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Case Study

Santander UK brings one-to-one customer engagement to life


  • Unified inbound and outbound marketing
  • Automated engagement with real-time event triggers
  • Increased relevancy of offers using predictive models and machine learning

The Business Issue

Santander UK’s mission is to “‘Help people and business to prosper.” However, disjointed legacy systems and a campaign-based marketing strategy made it impossible to deliver the desired results.

Santander was selecting customers in batch to meet campaign objectives by channel. But because their channels were not connected, customers received conflicting messages based upon where they chose to engage

And to make matters worse, the channel-specific campaigns were often leveraging data that was weeks old. Meaning by the time Santander UK was able to engage they were showing irrelevant information and offers.

The Solution

With Pega, Santander UK is evolving their customer engagement to become more customer centric with better quality conversations with their customers.

Santander UK analyzes customer attributes in real time to identify their customer’s needs – from managing their money, to buying a home, to starting a business – and uses this information optimize each engagement for customer value.

Santander uses Pega to optimize both inbound and outbound channels including: web, mobile, online banking, paid media (Facebook, Twitter, Google), email, SMS, direct mail, in branch, ATMs, and in the call center.

The Results

Since implementing Pega, Santander UK has:

  • 46M+ conversations optimized per month
  • 2,000 customer attributes analyzed in real time
  • Improved CSAT, ESAT (employee satisfaction), customer loyalty, and commercial performance
  • Financial Services
  • Marketing
See Santander UK’s journey to customer centricity | 37:28

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Related Resources

  • PegaWorld Video

    Learn how Santander markets successfully in a highly-regulated industry

  • Whitepaper

    See How to Move From One-to-Many Segmentation to a One-to-One customer approach