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Identity Resolution

Connect every signal to a real person — across devices, channels, and identifiers — with industry-leading match rates.

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Product questions, answered

What does identity resolution return?

Identity resolution connects supplied identifiers to a resolved profile. BIGDBM describes outputs with associated identifiers and RFIS confidence scoring. Confirm the returned fields and allowed uses for your selected service.

Which identifiers can I start with?

The documented inputs include email and hashed email, phone, MAID, IP, address and personal identifiers. Required combinations and supported formats depend on the endpoint or batch workflow.

How does this differ from Scored Identity Resolution?

Identity Resolution describes the matching workflow and supported identifiers. Scored Identity Resolution describes how confidence scores help evaluate and filter identity links. Confirm the service scope for your use case.

What affects match rate?

Input recency, normalization, identifier coverage and confidence thresholds affect match rate. Compare providers using the same deduplicated input cohort and measure false matches separately.

Coverage definitions and performance context · API reference · RFIS reference · Discuss scope and pricing

Example: evaluating a resolved record

Illustrative workflow using synthetic data; field labels below describe concepts, not an API response contract.

Synthetic resolution example
StepExample
Inputalex@example.com
Candidate outputA linked profile and its available identifiers, with confidence information
EvaluationReview the match score and permitted use; accept or reject according to your validated threshold

What's Inside

BIGDBM's Identity Resolution service resolves fragmented signals into unified, confidence-scored identity profiles. Whether you're starting from a mobile device ID, a hashed email, a phone number, or an IP address — we connect it back to a verified individual or business.

MAID

Resolve Android and Apple mobile device IDs to consumer or B2B profiles.

Hashed Email (HEM)

Match hashed emails to full consumer and B2B profiles with ~80% match rates.

Phone

Resolve mobile and landline numbers to verified identities using PQL scoring.

IP Address

Match residential and business IP addresses to identified individuals using billions of observed linkages.

PII

Resolve name, address, date of birth, and personal identifiers to a unified profile with 400+ fields.

Email

Match plain-text or hashed emails against 700M+ email records for high-confidence identity linkage.

Address

Resolve physical addresses to full consumer or B2B profiles with property and demographic data appended.

Businesses

Match company names, domains, and firmographic signals to verified business profiles.

Employees

Resolve professional identifiers to full employee records linked to verified company data.

Intenders

Identify in-market individuals from behavioral and intent signals, resolved and ready for outreach.

Engineered for Trust

Multi-Signal Matching

Deterministic and probabilistic matching tuned for accuracy — resolving MAIDs, HEMs, IPs, phones, and PII into a single unified profile.

Confidence-Scored

Every resolved identity is ranked on recency, frequency, intensity, and strength — so you always know how much to trust a match before activating it.

Privacy-Compliant

All resolution workflows operate on consent-managed data with opt-out processing, CCPA compliance, and deceased record suppression built in.

Continuously Refreshed

Identity linkages are updated daily for behavioral signals and monthly for device and address files — keeping resolutions current as people change devices and locations.

MAID
HEM/Email
Phone IP
PII/Address
↓
Confidence Scored
Matching Engine
↓
Resolved Identity

How Teams Get Results

Marketing & Growth
  • Resolve anonymous website visitors into known consumer or B2B profiles for immediate outreach.
  • Match hashed emails and device IDs to full identity records to power omnichannel campaigns.
  • Reconnect lapsed customers using updated phone, email, and address resolution.
Analytics & Data Science
  • Build people-based measurement frameworks by resolving cross-device signals to a single identity.
  • Validate internal identity graphs against BIGDBM's resolution layer to improve match coverage.
  • Enrich resolved profiles with behavioral and intent data for deeper audience modeling.
Risk, Compliance, and Ops
  • Verify consumer identity at account creation using multi-signal PII resolution.
  • Detect duplicate or synthetic identities by cross-referencing resolved signals.
  • Maintain audit-ready identity workflows with full consent and opt-out lineage.

