Virtual Assistant Performance Metrics
SUVA Analytics provides quantifiable measures to evaluate the effectiveness and efficiency of the Virtual Assistant in resolving customer queries. These include Deflection Rate, Handover Rate, and Abandon Rate. The Deflection Rate measures the percentage of queries effectively resolved by SUVA independently, showcasing its ability to handle user inquiries. Handover Rate assesses the rate at which queries are transferred to human agents, highlighting areas for improvement. Abandon Rate tracks the percentage of queries that go unanswered, indicating potential gaps in Virtual Assistant performance.
Business Metrics
The Business Metrics offered by SUVA Analytics focus on the return on investment (ROI) associated with the Virtual Assistant implementation. Customer Satisfaction Score (CSAT) and Cost Savings are key factors. CSAT measures user satisfaction levels through post-interaction surveys, providing insights into service quality. Cost Savings evaluate the monetary benefits of implementing SUVA based on efficiency gains and reduced operational costs, showcasing the value proposition of the Virtual Assistant.
LLM Usage Metrics
SUVA Analytics delves into key aspects of user interactions with large language models (LLMs) to optimize performance. Metrics like Total Requests, Input Tokens, Output Tokens, Average Response Time(s), and Average Error Rate(%) offer comprehensive insights. Total Requests quantify user interactions with LLMs, highlighting usage patterns. Input Tokens measure the complexity of user queries processed by LLMs, reflecting the depth of interactions. Output Tokens assess the richness of LLM-generated responses. Average Response Time(s) track the efficiency of LLM interactions, while Average Error Rate(%) identifies areas for enhancement and fine-tuning.
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