Banking on Data: Driving Customer Loyalty in Financial Services
As financial services become increasingly digitalised, banks must provide a winning customer experience to survive. This is only possible through a sound data strategy.
Helena Schwenk, Market Intelligence Manager at Exasol, explains how banks can use data and analytics to capture customer loyalty.
Driving customer loyalty has always been an important initiative for financial institutions, but COVID-19’s profound impact on the world has fundamentally changed how financial services companies now view loyalty. As more and more interactions shift online to virtual channels; customer behaviour changes as economic constraints hit home; approaches to risk change; and digital sales and services accelerate – the value of progressive data strategy and culture is all the more crucial.
As McKinsey’s recent report highlights, as revenue growth and customer relationships come under pressure, banks will need to rethink their revenue drivers, looking for new product launch opportunities, as well as reorienting offerings toward an advisory and protection focus. Advanced analytics can help identify those relevant niches of prudent growth.
However, the high prevalence of data silos and the unprecedented growth in data volumes severely impacts financial institutions’ ability to rise to this challenge efficiently. And with IDC conservatively predicting a 26% CAGR data growth in financial services organisations between 2018-2025, there are no signs that managing data is going to get any easier.
The financial services sector was already extremely data-intense due its the large number of customer touchpoints and the lasting legacy of COVID-19 will see this expand even further. Beating this challenge will require financial institutions to focus on turning their quantity and quality of their data into governed and operationalised data. To gain competitive advantage and win the fight in driving customer loyalty, financial services firms need to eradicate their data silos and start benefiting from real-time business decision making.
Beating this challenge will require financial institutions to focus on turning their quantity and quality of their data into governed and operationalised data.
Adopt a robust data analytics strategy
Defining a data analytics strategy is crucial for financial services organisations to increase customer loyalty and deliver a better customer experience. A solid data strategy holds the key to uncovering invaluable insights that can help improve business operations, new products and services and, crucially, customer lifetime value — allowing organisations to understand and measure loyalty.
In addition, a robust data strategy will help organisations keep a sharper eye on customer retention, using data to actively identify clients at risk of attrition, by using behavioural analytics, and then generating individual customer action plans tailored to each client’s specific needs.
In our survey of senior financial sector decision-makers, 80% confirmed that customer loyalty is a key priority, given that consumer-facing aspects of financial services generate revenue and are a critical differentiator. And, according to Bain & Co., increasing customer retention rates by 5% can increase profits by anywhere from 25% to 95%.
Recognise the challenges of customer retention
But increasing customer retention and improving loyalty is not easy. There are ongoing challenges to earn and maintain. For example, 54% of our survey respondents believe that customers have higher expectations of financial services experiences and 42% agree that digital disruptors that support new digital experiences, offerings and alternative business models, are encroaching on their customer base.
At the same time, regulation is a concern too, with 41% saying PSD2 and GDPR are impacting their ability to develop and improve customer loyalty initiatives.
Despite all these challenges, the business impact of poor customer loyalty – such as lost opportunities for customer engagement and advocacy (45%), higher levels of customer churn (45%) and lost revenue-generating opportunities (42%) – is too important to ignore. Given that it costs five times more to acquire a new customer than sell to an existing one — gambling on customer loyalty in today’s highly competitive environment is a big risk to take.
Strive for constant improvement
That said, in a heavily regulated industry with a wave of tech-disruptors, keeping customers happy and loyal is no mean feat. But driving a deeper understanding of customer lifetime value and measuring the loyalty of customers is possible. The good news is that almost all organisations (97%) use predictive analytics as part of their customer insights and loyalty initiatives, with three fifths (62%) using it as a key part. 65% also agree that data analytics enables them to offer personalisation and predict customers’ future behaviour.
Overall use of data analytics is maturing in financial services compared to other industries; 96% of the people we surveyed were very positive about their firm’s data strategy and how it is communicated for the workforce to implement. Although 48% did admit it could be improved.
This consistent need to improve is backed by McKinsey. Its survey of banks saw half saying that while analytics was a strategic theme, it was a struggle to connect the high-level analytics strategy into an orchestrated and targeted selection and prioritisation of use cases.
Revolutionising data analytics
Revolut is one disruptor bank showing the world what a thriving data-driven organisation looks like. By reducing the time it takes to analyse data across its large datasets and several data sources, it has reached incredible levels of granular personalisation for its 13 million global users.
Within a year, the data volumes at Revolut had increased 20-fold and it was an ongoing challenge to maintain approximately 800 dashboards and 100,000 SQL queries across the organisation every day. To suit its demands and its hybrid cloud environment it needed a flexible data analytics platform.
An in-memory data analytics database was the answer. Acting as a central data repository, tasks such as queries and reports can be completed in seconds instead of hours, saving time across multiple business departments. This has meant improved decision-making processes, where query time rates are now 100 times faster than the previous solution according to the company’s data scientists.
Revolut can explore customer demographics, online and mobile transfers, payments data, debit card statements, and transaction and point of sale data. As a result, it’s been able to define tens of thousands of micro-segmentations in its customer base and build ‘next product to purchase’ models that increase sales and customer retention.
The 2 million users of the Revolut app also benefit as the company can now analyse large datasets spanning several sources – driving customer experiences and satisfaction.
Revolut can explore customer demographics, online and mobile transfers, payments data, debit card statements, and transaction and point of sale data.
Every employee has access to the real-time “single source of truth” central repository with an open-source business intelligence (BI) tool and self-service access, not just the data scientists. And critical key performance indicators (KPIs) for every team are based on this data, meaning everyone across the business has an understanding of the company’s goals, industry trends and insights, and are empowered to act upon it.
Predict what your customers want faster
A progressive data strategy that optimises the collection, integration and management of data so that users are empowered to make and take informed actions, is a clear route to creating competitive advantage for financial services organisations.
Whether you’re a longstanding brand or challenger bank, the key to success is the same – you need to provide your services in a timely, simple and satisfying way for customers. Whether you store your data in the cloud, on-premise, or a hybrid, the right analytics database is central to understanding your customers better than ever before. By using data to predict and detect customer trends you will improve their experience and get the payback of increased loyalty, which is even more essential in a post-COVID world.