voc-analytics

Automating voice-of-customer analytics tracks NPS, CSAT, and CES metrics directly from survey data, applying bootstrap confidence intervals to surface reliable trends. It processes customer feedback deterministically to generate dashboard-ready metric outputs for stakeholder reporting.

Key driver analysis identifies top themes influencing Promoter versus Detractor classifications using permutation importance. This mechanism ranks extracted open-text themes by their statistical impact on customer satisfaction scores, isolating the specific feedback drivers behind metric shifts.

Extracting themes from open-text customer feedback uses LLM-based theme extraction with deterministic fallbacks. This approach processes survey responses to surface underlying drivers of satisfaction, applying sentiment analysis to categorize qualitative data into actionable insights.

Analyzing customer satisfaction scores across segments and time periods uses cross-tabulation to enable targeted insights. The workflow processes survey data to break down NPS, CSAT, and CES metrics by various segments, channels, and timeframes for granular reporting.

Running voice-of-customer analytics workflows requires Python data libraries including numpy, scipy, pandas, and scikit-learn. These dependencies support the deterministic computations, bootstrap confidence intervals, and permutation-importance driver analysis needed to process survey data.

LLM-based theme extraction for survey data implements deterministic fallbacks to handle processing limitations. If the primary language model extraction encounters issues, the system relies on these fallback mechanisms to ensure continuous text-analytics processing for theme and sentiment reporting.