# Product recommendations for ecommerce: How they work and what to look for

> Product recommendations explained, types, placements, algorithms, and evaluation criteria. A practical guide for ecommerce teams.

How they work, where to place them, and what to look for in a recommendation engine

## Types of product recommendations

This guide covers the types of recommendations, where to place them, how the algorithms work, and how to evaluate a recommendation engine for your store.

## Where to place recommendations (and why)

## How recommendation algorithms work

Products commonly purchased in the same order. A phone case with a screen protector. Running shoes with performance socks. This is the primary cross-sell driver and the recommendation type with the most direct AOV impact.

## Platform-specific considerations

## How to evaluate a recommendation engine

Recommendations based on the individual shopper's browsing history, purchase patterns, and real-time behavior. The most effective type for returning visitors. Requires behavioral data from at least one session to begin working.

## See recommendations powered by Product Intelligence

"People who bought X also bought Y." Analyzes purchase patterns across all shoppers to find correlations. Strengths: discovers non-obvious relationships (beer + diapers). Weaknesses: cold-start problem (new products with no purchase history get no recommendations), popularity bias (tends to recommend bestsellers).

## Frequently asked questions

### What are product recommendations in ecommerce?

Product recommendations are automated suggestions shown to shoppers based on their behavior, the product they're viewing, or broader trends. They appear throughout the shopping journey, homepage, category pages, product detail pages, cart, checkout, and email. The widely cited McKinsey benchmark (2017, citing 2013 data) is that 35 percent of what consumers purchase on Amazon comes from recommendations, a long-standing reference point for what mature, large-scale recommendation systems can drive.

### What types of product recommendations are there?

The main types are: similar products (alternatives to what you're viewing), frequently bought together (complements), trending/popular items, personalized for you (based on individual behavior), recently viewed, and new arrivals in categories you browse. Each type serves a different intent, similar products help comparison shoppers, while frequently bought together drives cross-sell.

### Where should I place product recommendations on my store?

The highest-converting placements are: product detail page (similar and complementary items), cart page (add-ons and accessories), homepage (personalized picks for returning visitors, trending items for new visitors), category pages (contextual suggestions), post-purchase email (cross-sell and replenishment), and search results page (for zero-result queries). Start with PDP and cart, they have the highest conversion impact per impression.

### How do recommendation algorithms work?

Three main approaches: collaborative filtering (people who bought X also bought Y, uses purchase patterns across all shoppers), content-based filtering (products with similar attributes to what you've viewed, uses catalog data), and hybrid approaches that combine both with behavioral signals. Modern systems use vector embeddings to represent products in high-dimensional space, enabling nuanced similarity matching beyond simple rules.

### What is the difference between rule-based and AI recommendations?

Rule-based recommendations follow explicit logic: 'show bestsellers

### What conversion rates should I expect from product recommendations?

Recommendation CTR varies widely by placement and relevance, sponsored product slots in search results convert differently from sidebar carousels on a product detail page. Conversion rates from recommendation clicks are usually higher than site average because the shopper has already shown intent by engaging with a suggestion. Measure direct recommendation revenue and assisted revenue (sessions that touched a recommendation before converting) separately, the assisted figure is usually larger and easier to under-credit.

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For the full interactive experience, visit [helloretail.com/en/learn/product-recommendations](https://helloretail.com/en/learn/product-recommendations)

## About Hello Retail

Hello Retail is an AI-powered ecommerce personalization platform based in Copenhagen, Denmark. We help online retailers improve search, recommendations, merchandising, email, and retail media through our proprietary Product Intelligence engine.

- **Website**: [helloretail.com](https://helloretail.com)
- **Demo**: [Book a Demo](https://helloretail.com/en/demo/)
- **AI information**: [helloretail.com/en/ai-info](https://helloretail.com/en/ai-info)

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