Seeking an edge: Exploiting alternative big data sources and customer data for financial gain
The alpha impact: Competitive differentiation
The notion of alpha is gaining prominence throughout financial services because of its monetization capacity. Technically, alpha refers to “the profit gap,” Loughlin explained. The market is predicting a certain level of performance based on all the information available. “If you can use better information to predict, say, higher performance, that gap is your specialized information. That’s alpha.” This concept is also applicable within organizations, as illustrated by the transfer of wealth from parents to millennials. “It would be good to know that the person who opened a checking account with $250 in it could be someone who’s going to inherit a million dollars because of their parent’s wealth,” Loughlin remarked. “That customer is someone you may want to pay more attention to than someone who’s got $250 in their account and that’s all they’ll ever have.”
Organizations that truly know their customers and have the data management rigors in place to understand customer relationships can capitalize on internal data from traditional data sources. Couples may have a joint checking account, for example, but each spouse has individual products and services as well. Centralizing the big data management on the back end to understand how those products might relate to the joint account (and vice versa) can lead to offers for additional products and services for each person. Information about customer relationships may stem from internal or external sources, traditional or alternative ones. The 360-degree view of the customer is based not only on the databases and silos inside an organization, but ones outside as well, Loughlin said.
Customer lifetime value
The millennial use case is important because it involves assessing customer lifetime value—a process that’s considerably improved by integrating alternative data sources with traditional ones in a centralized manner. In order for organizations to prioritize customers with a higher lifetime value than others, they need comprehensive customer overviews involving alternative big data sources. According to Loughlin, those sources “could be familial wealth, it could be where they work, it could be their educational background, it could be who their friends are, it could be social media. It could be a lot of pieces of information about that person.”
Predictive analytics and machine learning play an integral role in determining customer lifetime value. According to Jeff Lee, CMO of Seacoast Bank, which implemented a customer lifetime value model across its organization, this model features a machine learning algorithm called “an opportunity sizing engine.” It is combing through the entire customer base on a regular basis doing analysis. “So if I’m a CPA and I bank at Seacoast, and Rob is a CPA and he banks at Seacoast, it’s comparing the upper core value and the lower core value [to determine] what we need to do to increase the value of that lower core value customer.”
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