IBM Case Studies A foundation of analytics and personalized engagement build a customer-centric business model
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A foundation of analytics and personalized engagement build a customer-centric business model

IBM
Application Infrastructure & Middleware - API Integration & Management
Application Infrastructure & Middleware - Data Exchange & Integration
Finance & Insurance
Business Operation
Sales & Marketing
Data Science Services
System Integration
Grupo Financiero Banorte, Mexico’s third-largest bank, was facing a challenge of maintaining personal connections with customers due to rapid growth, especially through acquisitions. The bank wanted to strengthen its connection with customers by infusing all of its frontline processes with deep insights into the customer to drive a more engaging multichannel experience. The goal was to deliver precise personalized customer interaction on a very large scale.
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Founded in 1899 and based in Monterrey, Grupo Financiero Banorte became Mexico’s third-largest bank, and the biggest Mexican-controlled bank, after merging with IXE Financial Group in 2011. Banorte operates a network of more than 1,300 branches, over 6,700 ATMs, 110,000 point- of-sale (POS) terminals and 25,000 employees. The organization had revenues of USD3.2 billion in 2012 on total assets of USD71.7 billion.
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Banorte implemented a solution that uses models to extract insights into how its customers’ lives are changing and uses those insights to craft highly targeted cross-selling offers. The company is using “next best action” decision optimization algorithms to guide local branch and call-center representatives in their direct dealings with customers. The solution components include IBM InfoSphere Master Data Management, IBM WebSphere Multichannel Banking Transformation Toolkit, IBM WebSphere Application Server, IBM Campaign, IBM Operational Decision Manager, IBM Global Services — Global Business Services, and IBM Research.
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Banorte is delivering precise personalized customer interaction on a very large scale.
The business uses models to extract insights into how its customers’ lives are changing.
The company is using “next best action” decision optimization algorithms to guide local branch and call-center representatives in their direct dealings with customers.
Banorte expects to generate a bottom-line benefit of USD200 million in the first 24 months of the project.
The bank anticipates increasing its operating efficiency by 40 percent.
Significant boost in both customer retention and revenue per customer.
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