Since the financial crash, eight of Europe’s biggest banks have announced layoffs adding up to about 100,000 employees, paid £44bn in legal penalties and lost £300bn in market value.
Yet despite these severe reprimands, customers still do not trust their banks. The advent of the internet has amplified this problem as an increasingly dissatisfied customer base voice their concerns about poor service online. Consequently, retail banks are now seriously looking to improve their customer service practices.
Today, most British retail banks have finally identified a way to improve customer relations and ultimately repair their public image; using Big Data to develop services tailored to each customer. According to recent research by the Centre of Economics and Business Research (CEBR), 81 per cent of retail banks will have adopted Big Data analytics by 2020. Not only will retail banks be able to track general trends and adapt their services accordingly but also profile individual customers.
As part of their strategy to improve customer relations, banks are using the insight from customer data to reduce friction between the customer and their retail bank at every stage in the client lifecycle. At first, Big Data will help improve the process of acquiring new customers as it improves the ability of a bank to estimate credit worthiness and overall risk. Then when it comes to customer retention, Big Data will allow retail banks to keep their services ticking over, making specific changes to their services and offering customer-specific deals which should improve the customer experience and reduce friction.
Challengers: A changing of the guard
New entrants into the financial services sector have undoubtedly hastened this change in British retail banking strategy. Increased competition, from challenger banks to established supermarket chains, has forced retail banks to look to the power of their data in order to respond to consumer-driven changes in the market. Data is fast becoming the currency of financial service providers who need to understand customer behaviours and anticipate changes in the consumption of services.
This is because data is a currency which is easily accessible. In recent years, supermarket chains have taken a proactive approach in capturing and analysing this data. For example, Tesco’s Clubcard service allows them to assess which products their customers want to buy and where. As a result, Tesco can target product deals at the regional stores where a specific product is purchased in high quantities.
In addition, we are now seeing challengers to the big retail banks making significant inroads against their traditional competitors. These new entrants have capitalised on the lack of trust in financial institutions by responding more accurately to customer expectations and needs. This is in part down to their use of data and behavioural economics to drive customer acquisition.
Ultimately, it’s the agility of the challengers which has given them this competitive edge. Simple and state of the art technology systems have been set up to make the most out of customer data from the get-go. This is a huge advantage over their larger, more established retail banks - which are in danger of lagging behind – whose clunky legacy systems are worryingly complex and inefficient.
It is no small feat for retail banks to ingratiate Big Data into their processes as it often requires a daunting technological overhaul. Yet, these complex legacy systems have stopped bigger banks from capturing and understanding their data. To rectify this, banks will need to make better use of growing data sets such as correspondence, loan facility letters, contracts and the diversity of customer interactions if they want to offer bespoke consumer products that will allow them to fend off their more agile competitors.
The data problem: Gathering effective insights
Retail banks face two key challenges when analysing Big Data. The first is how it extracts relevant information from the huge amount of data available that will enable the company to make educated decisions. The other is to collate all the information in time for the decision to still have the desired impact on the targeted audience. In today’s fast-paced world, having the ability to extract, analyse and act on insights gained from data is key if a company wants to maintain a competitive advantage.
In recent years, there have been significant advances in dealing with these vast amounts of structured and unstructured data. Further improvements to data analysis will be brought on by the automation of the way data can be collected and processed in any form. This evolution of the handling of data will be facilitated by the development of artificial intelligence. AI is likely to be increasingly used to assist the user in questioning and understanding their data in real time, allowing for fast decision making, or enabling them to act on alerts and notifications.
Big Data’s big value
The CEBR’s research highlighted the link between insights gained from Big Data and the corresponding growth to the UK economy. Effective analysis of data could contribute £240.5bn to the economy which will come largely as a result of increased efficiency.
Retail banks in the UK have reached a tipping point. As challenger banks cut into the profits of their established rivals, the cost of leaving data untouched rises. Retail banks who are serious about repairing their customer relationships need to board the Big Data train before it leaves the station.
Steve Edkins, CEO, FusionExperience
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