Definition: The New Arrival Recommendation Model is designed to showcase newly added products in your store. This model serves the purpose of keeping both existing and potential customers informed about the latest additions to your product catalog.

We utilize the product's published date to identify which items should be recommended via this widget. Operators also have the option to select specific collections from which products should be recommended, allowing for a more tailored approach.

Filtering Criteria: To ensure that the products displayed in the widget provide the best user experience, we apply the following filters:

1. Availability: Products with at least 1 or more units in inventory are recommended.

2. Image Availability: Recommended products must have a main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and redirect customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: The algorithm selects products based on their published date, with the most recent items displayed at the top. It can be limited to specific product collections, where applicable.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. Highlight New Collections: Promote new collections prominently on your homepage to catch the attention of your visitors.

2. Target Specific Audiences: Recommend new products that belong to a specific collection on relevant collection or product pages. For instance, you can showcase kids' products exclusively on the kids' collection page or display a new jewelry collection when customers are browsing specific jewelry products.
   
   

By leveraging the New Arrival Recommendation Model, you can effectively inform your audience about the latest additions to your product offerings and enhance their shopping experience.

Definition: The Featured Products Recommendation Model showcases products on the homepage of an e-commerce site that are typically best-selling, highly rated, or brand new. The importance of making a strong first impression on customers cannot be overstated. Just as in physical stores, where well-branded exteriors and appealing product displays draw customers in, featuring select items on your homepage can greatly influence online shoppers.

This algorithm is specifically designed to select products that belong to a featured item collection. Additionally, operators have the flexibility to add additional product collections to recommend items related to specific categories.

Filtering Criteria: To ensure the products displayed in the widget provide the best user experience, we apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Availability: Products must have a main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and redirect customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: The algorithm selects products from the featured collection and can be limited to specific product collections, where applicable.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. 1. Showcase New Collections: Use this model to prominently display new product collections on your homepage, making a strong visual impact on visitors.

   2. Target Specific Audiences: Recommend new products that belong to a specific collection on relevant collection or product pages. For example, exclusively promote kids' products on the kids' collection page or feature a new jewelry collection when customers are browsing specific jewelry products.

Leveraging the Featured Products Recommendation Model allows you to effectively highlight top-performing or newly added products, making a lasting impression on your online shoppers.

Definition: Related Products, also known as Up-Sell Recommendations, are additional product suggestions presented alongside items a customer is currently viewing. These recommendations are strategically curated to serve several purposes, including enhancing the usage of the main product, complementing it, improving its overall usability, or mitigating any potential limitations.

Our sophisticated model harnesses the power of AI and ML to identify the best matches based on various product properties such as price, product type, tags, and vendor.

Filtering Criteria: To ensure the products displayed in the widget provide the best user experience, we apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Availability: Products must have a main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and redirect customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: Our algorithm identifies related products based on various product properties, including:

1. Price

2. Product Type

3. Tags

4. Vendor
   
   

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. Ideal for Enhancing Product Detail, Cart, Add to Cart, and Thank You Pages: This model is well-suited for providing recommendations on these crucial pages.

2. Elevate Promotions: Create campaigns to offer complementary products, enabling more targeted promotions by utilizing collections. For example, if a customer is browsing or about to purchase a laptop, you can entice them with discounted accessories that perfectly complement their choice.

The Related Products / Up-Sell Recommendation Model empowers you to enhance the shopping experience by suggesting relevant additional products to customers, thereby increasing overall user satisfaction and potentially boosting sales.

Definition: Cross-Sell Recommendations are a powerful technique that allows you to offer additional or complementary products to your customers, enhancing their shopping experience. For example, when a user is purchasing a dress, you can seize the opportunity to suggest matching shoes, bags, and accessories to help them complete their desired "look."

This algorithm is specifically designed to recommend products that meet the following criteria:

1. The Vendor is the same as the product page where the recommendations are being displayed.

2. The Product Type for the recommendations is different from the product in question.
   
   

Filtering Criteria: To ensure that the products displayed in the widget provide the best user experience, we apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Availability: Products must have a main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and redirect customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: Our algorithm selects products and prioritizes them based on the following properties:

1. Vendor: The vendor is the same for the product in question and the recommended products.

2. Product Type: The product type differs between the product in question and the recommended product.
   
   

Subscription Tiers: This model is available across all subscription tiers.

Tips:

• Leverage the Add-to-Basket Feature: If you wish to recommend this model on the basket or checkout page, make effective use of the add-to-basket feature to suggest complementary products.

• Consider Up-Sell Opportunities: In addition to recommending complementary products, you can also explore up-sell opportunities by suggesting higher-end versions of the same product or proposing upgrades and additional features.
  
  

By implementing the Cross-Sell Recommendation Model, you can enhance your customers' shopping journey, offer valuable suggestions, and potentially increase sales through strategic product recommendations.

