Record Linkage

What is Record Linkage?

Record linkage is the data science process of identifying and connecting records that refer to the same real-world entity across different databases, systems, or datasets, even when those records use different identifiers or contain inconsistent information. Also known as entity resolution, data matching, or deduplication, this technique enables organizations to create unified views of customers, accounts, or contacts from fragmented data sources.

For B2B SaaS go-to-market teams, record linkage solves the critical challenge of maintaining accurate customer identity when data flows through multiple systems—marketing automation platforms, CRMs, product analytics tools, customer success platforms, and data warehouses. A single customer might appear as "John Smith" at "Acme Corp" in Salesforce, "J. Smith" at "Acme Corporation" in HubSpot, and user ID "abc123" in product analytics. Record linkage algorithms match these disparate records to the same entity, enabling complete customer journey tracking and preventing duplicate outreach.

The business value is substantial: organizations with effective record linkage improve marketing attribution accuracy by 40-60%, reduce duplicate contact outreach that damages brand perception, and enable revenue operations teams to calculate accurate customer lifetime value across all touchpoints. According to Gartner's 2023 Data Quality Market Guide, poor record linkage costs B2B organizations an average of $15 million annually in missed sales opportunities, wasted marketing spend, and compliance risks from maintaining duplicate customer records.

Key Takeaways

• Identity Resolution: Record linkage connects multiple data records representing the same person, company, or entity across different systems and data sources

• Matching Algorithms: Uses deterministic (exact matching) and probabilistic (statistical similarity) techniques to identify records that likely represent the same entity

• Unified Customer View: Enables creation of complete customer profiles by merging data from CRM, marketing automation, product usage, and support systems

• Data Quality Improvement: Reduces duplicate records by 60-80% and improves data accuracy by consolidating fragmented information into authoritative records

• Cross-System Intelligence: Powers advanced analytics, attribution modeling, and personalization by connecting customer behavior across multiple touchpoints

How It Works

Record linkage operates through a multi-stage process that compares record attributes, calculates similarity scores, and determines match confidence levels. The process begins with standardization and normalization, where incoming data is cleaned and formatted consistently—converting "IBM Corp.", "International Business Machines", and "I.B.M." to standardized forms that can be compared accurately.

The next stage implements blocking or indexing techniques to reduce computational complexity. Rather than comparing every record against every other record (which would require billions of comparisons in large datasets), blocking groups potentially matching records based on shared characteristics. For example, records might be blocked by first letter of company name and country, so only records within the same block are compared—dramatically reducing processing requirements while maintaining accuracy.

The matching stage applies deterministic or probabilistic algorithms to calculate similarity between record pairs. Deterministic matching requires exact matches on specified fields (email address, phone number with country code, tax ID). This approach offers high precision but misses matches when data contains typos or variations. Probabilistic matching uses statistical models to calculate match probability based on multiple field comparisons, assigning weights based on each field's discriminating power.

For example, matching on "email domain + company name + country" might work as follows: exact email domain match (40 points), Levenshtein distance similarity on company name above 85% (30 points), exact country match (15 points), similar employee count within 20% (15 points). Records scoring above 80 points are classified as matches, 50-80 points as possible matches requiring review, and below 50 points as non-matches. Advanced systems use machine learning models trained on historical match decisions to continuously improve accuracy.

The final stage involves merge/purge operations that consolidate matched records into golden records—authoritative versions that combine the most accurate and complete information from all matched sources. Master data management systems maintain these golden records and propagate updates back to source systems. According to MIT research on entity resolution, modern record linkage systems achieve 90-95% accuracy with properly configured algorithms and sufficient training data.

Key Features

• Multi-Algorithm Matching: Combines exact matching, fuzzy string matching, and machine learning models to handle various data quality scenarios

• Confidence Scoring: Assigns probabilistic scores indicating match likelihood, enabling automated decisions for high-confidence matches and human review for ambiguous cases

• Survivorship Rules: Defines logic for selecting which data values to preserve when merging duplicate records (most recent, most complete, most trusted source)

• Cross-System Resolution: Links records across multiple platforms using different identifiers (email, phone, cookies, device IDs, account IDs)

• Incremental Processing: Continuously matches new incoming records against existing master data without reprocessing entire datasets

Use Cases

Unified Customer Journey Tracking

GTM analytics teams implement record linkage to connect anonymous website visitors with known leads, CRM contacts, and product users across the entire customer journey. When a prospect first visits the website anonymously, views pricing pages, downloads content as a known lead, engages with sales, and eventually becomes a product user, record linkage connects these identities to create a complete attribution path. This enables accurate multi-touch attribution modeling, identifies which marketing touchpoints actually influence pipeline, and calculates true customer acquisition costs across all channels.

