Your CRM has 50,000 contacts. About 60% have an email address. Maybe 35% have a phone number. Title fields are spotty — some were entered manually, some imported from a form submission that didn’t ask for title. Company names are inconsistent: “Google” and “Alphabet” and “Google LLC” and “Google Cloud” all appear.

Meanwhile, you just scraped a conference attendee list with full names, titles, and companies. Your team pulled a business directory export with phone numbers and addresses. Someone ran a LinkedIn scrape that returned current titles and company affiliations.

The data you need exists. It’s just split across sources that don’t share a common key.

The matching problem

The core challenge is deceptively simple: connect the record “Robert Chen, VP Engineering, Acme Corp” in your CRM to the same person appearing differently in each scraped source.

The same person across CRM and 3 scraped sources
Source Name Title Company Email Phone
CRM Robert Chen VP Engineering Acme Corp rchen@acme.com —
LinkedIn scrape Bob Chen Vice President of Engineering Acme Corporation — —
Conference list R. Chen VP Eng Acme — —
Directory export Robert Chen — Acme Corp. (415) 555-0142 robert.chen@acme.com

Each source captures different fields with different formatting. No single source is complete.

A human looking at this table immediately sees these are all the same person. A database join sees four unrelated records:

  • “Robert” vs “Bob” vs “R.” — name variants that share no common string
  • “VP Engineering” vs “Vice President of Engineering” vs “VP Eng” — same role, three formats
  • “Acme Corp” vs “Acme Corporation” vs “Acme” vs “Acme Corp.” — four representations of one company

An exact join on any single field fails. Even combining name + company, the variations are enough to prevent a match in most cases.

Why multi-field matching matters

Single-field matching is fragile. Names are ambiguous — there are plenty of real Robert Chens. Company names vary. Titles change over time.

Multi-field matching is robust. When you combine name similarity + company similarity + title similarity, the signal reinforces across fields:

  • “Robert” and “Bob” are weak name matches in isolation, but strong when both records also say “Acme” and “VP Engineering”
  • “Acme Corp” and “Acme Corporation” are borderline company matches alone, but strong when the contact name and title also align
  • An outdated title like “Director of Engineering” in your CRM matching against “VP Engineering” in a recent scrape isn’t a contradiction — it’s evidence of a promotion

AI embeddings capture this multi-field reinforcement naturally. The embedding of the full record — name + title + company — creates a vector where semantically equivalent records cluster together even when the individual strings differ significantly.

The enrichment payoff

Matching isn’t just about identifying duplicates. When you match records across sources, you can merge fields — filling gaps in your CRM with data from the scraped sources.

Before matching, your 50,000-contact CRM has significant coverage gaps. After matching against scraped sources and merging fields, the picture improves substantially:

CRM field coverage — before and after enrichment
Email address Before enrichment
60%
Email address After enrichment
81%
Phone number Before enrichment
35%
Phone number After enrichment
58%
Current title Before enrichment
42%
Current title After enrichment
74%
Company (standardized) Before enrichment
67%
Company (standardized) After enrichment
88%

Illustrative figures for a 50K-contact B2B CRM enriched against 3 scraped sources.

The phone number improvement alone — from 35% to 58% coverage — represents 11,500 additional contacts your sales team can call. At typical B2B conversion rates, that’s a meaningful pipeline impact.

Title updates are equally valuable. A contact who was a “Senior Engineer” when they entered your CRM three years ago may now be a “VP of Engineering” — a much more qualified buyer for enterprise deals. The LinkedIn scrape catches this change; the matching pipeline connects it to the right CRM record.

Deduplication as enrichment

There’s a useful reframe here: deduplication and enrichment are the same operation.

When you match two records for the same person, you identify a duplicate. When you merge the non-overlapping fields from both records into a single enriched record, you’ve enriched your data. The matching step enables both outcomes simultaneously.

This means the enrichment workflow is:

  1. Export your CRM contacts as CSV
  2. Prepare each scraped source as a separate CSV
  3. Match your CRM export against each scraped source
  4. Merge fields from matched records — filling empty CRM fields with scraped data, updating stale fields with fresher data
  5. Review borderline matches before merging to prevent false enrichment

The review step matters. A false positive — incorrectly matching two different people — doesn’t just create a duplicate; it contaminates one record with another person’s data. That’s worse than having a gap. For borderline matches, the LLM confirmation step provides reasoning you can audit: “Names match as nickname variant, company names are equivalent, titles are consistent with seniority progression. High confidence same person.”

Handling multiple scraped sources

When you have three or more scraped sources, the matching becomes iterative:

Round 1: Match CRM against LinkedIn scrape. Merge high-confidence matches. This is typically your highest-yield round because LinkedIn has the broadest professional coverage.

Round 2: Match the updated CRM (now enriched with LinkedIn data) against the conference attendee list. The enriched records match more reliably because they now have more fields populated.

Round 3: Match against the directory export for phone numbers and secondary email addresses.

Each round enriches the CRM further, and each subsequent round benefits from the enrichment of prior rounds. A record that was unmatchable in Round 1 (only a name and company) might become matchable in Round 2 after gaining a title from the LinkedIn merge.

Practical considerations

Field conflict resolution

When the CRM has a value and the scraped source has a different value for the same field, you need a resolution strategy:

  • Recency wins: For titles and company names, the most recent data is usually most accurate. A 2026 LinkedIn scrape beats a 2023 CRM entry.
  • CRM wins for contact info: If your CRM has a verified email and the scrape has a different one, keep both. The scraped email may be a secondary address, not a replacement.
  • Flag for review: When company names differ substantially (“Acme Corp” vs “TechStart Inc”), it may indicate the person changed jobs rather than a data formatting difference. Don’t auto-merge; flag for human review.

Scale matters

Matching 50,000 CRM records against a 10,000-record scraped list means up to 500 million potential pairs. Blocking — comparing only within the same industry, geography, or company name cluster — reduces this to a manageable number. The AI pipeline handles the blocking automatically based on your field configuration.

Privacy and compliance

A brief but important note: the legality and ethics of scraping and matching personal data vary by jurisdiction and context.

Scraping: Respect robots.txt. Don’t scrape behind authentication without authorization. Understand the terms of service of the platforms you’re scraping.

Data handling: If you’re matching records that include personal information, ensure your data processing complies with applicable regulations (GDPR, CCPA, etc.). The matching itself is a form of data processing that may require a legal basis.

Storage and retention: Don’t retain scraped personal data longer than necessary for the matching and enrichment purpose. Once fields are merged into your CRM, the intermediate scraped datasets should be deleted.

None of this is legal advice. Consult your compliance team before operationalizing scraped data enrichment at scale.

The ROI calculation

CRM enrichment through matching has a straightforward business case:

  • Data vendor alternative: Professional enrichment services charge $0.10–$0.50 per record for similar data. Enriching 50,000 records costs $5,000–$25,000 per refresh cycle.
  • Scraping + matching: The data is available publicly. The matching step is the bottleneck — and it runs in minutes rather than requiring a vendor contract and a two-week turnaround.
  • Freshness: You control the refresh cycle. Scrape monthly, match monthly, keep your CRM current. Vendor data is typically refreshed quarterly at best.

The constraint isn’t data availability. It’s the matching step. Public professional data exists across dozens of platforms. Connecting it to your internal records reliably is the hard part.

Match Data Studio connects your CRM records to scraped sources using AI that handles name variants, title differences, and company name inconsistencies. Start enriching your CRM →

Keep reading