Related Products vs Product Recommendations: Which Drives More Sales?

Related Products Products shown near a listing to encourage additional browsing or purchase. Merchants often use the phrase interchangeably with product recommendations, but the two approaches have distinct technical and commercial trade-offs that affect sales and long-term customer value.

In practice, "related products" is a broader merchandising concept that includes editorially-curated lists, rule-based pairings, and simple "also bought" sections. "Product recommendations" usually refers to algorithmic, personalized suggestions derived from machine learning models or collaborative filtering. The former is lightweight and predictable; the latter is data-hungry and scalable. Choosing between them, or combining both, depends on business goals, data maturity, and technical resources.

Key Differences

There are four axes where related products and algorithmic recommendations diverge: personalization, scalability, transparency, and cold-start behavior. Rule-based related products are transparent and immediate to implement but don’t adapt to individual shopper signals. Algorithmic recommendations personalize at scale but require sufficient behavioral data and may produce irrelevant results without tuning.

• Personalization: Recommendations aim to match individual preferences; related products often match the SKU or curator intent.
• Scalability: Algorithms scale across catalogs automatically; manual lists require ongoing merchandising effort.
• Transparency: Merchants can easily audit rule-based lists; ML systems are often opaque and need monitoring.
• Cold Start: New SKUs and stores with low traffic perform better with manual related-product rules.

Which Drives More Sales — Short Answer

Neither method is universally superior. For mature sites with substantial traffic and historical data, personalized product recommendations typically drive higher incremental revenue because they match the shopper’s preferences and lifecycle stage. However, for niche catalogs, seasonal assortments, or new stores, curated related-product lists outperform because they avoid poor algorithmic suggestions and can be merchandise-led to promote strategic items.

When To Use Each Approach

Consider business context when deciding which approach to prioritize. If your store has thousands of SKUs and millions of sessions monthly, invest in recommendation engines that use collaborative filtering and contextual signals. If you run a boutique store, new marketplace, or a catalog that changes frequently, start with curated related-product modules and simple rules (e.g., “frequently bought together”, “complements”). Most high-performing retailers use both: editorials for marketing objectives and algorithms for session-level personalization.

Implementation Considerations

Whether rule-based or algorithmic, operators must address placement, creative, and data integrity. Place recommendations where shoppers are most likely to add additional items (product pages, cart, post-purchase). Use clear CTAs and prices, and ensure real-time inventory and shipping constraints are respected. For algorithms, invest in tracking, privacy compliance, and retraining cadence; for editorial lists, schedule reviews and alignment with promotions and inventory.

Measuring Success

Compare related-product modules and recommendation engines using controlled experiments. Key metrics include attachment rate (percentage of orders with at least one recommended item), incrementality (A/B-tested revenue lift), conversion rate, average order value, and return rates. Also track engagement signals (click-through on recommendations) to detect relevance decay. Use holdout tests to measure true incremental lift, since correlation does not equal causation.

Practical Example

A sporting goods marketplace rolled out a simple "Customers Also Bought" list for accessory SKUs while piloting a machine-learning recommendation engine on 20% of traffic. The rule-based list increased accessory attachment by 8% immediately. The ML pilot produced a 14% lift for returning customers after three months, once enough sessions trained the model. The marketplace deployed a hybrid model: curated lists for new items and ML for personalized cross-sells on high-traffic pages.

Tips For Merchants

• Start where you are: Use curated related products for small catalogs; graduate to algorithms as data accrues.
• Mix tactics: Combine editorial messaging to promote margin or clearance items with algorithmic personalization for repeat customers.
• Monitor quality: Regularly review recommendations for irrelevant or out-of-stock items.
• Test rigorously: Run A/B tests with proper holdouts to measure true incremental sales impact.

In short, the Related Products concept covers both simple merchandising lists and complex recommendation systems; the choice depends on data, scale, and commercial priorities. Mixed strategies — editorial plus personalization — typically deliver the best balance of immediate returns and long-term growth.