Purpose
Turn raw contact reason data into a prioritized reduction roadmap — categorizing every contact type, separating what customers must contact you for from what they were forced to, and ranking the highest-leverage opportunities by effort and impact. Solves the "we're drowning in tickets but don't know which fixes actually move volume" problem.
When to Use
Invoke this skill when the user asks to:
- Understand what's driving their contact or ticket volume
- Categorize contact reasons or build a contact reason taxonomy
- Identify which contacts are avoidable vs. unavoidable
- Find the root causes behind repeat contacts or spikes
- Prioritize self-service, deflection, or process-fix investments
- Build a contact reduction or cost-to-serve roadmap
Inputs Required
Before starting, collect:
- Contact reason data: Volume by reason/category — from tags, IVR selections, disposition codes, or a pasted export
- Total volume & timeframe: Total contacts and the period they cover (e.g., 42,000 contacts / last 90 days)
- Channel mix: Breakdown across phone, email, chat, self-service, social (or best estimate)
- Cost per contact: Fully-loaded cost per contact by channel, or a blended average
- Existing self-service: What help center, chatbot, IVR, or automation already exists
- Repeat contact signal: Any data on repeat/re-contact rate or same-issue follow-ups
- Business context: Product/service type and any known recent changes (launches, outages, policy shifts, billing cycles)
If cost per contact is missing, use $6 for self-service-adjacent channels and $12–$15 for live phone/chat as working defaults and label the assumption clearly.
Instructions
Step 1: Normalize and Map to a Contact Reason Taxonomy
Consolidate messy tags into a clean two-level taxonomy. Map every reported reason into one of these top-level categories, then a specific sub-reason:
- Billing & Payments — charges, refunds, failed payments, invoice questions, cancellations
- Order & Fulfillment — status, delays, wrong/missing items, returns, tracking
- Account & Access — login, password reset, profile changes, permissions
- Product/Service Usage — how-to, setup, configuration, feature confusion
- Technical Issues / Defects — bugs, errors, outages, broken flows
- Policy & Eligibility — terms, coverage, qualification, disputes
- Sales & Pre-Purchase — pricing, availability, upgrade/downgrade
- Complaints & Escalations — dissatisfaction, retention risk, formal complaints
Collapse near-duplicate tags, flag any "Other/Uncategorized" bucket exceeding 10% as a data-quality risk, and report the % of volume that could not be cleanly mapped.
Step 2: Apply Pareto Analysis
Rank sub-reasons by volume descending and identify the vital few:
- Build a cumulative percentage running total
- Isolate the reasons that make up the top 80% of volume (typically 20–30% of reason types)
- Flag the single largest driver and the top 5 drivers explicitly
- Note any "long tail" — the count of low-volume reasons collectively consuming meaningful cost
Present this as a ranked table: Reason | Volume | % of Total | Cumulative %.
Step 3: Classify Avoidable vs. Unavoidable
For each of the top drivers, assign one classification:
- Unavoidable (Value-Add) — the customer needed a human and the contact reflected working-as-intended demand (e.g., complex sales question, genuine dispute)
- Avoidable — Self-Service Gap — answer exists but customer couldn't find or trust it
- Avoidable — Process/Upstream Defect — a broken process, unclear communication, or product defect caused the contact (e.g., confusing invoice, failed delivery, ambiguous email)
- Avoidable — Repeat/Failure Demand — contact only exists because a prior contact didn't resolve the issue
Failure demand and upstream defects are the highest-value targets — they eliminate the contact at the source rather than deflecting it.
Step 4: Trace Root Causes
For each avoidable top driver, go past the symptom to the cause using a short "why chain" (2–4 levels). Distinguish:
- Trigger — what event generates the contact
- Root cause — the underlying process, product, or communication defect
- Current failure point — why existing self-service or process doesn't prevent it
Do not stop at "customers ask about X" — specify why they're forced to ask.
Step 5: Generate & Score Reduction Opportunities
For each avoidable driver, define a concrete reduction opportunity and score it:
- Impact (1–5): Based on volume × cost × addressable share (what % of that reason's volume could realistically be eliminated, not just the full count)
- Effort (1–5): Implementation cost, cross-team dependencies, and time to value
- Type: Deflect (self-service/automation) / Fix (process) / Eliminate (upstream defect) / Contain (better routing)
- Estimated annual contacts removed and estimated annual cost saved
Step 6: Rank and Sequence
Sort opportunities into an action tier map:
- Quick Wins — high impact, low effort → do first
- Major Projects — high impact, high effort → plan and resource
- Fill-Ins — low impact, low effort → batch opportunistically
- Deprioritize — low impact, high effort → park
Deliver a final ranked list (top 8–12 opportunities) ordered by impact-to-effort ratio, with the recommended first three called out explicitly.
Output Format
- Structure: Taxonomy summary → Pareto table → avoidability classification → root-cause chains → scored opportunity table → tiered action map → recommended first moves
- Length: 1,000–1,400 words
- Format: Mixed — ranked tables for Pareto and opportunity scoring; structured bullets for root causes; short prose for recommendations
- Sections: Contact Reason Taxonomy & Data Quality → Pareto Analysis (top drivers) → Avoidable vs. Unavoidable Breakdown → Root-Cause Traces → Scored Reduction Opportunities → Effort/Impact Tier Map → Recommended First 3 Moves
- Tone: Operations analyst — data-first, quantified, decisive about priorities
Quality Checklist
Before delivering output, verify:
- [ ] Every reported contact reason is mapped to the taxonomy; the uncategorized % is stated
- [ ] The Pareto table includes a cumulative % column and names the top 5 drivers
- [ ] Each top driver carries an avoidable/unavoidable classification with a named subtype
- [ ] Each avoidable driver has a root-cause chain that goes at least 2 levels past the symptom
- [ ] Every opportunity has both an impact score AND an effort score (1–5), plus estimated contacts/cost removed
- [ ] The final ranking uses impact-to-effort ratio and names the recommended first 3 moves
Notes
- Impact scoring should use addressable volume, not raw volume — a huge driver that's 90% unavoidable is a smaller prize than a mid-size driver that's fully deflectable
- Failure demand (repeat contacts) is frequently the most underestimated driver; if repeat-rate data is thin, flag it as the highest-priority measurement gap rather than ignoring it
- Upstream fixes (a clearer invoice, a fixed delivery notification) beat deflection because they remove the contact instead of relocating it to a chatbot the customer still abandons
- If contact data is only available at the top-level category (not sub-reason), run the Pareto at category level and note that sub-reason granularity would sharpen the opportunity scoring
How to use this skill
This file is a Claude Code skill in Anthropic's standard format — 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:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills