The 10 Types of First-Party Data (With Examples)
When people hear first-party data, they often think of an email list. In reality, you are sitting on far more than that. Here are the ten types of first-party data most businesses already generate, and what each one is good for.
Fundamentals · 6 min read
1. Identity data
Names, email addresses, phone numbers, and account IDs. This is the connective tissue that lets you tie every other type of data back to a real person.
2. Behavioral data
What people do: pages viewed, products browsed, features used, videos watched, clicks and scroll depth. Behavioral data reveals intent and interest in real time.
3. Transactional data
Orders, purchases, refunds, subscriptions, and payment history. This is the highest-signal data you own because it reflects what people actually paid for.
4. Declared (zero-party) data
Preferences, intentions, and context customers tell you on purpose through quizzes, surveys, and preference centers. See first-party vs zero-party data for the distinction.
5. Engagement data
Email opens and clicks, SMS responses, app notifications tapped, and campaign interactions. Engagement data shows how people respond to your outreach across channels.
6. Support and service data
Tickets, chats, calls, returns, and complaints. Often siloed in a help desk, this data is rich with signals about satisfaction, churn risk, and product gaps.
7. Account and profile data
Settings, saved items, plan tier, role, and account configuration. This tells you how a customer has set themselves up to use you.
8. Loyalty and rewards data
Points, tiers, referrals, and redemption history. Loyalty programs are powerful because they trade a clear benefit for richer, consented data.
9. Offline and in-person data
In-store purchases, event attendance, point-of-sale activity, and phone orders. Bringing offline interactions back into your data foundation closes major gaps.
10. Contextual and device data
Location, device type, and channel of interaction, collected with consent. Used carefully, contextual data sharpens timing and relevance.
The point: connect them, do not collect them in silos
Each type is useful alone, but the value compounds when they are unified to one customer record. A purchase plus a support ticket plus a preference tells a story that none of them tells by itself.
Unifying these scattered types into a single profile is the job of identity resolution, and it is what turns raw data into something you can actually act on.
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