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Personalization

Personalization re-ranks search results for each individual user based on their history, affinities, price sensitivity, size/color preferences, and behavioral patterns.

Overview

Personalization creates unique search experiences for each user by learning from their past behavior and preferences, then applying those learnings to rank products in a way that's most likely to resonate with them. The Marqo pixel automatically collects user interaction data (clicks, purchases, browsing behavior) from your site, so personalization works automatically without requiring any manual data submission.

Personalization Signals

User History

  • Past purchases: Products the user has bought before
  • Browsing history: Products the user has viewed
  • Search history: Queries the user has searched
  • Cart history: Products added to cart but not purchased

User Affinities

  • Brand preferences: Brands the user prefers
  • Category preferences: Categories the user shops most
  • Price range: User's typical price range
  • Style preferences: Colors, sizes, styles the user prefers

Behavioral Patterns

  • Purchase frequency: How often the user purchases
  • Session patterns: When and how the user shops
  • Device preferences: Preferred device or channel
  • Engagement level: How engaged the user is

Personalization Types

Purchase-Based Personalization

Rank products based on purchase history:

{
  "personalization_type": "purchase_based",
  "signals": [
    "past_purchases",
    "purchase_frequency",
    "purchase_categories"
  ],
  "weight": 0.4
}

Behavioral Personalization

Rank based on browsing and engagement:

{
  "personalization_type": "behavioral",
  "signals": [
    "viewed_products",
    "clicked_products",
    "time_on_page",
    "add_to_cart_rate"
  ],
  "weight": 0.3
}

Preference-Based Personalization

Rank based on explicit and inferred preferences:

{
  "personalization_type": "preference_based",
  "signals": [
    "brand_preferences",
    "price_sensitivity",
    "size_preferences",
    "color_preferences"
  ],
  "weight": 0.3
}

Personalization Models

Collaborative Filtering

Recommend products similar users liked:

{
  "model_type": "collaborative_filtering",
  "algorithm": "matrix_factorization",
  "similarity_metric": "cosine",
  "min_users": 10
}

Content-Based Filtering

Recommend products similar to user's history:

{
  "model_type": "content_based",
  "features": [
    "category",
    "brand",
    "attributes",
    "price_range"
  ],
  "similarity_metric": "cosine"
}

Hybrid Approach

Combine collaborative and content-based:

{
  "model_type": "hybrid",
  "collaborative_weight": 0.6,
  "content_based_weight": 0.4
}

Personalization Strength

Control how much personalization affects rankings:

{
  "personalization_strength": {
    "new_users": 0.1,
    "returning_users": 0.3,
    "vip_users": 0.5
  }
}

Cold-Start Handling

Handle users with no history:

{
  "cold_start_strategy": {
    "use_popular_products": true,
    "use_category_trends": true,
    "use_demographic_segments": true
  }
}

Respect user privacy preferences:

{
  "privacy_settings": {
    "require_consent": true,
    "allow_opt_out": true,
    "anonymize_data": true
  }
}

Personalization Examples

Example 1: Brand Preference

{
  "user_id": "user_123",
  "preferences": {
    "preferred_brands": ["Nike", "Adidas"],
    "boost_strength": 1.3
  },
  "ranking_adjustment": {
    "boost_brands": ["Nike", "Adidas"],
    "boost_strength": 1.3
  }
}

Example 2: Price Sensitivity

{
  "user_id": "user_456",
  "preferences": {
    "price_sensitivity": "high",
    "typical_price_range": [50, 150]
  },
  "ranking_adjustment": {
    "boost_price_range": [50, 150],
    "boost_strength": 1.2
  }
}

Example 3: Size Preference

{
  "user_id": "user_789",
  "preferences": {
    "preferred_size": "large",
    "preferred_color": "blue"
  },
  "ranking_adjustment": {
    "boost_attributes": {
      "size": "large",
      "color": "blue"
    },
    "boost_strength": 1.15
  }
}

Personalization Analytics

Track personalization effectiveness:

  • Personalization lift: How much does personalization improve conversion?
  • User satisfaction: Do users prefer personalized results?
  • Diversity impact: Does personalization reduce diversity too much?
  • Segment performance: How does personalization perform for different user segments?

Best Practices

  1. Start subtle: Begin with light personalization, increase gradually
  2. Balance relevance: Don't sacrifice relevance for personalization
  3. Maintain diversity: Ensure personalized results aren't too narrow
  4. Respect privacy: Always get user consent for personalization
  5. Test continuously: A/B test personalization approaches
  6. Monitor performance: Track how personalization affects key metrics