FAQ

What identifiers can I use as a starting point for resolution?
You can start from any of the following: MAID (Android AAID or Apple IDFA), hashed email in MD5, SHA256, or SHA1, plain-text email address, phone number, IP address (residential or business), physical address, or PII fields such as full name, date of birth, and SSN fragments. BIGDBM's identity graph is built to resolve from whichever signal you have — even partial or low-quality inputs — and expand outward to every associated identifier it can reliably link. The output is a unified identity profile containing all verified signals mapped to that individual or household, confidence-scored using the RFIS framework so you can see exactly how strong each link is before activating.
What is the typical match rate?
BIGDBM reports an ~80% hashed-email match rate as of October 1, 2026. Results depend on input recency, normalization, coverage and confidence threshold. The September 2026 reporting workload exceeded 20 billion internal API requests. The eligible matching denominator and endpoint mix are not specified here. Evaluate a representative sample before estimating coverage.
What is a Phone Quality Level (PQL) score?
PQL (Phone Quality Level) is a confidence score assigned to every phone number that tells you whether it's worth calling or texting before you spend a dollar on outreach. The score evaluates carrier status (is the number active with a carrier?), connection type (landline, mobile, VoIP), line health, and DNC (Do Not Call) registry compliance. Numbers are scored on a tiered scale so you can set a threshold — for example, only contact numbers above a certain PQL score — and eliminate wasted outreach, compliance risk, and carrier penalties in one step. For SMS campaigns in particular, PQL scoring is essential: sending to invalid or DNC-registered numbers increases carrier filtering, damages sender reputation, and creates TCPA exposure. BIGDBM appends PQL scores at resolution time so every phone number in your output is pre-qualified.
Is deterministic or probabilistic matching used?
BIGDBM uses both methods, and the combination is what makes its match rates industry-leading. Deterministic matching is exact: when two records share the same verified identifier — a hashed email, confirmed phone number, or government-issued ID fragment — they are linked with certainty. This is the gold standard but only works when the input data is clean and complete. Probabilistic matching takes over when deterministic signals are absent or ambiguous — using device co-location patterns, behavioral clusters, household proximity, and observed signal overlap to build high-confidence links even across fragmented input data. Every probabilistic link is scored with RFIS (Recency, Frequency, Intensity, Strength), so you know how confident the match is and can set your own threshold for activation. The result is the broadest possible identity coverage without sacrificing precision — you see both the match and the score that justifies it.
How is privacy handled during resolution?
Privacy compliance is not a post-processing step at BIGDBM — it is infrastructure. Every identity resolution workflow runs on data that has been sourced with consent management from day one, with automated opt-out processing applied continuously so individuals who have exercised their CCPA rights are never returned in resolution outputs. Deceased record suppression removes individuals who are no longer contactable, protecting both compliance and deliverability. BIGDBM is SOC 2 Type II certified, TrustArc certified, and IAB Transparency compliant, with full audit trails maintained on every data workflow. For clients operating in regulated industries or handling sensitive audience segments, BIGDBM's compliance infrastructure means you can resolve identities at scale without building a separate compliance layer on top of the output.
Can resolution outputs be used for identity verification workflows?
Yes — and this is one of the strongest use cases for multi-signal resolution. When a new account is created or a transaction is submitted, BIGDBM's resolution APIs can cross-reference the supplied PII (name, address, phone, email, date of birth) against the identity graph in real time to confirm that the individual exists, that their signals are consistent with known records, and that no red flags exist — such as mismatched devices, address inconsistencies, or signals associated with known synthetic identity patterns. For fraud prevention, the RFIS confidence scores on each resolved field make it easy to flag low-confidence resolutions for manual review while auto-approving high-confidence ones. Typical API response time is 100–300ms, which integrates cleanly into onboarding, checkout, and account opening flows without adding noticeable latency.

Further Reading

Go deeper on identity resolution, match rate benchmarks, and data architecture.

Data Quality

How to Audit Match Rates and Improve Data Quality

Step-by-step guide to diagnosing low match rates, benchmarking performance, and improving resolution outcomes.

Identity Architecture

Identity Graph vs. CDP: Key Differences Explained

Understand when you need an identity graph versus a CDP, and how the two work together in a mature data stack.

Ready to Resolve Every Signal to a Real Identity?

Share a sample file or tell us which identifiers you're working with, and we'll show you what resolution rates and enrichment outputs you can expect.

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Note: before you can receive a sample, you must have a published Privacy Policy on your website, and be registered in the appropriate US States. Other laws might apply. Seek advice from your attorney if you are unsure.