A focus on predictive analytics can position retail banks for continued success in slow or very competitive markets.
While this is not a comprehensive look at how predictive modeling can give your financial organization a leg up, we’ve examined some promising research to bring you three areas where predictive analytics will be key this year and beyond.
1. Increased customer knowledge and acquisition
The companies that tap into the power of forecasting with predictive analytics rather than relying on historical data will see an increase in profitability of up to 20% in the next three years, according to a recent report from Gartner Inc.
A recent survey of 123 financial services executives by Aberdeen Research confirms this.
Adopters of predictive analytics have realized a 10% increase in new customer opportunities identified in the past year. And the increased visibility into potential customers leads to “longstanding, profitable relationships,” according to Peter Krensky, Aberdeen’s senior research associate for business analytics.
Predictive analytics reveal untapped market segments full of prospects that business development decision makers can focus on, he notes. And retail banks can use predictive analytics in real time to personalize services for their clients as well as make better business decisions.
2. Customer retention
Just like with new customer acquisition, predictive analytics can give retail banks a competitive advantage in customer retention. The retention ratio measures how many customers renew services.
Companies leveraging predictive analytics report an “average 8% increase in cross-sell and upsell revenue,” Krensky notes.
Only about 10% of a retail bank’s customers make up 50% of its profitable revenue. So banks that want to unlock further profit can use predictive analytics to understand how to market to the other 90%, according to Edwin van der Ouderaa, senior executive in Accenture’s Financial Services group.
“So, customer centricity and analytics will in the future all be about how to understand what that 90 percent is, and unlock them, that will typically be done by tapping into social network data, other behavioral statistical data and models for trends and styles and values and fashion even of those customer segments,” he says.
3. More real-time capabilities
Finally, Krensky’s research shows that “financial services firms with predictive analytics are twice as likely as those without to have real-time analytics capabilities.” The advantage here is that they are working in the present and are better poised for the future.
And Krensky notes that real-time analytics bring value to other areas of the business including “operational intelligence, monitoring markets and risk management.”
Looking ahead, the next generation of high-performing retail banks will be the ones that can “harness the real-time and predictive aspects of analytics and combine it with the user experience,” van der Ouderaa says.
- We invite you to watch our complimentary, on-demand webcast, “Retail Banking & Insurance: Customer Data Analysis.” In this webcast you will learn how leading financial institutions are using customer data to drive superior decisions, improve customer experience and make the most of their marketing budgets.
- Subscribe to our blog to stay up to date on the latest insights and trends in big data and big data analytics.