In the ever-evolving landscape of financial services, effective communication with customers is paramount.
However, ensuring the quality of these interactions has traditionally been a challenging task for QA teams.
They have long relied on sampling small numbers of conversations using keyword analysis, making it tricky to build a detailed picture of agent performance, compliance adherence and customer satisfaction. It’s a manual and time-consuming process that only provides a glimpse into the quality of an organisation’s conversations.
GenAI-powered conversation analysis can determine the emotional tone of the conversation and is emerging as the future of customer service QA for several reasons:
1. Precision through Contextual Understanding
One of the primary drawbacks of keyword-based or ‘presumed sentiment’ analysis is its inability to grasp the context in which words are used.
Communication is a complex thing, involving nuance, cultural influences, sarcasm, humour and industry-specific jargon. This makes it hard for traditional keyword analysis to discern the true meaning.
On the other hand, GenAI leverages advanced large language models to understand the wider context and meaning of the conversations, enabling precise evaluation.
2. Comprehensive Evaluation
Keyword analysis is limited by the predefined set of terms it searches for, leaving room for important information to slip through the cracks.
GenAI, however, extracts valuable insights beyond the scope of keywords, ensuring QA teams don't miss crucial details that could impact compliance, call flow adherence, and overall communication quality.
3. Real-time Analysis of Conversation Meaning
Traditional QA methods often involve retrospective analysis, with teams reviewing a sample of conversations after they occur.
GenAI-powered conversation evaluations allow for real-time monitoring of customer interactions. This enables organisations to identify issues as they arise, allowing for proactive correction and preventing potential compliance breaches or communication breakdowns before they escalate.
4. Adaptable and Scalable
Financial services organisations operate in a sea of regulatory changes and market dynamics.
Their communication frameworks must evolve with them. Keyword analysis struggles to keep pace with these changes, requiring constant updates to keyword lists. GenAI-based analysis, on the other hand, is adaptable and scalable.
It can learn and evolve alongside the organization, ensuring that QA processes remain effective even as communication strategies evolve.
In conclusion, the shift from traditional keyword-based analysis to Gen-AI-based conversation analysis represents a paradigm shift in communication QA for financial services organisations.
As financial services institutions further delve into new GenAI-powered conversation analysis technology, they can unlock a series of competitive advantages, including:
Improved agent productivity
Reduced compliance risks and penalties
Faster speed-to-competency training rates
Better customer satisfaction stemming from a deeper understanding of their needs
Wider business insights that can inform process, product and more
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Brocaly’s AutoQA uses GenAI so you can monitor and evaluate 100% of your customer conversations. Find out more on our website.
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