--- name: contact-volume-driver-analyzer description: "Analyzes inbound contact data to categorize contact reasons into a structured taxonomy, separate avoidable from unavoidable contacts, apply Pareto analysis to isolate the vital few volume drivers, trace root causes, and deliver a ranked list of deflection and reduction opportunities scored by effort and impact. Use when diagnosing what is driving contact center volume, building a contact reduction roadmap, or deciding where to invest in self-service, process fixes, or upstream defect removal." argument-hint: "[contact-data] [channel-mix]" allowed-tools: ["Read"] --- # Contact Volume Driver Analyzer ## 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](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//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. 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