First Party Data Collection

First-Party Data Collection

Overview

Third-party cookies are deprecated across all major browsers, and iOS tracking restrictions have cut paid signal quality by 30–60%. First-party data — information customers actively share with you — is now your most defensible marketing asset. This skill covers collecting preference data through quizzes, surveys, and preference centers, then using that data to personalize email, SMS, and product recommendations. Klaviyo and dedicated quiz apps make this achievable without custom development for most stores.

When to Use This Skill

• When ROAS on Meta and Google is declining due to signal loss from tracking restrictions
• When launching a preference center as part of an email list health initiative
• When wanting to reduce generic emails in favor of category-specific recommendations
• When launching a loyalty or VIP program that requires enriched customer profiles
• When preparing for GDPR/CCPA compliance with a structured consent management system

Core Instructions

Step 1: Choose the right tool for each data collection touchpoint

Step 2: Set up a product quiz for zero-party data

A product quiz collects preferences while immediately rewarding the customer with relevant recommendations — the highest-converting data collection method.

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Shopify

Using Octane AI (recommended for Shopify):

1. Install Octane AI from the Shopify App Store 2. Go to Octane AI → Quizzes → Create Quiz 3. Build a 3-question flow (keep it under 4 questions to maintain completion rate):

• Q1: "What brings you here today?" (Self / Gift / Work)
• Q2: "Which categories interest you most?" (multi-select with your product categories)
• Q3: "What's your typical budget?" (Under $50 / $50-$100 / $100+)

4. On the results screen, connect each answer combination to specific Shopify collections or products 5. Under Integrations → Klaviyo, enable data sync — Octane AI automatically updates Klaviyo profile properties with quiz answers (e.g., preferred_category: skincare, budget_range: 50-100) 6. Place the quiz at: homepage, dedicated /quiz page, or as a popup for new visitors

Using Typeform + Klaviyo:

1. Create a Typeform quiz at typeform.com 2. Enable the Klaviyo integration in Typeform — quiz answers push to Klaviyo as custom profile properties 3. Embed the Typeform on a Shopify page using an embed block in your theme editor 4. In Klaviyo, use the quiz answers as segment conditions and personalization variables in emails

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WooCommerce

1. Install Quiz and Survey Master (free WordPress plugin) or use Typeform embedded on a page 2. For product recommendations: configure the quiz to redirect to specific WooCommerce category pages based on answers 3. For Klaviyo sync: use the Klaviyo WooCommerce plugin and Typeform's Klaviyo integration 4. For Mailchimp: use Typeform's Mailchimp integration to tag subscribers with their quiz answers

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Step 3: Build a preference center

A preference center lets customers control what they receive — reducing unsubscribes while collecting valuable preference data.

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Shopify with Klaviyo

1. Go to Klaviyo → Sign-up Forms → Create Form 2. Choose Embedded Form type (for a dedicated preferences page) 3. Add fields:

• Email frequency: radio buttons (Daily deals / Weekly digest / Special occasions only)
• Favorite categories: checkboxes for your product categories
• Communication channels: checkbox for SMS consent

4. Embed this form on a /preferences page in your Shopify store 5. Link to this page from every email footer: "Update your preferences" next to the unsubscribe link 6. Klaviyo automatically saves form submissions as custom profile properties — use these as segment conditions

Alternative using Klaviyo's email preferences page: 1. Every Klaviyo email includes an auto-generated preferences link 2. Customers can unsubscribe or adjust frequency from this page without any setup

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WooCommerce

1. Use Mailchimp or Klaviyo and create an embedded form with preference fields 2. Host the form on a WordPress page (/email-preferences) 3. Link from email footers: "Manage your email preferences"

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Step 4: Set up progressive profiling

Ask one question at a time across different customer touchpoints — never ask for 10 fields at signup.

In Klaviyo, create different forms for different triggers:

• Post-signup (immediate): "What should we call you?" — captures first name
• Post-first-order (day 2 email): "When is your birthday? (We'll send a gift!)" — captures birth month
• Third email opened: "How often would you like to hear from us?" — captures frequency preference
• After browsing apparel category: "What's your size? We'll filter recommendations" — captures size

Each form is a separate Klaviyo sign-up form shown in the right context, updating the same customer profile properties.

Step 5: Use collected data in personalization

Connect the data to actual campaigns — data that isn't used adds no value:

In Klaviyo:

• Use custom profile properties as segment conditions: "preferred_category equals skincare" → send only skincare product launches to this segment
• Use properties as email personalization variables: {{ person.clothing_size }} in a size guide email
• Use in flow filters: "Birthday month equals current month" → trigger a birthday discount flow

Measuring data collection effectiveness:

• Track profile completeness: go to Klaviyo → Profiles and check what percentage have the key properties filled in
• Target: 50%+ of active subscribers with at least 3 preference fields filled
• Compare email revenue per recipient for profiles with/without preference data — data-driven segments should be 2–4x higher

Best Practices

• Value exchange is mandatory — always explain what the customer gets from sharing data ("so we can send fewer, more relevant emails" or "get personalized product recommendations")
• Keep quizzes to 3 questions maximum — quiz completion drops below 50% at 5+ questions; use progressive profiling to collect more over time
• Quiz completion must lead to immediate value — show product recommendations on the results page; do not just collect data without a reward
• Data minimization — only collect fields you actively use for personalization; every extra field is a privacy liability
• Store consent with timestamp and method — for GDPR compliance, log when and how each preference was set
• Annual re-confirmation — preferences go stale; send an annual "update your preferences" email to refresh data

Common Pitfalls

Related Skills

• @email-list-segmentation
• @predictive-personalization
• @lifecycle-marketing-automation
• @email-marketing-automation
• @exit-intent-popups

Privacy Compliance Module for a Multi-Region E-Commerce Platform

Problem/Feature Description

BrightCart is a fashion e-commerce platform operating in both the EU and United States. Following a recent external GDPR audit, their legal team has raised concerns that BrightCart's current consent management system would be difficult to defend in a regulatory investigation: the audit report noted that consent records lacked sufficient metadata to reconstruct what a customer agreed to and under what circumstances, and that historical consent states could not be reliably verified. The auditors also flagged that customer data removal requests were being fulfilled incompletely.

