Smart Recommender can recommend products with different algorithms that match your potential customers' interests. Machine Learning-powered Smart Recommender Algorithms can work with varying types of pages and aim to increase clicks and conversions. Insider classifies the algorithms into four different groups:
Generic Algorithms
In Generic Algorithms, product recommendations are based on product performance rather than individual consumer behavior. These algorithms assume that the most frequently interacted-with items will likely appeal to a broader audience, making them ideal for scenarios with limited individual user data. The Generic Algorithms are:
Most Popular Items
Top Sellers
Highest Discounted Products
Manual Merchandising
New Arrivals
Trending Products
Most Valuable Products
Contextual Algorithms
In Contextual Algorithms, product recommendations are based on the user's current context, such as the product they are viewing, and product relationships, like items frequently viewed or purchased together. The Contextual Algorithms are:
Viewed Together
Purchased Together
Location-based Top Sellers
Checkout Recommendation
Most Popular Items of the Category
Most Valuable Items of the Category
Substitute Products
Complementary Products
Recently Viewed
Purchased with Last Purchased
Personalized Algorithms
In Personalized Algorithms, recommendations are tailored to individual users by analyzing their behavior and interactions. These algorithms aim to deliver highly relevant suggestions by leveraging user-specific data, such as browsing history, past purchases, and real-time activity. The Personalized Algorithms are:
User-Based
Real-Time User Engagement
Algorithms with Multi-strategies
Algorithms with multi-strategies combine multiple recommendation algorithms to improve relevance and diversity. Leveraging the strengths of each algorithm, they deliver more balanced and effective recommendations. The algorithms with multi-strategies are:
Mixed Strategy
Chef - Auto Optimization for Recommendation Algorithms