Duplicate records are hiding your best leads. Identity resolution reveals true buyer intent and fixes your pipeline.
High Intent Looks Like Low Intent
In most PropTech platforms, 30% to 40% of CRM records are duplicates, created from multiple listing portals, lead forms, and integrations.
A single buyer might submit inquiries across Zillow, Realtor.com, your website, and paid campaigns. Each submission creates a new record, often with slight variations in name, email, or phone number.
In one PropTech platform, a buyer submitted inquiries across three portals and one direct website form. This created four separate CRM entries with slightly different data points.
Each record was scored independently. None of them appeared as high priority. The buyer did not receive timely follow-up from agents.
After implementing identity resolution, those records were merged into a single unified profile. The system identified multiple touchpoints, signaling strong buying intent.
Instead of exact matches, systems use similarity scoring across name, email, phone, location, and behavior patterns to identify duplicates.
Different portals export data in different formats. A normalization layer converts all inputs into a consistent schema before processing.
Deduplication should happen at the point of ingestion, not as a batch process. This ensures new leads are immediately matched against existing profiles.
What Changes When Identity Is Fixed
Lead scoring models become significantly more accurate because they operate on complete buyer journeys instead of fragmented signals.
Sales teams engage high-intent buyers faster, improving response times and conversion rates.
Marketing attribution improves because touchpoints are correctly linked to a single user.
Identity resolution is the process of linking multiple records that belong to the same individual into a single unified profile. In real estate, this is critical because buyers interact across multiple channels, creating fragmented records.
Lead scoring models rely on signals like frequency of interaction and engagement. When those signals are split across multiple records, the system underestimates buyer intent.
Exact matching fails when there are variations in spelling, formatting, or missing data. For example, “John Smith” vs “Jon Smith” or different phone formats would not match exactly.
Duplicates occur because leads come from multiple sources such as portals, ads, and direct traffic. Each source captures slightly different data, creating multiple records for the same person.
Probabilistic matching uses algorithms to determine the likelihood that two records belong to the same person, even if they are not exact matches. This is essential because real-world data is inconsistent.
A golden record is a unified profile created by merging duplicate records. It contains all interactions, data points, and history associated with a single individual.
