Investing in data and analytics to support the customer experience have been top trends in the financial industry for a number of years, delivering profitable outcomes. Yet, as banks and credit unions continue to develop expertise and drive innovation in these areas, technology
Deloitte highlights in it’s, 2019 Banking Industry Outlook report that with these market changes, the nature of customer demand is shifting rapidly. Customer expectations are changing and they are increasingly expecting bespoke, value-added services. With global volatility and external change occurring faster than before, Deloitte suggests financial institutions reimagine transformation as a “holistic, multi-year process” and “discard grand visions of becoming “a technology company” and instead focus on customers … with data as the bond”.
With this recommendation in mind, here are three ways banks and credit unions can support customer decision-making and engagement using data and analytics:
Integrate feedback across channels
Inviting, reviewing and learning, and actioning customer feedback has become an industry standard best practice across all levels and departments, and all sizes of financial institutions. Doing so regularly has enabled organizations to continuously improve its customer experience while creating the personalization customers are looking for.
While it remains important to action the explicit feedback obtained from your customers, to get the full picture, banks and credit unions must also ‘listen’ to what customers are telling them through their behaviour. Aggregating customer behaviour and feedback, across channels and the customer journey, provides deeper insights and leads to better more innovative solutions. With today’s data capabilities, financial institutions are able to manage this data at different levels to serve a multitude of purposes, from the high level ‘big picture’ down to the personalization of individual clients experience.
Drive revenue, reduce risk
WIthin financial institutions, nearly all decisions are made to: drive revenue, control costs or mitigate risk. While human nature introduces emotional influences, the decision making of customers is often guided by the same principles.
With this fundamental in mind, data and analytics can help banks and credit unions not just personalize and support customers with customized experiences and solutions that meets their needs, but move towards predicting their decision-making requirements. These predictions can improve programs to support user experiences by offering personalized information, when customers need it, to assist them in making decisions.
Differentiating is a critical strategy to support customers in choosing a bank or credit union to do business with. Business fundamentals suggest that financial institutions can differentiate to attract clients through two paths: cost leadership or product differentiation. Data and analytics can help achieve both.
Achieving cost leadership requires operations to be effective, at the lowest cost. Over the last decade, financial institutions have implemented automation and data analytics programs to reduce cost and effort across the business. These savings have been reinvested back into the business to support innovation or enabled a reduction in client pricing to retain competitiveness or achieve cost leadership. As the data insights become deeper, the potential for cost savings remains available for financial institutions to realize.
Creating product differentiation requires more than creativity and innovation leadership. It requires a strong foundation of data including a thorough understanding of the buyer’s expectations and needs. Here data and analytics play a role both to establish market needs, sizing and competition, but also to predict adoption, pricing and competitive response.
Carly Fiorina, Former CEO of HP has said that “the goal is to turn data into information, and information into insight.” With the financial industry actively developing their information into insights, the winners will be those that can successfully put them into action. How are you using data and analytics to support customer decision-making and engagement? We’d love to hear. Share with us at email@example.com.