Dynamic Segments Using Product and On-Site Behavior Data
Creates advanced segments combining website browsing data, product usage, and purchase history for hyper-personalization.
Implement dynamic segmentation that auto-updates based on on-site and in-product behavior data, enabling contextual emails that feel tailor-made.
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Prompt objective
Implement dynamic segmentation that auto-updates based on on-site and in-product behavior data, enabling contextual emails that feel tailor-made.
Real use case
ChefBox, a recipe kit subscription service, wants to personalize emails based on preferred cuisine type (browsing data), cooking frequency (app data), and avoided ingredients (customer profile). Currently they send the same newsletter to 18,000 subscribers.
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Prompt
Design a dynamic segment system for [COMPANY NAME], operating in the [INDUSTRY] sector, combining browsing, product, and transaction data. Context: - Active users: [NUMBER] - Email platform: [PLATFORM: ActiveCampaign/HubSpot/Customer.io/Klaviyo] - On-site tracking: [PLATFORM: Google Analytics/Segment/Mixpanel] - Available product data: [LIST OF EVENTS/PROPERTIES] - E-commerce/app: [PLATFORM] **1) Data sources for segmentation:** **Browsing data (on-site):** - Most visited categories (top 3) - Viewed product pages (without purchase) - Blog content consumed (topics) - Internal search (terms used) - Primary device (mobile/desktop) - Peak browsing hours **Product data (in-app):** - Most used features - Usage frequency (daily/weekly/monthly) - Adoption level (% of features activated) - Explicit preferences (profile/settings) - Unperformed actions (ignored features) **Transactional data:** - Purchased categories - Individual transaction value - Preferred payment method - Purchase seasonality - Discount sensitivity (purchased with/without coupon) **2) Compound dynamic segments (minimum 10):** For each segment: - Descriptive name - Compound rule (browsing + product + transaction data) - Example: '[CATEGORY] explorer who uses [FEATURE] weekly and purchased 2+ times in the last 90 days' - Estimated segment size - Estimated value (potential revenue) - Recommended personalized email (topic + CTA) - Segment update frequency **3) Automatic entry/exit triggers:** - Events that add to segment - Events that remove from segment - Validity period (e.g., 'visited category X in the last 7 days') - Overlap handling: What to do when a contact belongs to multiple segments **4) Technical implementation:** - Required integrations (tracking → email platform) - Events to configure in [TRACKING PLATFORM] - Custom properties/fields in email platform - Webhook or native sync - ETL if needed (BigQuery/Fivetran for large volumes) **5) Hyper-personalized email examples:** - 3 complete email examples only possible with these segments - Show the difference vs. generic email (before/after) - Expected impact on click rate and conversion
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- 1Replace the key placeholders first: COMPANY NAME, INDUSTRY, NUMBER, PLATFORM: ActiveCampaign/HubSpot/Customer.io/Klaviyo.
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