BID® Daily Newsletter
Jul 29, 2022

BID® Daily Newsletter

Jul 29, 2022

Predictive Analytics Help Personalize Customer Relationships

Summary: According to a recent report from McKinsey & Co., 71% of customers expect companies to personalize their interactions. Community Financial Institutions (CFIs) have traditionally delivered personalized experiences through their customer interactions and building strong relationships. But now, it’s vital to aggregate, integrate, and analyze customer data in ways that let CFIs anticipate and predict future needs.

Fans of the television show “M*A*S*H” may remember Corporal Walter “Radar” O'Reilly, a company clerk who earned his nickname by finishing his boss’s sentences, appearing moments before anyone called him, and hearing helicopters bearing wounded soldiers before anyone else could. Radar’s talents were courtesy of the show’s script writers. In predictive finance, however, results come from targeted data analysis.
Predictive analytics are part of personalization

By analyzing data to predict what customers will need next, financial institutions are following a broad trend. According to a recent report from McKinsey & Co., 71% of customers expect companies to personalize their interactions, and 76% feel frustrated when this doesn’t happen. The consulting and research firm also reports that faster-growing companies get 40% more of their revenue from personalization than companies with slower growth.
How CFIs can employ predictive analytics

Community financial institutions (CFIs) have traditionally delivered personalized experiences by forming strong relationships with their customers and maintaining those relationships. In a world filled with online and mobile banking, however, it’s vital that smaller financial providers aggregate, integrate, and analyze customer data in ways that let them anticipate customer needs.
Advanced analytics. Data platforms and analytics solutions use AI, advanced analytics, and machine learning to help organizations make the most of customer insights by providing greater personalization. A CFI can use this technology to understand their customers even better, predict which customers may be looking for other financial providers, and gain insight into which products they should present to customers and when.
For example, an analysis of customer data could help you identify customers who have loans with your competitors. Once you can determine the investment amount or other terms of the debt, your institution can market them a similar offer, emphasizing the ease of transactions between these account types within your CFI.
Cross-selling opportunities. Data analysis can offer timeliness in other ways, too. For example, analyzing customer data could keep your institution from marketing a mortgage to customers who have recently refinanced their home loans. It could also point out the logical next step for other customers. Clients who have recently set up a custodial savings account might be interested in 529 account offerings. Those who have loans with your institution but don’t have deposit accounts might be tempted by a good rate on a deposit account. A customer who recently went from a single to a joint account might be interested in your mortgage products.
As we’ve mentioned before, customers want personalized service from financial providers, but they’re reluctant to share the personal data that would power that service, until a provider proves that they can be trusted and there is value in this exchange for the customer.
A user-friendly, authentication process. As the first point of contact between your institution and your customers, it’s important for an authentication process to create a positive experience. This can help build the trust that customers need to continue entering into a new or ongoing banking relationship.
By using data analysis, your institution can predict what customers are likely to need — or not need — next from you.
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