A Truthset study of nearly one billion IP records, summarized by PPC Land, found IP-to-postal linkages accurate just 13% of the time. That is what inference looks like when you lean on it for targeting. For identity resolution for creator campaigns, the real question is how much inference your budget can tolerate. Identity graphs and hashed email matching sit at opposite ends of that dial. Here is how to pick.
Two approaches, two different bets
A hashed email match takes an email your brand already holds (a purchaser, a newsletter subscriber, a creator-code redeemer), converts it into a hash, and compares it with the same hash on a platform or partner. It is deterministic. Either the hash matches or it doesn’t.
An identity graph is a vendor-maintained map that links many identifiers to a person or household: emails, phone numbers, device IDs, cookies, postal addresses, sometimes IP data. It blends deterministic links with probabilistic ones. The pitch is scale. You bring one identifier, and the graph reaches the others.
Creator campaigns stress both models in unusual ways. A viewer watches a TikTok or YouTube integration on a phone, clicks through a link-in-bio page, and buys on a laptop three days later. Sometimes they buy through a retailer and you never see them at all. Your identity method decides whether that chain gets stitched together or reported as three strangers.
Where hashed email matching wins
Precision, first. When a hash matches, you know the two records refer to the same address. That makes it the cleanest way to suppress existing customers from creator-driven paid amplification, build lookalike seeds from real buyers, and send conversion events back to ad platforms.
Adoption backs this up. PPC Land’s explainer on deterministic signals notes that the IAB’s October 2025 connected TV conversion API guide cited 68% of advertisers using deterministic signals such as hashed emails and device IDs.
Cost and control are the other advantages. You hold the source data. You can see what went out, to whom, and under what consent. There is no black box in the middle deciding that two records “look like” the same person.
The weak spot is coverage. Hashed email only works on people who gave you an email, and creator audiences are mostly anonymous top-of-funnel viewers. If your campaign is awareness-heavy, your matchable pool may be small. Match rates also depend on whether the viewer used the same email on the platform as on your checkout page. Many don’t.
Where identity graphs earn their fee
Graphs shine when you need reach beyond your first-party list. They can connect a mobile ad exposure to a desktop purchase, extend a creator-informed audience across channels, and fill gaps when the email you hold isn’t the email the platform has.
That reach comes with a quality question vendors rarely volunteer. Probabilistic links are inference, and inference decays. The same Truthset research summarized by PPC Land found IP-to-email linkages accurate 16% of the time, and six providers agreed on the same household only 6.4% of the time. Per PPC Land, Truthset’s State of Data Accuracy report rated email-to-postal matches accurate 51% of the time, the strongest of the three linkages it measured. Even the best link in that study was wrong about half the time.
A graph is only as good as its weakest link type. Ask every vendor what share of its matches are deterministic, and what share rely on IP or device inference.
Not every graph is built on IP data, and those figures describe specific linkage types, not every vendor. But they are the right reason to demand a breakdown before you sign. For a deeper look at how vendors package these claims, see our piece on vetting match rate claims.
The compliance trap nobody wants to discuss
Many teams choose hashed email because it feels safe. Hashing sounds like anonymization. It isn’t.
The FTC said so plainly in July 2024. As AdExchanger reported, regulators including the FTC consider hashed identifiers to be personal information. The firm also flagged list matching and custom audience building as practices worth reviewing.
What does that mean for creator programs? Three things:
- Your privacy policy and your creator contracts must not call hashed data “anonymous.” Say “pseudonymized” or “hashed personal data” and mean it.
- Consent travels with the identifier. If a customer opted out of marketing, their hash should not appear in an upload to a creator whitelisting or paid amplification audience.
- Graph vendors carry the same risk, plus provenance risk. You inherit questions about where their links came from and whether the people involved agreed to it.
Consent plumbing deserves its own audit. Our guide on consent platform data flows covers what to check before peak season traffic hits.
Does the cookie question change the answer?
Less than it used to, though it still matters. Google has spent years backing away from third-party cookie removal. SiliconANGLE reported in April 2025 that Google dropped its plan for a Chrome opt-out tool for third-party cookies and would revise the Privacy Sandbox roadmap.
Don’t read that as a reprieve. Safari and Firefox already restrict cookies, and creator traffic skews heavily mobile and in-app, where cookies barely matter anyway. Your identity strategy should assume cookies are a bonus, not a foundation.
A decision framework for creator campaigns
Skip the either/or debate. Match the method to the job.
- Conversion tracking and suppression: Use hashed email. You need accuracy, and you already hold the data.
- Seed audiences for lookalikes: Use hashed email from real purchasers or code redeemers. Clean seeds beat large, noisy ones.
- Cross-device and cross-channel reach: Test a graph, but demand deterministic share and holdout results.
- Sensitive or regulated categories: Lean toward the method with the least third-party data movement. Clean rooms often fit here.
- Retail or marketplace sales you can’t see: Neither method fixes this alone. Use creator codes, unique landing pages, and retailer reporting.
Most mid-size brands end up hybrid: hashed email as the backbone, a graph for selective extension. If you run a customer data platform, check how it handles that split. Our comparison of CDP and identity resolution overlap explains where you may be paying twice.
Questions to put to any vendor
- What percentage of your matches are deterministic, and how is that verified?
- Can you show match rates by identifier type, not one blended number?
- How do you handle opt-outs and deletion requests downstream?
- Where do your links come from, and can you document consent for them?
- Will you run an incrementality holdout, or only report match volume?
Match rate is an input, not an outcome. A high match rate on the wrong people is just an expensive mistake. Tie any pilot to lift, not to the vendor’s headline figure.
Where clean rooms fit
If neither option sits right, a clean room can reduce raw data exchange while still allowing matching. They add cost and setup time, and creator programs often lack the volume to justify them. Read the clean room launch checklist before committing budget.
FAQs
What is the difference between an identity graph and hashed email matching?
Hashed email matching compares one-way encoded email addresses between two datasets and returns exact matches. An identity graph links many identifiers (emails, devices, phone numbers, postal data) to a person or household, mixing deterministic and probabilistic connections. Hashed matching is narrower and more precise. Graphs are broader and less certain.
Is hashed email data considered anonymous?
No. The FTC has stated that hashing does not make data anonymous, and that treating hashed identifiers as anonymous can be deceptive when they allow tracking over time. Treat hashes as personal data, with consent and opt-out handling to match.
Which approach is better for measuring creator campaign results?
Hashed email usually gives cleaner conversion measurement because the match is exact and you control the source data. Graphs can help connect exposures across devices, but you should validate them with holdout tests. Pair either with creator codes and tracked links to catch sales outside your own checkout.
Do I need an identity graph if I already have a CDP?
Not necessarily. Many CDPs include identity resolution on first-party data. A third-party graph adds value mainly when you need reach beyond known customers. Audit what your CDP already does before buying overlapping capability.
How accurate are identity graph matches?
Accuracy depends on the link type. Ask vendors for accuracy by identifier type, not a single blended rate.
What should a small brand with limited first-party data do?
Start with hashed email from whatever you have, and build the list through creator-specific landing pages, gated offers, and discount codes. Consider a graph only after you can measure incrementality, so you know whether it adds anything.
Before your next creator flight, pull the three most recent audience uploads and confirm each was built from consented, deterministic records. Then ask your graph vendor, in writing, for its deterministic share.
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