By Helena Schwenk, Market Intelligence Manager, Exasol
It’s a fact. The exponential growth of data directly impacts financial institutions’ ability to do business efficiently. And there’s no sign of that growth slowing down, with IDC conservatively predicting a 26% CAGR data growth in financial services companies between 2018-2025.
One of the main reasons why the financial services sector is so complex and data-intense stems from a large number of customer touchpoints. As an illustration, think of all the conceivable ways a single customer could interact and share data with different departments of their bank in a single day. They could check their current account balance on the bank’s smartphone app on their way to work; go into the branch in their lunch break to discuss a mortgage application; call the telephone banking call centre to check on the status of their savings; use the desktop website to transfer a credit card balance or renew their home insurance; and use their debit card throughout the day to make numerous transactions. Times all this by millions of customers and 365 days a year, and the data volumes quickly escalate.
Turning data of this quantity and complexity into governed and operationalised data that can be used to gain competitive advantage, drive customer loyalty, improve compliance and inform near-real-time business decisions, is an ongoing challenge. A challenge compounded by the prevalence of data silos which are endemic in financial services firms.
Data analytics to the rescue
Data analytics holds the key to helping organisations better connect to their data and uncover the valuable insights they need to improve business operations, develop new products and services and, crucially, enhance their customer experience.
Data analytics is an essential part of any customer retention strategy as data insights can be used to understand customers better, identify business opportunities and reduce costs. Data analytics also allows financial institutions to actively identify clients at risk of attrition by using behavioural analytics to generate individual customer action plans — which they can then choose to implement, tailored to the client’s specific needs.
With the help of Sapio Research, we recently surveyed 457 senior financial sector decision-makers across Germany, Austria, Switzerland, the US and the UK, whose organisations are focused on customer loyalty through data and analytics technologies. The objective was to decipher how exactly organisations are using data to increase customer loyalty and deliver a better experience. Our findings indicate that data strategy is crucial to customer insights and loyalty efforts. Respondents using data analytics agree they now have a deeper understanding of customer lifetime value, allowing their organisations to understand and measure the loyalty of customers.
The importance of customer loyalty
Data analytics is having a significant impact on customer loyalty within the financial services sector. 80% of our respondents agreed that improving customer loyalty is a key priority, given that consumer-facing aspects of financial services are seen as a critical differentiator and revenue generator. According to Bain & Co., increasing customer retention rates by 5% can increase profits by anywhere from 25% to 95%.
But increasing customer retention and improving loyalty is hard. More than half (54%) of respondents believe customers have higher expectations around their experience when interacting with financial organisations, which is making loyalty increasingly challenging to earn and maintain.
And there are other challenges: 41% of respondents think that regulations such as PSD2 and GDPR are impacting their ability to develop and improve customer loyalty initiatives. While 42% agree that digital disruptors are challenging established financial organisations when it comes to customer loyalty because they support new customer experiences, offerings and alternative business models.
The most significant impacts of poor customer loyalty are lost opportunities for customer engagement and advocacy (45%), higher levels of customer churn (45%) and lost revenue-generating opportunities (42%). Clearly, in today’s competitive environment, that’s not acceptable, especially as it costs five times more to acquire a new customer than sell to an existing one.
Data and customer loyalty
Keeping customers happy and loyal is easier said than done, in a heavily regulated industry experiencing a wave of tech-disruptors. When looking at the impact of data and analytics, respondents agreed that they benefit from customer loyalty initiatives mainly by giving a deeper understanding of customer lifetime value and allowing organisations to understand and measure the loyalty of customers. 65% of respondents agree that data analytics give them the ability to predict customers’ future behaviour and offer personalisation.
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. However, there are big geographical inconsistencies. For example, in the US, 93% of organisations’ departments have embraced data analytics, compared to just 37% in the UK.
That said, overall use of data analytics does appear to be more mature in financial services compared to other sectors; 96% of respondents were very positive about their organisation’s data strategy and how it is communicated for the workforce to implement it. Even if almost half of these (48%) believe it could be improved. This need to improve is consistent with research by McKinsey which found that even though for more than half of the banks surveyed analytics is a strategic theme, the majority struggle to connect the high-level analytics strategy to a targeted selection and prioritisation of use cases, and to implement them in an orchestrated way.
As financial services companies attempt more sophisticated data analytics, the skills required mean that some struggle with their inability to use advanced analytic methods for their desired analyses and activities (38%) or with a lack of access to external and more detailed customer data (35%).
Revolut and in-memory data analytics
Revolut is an excellent example of a company thriving by using their data. The company has reduced the time it takes to crunch data across large datasets that span several sources to help with customer insights as well as areas like fraud detection.
The company’s data volumes increased 20-fold in a year, and it has the ongoing challenge of maintaining approximately 800 dashboards and 100,000 SQL queries across the organisation daily. It uses a flexible data analytics platform that suits these demands and its hybrid cloud environment.
With an in-memory data analytics database as a central data repository, queries and reports that used to take hours can be completed in seconds — saving hours across all every business departments. Since implementation, the database has also improved decision-making processes, with data scientists estimating that query time rates are now 100 times faster than with the previous solution.
As for the Revolut app, which has approximately 2 million users, the company can now analyse large datasets spanning several sources to drive customer satisfaction.
Moreover, it’s not only data scientists benefiting from the data. Revolut provides every employee with an open-source BI tool and self-service access to the central repository. This data underpins the critical performance indicators (KPIs) for every team, meaning everyone across the business has a much better grasp of company goals and data trends – and the ability to act upon it.
Unlocking the value of customer data
With the demand for fast information higher than ever, a progressive data strategy that effectively collects, integrates and manages data so that it can be acted on, is the best way for financial services businesses to stay ahead.
In the last year, the majority of organisations (86%) we surveyed are using data and analytics to support the development of new products as a result of access to previously unattainable data.Some banks have reached incredible levels of granular personalisation to build ‘next product to purchase’ models that increase sales and customer retention. They do this by analysing customer demographics, credit card statements, point of sale data, online and mobile transfers, payments and credit bureau data, to discover similarities that lead to tens of thousands of micro-segmentations in a customer base.
Choosing the right analytics database is central to making all this possible and ultimately driving better customer experiences and loyalty for financial organisations.