By Alan Shields, RFi Group
I think most would agree that it seems almost impossible to have a conversation in the banking industry these days without the word ‘digital’ being present – digital is clearly a strategic imperative of almost every bank in Australia.
It is interesting to note that when we talk about digital initiatives, it encompasses so many different aspects of the banking sphere. One of those aspects is the use of data and it goes hand in glove with digital in better understanding banking customers.
In conversation we have with the banks here in Australia, there is always lots of interest in how they can make better use of the data. This includes how to leverage third party data to deliver greater insights and bring the two together to deliver better outcomes. Ultimately, the success in this regard comes down to a couple of key things:
- Operationalising insights
- Using third party data to forecast business metrics
In the last 18 months RFi Research has been focusing closely on the main bank customer experience and usage (including product holdings and channel interactions) and using this to help our clients to overlay consumer segments onto their own data to better understand:
- Who they should be targeting from a customer segment perspective
- What customers hold outside of their main bank relationship in terms of products
- When these customers will be most receptive to targeted offers based on life stage
- Why each segment should be focused on from a KPI perspective
- How to pull the right levers that will result in improvements in NPS or cross-sell/ buy
Understanding share of wallet and operationalising consumer segments
Using third party data to forecast business metrics
As a company, we have also been leveraging advancements in machine learning to bring together disparate data sources and deliver insights that help predict how key business metrics will move going forward. Just last year we brought together over 2,400 macro-economic indicators across the Australian states to help predict what would happen to mortgage arrears in the future. Using an agile methodology, project sprints and a machine learning environment, RFi Analytics were able to determine which of the 2,400 indicators was most correlated to arrears in each state and with what lag.
RFi Analytics’ Predictive Model
Going forward, the overlap between data analytics and innovation in the digital space will only become greater. As a company we are always looking to better understand our clients and help them to better understand their customers and drive tangible business outcomes.
For more information please contact me, I'm always open to a conversation.
Managing Director – Consulting
Ph: +61 2 9126 2600
By Alan Shields, RFi Group