Avi Patel, CMO
Fulton Financial Corporation
The financial services industry has been challenged to continue to transform itself in light of technology advancements and rapidly changing customer expectations. Given this, the brick-and-mortar financial center network needed to be evaluated and optimized in order to produce optimal shareholder return while meeting the needs of customers and the marketplace.
To address this challenge, an unsupervised machine learning technique was used to cluster the existing financial center locations. Financial centers were assigned to a cluster which was indicative of the customer base, transaction levels, sales opportunities, and surrounding market opportunity. Each cluster was then profiled to help rationalize the required financial center design. Ultimately, this work influenced staffing and technology within a financial center to maximize revenue while minimizing expense. A network optimization exercise was then conducted to identify potential closure and consolidation opportunities across the franchise. The objective of this analysis was to reduce expense while minimizing attrition and customer impact, thus increasing overall revenue. A multi-optimization technique was used to uniquely evaluate hundreds of variables to yield recommendations for closures/consolidation. By incorporating transactional data into the analysis and combining that with customer behavior analytics, customer impact of closures and consolidations was much more effectively predicted and managed.
This work had a far-reaching impact on the organization. It influenced an initial optimization pilot of financial centers based on the defined formats with reimagined staffing and technology. Initial results of the pilot reflected an increase to product cross-sales and an increase to customer satisfaction by 17% as financial center staffing was properly aligned to customer opportunity with supporting technology to support transactions efficiently. The closure/consolidation work has yielded analytically-based recommendations that account for the financial impact of business decisions but - equally as important - the customer impact of those decisions.