How banks fail to make use of data – and what to do about it

PCM spoke to Andrej Andelic, Business Development Executive at Nets, about what banks are missing out on by not maximising the benefits of customer transaction data.

How banks fail to make use of data

PCM: Many larger banks appear to have sophisticated data management teams. So what, in your view, is the data management problem?

AA: Firstly, large banks may make significant investments into data management techniques – but that doesn’t mean those techniques are relevant, sophisticated or up to date.

Furthermore, time poverty and budget constraints mean that banks of all sizes don’t keep up with the latest trends in data analysis – so their interpretations of customer data are not as effective as they could be.

Finally – as we argue in a new paper most existing systems are inflexible and expensive to update. These factors prevent a lot of banks from making the most of their data.

PCM: But why is data management so important to banks?

Customer data is an unexplored goldmine for most banks. If managed and interpreted better, these data  sets could deliver to outstanding business performance.

By using sophisticated interpretation techniques, banks could discover the costs and benefits of their card programs, targeted their marketing activities to make them more effective – and develop new products and a better understanding of their customers.

As we show on our website, banks that fail to properly exploit their customer data are missing out on strategic and tactical opportunities to improve the efficiency and profitability of their card business, increase transaction volumes and values and better serve different customer demographics, as well as creating new products.

PCM: So how can banks improve their data analytics?

AA: They should be looking at a sophisticated suite of data analytics tools that can be applied to their card products. At Nets, we call this Data Analytics as a Service, or DAaaS.

Our product suite filters and aggregates customer transaction data and interprets it to deliver fully customised reports that group and visualise customer behaviours using RFM analysis.

RFM analysis allows banks to interpret the recency (R), frequency (F) and monetary value (M) of user behaviour, all in a cost-effective, continuously updated package.

These product suites save business analysts time by aggregating transaction data and comparing it to customer profiles, as well as providing novel analytics through segmentation.

Business analysts can group transactions by product, customer demographic, time series, location and a host of other options.

Results can be compared against targets for that payment product, customer segment or other criteria, enabling the rapid development of an accurate, effective business strategy.

Find out more about how your bank can improve its data aggregation, management and analysis at low cost. Download more information about DAaaS from Nets now.

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