First-party vs Zero-party Data: Understanding the Key Differences
- Harold Bell
- Apr 25
- 11 min read
Updated: Aug 25
Key takeaways
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I've spent more than 16 years building B2B marketing programs, and I've watched the zero-party data conversation get distorted by a prediction that didn't come true. Most of what's been written about it was written to answer a question that's now moot. So let's do this properly: what each type of data actually is, what changed, and what a B2B team should do about it now.
What is first-party data
First-party data is information you collect directly on your own properties about how people behave. Page views, clicks, downloads, email opens, session duration, product usage, purchase history. You own the collection infrastructure, so you own the data, and no third party sits between you and it. The defining characteristic is that it’s observed rather than stated. Your buyer produced it without consciously deciding to tell you anything. |
First-party data is the backbone of analytics, attribution, retargeting, and behavioural scoring. It's abundant, it's continuous, and it's honest in the sense that people do what they do regardless of what they'd say in a survey.
Its weakness is that every conclusion drawn from it is an inference. Someone visited your pricing page four times. Are they evaluating you seriously, or are they a competitor, or a student, or a consultant building a comparison for someone else? The behaviour is a fact. The motive is a guess.
What is zero-party data
Zero-party data is information a customer intentionally and proactively shares with you, knowing they’re sharing it. Stated preferences, purchase intentions, budget range, timeline, role, the problem they’re trying to solve. The term was coined by Forrester analyst Fatemeh Khatibloo around 2018 to distinguish explicitly volunteered information from data collected by observation or inference. The defining word is intentionally. |
Forrester’s framing was that this is data earned through trust rather than harvested, and that it can be used to verify assumptions rather than replace them. That's a more precise idea than the way the term is usually used now.
In B2B, zero-party data looks like a form field where someone selects their evaluation timeline, a preference centre where they choose which topics they want to hear about, a self-assessment tool that asks about their current stack, or a demo request where they describe the problem in their own words. Every one of those is a person choosing to tell you something.
How do first-party and zero-party data actually differ
Four axes. Collection: observed versus volunteered. Accuracy: inferred motive versus stated motive. Volume: abundant versus scarce. Consent: implied through use versus explicit by definition. The practical consequence is that first-party data tells you what happened at scale, and zero-party data tells you why, for the smaller number of people willing to say. |
First-party data | Zero-party data | |
How it’s collected | Observed passively on your own properties | Volunteered deliberately by the person |
What it tells you | What someone did | What someone wants, intends, or is constrained by |
Accuracy of motive | Inferred, so it can be wrong | Stated, so it’s wrong only if they lied or misunderstood |
Volume | High. Every visitor generates it. | Low. Only the people who choose to engage. |
Decay rate | Fast. Behavior from six months ago says little. | Slower for stable attributes, fast for timeline and budget. |
Consent posture | Implied, and subject to consent frameworks | Explicit by definition |
Best used for | Analytics, attribution, retargeting, behavioral scoring | Qualification, segmentation, content targeting, personalization |
Main failure mode | Confidently acting on a wrong inference | Asking for more than the exchange justifies, so nobody answers |
Is zero-party data just a subset of first-party data
Yes, structurally. Both are collected on infrastructure you control, so zero-party data sits inside first-party data rather than beside it. The distinction is still worth keeping, because declared and observed data have different accuracy, different volume, and different consent characteristics, and treating them as interchangeable is exactly how teams end up making confident decisions on weak inferences. |
I make this point because a lot of writing on the subject presents the two as competing options, as if you should choose. You shouldn't. The useful framing is that zero-party data is the small, high-confidence core of your first-party data, and its job is to correct and anchor the much larger volume of inference around it.
Why did zero-party data become a topic and what actually changed
Zero-party data became a marketing topic because the industry expected third-party cookies to disappear. That expectation was wrong. Google reversed its deprecation plan in July 2024, confirmed on 22 April 2025 that it would keep third-party cookies in Chrome and would not roll out a standalone consent prompt, and retired the remaining Privacy Sandbox APIs in October 2025. The urgency was real. The deadline was not. |
This matters because it means most published advice on zero-party data is answering a question that no longer exists. Here's the actual sequence.
When | What happened |
2017 and 2019 | Safari introduced Intelligent Tracking Prevention and Firefox introduced Enhanced Tracking Protection, both blocking third-party cookies by default |
2019 | Google announced Privacy Sandbox as the replacement architecture for cookie-based advertising |
Around 2018 | Forrester analyst Fatemeh Khatibloo coined the term zero-party data |
Early 2024 | Chrome began restricting third-party cookies for 1% of users as a test |
July 2024 | Google reversed course, saying it would not remove third-party cookies and would instead offer users a choice |
22 April 2025 | Google confirmed it would not introduce a standalone cookie prompt and restated its intention not to deprecate third-party cookies |
October 2025 | Google retired the remaining Privacy Sandbox APIs including Attribution Reporting, Topics, and Protected Audience |
The April 2025 decision is documented in Google’s Privacy Sandbox announcement as reported by IAB UK, and the legal and compliance reading of it is covered by Hunton’s privacy practice and OneTrust. The subsequent retirement of the Privacy Sandbox APIs closed the chapter.
