From ATMs, mobile apps, social media to USSD, customers are adopting and getting more comfortable with digital channels as touch-points for interacting with financial service providers. This presents an enormous wealth of data from which more value can be created for customers.
Taking this from potential to reality requires an understanding of what big data is and what distinguishes it from any other kind of data. Big data has come to be characterized by the metrics of volume, variability, and velocity. Millions of customers generate data every day, from activities around customer engagement, transactions, channel usage, product selection and more — both structured and unstructured in format; the perfect mould of big data.
In a time where customers have increasingly become digitally savvy from experiences trading with e-commerce, connecting on social networks and getting entertained via online media, expectations have risen as to what makes a quality digital service. Thankfully, with the huge amount of data generated by consumers, Big Data provides the ingredient required in giving customers a delightful digital experience; financial service providers can also achieve more efficiency in their operations.
Since all customers are not the same, implementing a one-size-fits-all strategy in the way a financial institution digitally interact with all its customers, results in wastage and missed opportunities.
Financial institutions must understand that customers are more than just a segment. An in-depth analysis of all data sets on customers can help to gain a better view of customers at a granular level. Big data goes further to observe patterns in the relationship between these segments and the correlations that result into each of them.
Well-defined customer segmentation isn’t the end in itself. Rather, it serves as the basis on which other services such as customer engagement, personalisation, continual service improvement, fraud detection and more can be built.
HDFC Bank of India’s customer engagement based on big data has helped it to boost the activation of credit cards. This has been made possible by the use of customer lifecycle events, with promotions targeted to each customer based on the lifecycle segment they are most likely to belong. This has also resulted in a reduction in the cost per acquisition of each customer.
A world of digital communications presents a lot of opportunities; financial institutions must, however, ensure that they can get to customers as signals separated from the noise. Building on segmentation, this can be achieved by truly knowing the customer, their relationship with the institution and determining their interests and preferences beyond what they say it is. The products promoted to each group and sub-group of customers are then selected on this basis.
This can also be used to determine the best channel of communication-based on the product or target group — learning from the data on what has worked best for each. A big data analysis of unknown attributes most common to a specific user group of a product can help in identifying which customers would most likely be interested in it and then promote it to them with the channel that converts the most.
Singapore’s largest local bank with operations engages it customers based on an analysis of their actions, lifetime events, and demographic profiles. With this, it has been able to achieve a 20% higher level of satisfaction in its engagement of customers.
Personalisation today has gone beyond interacting with the customer by first name and targeted promotions based on broad customer segments. Big data presents an opportunity to personalize the service experience to each customer.
As identified by FINEX solutions, a leading German Fintech Consulting Firm, the customer’s digital experience can now be tailored down to the language, user interface functionality, next best actions and call to actions based on the personality type of each customer. This depth of Personalisation is now possible due to the huge amount of data accessible to financial service providers as users engage with their digital channels.
On its iPhone app, American Express uses big data analytics to personalize the offers delivered to users. This ensures that users are not bombarded with offers, but only offered those relevant to them, subsequently leading to an increase in conversion.
The millennial generation now resorts to digital channels for accessing customer care services before they consider walking into the nearest branch. Gone are the days when service providers had to rely on direct feedback only to get the feedback from customers on user experience and the quality of service. As data is being generated across all digital touch-points — from self-initiated customer service requests to product usage — customers are always generating data that can be mined for insights in the area of service delivery.
An analysis of compliant emails, tweets and Facebook posts for the product or channel which it is most associated can provide a cue on which products or channel of service delivery needs the most improvement; this can dig deeper into the demography of the customers making these complaints, provided iterations need to be made or a new product needs to be created for these customers.
Hong Kong’s DBS bank processes more than 25 million transactions per month. Using data analytics, the bank has understood the patterns of ATM cash flow and to predict when ATMs would run out of cash. This has led to almost zero cash out and an increase in customers’ confidence in the reliability of their ATMs.
The benefits big data offer financial service providers extend beyond just the customer. It is helping service providers improve their internal operations in the areas of compliance, planning, detection of fraudulent activities, communications and brand management.
Though generating this data has become an innate capability of most financial service providers due to investments in e-banking infrastructure, obstacles mostly cited about big data initiatives are the existence of data in silos, the difficulty of getting management buy-in, and regulatory issues.
It is therefore recommended that beyond the readiness and capacity to convert these data into insights, big data initiatives are piloted with smaller projects. This being less resource intensive, will provide lessons and the case for scaling up or scaling down such initiatives.