Feedback Synthesizer

Aggregate feedback from surveys, support tickets, reviews, and social media, then clean and normalize it before applying sentiment analysis and theme tagging. This Skill's pipeline covers ingestion, categorization, and quality assurance across all these sources.

Score requests with frameworks like RICE, MoSCoW, or Kano based on feedback frequency, user impact, and business value. The Skill performs multi-criteria decision analysis and produces priority matrices with effort and ROI estimates.

Yes, feedback patterns such as declining sentiment, recurring complaints, and dropping satisfaction scores can signal churn risk. The Skill builds satisfaction models and early warning systems targeting 90% precision for satisfaction drops.

NPS measures loyalty likelihood, CSAT measures satisfaction with specific interactions, and CES measures effort required to complete tasks. The Skill correlates all three scores with feedback themes to model overall customer satisfaction.

Automated sentiment detection can misclassify sarcasm, domain-specific language, and mixed-sentiment feedback. The Skill mitigates this with confidence scoring, manual review, bias checking, and stakeholder validation of themes.