Basic Recommendation Strategies

👉 Customers viewed these products together

This strategy identifies product associations by analyzing customer browsing behavior. When a customer views a specific product, this approach suggests other products that were frequently viewed together with it. For instance, if a customer is looking at a laptop, this strategy might recommend accessories like laptop bags or external hard drives that are commonly viewed alongside laptops. By leveraging collective viewing patterns, it enhances product discovery and provides customers with relevant suggestions aligned with their interests.

Possible Placements: Product Detail Page, Cart

👉 Personalized alternative products from the same category

Tailored to individual preferences, this strategy offers personalized alternative products within the same category. By understanding the customer's browsing and purchase history, it identifies products that share similarities with the initially viewed item but may offer different features, styles, or brands. For example, if a customer is exploring smartphones, this strategy might suggest alternative models with similar specifications or price ranges. It aims to broaden customers' choices within their desired category, facilitating informed decision-making and potentially increasing engagement and conversion rates.

Possible Placements: Product Detail Page, Cart

👉 Higher-priced alternatives from the same category

This recommendation strategy presents personalized alternative products within the same category, but at higher price points. By analyzing the customer's preferences and purchase behavior, it identifies opportunities to promote premium or upgraded versions of products that align with their interests. For example, if a customer is considering a basic digital camera, this strategy might recommend a more advanced model with additional features or accessories. It aims to encourage customers to explore higher-value options that meet their needs and preferences, potentially leading to increased average order value and revenue.

Possible Placements: Product Detail Page, Cart

👉 Customers bought these products together.

Leveraging transaction data and purchase history, this strategy identifies products frequently bought together by customers. It employs association rules and collaborative filtering techniques to recommend complementary or related items often purchased as a set. For instance, if customers commonly purchase a camera and a camera case together, this strategy might suggest other camera accessories or compatible products. By offering bundled recommendations, it aims to enhance cross-selling opportunities and encourage customers to complete their purchase with complementary items, ultimately driving higher sales and customer satisfaction.

Possible Placements: Product Detail Page, Cart

👉 Alternative products from other categories.

Differing from strategies focusing solely within the same category, this strategy recommends alternative products from different categories based on customer preferences and behavior. By analyzing browsing and purchase history, it identifies cross-category connections and recommends items that may align with the customer's interests but belong to different product categories. For example, if a customer is browsing for a laptop, this strategy might recommend related products such as laptop stands, wireless mice, or software subscriptions from different categories. It broadens the scope of recommendations, introducing customers to a diverse range of products that align with their interests and needs across multiple categories.

Possible Placements: Product Detail Page, Cart

👉 Most viewed products

This recommendation strategy highlights products with the highest viewing frequency across the shop or within specific time frames. By analyzing browsing behavior and popularity metrics, it identifies trending items generating significant interest among customers. For example, if a particular smartphone model consistently attracts a large number of views, this strategy might feature it as a top recommendation. It aims to leverage social proof and popularity signals to guide customers towards products currently in demand or trending, potentially influencing their purchasing decisions and driving engagement.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Bestselling products

This strategy focuses on showcasing products with high sales volumes and popularity rankings. By analyzing transaction data and sales metrics, it identifies best-selling items across the platform or specific product categories. For instance, if a certain book title consistently sells well, this strategy might highlight it as a top recommendation in the books category. It aims to leverage the authority of best-seller rankings to guide customers towards products proven to be popular choices among other shoppers, instilling confidence and facilitating purchase decisions.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Personalized products based on item relationships in customer's purchase history.

Utilizing collaborative filtering techniques, this strategy generates personalized product recommendations based on the customer's purchase history and similarities with other users. By analyzing historical transaction data and identifying patterns of item relationships, it recommends products aligned with the customer's preferences and tastes. For instance, if a customer previously purchased a digital camera, this strategy might recommend related accessories such as memory cards or camera bags based on the purchase behavior of similar customers. It aims to leverage collective user behavior to deliver tailored recommendations resonating with the individual customer's preferences, increasing relevance and improving the overall shopping experience.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Personalized category-specific products from purchase history's item relationships.

