CSAT Driver Analysis

To analyze customer satisfaction score changes, you decompose CSAT movements into contributing factors across service dimensions. This process uses statistical methods like Key Driver Analysis to pinpoint specific operational and experiential factors impacting your scores.

Identifying CSAT drivers requires individual survey responses, survey dimensions, customer attributes, operational metrics, verbatim comments, change logs, and historical CSAT data. This comprehensive dataset enables the statistical correlation of satisfaction shifts to specific operational events.

Correlating operational metrics with CSAT drops involves linking satisfaction shifts to specific operational events and service dimensions. By analyzing historical data and change logs alongside survey responses, you can isolate the exact operational factors causing score movements.

Verbatim comments are analyzed to extract themes and sentiment, providing qualitative context for CSAT driver analysis. This textual feedback is processed alongside operational metrics to explain why customer satisfaction scores have changed.

The best way to decompose CSAT changes is to break down score movements by dimension, segment, and channel using statistical rigor. This method isolates specific experiential drivers, allowing you to target exactly which segments are impacting overall satisfaction.

Generating actionable recommendations from CSAT driver analysis involves using the identified operational and experiential drivers to suggest data-driven improvements. This translates isolated factors, like delivery speed issues, into clear next actions to improve overall satisfaction.