Why should my bank start making data-driven decisions? It’s a common question many bank executives are asking as they see the competition leveraging customer data to improve service, better segment, mitigate risk, enhance marketing messages, and drive new business opportunities.
Banks are sitting on mountains of customer data that can be used to make faster, smarter, more accurate decisions that impact everything from service to the bottom line. Some industry experts expect a sevenfold increase in the volume of data before 2020. Validation that starting on the advanced analytics journey today, will help those who embrace data-driven decisions, remain competitive. For those bank executives grappling with the pay-off of an advanced analytics initiative, consider these three compelling advantages:
The competitive advantage
Over the last few years, several tier one financial institutions have redirected and reinvested capital into an analytics initiative. In 2014, Wells Fargo hired their first ever chief data officer and employed an army of data analysts and scientists. Through intelligence extracted from customer analytics and data, they aim to make their customers’ lives easier, to enhance the customer experience, to mitigate risk, and to ensure that they are fully compliant with privacy regulations as well as their own privacy policies. Just over a year ago, Fifth Third Bancorp invested in data science and analytic platforms to progress toward developing a pipeline of intelligent commerce solutions.
The rise of advanced analytics has been a transformative change for every organization leveraging its power to identify models and algorithms to lift customer service, contribution and loyalty, improve efficiency, quickly solve business challenges, mitigate risk, and to make the next best offers in real time via digital channels. Keeping the competition in the rearview mirror requires gaining a deeper understanding of the organization’s data, and how to transform this information into an asset.
Many community banks hold a key differentiator from the mega banks—local community service and involvement and building customer relationships—but the best customers are the ideal targets of a competitor’s advanced analytical model. I submit that community banks should begin adding a data-driven culture to their business model; this requires that advanced analytics be at the forefront of the business plan. The algorithms and models for action will help deliver highly personalized and tailored services to customers that will translate into increased market share, revenue and customer loyalty for years to come.
Start with the end In mind
Turning the data generated by customers into actionable, comprehensive insights is a big task—and a long journey. The initial steps should include establishing short-term and long-term business goals. Once ready with an effective process to collect, cleanse, compute and consume the data, a solid multi-step process needs to be established to use this complex data to better pinpoint customer needs and interests or identify business opportunities to extract untapped value.
On average, an analytics implementation can take 120-180 days. But post implementation, the ultimate reward is when the bank can establish a set of recurring best practices derived by specific actions on specific data segments that deliver the right message, to the right customer through the right channel, at the right time. These statistically predictive results lead to prescriptive actions that can be repeated and further refined over time for ongoing success. While there are many immediate actions that will deliver a return, building prescriptive action models require an average of 18 to 36 months; it is best to take that first step now.
Investing in the future
In time, banks will have the ability to take action on specific data segments based on customer contribution, next best product models and algorithms that identify opportunity. Such action lists are important resources to the frontline teams and should both align with strategic goals and be measured through scorecard or incentive module feedback. Top performers can be recognized and by becoming a learning organization (a concept coined through the work and research of Peter Senge), the bank can facilitate the learning of its customers and continuously transform itself. The culture of the bank begins to change, strategic initiatives are measured, resources are provided to team members in the form of information and action lists, and performance can be assessed. Data science is the next step in business intelligence and will drive improved customer service, retention and contribution.
It is estimated that by 2020 the number of analytics service providers used by financial institutions will quadruple. Budgeting and planning for a robust advanced analytics initiative now will separate the market leaders from those who chose to lag behind. It’s time to embrace one of the greatest technological advancements of the 21st century.