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    Generic Algorithms
    • 26 Oct 2023
    • 4 Minutes to read
      Contents

      Generic Algorithms

      • Updated on 26 Oct 2023
      • 4 Minutes to read
        Article Summary

        In Generic Algorithms, product recommendations are based on product performance rather than individual consumer behavior or current context. 

        The algorithms covered under Generic Algorithms are:

        • Most Popular Items
        • Top Sellers
        • Highest Discounted Products
        • Manual Merchandising
        • New Arrivals
        • Trending Products
        • Most Valuable Products

        The algorithm generates recommendations based on page view counts during the last 30 days in the same locale (the language of the website that the user visits). Most Popular Items of the Category works with the same logic but brings results from the same category with the product or category that is currently being viewed. After generating recommendations, the algorithms order the results with descending page view counts and place them on the smart recommender widget.

        • Page Type:
          • Most Popular Items: Home Page, Product/Article Pages, Category Pages, Cart Pages
          • Most Popular Items of the Category: Product/Article Pages, Category Pages
        • Example Use Case: Display the most popular products and promote the hottest products that are viewed or sold most on the website. Display the products that gather the attention of the users from the same category, and apply filters to show the products that have a higher price to create upsell opportunities from both the same category or cross categories.
        Your title goes here
        The algorithm does not take user attributes or affinities into account, thus there is no user-based personalization in these algorithms. The same results are shown to all users of the target segment.
        • Fallbacks: Top Sellers Of Parent Category
        • Prerequisites: 30 days of product views.
        • Maximum Number of Products to Recommend (in the same variant): 90

        Top Sellers

        The algorithm generates recommendations based on purchase counts during the last 30 days in the same locale (the language of the website that the user visits). Top Sellers of the Category works with the same logic but brings results from the same category with the product or category that is currently being viewed. After generating recommendations, the algorithms order the results with descending purchase counts and place them on the Smart Recommender widget.

        • Page Type: 
          • Top Sellers: All Pages: Home Page, Product/Article Pages, Category Pages, Cart Pages
          • Top Sellers of the Category: Product/Article Pages, Category Pages
        • Example Use Case: Display the products that are able to complete the user flow of discovery, add to cart, and purchase. These products indicate good price-value performance and give the other users the feeling of being preferred. With price or category filters, you can increase purchase rates of unexplored categories and create upsell opportunities.
        Your title goes here
        The algorithm does not take user attributes or affinities into account, thus there is no user-based personalization in these algorithms. The same results are shown to all users of the target segment.
        • Fallbacks: Top Sellers Of Parent Category
        • Prerequisites: 30 days of product purchase data.
        • Maximum Number of Products to Recommend (in the same variant): 90

        Highest Discounted Products

        The algorithm generates recommendations based on discount ratios during the last 30 days in the same locale (the language of the website that the user visits). It recommends products in a sequence based on the extent of their discount ratios, prioritizing those with the highest discounts. Insider receives the original price and discounted price with Insider system rule and calculates the discount ratio % for each product. Recommended products will be sorted from the highest discount to the lowest for the given size. For each currency type, the discount ratio is calculated separately. All users see the same recommendation.

        • Page Type: All pages, Category pages
        • Example Use Case: Promote the highest discounted products to discount seekers (discount affinity high segment) to grab their attention more easily.
        • Fallbacks: Highest Discounted Products Of Parent Category
        • Prerequisites: Original Price and Price attributes.
        • Maximum Number of Products to Recommend (in the same variant): 90

        Manual Merchandising

        This algorithm retrieves details of products that are manually specified, ensuring that only items currently in stock are included in the recommendations. It allows for the display of particular products or content, specified in the campaign configuration, making it ideal for showcasing specific items, especially during special occasions or events. All users see the same recommendation.

        • Page Type: All Pages, Product/Article Pages, Category Pages Cart Pages
        • Use Cases: Promote certain products on the home page if you want to finish their stock quickly or emphasize new arrivals.
        • Fallbacks: NA
        • Prerequisites: NA
        • Maximum Number of Products to Recommend (in the same variant): 50

        New Arrivals

        This algorithm highlights products recently added to your website and suggests them in a sequence based on their publish date. It prioritizes recommendations according to the chronological order of products' appearance on your website. 

        • Page Type: All pages, Category pages
        • Example Use Case: Promote new arrivals, especially new season products to increase their sales; emphasize newly arrived items based on the first visit, rather than the time added to the website.
        • Fallbacks: New Arrivals Of Parent Category
        • Prerequisites: item_update_date attribute
        • Maximum Number of Products to Recommend (in the same variant): 90

        This algorithm employs a scoring system to recommend items, focusing on this week's trending products compared to the previous week. It assigns scores based on weekly view and purchase information, allowing for a dynamic and data-driven approach to suggest trending items. 

        • Page Type: All pages, Category pages
        • Example Use Case: Influence users by showing them products that are getting the most attention in terms of visit/purchase metrics over time. No real personalization unless the user is interested in prominent items.
        • Fallbacks: Trending Products Of Parent Category
        • Prerequisites: 2 Weeks of product purchase and views.
        • Maximum Number of Products to Recommend (in the same variant): 90
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        We do not recommend you to use this algorithm unless you have 2 weeks of product purchase and views.

        Most Valuable Products

        This algorithm recommends products that generate higher revenue across your site, considering both the contribution to revenue and revenue per visit. All users see the same recommendation.

        • Page Type: All Pages, Product/Article Pages, Category Pages, Cart Pages
        • Example Use Case: Promote more revenue-generated products on your website. 
        • Fallbacks: Most Valuable Of Parent Category
        • Prerequisites: "Product Value Scoring" and "Most Valuable Products" should be enabled for your account to be able to use the Most Valuable Products algorithm.
        • Maximum Number of Products to Recommend (in the same variant): 90
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        To ensure accurate recommendations for products with higher revenue, it is recommended to select a minimum look-back period of 3 days.
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