The role of big data in banking and how it can be used to drive a successful omnichannel strategy

By Mark Aldred | 5 February 2018

With the proliferation of the internet and smart devices, businesses have access to more data than ever before. Finding ways to extract value from this data has been a goal for some time, and led to a great deal of hype around big data (so-called digital “black gold”).

Nonetheless, there are real rich insights that could be elicited from big data. According to a report by SAS and the Centre for Economics and Business Research, the value of big data could be worth £322bn to the UK by 2020 – that’s bigger than many of the UK’s biggest industries.

Retail banking is forecast to be one of the industries which will experience the most economic benefits from big data – to the tune of more than £16bn. However, the use of big data isn’t without its challenges for retail banks.

Why are banks chasing digital ‘black gold’ dreams?

Quite simply the answer is because banks have so much data that they have made little use of, and it is only set to grow rapidly in size and value moving forward.

Financial institutions have long had access to data which provides insight into how their customers live their lives. This resource will grow exponentially over the next few years, fuelled by multimedia, dependence on smartphones and social media. For banks this could mean an increasing number of new opportunities to interact with end customer and gain valuable insights that improve engagement, services and revenues.

The challenge has been while the data available to banks on their customers is rising, they have found it extremely difficult to correctly extract real value and insights that  are actionable.

Now, next generations of technology are providing better analytic tools that can extract, collate, process and present all the data from the various contact points used by bank customers. These tools, if well integrated with Big Data Analytics platforms, allow you to perform an unprecedented number of large scale operations such as: establish correlations in customer behaviour, analyse historical series more quickly and with more accuracy, determine trends or specific market segments to identify new business opportunities, and adopt more targeted market strategies.

The importance of big data in driving omnichannel success

Starting from the analysis of historical customer data collected through banking interactions across the channels, you can understand what motivates customers and improve how you connect with them through personalisation. For example, you can enhance customer interaction with both self-service devices (ATMs, ASDs, ASSTs, reception booths) and with bank staff. The greater the knowledge staff have of the customers, the better prepared they are to meet increasingly growing and complex needs. Big Data is crucial here to analyse their spending potential and identify the products they need, and of course identify new business opportunities in the medium to long term.

Intelligent use of data does not only affect customer experience, but also internal processes and bank operations. You can quantify the volume of operations for each ATM on a given day, by time zone, by type, which means you can evaluate how long customers have to queue and the likelihood they will give up before getting to the front. By using this type of information, you can make an advanced analysis of bank channels by effectively evaluating costs, revenue from a service, and also any losses or inefficiencies. It is also possible to minimise any operational risks, as well as develop new strategies, or plan where to place your technology in the future.

Looking to the future, the desire for more data analytics to support and drive the business forward is going to increase. What’s becoming clearer is that the appetite for greater correlation of increasing volumes and streams of dynamic data could be held back, unless systems are utilising more machine learning and artificial intelligence to do this complex work autonomously and in real time.

There also is a requirement to allow customers to see the value to them of sharing more of their data with their financial institutions. Trust needs to be won and carefully protected especially when customers agree to share personal data from their social media activity. Work will always need to be done here, though compliance with emerging data protection regulations could be a catalyst for customers to feel more comfortable sharing personal data with their financial services provider.

Banks are well positioned to use big data analytics to improve customer experiences and operational performance. The key is how well they think through the challenges of integration and whether the insights that they gain are used at the right time and in the right context. 

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