April 17, 2014
There are 90,000 bank branch offices across North America. Each branch has its own specific product needs depending on its demographic and surrounding culture. Banks purchase market specific data on a regular basis. The data is dated and not about their customers and prospects nor do they address the needs of the customers or businesses. Banks use this data to develop promotional as well as products and sales strategies. Unfortunately making strategic decisions based on what has happened in the past may not give the expected results.
Take the example of two branch offices, both located in the suburbs of small urban environments. Both are populated by young families, with the average age of 32, and with incomes around $50,000. One is located in Nebraska while the other is located in North Carolina. The group in Nebraska is primarily farmers and in farming-related industries where the group in North Carolina are primarily entrepreneurs and professional services. Both groups save, spend and live differently and they need different types of banking products but based on the data they look the same.
How can banks ensure that they are serving the needs of the customers that want to bank with them as well as what their current customers want today and not necessarily what they wanted last year? How can they easily qualify prospects and turn them into highly qualified leads. How can they create consistent recommendations and buying experiences across hundreds if not thousands of branch offices? The answer: with real-time product recommendations based on customer preferences, bank criteria/policies and predictive analytics.
Ignite’s solution scientifically ensures correct product recommendations across the branch network and consistently across all channels. It is able to track recommended products vs. account openings AND the gap between them. The branch manager can run reports that will tell them what has been recommended, what has been sold, and the gap between the two. This helps the branch manager and the entire retail executive teams manage the team to increase sales performance and enhance organizational efficiency.
Case in point – I was recently working with a bank that was following conventional wisdom which indicated the most profitable products are those that serve the top 5% – older wealthy clientele. Their entire product strategy at all of their branches targeted this market. After using recommendation guides that provide real-time data on prospective customers, the bank found out that the majority of people visiting their website and those that wanted to be served by their branches in urban areas were actually 28 year old females living in apartments. They had no idea. The branches were recommending one set of products but were opening a different set of accounts which was identified by the aforementioned “Gap Analysis.” Not only was this is a segment that previously ignored but it turned out to be more profitable than their original target as well as critical to the banks future growth.
Find and fill your GAP. To learn more about real-time product recommendation guides with analytics firstname.lastname@example.org.
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