CSAT response and detractor analysis
Delivers a structured csat response and detractor analysis result with evidence, decisions, and open points. Use when teams need csat response and detractor analysis, support operations review, customer success analysis, or a repeatable case-work deliverable.
Veröffentlicht 21. Aug. 2026 · Aktualisiert 26. Aug. 2026
Voraussetzungen
Add the company's approved definitions, rubric, policy, playbook, or reference document to the scope. Set source fields, date windows, terminology, thresholds, ownership, and calculation rules. Define privacy, redaction, retention, and reviewer requirements for customer content.
Skill-Dokument
Das vollständige SKILL.md, das dein Agent liest und befolgt.
CSAT response and detractor analysis
Purpose
Turn csat response and detractor analysis into a consistent, evidence-based support work product. The agent analyzes the customer-support corpus and writes a result that a support or success team can review and act on.
Scope
This template covers business analysis and drafting across support records, customer context, tables, and approved reference material. It is independent of a particular helpdesk or communication channel. Process key: support-csat-analysis.
Excluded: sending messages, changing source records, making approvals, deciding policy exceptions, promising outcomes, and operating external systems.
Data basis
- Tickets, customer records, conversations, tables, and approved documents relevant to csat response and detractor analysis.
- Source identifiers, timestamps, statuses, relationship keys, and relevant measures.
- Company terminology, policy, rubric, taxonomy, or playbook when provided.
Result
A structured analysis sheet and concise narrative report for csat response and detractor analysis. Every material finding cites its ticket, record, row, message, or document location. Missing and contradictory evidence remains visible.
Quality criteria
- Counts and calculations reconcile to the defined population for support-csat-analysis.
- Every material finding has a source citation in support-csat-analysis.
- Facts, calculations, interpretations, and recommendations are separated in support-csat-analysis.
- Ambiguous, missing, and excluded data is recorded as an open point for support-csat-analysis.
- The sheet and report use the same labels, totals, and priorities for support-csat-analysis.
Instructions
Use company definitions and reference material for support-csat-analysis before general practice. Preserve source identifiers and exact dates. Use the stated time zone for comparisons. Mark unknown rather than guessing. When sources conflict, cite both and explain the governing rule. Minimize personal data and redact it from examples. Every recommendation must point to an observed issue, measured pattern, or cited requirement.
Adapt before use
- Add the company's definitions, rubric, policy, playbook, or reference documents required for this process.
- Set source fields, date windows, terminology, thresholds, and calculation rules.
- Define privacy, redaction, retention, and reviewer requirements for support-csat-analysis.
- Name the team that owns follow-up items and escalation conditions for support-csat-analysis.
Verwandte Skills
- Complaint response and root-cause note
Delivers a structured complaint response and root-cause note result with evidence, decisions, and open points. Use when teams need complaint response and root-cause note, support operations review, customer success analysis, or a repeatable case-work deliverable.
- Customer health review
Delivers a structured customer health review result with evidence, decisions, and open points. Use when teams need customer health review, support operations review, customer success analysis, or a repeatable case-work deliverable.
- Customer onboarding plan
Delivers a structured customer onboarding plan result with evidence, decisions, and open points. Use when teams need customer onboarding plan, support operations review, customer success analysis, or a repeatable case-work deliverable.
- Customer review theme analysis
Clusters customer reviews into evidenced themes, separates praise from complaints, quantifies sentiment and product areas, and surfaces representative quotes with source identifiers. Use for review mining, voice-of-customer reporting, product feedback synthesis, or reputation monitoring.