sales-data-hygiene

CRM Data Hygiene & Quality

Help the user clean, deduplicate, normalize, and maintain CRM data quality. This skill is tool-agnostic but includes platform-specific guidance for ZoomInfo OperationsOS, Salesforce native tools, HubSpot Operations Hub, Clay, LeanData, RingLead, Openprise, and DemandTools.

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated knowledge.

Ask the user:

1. What's the main data problem?
   • A) Duplicate contacts, leads, or accounts
   • B) Stale/outdated records (job changes, company changes)
   • C) Missing fields (no phone, no email, incomplete company data)
   • D) Inconsistent data (job titles, industries, company names formatted differently)
   • E) Compliance issues (opt-outs, GDPR, stale consent)
   • F) General data audit — don't know what's wrong yet
   • G) Setting up ongoing data hygiene automation
   • H) Other — describe it

1. What CRM are you using?

   • A) Salesforce
   • B) HubSpot
   • C) Microsoft Dynamics
   • D) Pipedrive
   • E) Other CRM
   • F) Custom/in-house system

2. How many records are affected?

   • A) Under 1,000 (small cleanup)
   • B) 1,000-10,000 (moderate)
   • C) 10,000-100,000 (large)
   • D) 100,000+ (enterprise-scale)
   • E) Not sure — need to audit first

3. What tools do you have for data operations?

   • A) ZoomInfo OperationsOS
   • B) HubSpot Operations Hub
   • C) Salesforce native (duplicate management, data.com)
   • D) Clay
   • E) LeanData / RingLead / Openprise
   • F) DemandTools (Validity)
   • G) None — using manual processes
   • H) Other — describe it

Step 2 — Strategy and approach

Read references/platform-guide.md for detailed audit frameworks, deduplication strategies, normalization tables, enrichment automation, and platform-specific guidance.

You no longer need the platform guide details — focus on the user's specific situation.

Step 3 — Actionable guidance

Quick wins (do these first)

1. Remove obvious duplicates — exact email match dedup is safe and fast
2. Fix formatting — standardize phone numbers, capitalize names, normalize countries
3. Fill critical gaps — bulk enrich records missing email or phone
4. Remove dead records — hard bounces, invalid emails, disconnected phones

Ongoing hygiene program

1. Prevent duplicates at entry — enable duplicate rules on record creation
2. Enrich on create — auto-enrich new records within minutes of creation
3. Monthly dedup sweep — run fuzzy match dedup monthly, review and merge
4. Quarterly refresh — re-enrich all active records every 90 days
5. Annual purge — remove records with no activity in 12+ months (archive, don't delete)

Metrics to track

• Duplicate rate — % of records with duplicates (target: <2%)
• Field completeness — % of critical fields filled (target: 95%+)
• Bounce rate — email bounce rate on outbound (target: <3%)
• Data age — median days since last enrichment (target: <90)
• Merge rate — duplicates merged per month (should trend down over time)

Gotchas

1. Merge before you enrich — enriching duplicate records wastes credits. Dedup first, then enrich the surviving records.

2. Test dedup rules on a sample first — fuzzy matching can produce false positives (merging records that shouldn't be merged). Always review a sample of 50-100 merge candidates before running bulk operations.

3. Preserve lead source on merge — the most common post-merge complaint is losing original lead source attribution. Configure merge rules to keep the oldest record's lead source.

4. Don't delete — archive — instead of deleting stale records, move them to an archive status. Deleted records lose history; archived records can be reactivated if the contact returns.

5. GDPR and compliance — data hygiene must respect opt-out and consent records. Never re-enrich a contact who has opted out. Check compliance status before any bulk enrichment operation.

• Self-improving: If you discover something not covered here, append it to references/learnings.md with today's date.

Before recommending a specific platform skill

This skill covers a strategy domain across many platforms. Before pointing the user to any specific platform skill (any /sales-{platform} listed in ## Related skills, e.g., /sales-mailshake, /sales-klaviyo, /sales-apollo), read that platform skill's actual SKILL.md first. The 1-line description in ## Related skills is enough to identify a candidate — it's not enough to commit to it or to write a prompt that invokes it well.

