SUVA Analytics for Data-driven Insights and Actionable Decisions

Virtual Assistant Performance Metrics

These quantifiable measures evaluate how effectively and efficiently SUVA resolves customer queries.

Measure the percentage of queries effectively resolved by SUVA compared to the total chat volume, showcasing SUVA's ability to handle user inquiries independently.

Deflection Rate

Assess the percentage of queries where SUVA identifies the requirement for human intervention and facilitates a warm transfer to a live agent, ensuring seamless escalation for complex queries.

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Evaluate the percentage of customer interactions prematurely terminated by users before offering feedback or completing desired actions, providing insights into user engagement and satisfaction levels.

Accelerated Time to Value

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Business Metrics

These metrics assess the return on investment (ROI) associated with SUVA implementation.

Usually assessed through post-interaction surveys or feedback mechanisms, providing invaluable insights into user satisfaction levels and overall service quality.

Accelerated Time to Value

This metric is calculated using the formula: Deflection Volume (queries resolved by SUVA) multiplied by the Average Cost per Case, offering a clear understanding of the financial benefits derived from SUVA usage in terms of reduced support costs.

Accelerated Time to Value

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LLM Usage Metrics

These metrics encompass key aspects of user interactions with large language models (LLMs)

Total Requests

Usually assessed through post-interaction surveys or feedback mechanisms, providing invaluable insights into user satisfaction levels and overall service quality.

Input Tokens

Measure the total number of tokens used by the LLM to process user queries, offering insight into the complexity and depth of interactions with the LLM.

Output Tokens

Assess the total number of tokens utilized by the LLM to generate contextual chat responses, reflecting the richness and detail of the generated content.

Average Response Time(s)

Calculate the average duration taken by the LLM to process and respond to queries, aiding in understanding the efficiency and responsiveness of LLM-driven interactions.

Average Error Rate(%)

Evaluate the average percentage of queries where the LLM fails to respond, offering insights into areas for improvement and fine-tuning of LLM capabilities.

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