectively using traditional methods.

Of course, banks are also wary of the regulatory and ethical risks associated with holding so much data on customers, such as misuse of that data and the inevitable rise in security risks. If sensitive customer data stored by the banks is fraudulently obtained by third parties, for example through hacking, this could destroy that bank’s relationships with its customers. Retail banks will need to ensure their IT security is constantly maintained to prevent them being compromised and avoid potential legal risks.

Taking Advantage of Big Data Analytics to Enhance Customer Experience

Banks will need to develop and promote a company-wide analytics strategy, building the right organisational culture and driving a concerted shift towards data-driven behaviour, in order to design and implement effective Big Data initiatives. This will involve embedding analytics into core business processes, consolidating disparate Analytics teams and finding skilled people with the ability to apply the right data science techniques and draw out clear and actionable insights. Hiring the right people is crucial.

Banks will need to formalise the collection, storage and usage of customer data – both structured and unstructured – to ensure insights are accurate, meaningful and of a high quality. Such customer insights will support effective strategy design and should have particular impact in helping banks acquire new customers, increase share of wallet and limit customer attrition. Again, the challenge will be doing this in a cost-effective way across a variety of systems that may not be designed to speak to each other.

Most importantly, banks need to look outside the financial services industry for best practice in the creative application of data. Personalised communications, geolocation tools and well-timed push notifications are being done well (and badly) in every sector – and it’s not hard to envisage how these approaches could turn a bank from something of a ‘digital mattress’ (simply storing my money) to a brand that is trusted, required, and applauded regularly.

Selling Without Selling

One of the main ways in which incumbents can use Big Data to gain market share is by using it to address ‘unmet customer needs’ – in other words, as a basis for effective, meaningful cross-selling for current account customers.

Current accounts are the most basic product offered by traditional retail banks. Given the free-if-in-credit model, they are primarily only profitable for banks in the case of customers who live constantly in their overdrafts. They are seen by providers as a foundation product that can be used to build customer relationships, which can then be leveraged to sell them more profitable products.

However, in the aftermath of various mis-selling scandals, such as PPI, banks are far more cautious about taking a hard-sell approach. What is becoming increasingly common, especially amongst digital challengers more accustomed to analysing Big Data, is for providers to analyse customer spending and savings habits and identify products for them that would help improve their financial situation. For example, if the bank identifies that the customer is holding large amounts of disposable income in their current account, it may suggest an instant access account with a higher-paying interest rate to the customer within online or mobile banking. This is a much softer approach to selling and one that is likely to encourage customers to trust their financial provider.

As Mapa’s quarterly Current Accounts Dashboard shows, innovation within the current accounts space is currently limited, particularly from incumbent providers. This makes it relatively easy for disruptors, such as Fidor, B and Monese, to draw more digitally-savvy customers away from the UK banking behemoths. Incumbents should not disregard the potential benefits of big data and can fight back by becoming more customer-centric and data-driven.

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