AI-powered personal recommendations adapt to each customer's browsing behavior in real-time, analyzing their product views to suggest relevant items that match their evolving interests. This enables personalized shopping experiences where customers discover products tailored specifically to their preferences, which helps improve engagement and conversion rates.
The recommendation engine builds dynamic customer profiles by tracking viewed products, assigning greater weight to recent interactions, and analyzing over 100 product dimensions to identify subtle relationships. This helps ensure recommendations stay fresh and relevant while guiding customers to products they're likely to purchase, which tends to enhance both customer satisfaction and order values.
This type of Recommendations widget personalizes product suggestions based on each customer's browsing history, ensuring relevant and engaging shopping experiences.
User Profile Development
As customers navigate your store, our system dynamically builds a profile based on their viewed products. This profile:
- Updates in real-time with each interaction
- Assigns greater weight to recent views
- Continuously adapts to reflect evolving interests
Note: To enhance the shopping experience & promote exploration, our system excludes recently viewed products from personal recommendations. While these products are technically relevant, we prioritize offering fresh suggestions that better align with the shopper’s evolving interests and preferences.
Recommendation Logic
Consider the following example:
If a customer views:
- A red hat
- White jeans
- A green t-shirt (most recent)
Our system may recommend:
- More t-shirts (emphasizing recent interest)
- Items in red, white, or green
- Products that bridge categories (hats, t-shirts, and jeans)
- Frequently purchased complementary items
Advanced Similarity Matching
Beyond basic attributes like color, size, and category, our algorithm:
- Analyzes over 100 product dimensions
- Identifies subtle relationships between products
- Learns from collective user behavior
- Recognizes non-obvious connections, such as formality levels
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