Definition: The Best Seller Product Recommendation Model is meticulously designed to predict and suggest popular products based on an array of factors, including historical sales data, customer preferences, and prevailing market trends. The primary objective of this model is to empower users with insightful purchasing recommendations by showcasing products that have garnered significant popularity among other customers.

The Best Seller Model primarily relies on orders placed within the last 1 month to identify the most sought-after items, considering the criteria outlined below.

Filtering Criteria: To ensure the products displayed in the widget offer an exceptional user experience, we meticulously apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Clarity: Products must feature a clear and prominent main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and take customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: Our algorithm identifies products and prioritizes them based on their order history within the past 1 month.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. Showcase Bestsellers on the Homepage: Dedicate a section on your homepage to showcase bestseller products prominently. This allows visitors to quickly identify popular items, encouraging further exploration.

2. Leverage Bestsellers in the Cart or Checkout Page: Offer bestseller recommendations on the cart or checkout page to motivate additional purchases or enable effective upselling strategies.
   
   

By implementing the Best Seller Product Recommendation Model, you can effectively guide customers towards popular and relevant products, facilitating informed purchasing decisions and potentially increasing your sales.

Definition: The Frequently Bought Together Recommendation Model employs advanced algorithms to predict and suggest combinations of products that are frequently purchased together by customers. This recommendation model aims to enhance user experiences by providing additional product recommendations that complement their initial purchase. These suggestions are derived from patterns and associations observed in historical transaction data.

Filtering Criteria: To ensure that the products displayed in the widget offer the best possible user experience, we meticulously apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Clarity: Products must feature a clear and prominent main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and take customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: Our algorithm identifies products that are frequently purchased in conjunction with the product currently being viewed by the customer, while adhering to the aforementioned criteria.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

• Prominently Display Recommendations on Product Detail Pages: Consider placing the Frequently Bought Together recommendations prominently on the product detail page, ideally below the main product information. This provides customers with an easy way to discover related items and consider adding them to their purchase.

• Utilize Shopping Cart Page Recommendations: Create a dedicated section on the shopping cart page to showcase Frequently Bought Together recommendations. This strategic placement capitalizes on customers' existing purchasing mindset, making them more likely to consider adding complementary items to their order.
  
  

By leveraging the Frequently Bought Together Recommendation Model, you can enhance user engagement, increase sales, and provide a seamless shopping experience by suggesting relevant product combinations based on historical buying patterns.

Definition: The Last Viewed Recommendation Model utilizes an algorithm that suggests products or content based on a user's most recent interactions or views. This model focuses on the items a user has engaged with recently, offering personalized recommendations that align with their current interests and preferences.

Filtering Criteria: To ensure that the products displayed in the widget provide an exceptional user experience, we diligently apply the following filters:

1. Availability: Recommended products must have at least 1 or more units in inventory.

2. Image Clarity: Products must feature a clear and prominent main image.

3. Title Clarity: The product title must not be empty.

4. Valid Links: The product link must be valid and take customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: Our algorithm identifies products that have been viewed by the customer in descending order, adhering to the criteria outlined above.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. Display Last Viewed Items on Product Detail Pages: Consider including a dedicated section on the product detail page that showcases the user's recently viewed items. This feature allows users to easily reference the products they have explored recently while making decisions or considering alternative options.

2. Utilize User Account Dashboards: If your website features a user account system, incorporate a "Recently Viewed" tab or section within the user's account dashboard. This feature enables users to access their recently viewed products, even if they have logged out and returned to the site later.

By implementing the Last Viewed Recommendation Model, you can enhance user engagement, facilitate decision-making, and provide a seamless shopping experience by suggesting products aligned with a user's recent interactions and preferences.

Definition: The Recently Purchased Recommendation Model is a system or algorithm that suggests products or content to users based on their most recent purchases. It leverages the user's transaction history to provide personalized recommendations that align with their recent buying behavior.

Filtering Criteria: To ensure that the products displayed in the widget enhance the user experience, we apply the following filters:

1. Inventory Availability: Recommended products must have at least 1 or more units in inventory.

2. Clear Main Image: Products must feature a clear and prominent main image.

3. Descriptive Title: The product title must not be empty.

4. Valid Links: The product link must be valid and take customers to the correct product page upon clicking.

5. Avoid Redundancy: On product pages, the recommended product will not be the same as the one the customer is currently viewing.
   
   

Algorithm: The Ci app recommends products that have been recently purchased by the customer, prioritizing based on the most recent order date and adhering to the criteria outlined above.

Subscription Tiers: This model is available across all subscription tiers.

Tips:

1. Order Confirmation Page: Following a user's purchase, consider displaying a section on the order confirmation page that showcases recently purchased product recommendations. This feature allows users to discover related items or accessories that may complement their recent purchase.

2. User Account Dashboard: Create a dedicated section within the user's account dashboard that displays recently purchased product recommendations. This provides users with a centralized location to access and explore recommendations based on their past purchases.
   
   

By implementing the Recently Purchased Recommendation Model, you can enhance user engagement and provide valuable product suggestions based on a user's recent buying behavior, ultimately improving the overall shopping experience.