Investing in software and systems that can collect, collate, and analyze data has been as hot a topic in retail banking as in most other industries over the past few years. Mainly because of the promises – most vociferously made by the vendors of the software – that these investments will provide strong returns by giving bankers a clearer understanding of their customers.
But with these customers increasingly spreading their demand for financial services among different providers, including many new ones, retail banking executives are now more eager than ever to make sure these investments bear fruit. With a better understanding of customers, banks and credit unions hope to improve their marketing, acquire new clients, tailor products more precisely to different groups of people, and offer a more “seamless experience” for customers using different products from the same bank or different channels (a customer that increases their credit limit via a mobile app and then talks to customer service rep about opening a new savings account, for example).
The problem is that, despite managers’ fervent hopes, most banks have not yet achieved the results they hoped for from these investments. In part this is because they haven’t got a clear way to determine where to focus their analytics investments to improve customer engagement.
The Difference Between Not Losing Customers and Making Them More Loyal
When it comes to existing customers, there is a distinct difference between investments that will prevent “attrition” (the loss of customers) and those that will deepen customer relationships (i.e., persuade customers to purchase more services) and increase customer loyalty.
For example, many banks have invested in capabilities to help them share customer data across channels. This makes sense, as these capabilities are vital to providing a low-effort customer experience, which is the best way to ensure customers are not disloyal. The problem is that sharing information across channels will not increase loyalty or attract more customers, it will merely prevent customers from getting frustrated and leaving.
Customers have never understood why there are back-end siloes that prevent their data being shared – they only know that it does not make sense for their main bank to ask them how much money is in their checking account on a loan application when that checking accounts sits with the same institution. So these data investments are merely enabling banks to achieve the minimum standard customers expect.
Two Examples of How to Get it Right
The key to ensuring that investments in analytics investments increase customer engagement is for managers to ask themselves if any investment will enable them to use customer data to help uncover and address unmet customer challenges to reaching their financial goals.
Two retail banking firms provide good examples of how to use data analytics investments to engage customers.
Intercepting financial missteps before they can occur: Banco de Crédito del Perú analyzes customer data to send helpful alerts to customers before they run into financial trouble and so avoid frustrating, costly error resolution later on.
For example, if a customer uses her credit card to purchase an airline ticket, the bank contacts her and asks if it should set a travel alert on the account, rather than waiting for the customer to go abroad and have a card declined.
This helps to increase engagement by ensuring that the customer continues to use their card in the short-term, and in the long-term is more likely to stick with BCP for additional product needs since the bank supports her best interests.
Recommending the right next step: Digit analyzes user income and spending habits, and then determines a reasonable amount that can be saved regularly without meaningfully affecting the user’s lifestyle. The app then makes small, automatic withdrawals from the user’s checking account into an alternate savings account.
Digit keeps users engaged by helping them realize the benefits of incremental savings over time, while also using its data on customers to actually take steps on the customer’s behalf that help them increase their savings.