Account-Based Marketing Account Matching

Revenue operations teams use record linkage to match leads and contacts to target accounts in ABM programs. When inbound leads arrive from various sources (web forms, content downloads, events), record linkage algorithms match them to existing accounts based on email domain, company name similarity, and IP address intelligence. This prevents treating existing customer contacts as new leads, ensures all contacts from target accounts receive coordinated messaging, and enables account-level engagement scoring that aggregates activity across all associated contacts.

Data Warehouse Customer 360

Data engineering teams implement record linkage in data warehouses to build unified customer profiles from disparate source systems. Contact data from Salesforce, behavioral data from product analytics, support interactions from Zendesk, and payment information from billing systems are linked using multiple identifiers (email, user ID, account ID) and fuzzy matching on name and company attributes. The resulting customer 360 views power executive dashboards, enable advanced cohort analysis, and support AI-powered recommendations based on complete customer context rather than siloed system data.

Implementation Example

Here's a comprehensive record linkage framework for B2B SaaS customer data:

Record Linkage Architecture for B2B Customer Data
═══════════════════════════════════════════════════════════════════
<p>Source Systems          Linkage Process              Master Data Output<br>───────────────        ──────────────────           ──────────────────</p>

Record Linkage Matching Algorithm Framework

Matching Rules Example: B2B Contact Linkage

Rule 1: High Confidence Match (Auto-Link)
══════════════════════════════════════════
- Email exact match (100 points) → AUTO MATCH
OR
- Phone exact match (100 points) → AUTO MATCH
OR
- Company name similarity > 90% (40 points)
  + Person name similarity > 85% (35 points)
  + Country match (15 points)
  + Job title similarity > 70% (10 points)
  = 100 points → AUTO MATCH
<p>Rule 2: Possible Match (Manual Review)<br>══════════════════════════════════════════</p>
<ul>
<li>Email domain match (30 points)
<ul>
<li>Person name similarity > 80% (25 points)</li>
<li>Company name similarity > 75% (20 points)<br>= 75 points → REVIEW QUEUE</li>
</ul>
</li>
</ul>
<p>Rule 3: No Match (Create New Record)<br>══════════════════════════════════════════</p>

Survivorship Rules for Merged Records

When multiple records match and must be merged into a golden record, survivorship rules determine which values to keep:

Record Linkage Technology Stack Options:

• Open Source: Apache Spark MLlib for large-scale matching, Python RecordLinkage library, Splink

• Cloud Platforms: Google Cloud BigQuery ML entity resolution, AWS Entity Resolution, Snowflake Fuzzy Matching

• Enterprise MDM: Informatica MDM, SAP Master Data Governance, Oracle Customer Data Management

• CDP Native: Segment Identity Resolution, mParticle IDSync, Treasure Data Customer Data Cloud

• Specialized: Tamr, Senzing, Tilores for complex entity resolution

Related Terms

• Identity Resolution: Broader category that includes record linkage plus cross-device and anonymous-to-known visitor matching

• Entity Resolution: Synonym for record linkage focused on resolving entities across datasets

• Master Data Management: System architecture that maintains golden records created through record linkage

• Golden Record: The authoritative, consolidated record created by linking and merging duplicate records

• Identity Stitching: Process of connecting multiple identities and identifiers to a single customer profile

• Deterministic Matching: Exact-match linkage approach requiring perfect field agreement

• Probabilistic Matching: Statistical linkage approach using similarity scoring across multiple fields

• Customer Data Platform: Systems that heavily rely on record linkage for unified customer profiles

Frequently Asked Questions

What is record linkage?

