A data-driven approach to customer strategies
The ability to analyze data for relevant insights is critical to the effectiveness of banks’ acquisition, retention and engagement initiatives, especially during what is expected to be a prolonged environment of low interest rates and tough economic conditions. With the right data tools, banks can analyze their customers’ evolving behaviors – including fund transfers, savings patterns and demand for loans – to optimize initiatives for organizational goals.
Most banks mistakenly believe this involves huge financial outlays to acquire external data, AI and advanced analytics when in fact it more often comes down to leveraging capabilities that they already possess to translate the bank’s immense store of transactions and funds-movement data into actionable intelligence.
Banks have the data to build an interactive flow-of-funds model that can in real time analyze and extract insights from the movement of funds internally between a pair of products owned by the same customer and externally involving accounts held with other financial institutions.
Flow-of-funds models are not new in banking. In fact, many banks currently use something similar to determine customers’ price sensitivities in order to optimize deposit pricing. Mastery over deposit demand elasticities using flow-of-funds have given banks the ability to take surgical actions to raise or lower deposit rates in response to market conditions or to meet deposit portfolio targets.
Many banks have overlooked the fact that the data analytics capabilities that support deposit pricing also afford them the potential to extract critical intelligence to support acquisition, retention and engagement initiatives that increase their customers’ lifetime value with the bank.
Mapping money movement trends between various deposit, investing and lending accounts, can give a bank a 360-degree view of the nature of their customers’ relationship with them. Banks can measure and score customer relationships along commitment criteria such as engagement depth, loyalty, commitment and trust. Using a flow-of-funds model, a bank can also easily and accurately segment its deposit customer base based on key variables, including life stage, the degree of complexity in their financial lives and financial behaviors.
Big banks are already using these insights to deliver targeted offers and promotions on credit cards, loans and other banking products. More personalization is possible here. A well-timed deposit promotion customized for a high-value customer might be just the thing to keep him or her from transferring a large sum to an online bank paying higher rates. The ability to identify customers with a higher risk of attrition also gives the bank an opportunity to deploy personalized retention campaigns to improve customer interactions and value perception.
Close analysis of customers’ spending, savings, investing and borrowing patterns can uncover opportunities for product innovations. For example, a focused flow-of-funds trends analysis might reveal a large segment of high-earning bank customers struggling to save. The bank can consider introducing an AI-based auto-savings feature. Similarly, a service bundle featuring lucrative rewards might persuade another group of customers to consolidate their multi-bank financial accounts with the bank.
Another important application for flow-of-funds data is to support better decision-making. Recently, a bank was alarmed to see a steady loss in market share for long-term deposits, prompting them to wonder if they needed to price their high-yield products more aggressively. But on closer examination, using flow-of-funds analytics, the bank discovered client funds were actually moving into investments products within the bank, not to competitors. Having these insights helped this bank preserve net interest margins.
By observing banking and fund-movement behaviors of newly acquired customers over time, banks can also acquire the ability to distinguish between loyal customers versus rate chasers, who move money in and out of institutions in search of better deals. These observations can be used to develop more targeted and precise acquisition strategies.
Banks already in possession of data analytics capabilities in the form of flow-of-funds to support deposit pricing can consider expanding it for customer-relationship optimization, and to support lending, credit cards and financial advice. The ability to extract useful insights and business intelligence from transactions data is available to most banks. It can be an important step toward responding effectively to shifting market conditions and evolving banking behaviors.
As the retail banking landscape changes, sometimes in dramatic and unexpected ways, it is not just in pricing where we need to exhibit precision. We must also exercise greater precision in how we acquire, retain and engage customers.