CSAT & Voice of Customer Analyst Skill

Name: CSAT & Voice of Customer Analyst
Brand: KissMySkills
Price: 7.0 USD
Availability: InStock

CSAT & Voice of Customer Analyst

Turn your AI into a CX insights analyst that explains your CSAT and NPS and routes each issue to its owner.

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Ask it something hard.

📎 csat-voice-of-customer-analyst.md LOADED ✓

Here are 1,240 CSAT responses for September with comments. CSAT fell from 88 to 81 percent. Why?

CLAUDE · WITH THIS SKILL You get: five headline findings -> a metrics table with n and margin of error -> data notes and exclusions -> a theme codebook -> top drivers with score impact and owner -> slices by channel, queue and product -> the agent vs process vs product split -> anonymised quotes -> quick wins and owner asks -> a detractor follow-up list -> survey fixes.

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// the file itself

Read it before you buy it.

The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.

// le skill qui fait tourner votre IA

You are a customer experience insights analyst who turns survey scores and comments into a short report that says why the number moved and who should fix what. You have been activated to compute CSAT, NPS and CES correctly, code the comments and write the Voice of Customer report.

You do not connect to survey or helpdesk tools and you never contact customers. You work on exports the user pastes or uploads: survey responses (CSV or XLSX from Zendesk, Freshdesk, Intercom, Qualtrics, SurveyMonkey, Typeform, Medallia, Delighted), ticket tags, review exports and previous reports. · CSAT or NPS moved and nobody can explain why. · Hundreds of comments sit unread every month. · Managers argue whether low scores are about agents, policy or product. · A monthly or quarterly VoC report is due for leadership. · The survey itself needs fixing: low response rate, biased timing, vague questions. · General sentiment analysis of arbitrary text (social listening, news) - use a general sentiment analysis skill. · Coaching individual agents - hand the agent-driven findings to a coaching skill. · Statistical modelling beyond driver analysis (for example full key-driver regression on large datasets) - the skill can outline it but works best with spreadsheet-scale data. · Sentiment Analysis Specialist (Mira): general sentiment and emotion analysis of any text. This skill is the CX-specific workflow: survey metrics computed correctly, a support-oriented codebook, and an owner-by-owner VoC report. · Call center agent coach (Kofi): coaches agents. This skill separates agent-driven feedback from process- and product-driven feedback, so coaching targets only what agents can actually change. · AI Call QA Scorer: scores calls against a rubric. Combine the two: QA says what agents did, VoC says what customers felt. 1. Survey export (required): response id, date, score, question type (CSAT, NPS, CES), comment, and any of channel, queue, agent, product, ticket id, customer segment. Default period: last full month plus the month before for comparison. 2. Survey setup: question wording, scale, when and to whom it is sent, sends versus responses (for response rate).

127 lignes · .md · telechargement immediat

// what's inside

Que contient cette compétence ?

CSAT, NPS and CES with n and margin of error
Theme codebook and score impact per theme
Agent vs process vs policy vs product split
Quick wins, owner asks and detractor follow-up list

Support managers, CX leads and contact center heads who report on customer satisfaction.

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// ask your assistant

Or let your AI set up this skill for you.

Go to kissmyskills.com/pages/for-ai-agents and set up the "CSAT & Voice of Customer Analyst" skill (handle: csat-voice-of-customer-analyst) for me

Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →

What you're actually buying

CSAT & Voice of Customer Analyst turns your survey exports and comments into a report that says why the score moved and who should fix what. It computes CSAT, NPS and CES correctly with response counts and margin of error, codes every comment into a theme codebook, measures which themes pull scores down, and finds where problems concentrate by channel, queue, agent and product.

Built for support managers, CX leads and contact center heads who sit on hundreds of unread comments every month. It separates agent-driven feedback from process, policy and product issues, picks representative quotes, lists quick wins and issues to route to other owners, builds a closed-loop follow-up list for detractors and suggests survey fixes. It works inside Claude or ChatGPT as a skill file and does not connect to your survey or helpdesk tools. Unlike Mira - Sentiment Analysis Specialist, which analyses sentiment in any text, it is a CX metrics and VoC reporting workflow, and it tells Kofi - Call Center Agent Coach which issues agents can actually change.

Know why CSAT moved in one report instead of guessing from a dashboard number.

Compatible with: Zendesk, Freshdesk, Intercom, Qualtrics, SurveyMonkey, Typeform, Medallia and Delighted exports, CSV and Excel.

Does it calculate NPS and CSAT correctly? Yes - with n, period comparison and a margin of error, and it labels small segments as indicative.
Can it code thousands of comments? It codes every comment for spreadsheet-sized exports and a stratified sample for very large ones, and says which.
Does it blame agents for policy problems? No - each theme is tagged agent, process, policy, product or external.
Does it contact customers? No - it builds the follow-up list and your team makes the calls.

// installation Moins de 2 minutes

Quatre étapes. N’importe quel chat AI.

01
Télécharger le fichier
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02
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03
Collez le contenu du fichier
Insérez-le dans le prompt système, les instructions du projet ou le champ des instructions personnalisées.
04
Commencez à travailler
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Aucune connaissance technique requise.

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// faq

Questions concernant ce produit

What does CSAT & Voice of Customer Analyst do?+

It analyses CSAT, NPS and CES survey exports with their comments: computes the metrics correctly with sample sizes, codes comments into themes, measures which themes lower scores, finds where they concentrate, separates agent, process, policy and product issues, and writes a Voice of Customer report with quick wins, owner asks, a detractor follow-up list and survey fixes.

How is it different from a sentiment analysis tool?+

Sentiment analysis tells you whether text is positive or negative. This skill is built for customer experience: it ties each theme to a score impact and an owner, handles survey bias and small samples, and produces a report leadership can act on.

Can it explain why our CSAT dropped?+

Yes, when the export covers both periods. It checks whether the change is beyond normal variation, then shows which themes are new or growing, where they concentrate and which events in the period line up with them.

Is agent-level reporting included?+

Only when you ask for it and only for agents with enough responses. Quotes shared with leadership are anonymised and policy or product complaints are never counted against agents.