Insights gained from data analytics are set to have a profound effect on retail banking, explains Damian Young, director of banking EMEA Nomis Solutions. Yet the potential to refine future forecasts and tailor a better offering for their customers makes analytics too valuable an opportunity to ignore for much longer
Retail banks have a tremendous opportunity to use insight extracted from data to refine their customer offering. These insights add a level of refinement and specifics at the customer, transaction and event levels and can help explain not only what happened, but why it happened and when, to which customer, and even potentially predict if it will occur again and under what circumstances?
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A recent report by Ventana Research described price and revenue optimisation in banking as a natural fit for the application of big data analytics, sifting through large data sets to collect characteristics of consumer behaviour that enable banks to identify customer segments and quantify their price sensitivity.
The firm’s senior vice president of research, Robert Kugel explains that big data analytics software can help users manage more granularity in the process of defining offers for customers (and the levels of discretion they allow to account managers and sales people to set prices) as well as the terms and conditions of the transaction.
Upon identifying characteristics that influence buyers’ price sensitivity, banks can combine the most relevant factors to present a price that will enable them to optimise revenue and profits from those customers.
Enabling them to automate analytics and reporting as well as facilitating management of the related data, makes it easier for a bank to set prices in a way that best matches its strategic objectives, whether that is to be a market share leader in specific product categories or to maximise returns on certain assets.
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By GlobalDataKugel observes that using price and revenue optimisation rather than simplistic risk-based pricing can provide a competitive advantage in achieving higher returns on assets and lower costs of capital. It also has the advantage of often being counterintuitive and therefore offering strategies unavailable to less well-informed competitors.
Predictive analytics allows banks to quantify the expected movements of balances in a way that eliminates some of the uncertainty. Being able to predict which customers are more price sensitive to acquisition and retention rates, enables the bank to plan for trade-offs and ultimately make better long term decisions.
References to the longer term also remind us that there are many potential applications of data analytics in retail banking that have yet to be developed. For example, analysis of customer spending behaviour patterns, exposures, and risks could eventually enable retail banks to offer services such as dynamic monthly credit card allowances.
The main expectations that banks have for data analytics include:
- Enabling them to tailor their offerings to the needs of individual customers
- Improving trading strategies
- Providing better insights into market dynamics and improve market research
- Improving their ability to react to internal and external issues
- Speeding up high quality decision-making processes
- Identifying possibilities for revenue enhancement and cost reduction
Banks clearly recognise the need to get support in technology and data science to execute banking and customer strategies and many are partnering with us for this reason.
They are using such solutions for a variety of reasons: to predict future customer behaviour and thus tailor customer solutions; improve their bottom line; provide management insights for strategy solutions; provide answers to issues (including regulatory issues); make faster decisions; and enhance balance sheet and P&L management.
The value of this partnership is clear – we have helped these banking customers improve bottom line performance at the portfolio level by an average of 17%.