So does that mean zero-party data was a fad? No, and this is the part I'd push back on if someone brought it to me.
If you were collecting zero-party data as a hedge against cookie deprecation, your reason evaporated and you should reassess honestly. If you were collecting it because asking someone their budget is more accurate than guessing from their page views, nothing changed at all. The accuracy argument was always the stronger one. The compliance argument was just louder.
And in B2B specifically, third-party cookies were never doing much work anyway. Cross-site behavioral retargeting matters far more to consumer e-commerce than to a nine-month enterprise software evaluation involving seven people. The B2B case for asking buyers what they want has always rested on qualification, not on tracking.
When should you use first-party data
First-party data does four jobs well in B2B.
Behavioral scoring. Repeated visits to pricing, docs, or comparison pages are a real intent signal even though the motive is inferred. Score it, but hold the conclusion loosely.
Attribution and measurement. Which content touched which opportunity. This is unavoidable first-party work. See content marketing ROI measurement.
Content performance. What people actually read, how far they get, and where they leave. This is how you find out which topics are working, covered in data-driven content marketing.
Account-level intent. Aggregating anonymous behaviour by company domain is often more useful than individual-level tracking, and it degrades far less under privacy constraints.
Its limit is always the same. Behavior tells you something happened. It never tells you why, and the gap between those two is where expensive mistakes live.
When should you use zero-party data
Zero-party data earns its keep in B2B when the answer materially changes what you do next.
Qualification. Timeline, budget range, and decision authority are the three questions that separate a real opportunity from a curious reader, and no amount of behavioral inference substitutes for asking. This is the single highest-value use in B2B.
Segmentation for email. Letting people choose their topics beats guessing from click history, and it directly reduces the irrelevant-outreach problem. Gartner found 73% of B2B buyers actively avoid suppliers who send irrelevant outreach. See B2B email marketing strategy.
Content targeting. A stated role and a stated problem let you route someone to the right material immediately rather than after four visits of inference.
Persona validation. Zero-party data is how you find out your personas are wrong. See writing for your buyer personas.
The constraint is the exchange rate. Every question costs you completions, so a question only earns its place if the answer changes your behavior. If nobody will ever act on the answer, delete the field. That's the same discipline as gated vs ungated content: you're trading reach for information, and the trade has to be worth it.
How do the two work together in a B2B program
Use first-party data to detect that something is happening and zero-party data to find out what. Behavior identifies the account and the moment. The declared answer tells you the intent, the constraint, and the timeline. Programs that run on inference alone are confident and often wrong. Programs that rely on declaration alone have a very small dataset. The combination is what works. |
A concrete B2B loop looks like this. Anonymous behavioural data shows a spike in activity from one company domain across your docs and pricing pages. That's first-party, and on its own it justifies nothing more than attention. Someone from that domain then downloads a buying guide and answers two questions in the form: what they're evaluating and when. That's zero-party, and it converts an inference into a qualified opportunity with a stated timeline.
This matters more now than it did, because buyers arrive later and better informed. Gartner's B2B buying journey research found buyers spend only around 17% of the purchase journey with suppliers, and its 2026 survey found 45% used AI during a recent purchase and 70% prefer a fully digital self-service experience. If a buyer has already formed a view before they identify themselves, the few questions you get to ask are disproportionately valuable. Waste them on job title and you've learned nothing you couldn't have inferred.
There's a discovery-side consequence too. Pew Research found clicks to websites fall from 15% to 8% when a Google AI summary appears. Fewer identified visitors means each declared answer carries more weight, which raises the return on designing your forms properly.
How do you actually collect zero-party data in B2B
Five mechanisms, roughly in order of how much they return for the effort.
Two well-chosen form fields. Timeline and problem, as dropdowns rather than free text. Cheapest thing on this list and usually the highest yield.
An email preference centre. Let people choose topics and frequency. It reduces unsubscribes and produces segmentation data as a side effect.
Self-assessment tools. A maturity assessment or readiness checker gives the buyer something genuinely useful and gives you their stated current state. High effort, high return.
Progressive profiling. Ask one new question per interaction rather than nine at once. Spreads the cost across the relationship.
Sales call notes fed back to marketing. The richest zero-party data in most companies already exists in the CRM and marketing never reads it.
