success story

2.9 million personalized interactions driven by an AI-powered customer decision hub

The client, a premier national bank, wanted to improve client retention rates through customer engagement. However, its legacy system could not scale to meet growing customer expectations. Since many processes were still manual, and there was no omnichannel view or support for customers, they could not deliver personalized, contextual experiences in real-time. 

Their goal was to deliver effective marketing campaigns using predictive analytics and adaptive learning while enabling business users to contribute and coordinate work with IT. 

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The Challenge

The client sought to improve customer retention rates with AI/ML-led activities to increase customer engagement.

The client wanted to: 

  • Digitally provide customers with a personalized, branchlike experience to show their customers that they know and care about them as a banker would within a branch
  • Feed relevant and timely reminders and recommendations to current customers on their banking homepage and through email to demonstrate the bank's value 
  • Push next-best-actions and offers to customers via their preferred digital channels, including web, SMS, or automated phone calls, based on their needs, behaviors, demographics, buying patterns, and in-branch activities
The Solution

Implementing vEngage on Pega and integrating it with other technologies, including Adobe, enabled the client to use Pega Customer Decision Hub.

The Pega engine powers the bank's new customer decision hub to create personalized interactions with customers through:

  • AI-powered decisioning capabilities 
  • Analytics-driven real-time recommendations
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The Benefit

The bank delivered ~2.9 million personalized, analytics-driven interactions after implementation and can now hyper-personalize targeted product offerings and services through relevant channels at the most appropriate time.

Gaining the ability to assess the call deflection rate using adaptive modeling prioritization and the accuracy of intent prediction between traditional static ranking and adaptive AI-prioritization also helped the bank achieve significant results.

vEngage helped the bank:

  •  Increase customer interactions by 5% with the potential to scale up by 30-50%
  • Increase click-through rates by 3.5% with the potential to scale up by 25-50%
  • Send ~250,000 targeted emails per day to reach over 400,000 customers

 

Ensuring process improvements with digitization and machine learning (ML)

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