BBVA has deployed generative AI to analyze hundreds of thousands of monthly customer conversations, moving beyond traditional satisfaction surveys to build a more detailed picture of real user experience. In Mexico alone, AI systems now process over 220,000 calls between customers and remote relationship managers each month, along with roughly 4,000 monthly business customer calls to contact centers.
Analyzing calls at scale
In Spain, the bank uses generative AI to examine transcripts of phone calls and conversations, identifying the precise reasons customers reach out. In Mexico, the same technology scans massive call volumes to detect friction points in digital journeys and track how interactions evolve from first contact to resolution. By linking abandoned transactions in the mobile app with subsequent support calls, AI can pinpoint the underlying causes of customer frustration.
This approach helps the bank understand what happens when a customer encounters a problem, not just the final satisfaction score. The data reveals patterns that were previously invisible in manual review processes.
Richer insights from NPS surveys
AI is also reshaping how BBVA's Mexico operations interpret Net Promoter Score feedback. Instead of relying solely on numerical scores, the system categorizes open-ended comments, identifies the main drivers of dissatisfaction, and combines those findings with behavioral data from digital channels. Relationship managers receive an automatically generated summary before each customer conversation, providing context about the person's most recent banking experiences.
David Arconada, global head of listen and know your customers at BBVA, said: "For years, we've asked our customers what they thought about their experience. Today, we can build a comprehensive view of their interactions with the bank by analysing hundreds of thousands of conversations, helping us better understand what they need, what concerns them, and where they encounter difficulties. We're entering a new era of customer insight, with the goal of turning that knowledge into richer, more personalized, and more distinctive relationships."
Why this matters for customer support
For customer support teams, BBVA's move signals a shift from reactive measurement to proactive pattern recognition. AI surfaces hidden pain points that surveys alone cannot capture, such as the exact step in an app where a transaction fails and the follow-up call that results. When agents have access to automatically generated summaries of a customer's recent issues, they can skip repetitive fact-finding and focus on solving the problem. Teams looking to build similar AI-driven insight capabilities can explore resources like the AI Learning Path for Call Center Supervisors.
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