Quick Answer: Record linkage is the process of identifying and connecting records across different databases or systems that refer to the same real-world person, company, or entity, even when they use different identifiers or contain inconsistent information.

Record linkage uses matching algorithms that compare record attributes like names, email addresses, phone numbers, and company information to determine when separate records represent the same entity. The process creates unified customer views by linking CRM contacts, marketing leads, product users, and support tickets into complete profiles, enabling accurate customer journey tracking and preventing duplicate outreach.

What's the difference between record linkage and deduplication?

Quick Answer: Record linkage connects records representing the same entity across different data sources, while deduplication finds and removes duplicate records within a single dataset or system.

Deduplication is typically simpler because records are in the same system with consistent formatting and field structures—the challenge is identifying near-duplicates like "John Smith" and "J. Smith" at the same company. Record linkage is more complex because it matches records across systems with different schemas, identifiers, and data quality levels. However, both use similar matching algorithms (exact, fuzzy, probabilistic), and record linkage often includes deduplication as a component when consolidating matched records.

What are deterministic and probabilistic record linkage?

Quick Answer: Deterministic linkage requires exact matches on specified fields (like email or ID numbers), while probabilistic linkage uses statistical scoring across multiple fields to calculate match probability even with inexact data.

Deterministic matching is simpler and faster but only works when data contains reliable unique identifiers and is free from typos or variations. It's ideal for matching on email addresses, phone numbers, or tax IDs. Probabilistic matching is more flexible and catches matches that deterministic rules miss—like "International Business Machines" and "IBM Corp."—by calculating similarity scores across multiple attributes and applying weighted scoring. Most production systems use hybrid approaches: deterministic matching for high-confidence cases and probabilistic matching for records without exact identifier matches.

How accurate is record linkage?

Record linkage accuracy varies widely based on data quality, matching algorithms, and tuning. Systems using exact matching on unique identifiers (email, phone) achieve 99%+ accuracy. Fuzzy matching on names and addresses typically reaches 85-90% accuracy. Sophisticated probabilistic models combining multiple attributes achieve 90-95% accuracy with proper configuration. Machine learning approaches trained on historical match decisions can reach 92-96% accuracy. The challenge is balancing precision (avoiding false matches that link different entities) with recall (catching all true matches). Most organizations tune algorithms to prioritize precision, automatically linking high-confidence matches while routing ambiguous cases to human review queues.

What tools are used for record linkage in B2B SaaS?

B2B SaaS organizations implement record linkage through several technology categories. Customer Data Platforms (Segment, mParticle, Treasure Data) provide built-in identity resolution for linking customer touchpoints. Master Data Management platforms (Informatica, SAP, Tamr) offer enterprise-grade linkage for large-scale data. Data warehouse solutions (Snowflake, BigQuery, Databricks) include fuzzy matching and ML-based entity resolution functions. Reverse ETL platforms (Hightouch, Census) link warehouse golden records back to operational systems. Open-source libraries (Python RecordLinkage, Splink) enable custom implementations for organizations with data science teams. The choice depends on data volume, technical resources, and whether linkage occurs in operational systems or analytics environments.

Conclusion

Record linkage is foundational infrastructure for B2B SaaS organizations seeking unified views of customers across fragmented data sources. By implementing sophisticated matching algorithms that connect contacts, leads, users, and accounts across CRM, marketing automation, product analytics, and support systems, GTM teams gain complete customer journey visibility that drives more accurate attribution, prevents duplicate outreach, and enables true customer 360 analytics.

For revenue operations teams, record linkage transforms how organizations understand customer relationships—connecting anonymous website visitors to known leads to paying customers in cohesive journey maps. Marketing operations teams leverage linked records for multi-touch attribution that accurately credits campaigns and channels contributing to pipeline. Sales teams benefit from unified account views that aggregate engagement across all contacts, preventing redundant outreach and improving account intelligence.

As B2B organizations continue expanding their GTM tech stacks and collecting customer data across more touchpoints, effective record linkage will become even more critical. Organizations that implement robust identity resolution infrastructure early—using hybrid deterministic and probabilistic matching, maintaining golden records in master data management systems, and continuously improving match accuracy—build more reliable customer intelligence that drives better GTM decisions and stronger business outcomes.

Last Updated: January 18, 2026