Zero-party data

Zero-party data is information a customer intentionally shares with a brand—such as preferences, needs, and constraints—in order to get a better experience. In ecommerce, product finder quizzes are a common way to capture this declared intent and use it to guide recommendations.

Also known as: declared data, declared intent, self-reported data.

Why it matters

• It’s explicit: the shopper tells you what they want, instead of you guessing from clicks alone.
• It improves match quality when products are hard to compare (shade, fit, routine, compatibility, goals).
• It can power downstream segmentation and personalization when configured to pass intent into your lifecycle and analytics stack.
• It creates a structured funnel you can measure and improve over time.

What zero-party data looks like in ecommerce

Common examples include:

• goals (hydration, sensitive skin, anti-aging)
• constraints (budget range, size/fit constraints, ingredient preferences)
• preferences (finish, firmness, style, environment)
• outcomes (recommended set or routine type)

Example: Haus Labs shade matching combinations

Some experiences require a large number of possible “profiles” to deliver personalized results. For example, the Haus Labs shade matching experience supports 60,480 different profile combinations.

The practical point: when the number of possible outcomes is large, structured declared intent becomes far more useful than generic browsing signals because it provides explicit inputs you can use to generate a precise match.

Downstream (when configured), this intent can also be passed as events and attributes for segmentation and measurement.

How it’s captured

Zero-party data is typically captured through:

• product finder quizzes (guided selling)
• routine builders
• fit/sizing selectors
• preference selectors embedded on PDPs or collections

How it becomes useful (activation)

To be useful, declared intent needs to be:

1. normalized (consistent naming and value taxonomy)
2. mapped to outcomes (recommendation logic and guardrails)
3. passed downstream as events and attributes when configured (so other systems can use it)

Cartful context

In Cartful, zero-party data is captured as quiz-derived intent and outcomes that can be used in two ways:

• on-site: to guide controlled product recommendations with merchandising guardrails
• downstream (when configured): as events and attributes for segmentation, personalization, and measurement

Cartful is privacy-forward by default:

• aggregated analytics (not PII) by default
• no IP/device identifiers stored
• no free-text answer storage
• emails/names can be forwarded via encrypted API when configured

Common pitfalls

• Treating zero-party data as “anything a shopper does” (it’s specifically what they intentionally share)
• Capturing messy values without a taxonomy (segmentation becomes noisy)
• Not tying answers to real guardrails (recommendations drift or break when the catalog changes)
• Not wiring intent downstream (you lose long-term value beyond the quiz)
• Collecting more than you need (increases friction and reduces completion rate)