Leveraging the use of data to design, develop and deliver consumer services has become the norm in industry and this is no different in retail financial services.
Consumers’ everyday life consists of the transfer and transmission of huge amounts of data that ultimately shapes the way we all do business in society today. Whether it’s through social networking, shopping or business and pleasure travelling, data has become the cornerstone of delivering better experiences, faster delivery and right fit solutions. Banks are no exception in this area, but have been a little slower to respond to the significant signals that their data tells them about their customers.
Pricing analytics is one of these key areas in banking. Pricing analytics married with consumer behavioural insights allows banks to get closer to the nirvana of higher customer satisfaction and increased profitability.
Pricing analytics is not simply finding the right price point for a single transaction, but finding the optimum price points to deliver multiple business objectives. By deeply understanding consumer attributes, behaviours and sensitivity to pricing, banks are using price optimisation methodologies not only to find the right price position but to also drive customer retention strategies, acquisition strategies, customer satisfaction goals, product design, channel selection and marketing/sales strategies.
Banks have not been at the forefront of data science in the past, but more and more they are recognising, not only the criticality of this, but also the need to partner to find the best solutions quickly. There have been numerous barriers to banks leveraging data to inform strategy and customer propositions. Firstly, legacy systems built around product and financial reporting has reduced their ability to truly understand portfolio and customer views. Secondly, price sensitivity is not an observable behaviour and it is therefore difficult to isolate the factors that influence it, as well as the factors that influence customers who do not care as much about price. Finally, modelling for price sensitivities while considering customer attributes and behaviours is not easy and banks’ core competencies are not in data modelling.
As we enter a new era of financial services, where consumers expect more, delivery needs to be slick, transparency is a must and economic activity (including interest rates rises) is changing, portfolio strategies MUST deeply understand consumer behaviours and more importantly help to predict future behaviours.
In the next cycle of interest rates, applying analytics to pricing will be key. An improvement in interest margin of just 5bps in a year on a deposits portfolio of €50bn is a direct bottom line improvement of €25m per year. And it is not only about the price point as I alluded to earlier. Banks increasingly want to increase their share of main account holders, as the probability of increasing product holdings of these customers is far greater. Pricing analytics will help banks maximise the opportunity to both retain these customers as well as acquire new customers that are likely to become lifelong customers. It’s not simply the transactional benefit that needs to be examined here but the share of wallet and lifetime value benefits.
Of course, the highest immediate value is realised in a moving rate environment when banks have to constantly change their prices. Short-term rates move quickly when rates are rising, so banks have to be able to react to these changes and what their competitors are doing. In this situation, the ability to run calculations on a daily basis is clearly invaluable. This can sometimes present challenges within banks, given the restrictions on operationalising the modelling process.
A report by Ventana Research describes 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 granularly the process of defining offers to 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.
Banks have countless reasons to deploy data analytics to their pricing disciplines. Not only does it have a direct impact on bottom line, but also it allows banks to be more nimble, react faster and be more certain on outcomes and results. After all, consumers expect this.
By Damian Young, Managing Director, Nomis Solutions