How to read it:

• If ~/.claude/skills/{skill-name}/SKILL.md exists locally, Read it.
• For sales-* skills, WebFetch directly from this repo: https://raw.githubusercontent.com/sales-skills/sales/main/skills/{skill-name}/SKILL.md — e.g., for sales-mailshake: https://raw.githubusercontent.com/sales-skills/sales/main/skills/sales-mailshake/SKILL.md.
• For non-sales-* skills (third-party), look up {org}/{repo} in ~/.claude/skills/sales-do/references/skill-sources.md if installed and fetch the same skills/{skill-name}/SKILL.md path under that repo.

After reading, ground your recommendation in something concrete from the SKILL.md (its scope, a sub-flow, its argument-hint shape, or a "Do NOT use for..." negative trigger). Align any generated invocation with the platform skill's argument-hint. If the platform skill turns out not to fit the user's situation, swap to another or handle the question here directly rather than recommending a poor fit.

Related skills

• /sales-hubspot — HubSpot platform help (Data Hub data sync, data quality automation, deduplication)
• /sales-attio — Attio platform help (AI-native CRM with custom objects)
• /sales-blueconic — BlueConic CDP — profile unification, identity resolution, audience activation
• /sales-tealium — Tealium CDP — Real-Time CDP, identity resolution, 1300+ connectors
• /sales-cdp — CDP comparison and selection strategy across platforms
• /sales-clay — Clay platform help
• /sales-zoominfo — ZoomInfo platform help (for OperationsOS-specific setup)
• /sales-clearbit — Clearbit platform help (enrichment, reveal, prospector)
• /sales-enrich — enrichment strategy across all providers
• /sales-lead-routing — lead assignment and territory rules (often paired with dedup)
• /sales-lead-score — lead scoring models (depend on clean data)
• /sales-integration — connecting data tools to CRM
• /sales-prospect-list — building prospect lists (data quality at the source)
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Examples

Example 1: CRM data audit

User says: "Our Salesforce has 50,000 contacts and I suspect a lot of them are duplicates or outdated. Where do I start?" Skill does: Walks through the data quality audit framework — measure completeness, accuracy, duplication rate, and decay. Recommends starting with exact-match email dedup (safest), then running a field completeness report, then sampling 100 records against LinkedIn to estimate accuracy. Result: User has a data quality scorecard and prioritized cleanup plan.

Example 2: Setting up ongoing hygiene

User says: "We keep getting duplicates in HubSpot and our data goes stale within months. How do we automate this?" Skill does: Recommends HubSpot Operations Hub for dedup + ZoomInfo or Clay for enrichment. Sets up duplicate prevention rules on creation, auto-enrichment for new records, and a quarterly re-enrichment schedule. Result: User has an automated hygiene program that prevents duplicates and keeps data fresh.

Example 3: Pre-campaign data cleanup

User says: "We're about to launch a big outbound campaign to 10,000 contacts. How do I make sure the data is clean first?" Skill does: Recommends a pre-campaign checklist: dedup the list, verify emails with a dedicated verification tool, re-enrich records older than 90 days, remove contacts at companies that no longer fit ICP, and check opt-out/DNC status. Result: User launches campaign with verified, deduplicated, compliant data — lower bounce rate, higher deliverability.

Troubleshooting

Dedup merging wrong records

Symptom: Fuzzy match dedup merged two different people who happen to have similar names at the same company Cause: Match rules too loose — matching on name + company without additional criteria Solution: Tighten match rules: require email OR phone match in addition to name + company. Always run in "review" mode before "auto-merge" mode. Add title or department as a tiebreaker.

Enrichment not filling expected fields

Symptom: Auto-enrichment runs but many records still have empty phone or email fields Cause: Single enrichment provider doesn't have coverage for all contacts. Coverage varies by geography, seniority, and industry. Solution: Implement waterfall enrichment — try Provider A, if no result try Provider B, then Provider C. Use /sales-enrich for waterfall setup. Common waterfall: ZoomInfo → Apollo → Lusha.

Data quality metrics not improving

Symptom: Running monthly dedup and enrichment but duplicate rate and completeness aren't improving Cause: New duplicates are being created faster than they're being merged. Root cause is usually web forms, imports, or integrations creating records without duplicate checks. Solution: Fix the source — enable duplicate prevention rules on all record creation paths (web forms, API imports, manual creation, integration syncs). Prevention is more effective than cleanup.