Claude Code for Revenue Operations

Your CRM knows where your deals are dying. It knows which rep’s pipeline is real and which rep’s pipeline is theater. It knows your stage-2-to-3 conversion rate has been declining for two quarters. It knows you have 847 open opportunities that haven’t been touched in 90 days.

You know it knows. You just don’t have time to ask.

That’s the actual RevOps problem. It’s not a data problem — you have too much data. It’s a synthesis problem. The answers are in there. Getting them out requires either a data analyst you don’t have, a BI tool that takes three months to configure, or four hours of pivot tables on a Friday afternoon.

Claude Code does the pivot tables.

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What You’ll Build

1. CRM Data Quality Audit — A prioritized report of exactly what’s broken in your data and which records to fix first

2. Cohort Analysis — Win rates, ASP trends, and sales cycle changes by rep, segment, and quarter

3. Funnel Conversion Model — Stage-by-stage conversion rates, leakage points, and the math on hitting number

4. Revenue Forecast Model — A bottoms-up range (conservative/base/optimistic) from your current pipeline

5. Process Documentation Generator — Clean process docs, RACI, and workflow diagrams from plain-English descriptions

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Step 1: Setup

Claude Code runs at claude.ai — go to Projects, create a new project, open a conversation. No installation, no API configuration.

Pre-work checklist for your CRM export before you start:

• Remove or anonymize PII you don’t need (personal email addresses, direct phone numbers)

• Check that column headers are clean and descriptive — “Close Date” not “Col_G”

• Handle obvious nulls: decide whether blank fields mean zero, unknown, or N/A, and note that in your prompt

• Make sure stage names in your export match your actual defined stages

Export your data as CSV. Pull the full export — don’t filter it down first. The audit is designed to find what you don’t know to look for.

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Step 2: CRM Data Quality Audit

Run this first. It tells you exactly what’s broken before you build anything on top of it.

Export your full CRM contacts and deals/opportunities as CSV. Upload and paste this prompt:

You are a RevOps data analyst. I've uploaded a CSV export of our CRM deals/opportunities.

Analyze the data and produce a prioritized data quality report that includes:

1. Fields with >30% null or blank rates — list each field, the null percentage, and whether
   this field is critical for reporting
2. Stage name inconsistencies — identify any deal stages that don't match a standard
   progression (typos, deprecated stages, stages that shouldn't exist)
3. Potential duplicate company/account names — look for companies that appear multiple
   times with slight variations
4. Deals open longer than 180 days — list count, total pipeline value, and the oldest
   open date
5. Deals missing a close date or with a close date in the past that are still marked open

Format the output as:
- Executive Summary (3–5 bullets on the biggest data quality risks)
- Detailed Findings (one section per issue type, with specific counts and examples)
- Recommended Fixes (prioritized by impact on forecast accuracy)

Be specific. Include example record names or IDs where possible so I know exactly what
to fix.

What you get: A report that would have taken a data analyst two hours to build. Specific records to fix. A prioritized list based on what actually affects forecast accuracy.

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Step 3: First Result

Run the audit. Read the findings. Fix the critical-path issues — specifically anything affecting close dates, stage names, and owner assignment. Those three fields drive every model you’re about to build.

When your data is clean enough to trust, keep reading.

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