Reshaping the retail bank branch strategy
Optimise your physical footprint through machine learning and cross-channel management
Despite the growing importance of digital distribution in banking, human touch is still deemed important. In the UK, a third of retail banking customers1 prefer to use bank branches to accommodate all of their banking needs. 20% of customers will follow the digital route for basic products and services, whilst still using branches for more complex advice. Even those who are the most 'digitally savvy' still state their preference for face-to-face interaction when concerning some services and advice, albeit being comfortable using many other digital services.
This preference for branch interactions does not negate the willingness to move to more digital platforms, but it is important to understand the impact that the combination of physical and digital channels have on customer acquisition and retention.
Digital only banks have certainly harnessed the shifting customer preference towards digital, but there is a clear advantage for traditional banks to harness both platforms suitably.
Real estate is often the highest overhead and operating cost for retail banks, and presents a challenge for banks with a high number of physical branches. To address this, banks must ask several questions;
BearingPoint has pioneered an approach to optimise future physical footprint requirements, servicing customer driven demand across channels whilst minimising costs.
Our approach brings together customer / product and channel transaction level data over time with external demographics and location data to identify patterns in digital propensity aligned with customer and demographic behavioural segments.
This rich insight enables you to answers multiple questions;
Those questions are becoming more common amongst our clients. In our most recent example, a major French retail bank with more than 1,600 branches and 6.2 million customers was looking to optimise their channels to service their customers' changing needs. With a long-standing branch network and a full mobile and online offering, the question was not only around the format and optimal use of the branch network, but how the same data can be used to drive overall performance.
Through the use of Hypercube, a BearingPoint big data analytics tool, we applied machine learning to create customer, service, product, and geographical profiling, informing the bank's branch and channel strategy. The result was the introduction of a new process on reviewing branch performance, product and service mix, as well as the adoption of new ways of diverting customers to the different channels based on needs. The result was not only the optimisation of the bank's performance, but also increased customer satisfaction levels by responding to the different needs and means of communication to the various demographic segments.
1 McKinsey & Co, September 2017