More tactical detail in zero-party data collection and zero-party data strategy.
What mistakes do teams make with these data types
Treating an inference as a declaration
Someone read three articles about migration, so the system tags them as evaluating migration and starts sending migration content. They were doing research for a conference talk. This is the most common and most expensive error, and it compounds because the wrong tag drives the wrong follow-up which produces more misleading behavior.
Asking for information you will never use
Every field costs completions. If nobody will ever act differently based on company size, remove the company size field. Most B2B forms carry two or three fields that exist because someone asked for them in a meeting in 2022.
Collecting declarations and never acting on them
If someone tells you their timeline is twelve months and you put them into the same sequence as someone evaluating now, you've broken the exchange. They gave you information and got nothing for it, which makes them less likely to answer honestly next time.
Assuming one replaces the other
Zero-party data won't give you attribution and first-party data won't give you intent. Teams that go all-in on one end up rebuilding the other within a year.
Building the whole strategy on the cookie deadline
The deadline moved and then disappeared. If your zero-party data business case was written in 2023 and rested entirely on Chrome deprecation, it needs rewriting on accuracy grounds. The good news is that the accuracy argument is more durable than the compliance one ever was.
Where to start
Add two fields to your highest-intent form: evaluation timeline and the problem they're trying to solve. Make sure both are actually used in routing and follow-up. Then read your last twenty sales call notes and write down every piece of information a buyer volunteered that marketing never captured. That list is your zero-party data roadmap, and it costs an afternoon.
Frequently asked questions
What is the difference between first-party and zero-party data?
First-party data is observed on your own properties, such as page views, clicks, and purchases. Zero-party data is volunteered deliberately by the person, such as stated preferences, budget, or timeline. The practical difference is that first-party data tells you what someone did while zero-party data tells you what they want.
Is zero-party data a type of first-party data?
Structurally yes. Both are collected on infrastructure you control, so zero-party data is a subset of first-party data rather than a competing category. The distinction is worth keeping because declared and observed data have very different accuracy, volume, and consent characteristics.
Who invented the term zero-party data?
Forrester analyst Fatemeh Khatibloo coined it around 2018 to distinguish information a customer intentionally and proactively shares with a brand from data collected through observation or inference.
Are third-party cookies actually going away?
No. Google reversed its deprecation plan in July 2024, confirmed on 22 April 2025 that it would keep third-party cookies in Chrome without a standalone consent prompt, and retired the remaining Privacy Sandbox APIs in October 2025. Safari and Firefox blocked them years earlier, so they remain restricted on those browsers.
Does the cookie reversal mean zero-party data no longer matters?
No, but it changes the justification. If you were collecting zero-party data as a hedge against cookie deprecation, that reason has gone. If you were collecting it because a stated budget is more accurate than an inferred one, nothing has changed. The accuracy argument was always the stronger one.
What is an example of zero-party data in B2B?
A form field where someone selects their evaluation timeline, a preference centre where they choose which topics to receive, a self-assessment tool where they describe their current stack, or a demo request where they write out the problem they are trying to solve.
What is an example of first-party data in B2B?
Page views on your pricing page, email open and click history, documentation usage, session recordings, webinar attendance, and product usage telemetry. All of it is behavior you observed rather than information anyone chose to tell you.
Is zero-party data more accurate than first-party data?
It is more accurate about motive and intent, because the person stated it directly. It is not more accurate about behavior, and it is far scarcer. Use declared data to interpret observed data rather than to replace it.
How many fields should a B2B form have to collect zero-party data?
As few as possible, and every one should change what you do next. Timeline and problem are usually the two highest-value questions in B2B. If nobody will ever act differently based on a field, remove it, because each field costs completions.
Do you need consent to collect zero-party data?
Zero-party data is explicitly volunteered, so the act of providing it carries a clear consent signal. You still need to honor applicable privacy law regarding how you store, process, and use it, and you should state plainly what the data will be used for at the point of collection.
Which is better for B2B, first-party or zero-party data?
Neither alone. First-party data identifies the account and the moment at scale. Zero-party data supplies the intent, constraint, and timeline for the smaller set of people who engage. Programs running on inference alone are confident and often wrong; programs running on declaration alone have too little data.
How do you use first-party and zero-party data together?
Use behavioral first-party data to detect a spike in activity from an account, then use a small number of declared questions at the conversion point to confirm what is actually happening. That turns an inference into a qualified opportunity with a stated timeline.
Harold Bell is the founder of MQL Magnet and a Forbes Communications Council member. He has more than 16 years in B2B content and demand generation, working with AWS, Cisco, Google Cloud, and Ford. Book 30 minutes at cal.com/mqlmagnet/30min.
