Cresta Unveils New Advancements That Drive Powerful Business Outcomes

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Cresta, the end-to-end generative AI platform for contact centers, unveiled new human-in-the-loop AI capabilities to improve CX, accelerate revenue growth, and reduce operational costs. These enhancements provide agents with more effective real-time behavioral guidance based on proven results and empower customers to transform traditional quality management from a resource-drain into a true driver of better performance.
"Cresta is laser-focused on pushing the boundaries of LLM-powered applications to identify specific agent and customer behaviors that have an enormous influence on core KPIs – these enhancements empower sales and CX leaders to quickly take immediate and targeted actions that really move the needle for their business," said Ping Wu, CEO of Cresta.
The following capabilities expand what's possible for human-in-the-loop contact center AI:
Behavior Discovery
Despite recent advancements in AI-driven interaction analytics, contact center leaders have struggled to pinpoint situation-specific tactics and behavioral best practices that actually lead to better outcomes at scale – such as happier customers, higher sales conversion, and more efficient issue resolutions. With Behavior Discovery, Cresta empowers contact center and business leaders to discover these insights from completely unstructured conversation data — using proprietary large language models to identify and define previously unknown behaviors that are proven to drive better results.
Key Capabilities Include:
- Automatically discover and define more effective conversational behaviors using specific KPIs or groups of known top performers
- Zero in on situationally-specific best practices using natural language prompts and granular filters
- Easily see each behavior's impact on KPI outcomes across 100% of historical conversations to predict its potential to drive better performance
- Create the framework for each new intent model using a straight-forward natural language description
- Train the model by using a proprietary few-shot learning process based on labeling positive and negative examples from real conversations
- Preview the quality of each model using an industry standard F1 benchmark to confirm its accuracy and deploy models with confidence
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