Item Page Best Practices

Overview

The objectives of recommendations on the Search Page are to drive consideration of the incumbent product and promote alternative options and/or cross-sells.

  • Quantity: 2-3 placements
  • Location: We strive for one placement above-the-fold with the remaining interspersed within the page content. Remember, the main objective of the item page is to drive consideration of the current product—something which recommendations cannot help, only hurt. As such, be judicious in the positioning of recommendations so as not to disrupt customer absorption of salient content, which often becomes a vertical-specific consideration. For example, within Customer Electronics and Appliances (functionality-centric verticals), product specifications and descriptions are an important part of customer consideration. Algonomy avoids recommendations between the core product content (image, price, etc.) and the descriptions, instead opt for an above-the-fold vertical placement in the right margin. This is not the case in many softlines verticals such as Apparel.
  • Strategies: Recommendations are commonly a mix of Similar Items, Cross-sell, and Personalized strategies to support the various merchandising objectives of the page. This will usually result in similar products being presented in the top-most placement with the other recommendation types falling to the remaining zones.

Diagram

Description automatically generated

At times, it makes sense to fulfill the cross-sell objective with more curated recommendations (for example, “Complete the Look” or “Frequently Bought Together”) through Advanced Merchandising. Provided the retailer can supply the requisite attributes, this is an effective tactic across a number of verticals/categories—hardlines and softlines—where pairings are important: Apparel, Customer Electronics, Appliances, Home Furnishings, etc. Advanced Merchandising recommendations do not require a net new placement; rather they should replace the existing placement designated for cross-sells.

Strategy

Family

Description

Comments

ClickCP

Product Viewed Viewed

People who viewed this item also viewed

Products other customers have viewed given they viewed the current product

 

ClickEV

Product Viewed Viewed

People who viewed this item also viewed

Same as ClickCP but bias toward higherpriced items

 

CategorySiloed ViewCP

Product Viewed Viewed

People who viewed this item also viewed

Same as ClickCP but results are filtered to match the category of the seed item

Only works if a category hint is instrumented or the product has only one category

ViewedPurchased

Product Viewed Bought

People who viewed this item ultimately bought

Products other customers have bought given they viewed the current product

 

CategorySiloed ViewedPurchasedCP

Product Viewed Bought

People who viewed this item ultimately bought

Same as ViewedPurchased but results are filtered to match the category of the seed item

Only works if a category hint is instrumented or the product has only one category

ViewedPurchasedPercent

Product Viewed Bought

People who viewed this item ultimately bought​​​​​​​

Same as ViewedPurchased but returns seed product and % of customers that have purchased each item

Requires layout that exposes seed and conversion %. Only use if seed clearly delineated as customers will confuse it for a new item.

PurchaseCP

Product Bought Bought 

People who bought this item also bought  

Products other customers have bought given they bought the current product 

Also enable the V2 version of this strategy which de-prioritizes general top-sellers

PurchaseEV

Product Bought Bought 

People who bought this item also bought 
 Same as PurchaseCP but bias toward higher-priced items 

Also enable the V2 version of this strategy which de-prioritizes general top-sellers

SessionPurchase CP 

Product Bought Bought 

People who bought this item also bought  
Same as PurchaseCP but purchases must be from same session  

 

CategoryDiverse PurchaseCP 

Product Bought Bought 

People who bought this item also bought  
Same as PurchaseCP, but does not recommend products belonging to primary category as seed and only recommends one product from each unique category 

Includes name of category to which recommended product belongs; can be exposed in layout 

CategorySiloed PurchaseCP 

Product Bought Bought 

People who bought this item also bought  
Same as PurchaseCP but results are filtered to match the category of the seed item 

Only use in verticals where same-category cross-sell make sense, e.g. Apparel but not Consumer Electronics 

PersonalizedClick CPInCategory 

Personal- ized 

People who viewed items you browsed also viewed  
Products other customers have viewed given that they viewed a product the customer recently browsed. Uses category context, if available, to show only products in same category as seed. 

 

Personalized ViewedPurchased InCategory 

Personalized 

People who viewed items you browsed also viewed  
Products other customers have viewed given that they viewed a product that the customer recently browsed. Uses category context, if available, to show only products in same category as seed. 

 

MultiItem Personalized ViewCP 

Personalized 

People who viewed items you browsed also viewed  
Products other customers have viewed given that they viewed products that the customer recently browsed 

This can recommend items outside of the current category which may not be desired by client 

RecentHistorical Items 

Personalized 

Recently Viewed Items  
Items viewed during current and previous sessions

This can return items outside of the current category which may not be desired by client 

CategoryTop Sellers 

Category Best Sellers 

$STRATEGY_HINT$ Best Sellers  
Top products in the category based on unit sales 

 

CategoryTop Products 

Category Best Sellers 

Top Products in $STRATEGY_HINT$  
Top products in the category based on views, clicks and purchases 

 

PopularProductsInCategory 

Category Best Sellers 

Popular Products in $STRATEGY_HINT$   
Top products in the category based on # of line items * total revenue