What is Product recommendations?

Recommendations surface relevant products at the right moment - on a product page, in a cart, after a search, or inside a chat conversation. They reduce the effort a shopper has to put into discovery and increase the chance they find something they want to buy. Recommendations can be algorithmic, rule-based, or driven by a conversational interaction where the shopper explains what they need.

How does product recommendations work?

• Collaborative filtering: surfaces items bought together or by shoppers with similar histories
• Content-based filtering: matches products by shared attributes like category, price range, or material
• Rule-based merchandising: operators pin or boost specific items for a page or season
• Conversational: a shopper states a need ('I need a gift under $50') and the system retrieves matching products in real time

Why does it matter?

Shoppers who find a relevant product faster are more likely to complete a purchase. For ecommerce operators, recommendations increase average order value and reduce bounce from dead-end product pages. For dealerships, surfacing the right trim or add-on at the right moment can shorten a sales conversation that might otherwise end without a next step.

Nobi's assistant delivers product recommendations inside a shopping conversation, matching items to what the shopper just said rather than relying on click history alone.

Frequently asked questions

What is the difference between product recommendations and search results? Search results respond to an explicit query the shopper typed. Recommendations are proactive - they surface products the shopper has not searched for yet, based on context like the current page, cart contents, or stated preferences.

Do product recommendations require a lot of historical data to work? Collaborative filtering works best with large purchase histories, but content-based and rule-based approaches work well even for newer stores or thin catalogs. Conversational recommendations can be effective from day one because they respond to what the shopper says rather than past behavior.

How do operators control which products get recommended? Most platforms let operators set rules to boost, pin, or exclude specific items - for example, prioritizing high-margin products or suppressing out-of-stock inventory. These manual overrides work alongside the algorithm rather than replacing it.