With COVID-19 forcing consumers to access their finances through online services, financial institutions need to reevaluate how, when and where they engage with customers. The standard approach to delivering customer experience is no longer good enough.
To its credit, the industry knows this: According to a recent
If the last few weeks have been any indication, customer preferences are shifting radically, and businesses need to be agile enough to adapt to changing expectations. When looking back at the pandemic, customers will only remember which companies went above and beyond for them.
That said, digitally transforming how a company operates is no easy feat. How can organizations that are so focused on hyper-methodical processes, quickly adapt and transform to meet customers’ shifting expectations? Where should they start?
My own recommendation is that financial institutions consider employing a data-driven operating model (DDOM). By focusing on three pillars – KPIs, data management, and channel selection – they can not only engage but keep up with customers, while remaining agile and refining their playbook every step of the way.
The first step in adopting a DDOM approach to customer engagement and originations is developing a common set of KPIs. This allows financial institutions and their marketing teams to measure the full customer journey – discovery, trial, purchase, usage and renewal – in both financial and non-financial terms.
Having a common set of KPIs enables teams to uncover correlations and signals that drive customer value and conversion along the way, as well as course-correct as needed. Additionally, by adopting a common vocabulary and method of measuring, it ensures better alignment between teams and a laser focus on the interactions that matter most.
At this point, calls for organizations to break down siloes between departments is a little cliché and ignores the reason siloes exist in the first place. Financial institutions wear many hats, and those hats are often specialized, so it makes sense to let teams specialize in their areas of expertise and remain in their own lanes.
For example, a banking customer’s tax returns, payment history, and rental information are collected, assessed, and acted on differently depending on whether the customer is applying for a mortgage, a loan, or a credit card. Each silo might be conducting its business separately, but the roads stopping at them all lead to the same destination.
A top-down, bottom-up approach to data integration allows businesses to closely examine their customer’s journey to identify the most important pain points and opportunities, while specialized teams can connect those findings to a company’s data assets to uncover the level of effort required to integrate each one.
Identifying the data sources that are easiest to integrate and likely to have the largest impact allows the company’s marketing team to pursue a few quick wins, accelerating buy-in from other departments and enabling them to build up the workforce and budget required to integrate data sources that require more resources and effort.
