Data Enrichment RFP Template

Most data enrichment RFPs are 15 pages of procurement boilerplate that don't help you pick the right vendor. They ask about company history, organizational charts, and office locations instead of the things that actually determine whether a vendor will perform.

This template cuts the standard RFP down to the sections that matter. Use it as-is or adapt it to your procurement process.

Section 1: Project Scope

Start with what you need. Be specific enough that vendors can give you accurate pricing but don't over-specify the methodology (let them propose their approach).

Include these details:

• Database size: Total records to be enriched
• Record types: Contacts, companies, or both
• Target fields: Which data points you need (email, phone, title, company size, industry, etc.)
• Current state: What fields you already have, what's missing
• Target market: Geography, industry, company size, seniority level
• Frequency: One-time project vs. ongoing enrichment
• Additional services needed: Cleaning, deduplication, standardization

Section 2: Quality Requirements

This is where most RFPs fail. They don't define what "quality" means in measurable terms. Set specific thresholds:

Minimum quality thresholds (example):

• Email match rate: 80%+ on submitted records
• Email deliverability: 90%+ on delivered emails (measured via SMTP verification)
• Phone match rate: 60%+ direct dial coverage
• Phone accuracy: 85%+ of delivered phone numbers connect to the named contact
• Title accuracy: 90%+ of delivered titles match the contact's current role

Section 3: Test Batch Protocol

Require every vendor to process a test batch before you evaluate proposals. This is the single most important section of your RFP.

Test batch requirements:

• Size: 500-1,000 records from your actual database
• Selection: Random sample across your target market (not cherry-picked)
• Fields requested: Same fields as the full project
• Evaluation criteria:
  • Match rate per field
  • Manual accuracy check on 50 randomly selected records
  • Turnaround time
  • Data format and deliverability
• Cost: Test batch should be free or credited against the full project

Section 4: Pricing Structure

Ask vendors to provide pricing in a format you can compare directly:

Request pricing in this format:

• Per-record cost broken down by: records matched vs. records submitted, with separate costs per field if applicable
• Volume tiers if pricing varies by quantity
• What counts as a "record"? Do you pay for records attempted or only records enriched?
• Minimum commitment: Is there a minimum project size or annual spend?
• Additional costs: Setup fees, platform access fees, support tiers, custom field mapping

Section 5: Data Ownership and Security

These terms matter more than most teams realize. Get them in writing:

• Data ownership: Do you own the enriched data permanently, or does the vendor retain rights?
• Deletion clauses: Are you required to delete data if you cancel the contract?
• Re-licensing: Can you share enriched data with partners, clients, or subsidiaries?
• Data security: How is your data transmitted and stored during processing? SOC 2? Encryption?
• Compliance: GDPR, CCPA, or other relevant privacy regulations

Section 6: Turnaround and Support

• Expected turnaround: How many business days for the full project?
• Progress updates: Will you receive batch-level status updates?
• Point of contact: Dedicated account manager vs. support queue?
• Issue resolution: What happens if quality falls below agreed thresholds?
• Re-enrichment policy: If data goes stale within a defined period, is there a re-enrichment option?

Section 7: Evaluation Scoring

Weight your evaluation criteria to reflect what actually matters:

• Test batch performance: 40% (match rate, accuracy, fill rate on your data)
• Pricing: 25% (total cost for your specific project scope)
• Data ownership terms: 15% (permanent ownership, no deletion clauses)
• Turnaround and support: 10%
• Additional capabilities: 10% (cleaning, deduplication, custom research)

Notice what's not weighted heavily: vendor company size, years in business, number of clients. Those don't predict performance on your data.

Common Mistakes to Avoid

1. Skipping the test batch. No amount of reference calls replaces running your actual data through the vendor's process.
2. Comparing list prices. Always compare total project cost based on your specific volume and requirements.
3. Ignoring data ownership terms. A low per-record price means nothing if you have to delete the data when the contract ends.
4. Over-weighting database size. A vendor with 300M records and poor accuracy in your market is worse than a vendor with 50M records and excellent coverage of your ICP.
5. Signing long-term contracts before testing. Start with a single project before committing to an annual deal.

Frequently Asked Questions

What should a data enrichment RFP include?

Focus on measurable deliverables: project scope, quality thresholds, test batch protocol, pricing format, data ownership terms, and evaluation scoring. Skip generic vendor background questions.

How do you compare data enrichment vendors?

Run a blind test batch. Give 3-4 vendors the same 500-1,000 records and compare match rate, accuracy, fill rate, and pricing. Performance on your data matters more than demos or reputation.

What match rate should I require in a data enrichment RFP?

For US mid-market: 80%+ email, 60%+ direct dial, 90%+ firmographics. Adjust down 10-15 points for SMB or niche markets. Always measure against your test data, not the vendor's samples.