How can community banks gain competitive advantage with predictive analytics?

NEW YORK - Nov. 3, 2013 - PRLog -- BY YONI ELMALEM AND EMILIE PERIN, Inbox America. November 3, 2013

With 92% of retail banks’ operating costs driven by data management, managing data is truly at the center of the banks executives’ attention. Data management is too often seen as a burden rather than an opportunity - especially for small institutions which cannot afford to have in-house teams of data specialists. Community banks fall into that category.

As Eric Siegel –founder of Predictive Analytics World– puts it: “Data in its raw form is boring crud. The gold is what’s discovered therein”. Indeed every bit of available data should be used to strengthen the business, in a smart way: that’s predictive analytics’ value proposition.

Predictive analytics is an area of data mining which leverages customers’ data to predict customers’ activity trends and behavior patterns. According to Howard Rubin, in his book, Technology Economics: it is today “the best opportunity for banks to grow revenue at the lowest costs”.


Why is predictive analytics so important for community banks?

Data analytics is nowadays more and more used to analyze customer’s behaviors in order to make smarter decisions. And American banks have understood this, as 40% of US banks ramp-up to launch big data and analytics strategies, according to IDC financial insights. So, as community banks face tough competition from big players – such as JP Morgan Chase, Citibank, Bank of America, Wells Fargo, or PNC - predictive analytics can provide an crucial advantage to stay in the game or even be beat competitors as well as to increase their overall efficiency.

Bridging the “efficiency gap” between community banks and others networks.

In December 2012, the FDIC pointed out, in a study, an “efficiency gap” between non-community banks and community banks. Indeed, mainly because of their size, community banks are often less efficient than larger networks since they cannot perform economies of scale. On average their efficiency ratio is 9.2% below non-community banks’. Moreover, banks customers are today more demanding and less loyal. This affects even more the banks-customers relationship, which is at the core of community banks’ activity.

A solution to fill this gap is use innovative ways to detect changes in customer behaviors, predict next move and understand how to act appropriately –for the common interest of both the customer and the bank. Predictive analytics can help answer many of these issues: improving risk management, acquiring and retaining profitable customers, provide business insights to detect and unleash hidden value while improving customer experience and strengthen customer interactions.

Nowadays many US banks use analytics to change their marketing strategy from “product-centered” to “customer-centered”. But the issue is more to tailor their operations to the very needs of each individual customer.

This won’t be achieved with traditional segmentation techniques. One must apply predictive analytics to provide insights about each customer’s needs and preferences (i.e. products, campaigns, channels) and enhance your customer relationship.

Another example of predictive analytics benefits is the detection of early signs of disengagement to help prevent churn.  Thanks to transactions numbers and characteristics, a warning system could be implemented to inform bank’ sales teams when a relationship needs is at risk and needs to be nurtured. As everybody knows, acquiring a new customer is much more expensive than to retain one. This is why many banks already use predictive analytics to improve customer loyalty.

For sure predictive analytics can help turn banks data into profit and increase their profitability. But it doesn’t replace traditional customer advisors. Indeed, sales representatives are at the center of the relation with customers –which is supposed to be the strength of community banks. Since community banks have closer relationships with their customers, they should get more information about them and should be able to leverage it better than larger banks.

So, analytics can empower banks to better operational decisions and take full advantage of their customer’s capital. But today’s complex questions cannot be answered with static information that was computed weeks or months ago. The key success factor is to link predictive analytics real-time to bank’s sales teams’ operational processes and automated real-time insights.

As Howard Rubin points out: “If the problem with the future is that it’s not what it used to be, then those who figure out what it is first, will be the winners.” And predictive analytics is the best way to go there.

Sources:
1 Howard Rubin, author of “Technology Economics: the cost of data”.
2 “FDIC Community banking study” Dec. 2012
3 SAS - "Banking on Analytics"


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About Inbox America

Inbox is a global consulting firm that specializes in marketing and predictive analytics to provide customized solutions for data-intensive organizations. Inbox employs marketing and research professionals to design solutions to advise both private and public sector clients on how to meet the complex changing needs of their customers.