A separate incident this quarter highlighted a CCPA compliance gap: after a California customer submitted a "do not sell my data" request, the customer's data continued to be used in targeted digital advertising, resulting in a formal complaint. The root cause was that the opt-out process only updated an internal database flag without propagating to the downstream advertising systems BrightCart uses for audience targeting.

Additionally, the customer support team has received complaints from customers who opted out of marketing emails but then stopped receiving their order confirmations and shipping updates. Engineering identified that the consent logic was not distinguishing between different categories of customer communication.

The engineering team needs a TypeScript consent management module that addresses all of these issues. The module will be reviewed by the legal team, so the design documentation should explain the architectural decisions clearly.

Output Specification

Produce the following TypeScript files:

• consent-manager.ts — the recordConsent function and ConsentRecord type
• erasure-handler.ts — the handleErasureRequest function implementing GDPR right-to-erasure
• ccpa-handler.ts — the handleCcpaOptOut function
• consent-design.md — a document for the legal team describing:
• What data is captured for each consent record and why it is needed for compliance
• How the design ensures historical consent states are auditable over time
• How different categories of customer email are handled differently
• What external systems are notified during data removal and opt-out flows

Assume the following are available as imports:

• db — a database client with .consentLogs.create(), .customerProfiles.update(), etc.
• emailServiceClient — an ESP client with .deleteProfile(customerId) and .updateContactProperty(customerId, key, value) methods
• adPlatformClient — with .suppressFromCustomerMatch(customerId) and .suppressFromCustomAudiences(customerId) methods

Customer Profile System for a Fashion E-Commerce Platform

Problem/Feature Description

StyleNest is a mid-sized fashion e-commerce brand that has been running for three years. Their marketing team is struggling with three related problems: their paid ad performance has dropped significantly due to iOS and browser tracking changes, their email campaigns are untargeted (same newsletter to everyone), and their product recommendation engine returns generic results because it has no customer preference data to work with.

The engineering team has been asked to build the foundational data layer for a first-party data strategy. This means designing a customer profile schema that captures both what customers tell you (declared/zero-party data) and what you observe from their behavior, along with an incremental data collection system that asks customers for information progressively rather than overwhelming them with a long form.

The head of engineering wants a TypeScript implementation with two things: (1) a CustomerProfile interface that properly captures all relevant preference, behavioral, and consent fields, and (2) a progressive profiling module that determines what to ask next and computes how complete a customer's profile is. The system should be designed so it can later be connected to a preference center and quiz, but for this task just produce the schema and profiling logic.

Output Specification

Produce the following TypeScript files in your working directory:

• customer-profile.ts — the CustomerProfile interface and any supporting type definitions
• progressive-profiling.ts — the getNextProfileQuestion function, the PROFILE_QUESTIONS configuration array, and the computeProfileCompleteness function
• README.md — a short explanation of the schema design decisions, especially around privacy fields and what drives the profile completeness score

The README should document:

• Which fields contribute to the profile completeness score
• The list of trigger events used for progressive profiling questions (in order)
• The rationale for any privacy-sensitive field design choices

Product Recommendation Quiz for a Home Goods Brand

Problem/Feature Description

Hearth & Co. is a home goods and lifestyle brand that sells everything from kitchen accessories to bedroom furnishings. Their marketing director has noticed that new visitors who land on the homepage bounce at a rate of 72% because they face an undifferentiated product catalog of 800+ items with no clear starting point. Existing product filters help repeat customers who know what they want, but do nothing for first-time visitors.

The team wants to build a short onboarding quiz that helps new visitors find the right products while simultaneously collecting declared preference data that can be stored on the customer profile and fed into their email platform. The quiz will be embedded in the homepage hero for new visitors. Past attempts at long onboarding flows (7+ questions) had very poor completion rates, so the new quiz must be short and feel immediate — users should see relevant product suggestions as soon as they finish.

The quiz must connect to the rest of the data platform: answers should flow through to a named customer profile field, which is then used as a filter when pulling product recommendations, and finally synced as a property on the ESP audience so that targeted email campaigns can be built from the data.

Output Specification

Produce the following files:

• quiz-component.tsx — a React component implementing the quiz UI
• quiz-pipeline.ts — a module that maps quiz answers to profile fields, applies them as recommendation filters, and syncs them to an ESP contact property
• quiz-design.md — a short document describing the quiz UX decisions, including how many questions are included and why, what happens at the end of the quiz, and how users can opt out of completing it

The quiz-design.md should include:

• The number of questions and rationale
• A description of what the user sees after completing the quiz
• How the quiz handles users who do not want to complete it
• A diagram or written description of the data flow from quiz answer to ESP property