Building upon collaborative filtering, this strategy focuses on recommending personalized products within the same category based on the customer's purchase history and similarities with other users. By analyzing historical transaction data and item relationships within specific product categories, it identifies products aligned with the customer's preferences and interests within that category. For example, if a customer frequently purchases skincare products, this strategy might recommend personalized skincare items like moisturizers or serums based on the purchase behavior of similar customers within the skincare category. It aims to deliver tailored recommendations highly relevant to the customer's interests within a specific product category, enhancing the likelihood of engagement and conversion.

Possible Placements: Product Detail Page, Cart, Category Page

👉 Products from customer's favorite categories

This recommendation strategy determines the customer's most favorite product categories based on their browsing and purchase history. It then recommends personalized products from each favorite category to the customer. By analyzing customer preferences and behavior, it identifies the categories that the customer has consistently shown interest in and selects relevant products to showcase. For example, if a customer frequently explores the electronics category, this strategy might recommend one personalized electronics product such as a smartphone or headphones based on their preferences. It aims to enhance the shopping experience by presenting products aligned with the customer's favorite categories, increasing the likelihood of engagement and purchase.

Possible Placements: Product Detail Page, Cart, Category Page

👉 Products from customer's favorite brands

This strategy determines the customer's most favorite brands based on their browsing and purchase history. It then recommends personalized products from each favorite brand to the customer. By analyzing customer preferences and brand affinity, it identifies the brands that the customer has consistently favored and selects relevant products to feature. For example, if a customer has consistently purchased products from a specific clothing brand, this strategy might recommend personalized clothing items from that brand based on their preferences. It aims to enhance the shopping experience by presenting products from favored brands, increasing the likelihood of engagement and purchase.

Possible Placements: Product Detail Page, Cart, Category Page

👉 New products sorted by bestsellers

This recommendation strategy focuses on showcasing newly added products sorted by their popularity or bestseller status. By analyzing product release dates and popularity metrics, it identifies recently introduced items gaining traction among customers. For instance, if a new smartphone model has been recently launched and is quickly gaining popularity, this strategy might feature it as a top recommendation in the electronics category. It aims to highlight fresh and trending products to customers, keeping them informed about the latest offerings and encouraging exploration of new arrivals, ultimately driving engagement and sales.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Personalized new products

Building upon the NEW_OFFERS strategy, this approach delivers personalized recommendations of newly added products based on the customer's preferences and browsing history. By analyzing historical data and identifying patterns of product affinity, it selects recently introduced items aligned with the customer's interests. For example, if a customer has previously shown a preference for fitness equipment, this strategy might recommend personalized new fitness products like workout apparel or accessories. It aims to deliver tailored recommendations resonating with the customer's preferences, increasing relevance and encouraging exploration of new arrivals, ultimately driving engagement and sales.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Sale items sorted by bestsellers*

This recommendation strategy focuses on showcasing products currently on sale, sorted by their bestseller status. By analyzing the catalog data, it identifies discounted items popular among customers. For instance, if a particular clothing item is on sale and selling well, this strategy might highlight it as a top recommendation in the clothing category. It aims to attract customers with discounted offerings, increasing the likelihood of purchases and promoting customer satisfaction.
*requires catalog field "sale" which is not standard

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉 Personalized sale items*

This approach delivers personalized recommendations of products currently on sale based on the customer's preferences and browsing history. By analyzing historical data and identifying patterns of product affinity, it selects discounted items aligned with the customer's interests. For example, if a customer has previously shown a preference for outdoor gear, this strategy might recommend personalized outdoor products currently on sale like camping equipment or hiking gear. It aims to deliver tailored recommendations resonating with the customer's preferences, increasing relevance and encouraging purchases of discounted items.

*requires catalog field "sale" which is not standard

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page

👉Recently viewed products

This recommendation strategy presents products viewed by the customer in the last 30 days, sorted in reverse chronological order. By analyzing browsing behavior and session data, it identifies products the customer has recently shown interest in and presents them as recommendations. For instance, if a customer recently viewed a laptop and a printer, this strategy might prioritize recommending those products at the top of the list. It aims to remind customers of products they have recently considered, facilitating informed decision-making and potentially increasing conversion rates.

Possible Placements: Product Detail Page, Cart, Entry Page, Category Page