Real-Time Identity Graphs for B2B and HCP ABM: How to Evaluate Them
How to evaluate real-time identity graphs for account-based marketing in B2B and HCP settings: latency, accuracy, firmographic links, privacy, and testing.
The short answer
A real-time identity graph resolves who or which organization is behind a digital interaction as it happens, so account-based marketing can target or personalize immediately. Evaluate one on match accuracy against a known truth set, latency, coverage of your target accounts, how person and account links are built, refresh frequency, privacy and consent sourcing, and whether results improve outcomes in a controlled test.
Account-based marketing depends on recognizing target accounts, and the people in them, across channels. Real-time identity graphs promise to do that in the moment. The promise is attractive. Accuracy and privacy deserve close scrutiny before you buy.
What real-time graphs do
- Website recognition. Identify the company, or sometimes the person, visiting a page.
- Bid-time targeting. Decide whether an impression belongs to a target account.
- Personalization. Change content based on who is likely visiting.
- Lead routing. Alert teams when a target account engages.
How the matches are made
| Signal | Typical accuracy |
|---|---|
| Logged-in or form-fill identity | High |
| Hashed email from a partner network | Medium to high |
| IP address to company | Medium for offices; weak for remote workers |
| Device and cookie history | Varies; declining with browser limits |
Remote and hybrid work has reduced the reliability of IP-to-company matching. Ask how the provider handles it.
Evaluation criteria
- Accuracy. Test with a truth set: known contacts and their companies. Measure correct matches, wrong matches, and misses.
- Coverage. Share of your target accounts the graph recognizes.
- Latency. Fast enough for the use, such as page load or bidding.
- Granularity. Account-level only, or person-level? Person-level raises more privacy questions.
- Freshness. How quickly job changes and new devices are reflected.
- Sourcing. Where data comes from and what consent supports it.
- Outcome lift. Does using the graph improve results versus a control?
Testing approach
- Run a parallel test: half of target accounts get graph-driven targeting or personalization, half get standard treatment.
- Compare engagement and pipeline or, for HCPs, downstream actions.
- Check accuracy on a sample by verifying matched identities through other means.
HCP use cases
For pharma, HCP ABM often targets practices or health systems. Real-time identity might personalize an HCP website for verified HCPs or adjust media for accounts in a key system. Points to check:
- Is the HCP match based on NPI-linked data with appropriate consent?
- Are you personalizing promotional content in a way review has approved?
- Does any health data about patients enter the process? It should not.
See account-based marketing for HCPs.
Red flags
- Vendor will not run an accuracy test on your truth set.
- Person-level identification with vague consent sourcing.
- Coverage claims based on total graph size, not your accounts.
- No clear way to remove or correct data.
Common mistakes
- Buying on demo results with the vendor's own accounts.
- Treating IP-based company matches as person-level identity.
- Skipping a control group.
Practical takeaway
Give each real-time identity vendor the same truth set of known contacts and accounts and score matches as correct, wrong, or missed. Wrong matches cost more than misses, so weigh them more heavily.
Frequently asked questions
What does real-time mean for an identity graph?
The graph can return a match quickly enough to act during a session or bid, such as choosing which ad to serve or personalizing a web page.
Is real-time identity accurate?
Accuracy varies. IP-to-company matches are often weaker for remote workers. Test against known contacts or accounts.
Can real-time identity be used for HCPs?
Some providers resolve HCPs in real time for website personalization or media, using NPI-linked data. Privacy terms and consent must allow the use.
Sources
External guidance and platform documentation change. Links were current at publication; check them again before relying on them for a decision.
Editorial note. Analysis and frameworks are the author's own and do not represent Acxiom or any current or former employer, client, or named platform. Examples labeled hypothetical or illustrative are not results from real campaigns. Nothing here is legal, regulatory, or medical advice.
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