---
name: customer-effort-score-diagnostic
description: "Analyzes a described customer journey or service interaction to locate high-effort friction points, scores each on a 7-point Customer Effort Score scale, ranks friction sources by impact and fixability, and delivers low-effort redesign options with an implementation sequence. Use when diagnosing why customers churn or complain, auditing a support or self-service flow, or systematically reducing effort across a service journey."
argument-hint: "[journey-description] [current-ces-data]"
allowed-tools: ["Read"]
---

# Customer Effort Score Diagnostic

## Purpose
Pinpoint exactly where a customer journey forces too much work, score each friction point on the Customer Effort Score framework, and deliver a ranked set of low-effort redesign options — so CX teams fix the specific moments driving disloyalty rather than guessing.

## When to Use
Invoke this skill when the user asks to:
- Diagnose why customers complain about a service or support experience being "hard"
- Audit a support flow, onboarding sequence, or self-service journey for friction
- Interpret or act on Customer Effort Score survey results
- Reduce contact volume, repeat contacts, or channel-switching in a service journey
- Identify which friction point to fix first when resources are limited
- Redesign a specific interaction (returns, password reset, billing dispute, cancellation) to lower effort

## Inputs Required
Before starting, collect:
- **Journey or interaction description**: The step-by-step flow the customer goes through, in the customer's shoes
- **Channels involved**: Phone, chat, email, self-service portal, app, in-person, or combinations
- **Current CES data**: Existing CES scores, verbatims, or survey results if available
- **Known pain signals**: Complaints, escalation reasons, repeat-contact reasons, drop-off points
- **Volume/context**: How many customers hit this journey and how often
- **Constraints**: Systems, budget, or policy limits on what can be changed
- **Success metric**: What "better" looks like — lower CES, fewer contacts, higher resolution rate

If current CES data is missing, run the diagnostic on effort signals and flag where a CES survey should be instrumented.

## Instructions

### Step 1: Map the Journey Into Effort Stages
Break the described interaction into discrete stages a customer actually experiences. For each stage, note:
- **The customer's goal** at that stage (what they're trying to accomplish)
- **The action required** of them (what they must do)
- **The channel** they're in
- **Any transition** (channel switch, handoff, wait, or repeat) — these are the highest-effort moments

Flag every point where the customer must repeat information, switch channels, wait, hunt for information, or take an action the company could have removed.

### Step 2: Identify Friction Points Against the 7 Effort Drivers
Screen each stage for these known high-effort drivers:
- **Channel switching**: Forced to move from self-service to a live channel (or across live channels)
- **Repeating information**: Re-stating account details, the problem, or context already given
- **Transfers and handoffs**: Passed between agents, teams, or systems
- **Waiting**: Hold time, queue time, response-time gaps, or delayed resolution
- **Hunting**: Searching for information, the right contact, or the next step
- **Policy/process friction**: Rules that force extra steps (forms, approvals, verification loops)
- **Generic or wrong routing**: Being sent somewhere that can't resolve the issue

Name each friction point specifically — "customer must re-verify identity after being transferred from chat to phone" — not "identity verification is hard."

### Step 3: Score Each Friction Point on the CES Scale
Apply the standard 7-point Customer Effort Score framing ("The company made it easy for me to handle my issue" — 1 Strongly Disagree to 7 Strongly Agree). For each friction point, assign:
- **Effort rating**: The likely CES contribution (1–7, where lower = higher effort)
- **Effort type**: Physical (steps/actions), cognitive (figuring it out), or emotional (frustration/anxiety)
- **Evidence basis**: What signal supports this rating — verbatim, drop-off data, or driver inference
- **Loyalty risk**: How this moment affects likelihood to churn, complain, or switch

### Step 4: Rank Friction Points by Impact and Fixability
Build a 2-factor ranking:
- **Impact**: Volume of customers affected × severity of effort × loyalty risk
- **Fixability**: Effort and constraint required to change it (Quick fix / Moderate / Structural)

Prioritize in this order: high-impact/quick-fix first, then high-impact/structural, then low-impact/quick-fix. Explicitly deprioritize low-impact/structural.

### Step 5: Prescribe Low-Effort Redesign Options
For each of the top-ranked friction points, provide a concrete redesign using the effort-reduction hierarchy:
1. **Eliminate the step** entirely (best outcome — removed effort is zero effort)
2. **Preempt the need** (resolve before the customer has to ask — proactive messaging, defaults)
3. **Automate or self-serve** (make the low-effort path the default path)
4. **Streamline** (fewer clicks, no re-entry, warm transfers with context carried forward)
5. **Set expectations** (if effort can't be removed, reduce perceived effort with clear guidance)

Each redesign must name what changes, which system or team owns it, and the effort driver it removes.

### Step 6: Sequence the Fixes
Deliver an ordered implementation plan: what to fix first (highest impact, lowest effort), what to instrument or measure, and what CES improvement to expect. Note any friction point that should be validated with a CES survey before redesign.

## Output Format
- **Structure**: Journey map → friction point inventory → scored/ranked table → redesign prescriptions → sequenced plan
- **Length**: 900–1,300 words
- **Format**: Structured document with a scored friction table and prose redesign prescriptions
- **Sections**: Journey Effort Map → Friction Points Identified → CES-Scored Ranking Table → Redesign Recommendations → Implementation Sequence
- **Tone**: CX diagnostic lead — evidence-driven, specific, prioritizes action over theory

## Quality Checklist
Before delivering output, verify:
- [ ] Every friction point is named specifically to a moment in the journey — not stated as a general theme
- [ ] Each friction point is scored on the 1–7 CES scale with an effort type and evidence basis
- [ ] The ranking uses both impact and fixability — not effort score alone
- [ ] Each top-ranked friction point has a redesign that names which level of the effort-reduction hierarchy it applies
- [ ] The implementation sequence leads with high-impact/quick-fix items
- [ ] Where CES data is missing, the output flags exactly where to instrument a survey

## Notes
- The single strongest predictor of disloyalty is high effort — one hard interaction damages loyalty more than a delightful one repairs it. Weight loyalty risk accordingly.
- Channel switching and repeating information are the two most common and most damaging effort drivers; check for them first.
- Eliminating a step almost always beats optimizing it — push every redesign toward the top of the hierarchy before settling for streamlining.
- When a journey spans multiple channels, effort accumulates across handoffs even when each individual step looks acceptable in isolation.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

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