How can I use 'people also bought' recommendations effectively?

Question

I'm struggling to implement effective cross-selling strategies in my e-commerce store. While I understand the concept of 'people also bought' recommendations, I'm unsure how to execute them in a way that genuinely increases average order value and provides real value to customers. I want to create recommendations that feel authentic, helpful, and not just like a random sales tactic. What are the best practices for designing recommendation algorithms that actually convert and enhance the shopping experience?

Muhammed Tüfekyapan · Accepted Answer

Using 'People Also Bought' Recommendations Effectively
"People also bought" is one of e-commerce's most powerful AOV levers - Amazon attributes up to 35% of revenue to recommendation algorithms. But the strategy only works when recommendations feel genuinely useful rather than random upsells. The key is relevance: the recommended item must logically complement what the customer already wants.

Recommendation Placement Strategy

Location
      Recommendation Type
      Goal

Product page (below fold)
      Frequently bought together
      Pre-purchase bundle consideration

Cart page / drawer
      Complementary add-ons
      Last-moment AOV increase

Post-purchase page
      Related products, accessories
      Second purchase without abandonment risk

Email (order confirmation)
      What goes with your purchase
      Warm audience cross-sell

What Makes Recommendations Convert

Logical pairing - Camera bag with camera, not another camera
  Price compatibility - Add-ons should be 20-40% of the main item's price
  Social framing - "87% of customers who buy X also get Y" is more persuasive than generic "you might like"
  One-click add - Friction kills impulse adds; let customers add without leaving the page
  Bundle discount option - "Add both for $X, save $Y" significantly boosts uptake

Recommendation Mistakes to Avoid

Recommending competitors or substitutes - visitors may switch instead of add
  Showing too many options (more than 4-5) - paradox of choice reduces conversion
  Recommending out-of-stock items - kills trust and wastes the moment
  Generic "bestsellers" with no relevance to the viewed product

AI-Powered Cart Recommendations
  Growth Suite's Advanced Cart Drawer includes AI-powered product suggestions that surface genuinely complementary items based on cart contents and purchase history. The Frequently Bought Together widget automatically identifies high-affinity product pairs from your store's actual order data - eliminating the guesswork of manual curation while maximizing relevance.

How can I use 'people also bought' recommendations effectively?

In depth

Using 'People Also Bought' Recommendations Effectively

Recommendation Placement Strategy

What Makes Recommendations Convert

Recommendation Mistakes to Avoid

Put this answer to work in your store.

Use Cases

Location	Recommendation Type	Goal
Product page (below fold)	Frequently bought together	Pre-purchase bundle consideration
Cart page / drawer	Complementary add-ons	Last-moment AOV increase
Post-purchase page	Related products, accessories	Second purchase without abandonment risk
Email (order confirmation)	What goes with your purchase	Warm audience cross-sell

In depth

Using 'People Also Bought' Recommendations Effectively

Recommendation Placement Strategy

What Makes Recommendations Convert

Recommendation Mistakes to Avoid

Put this answer to work in your store.

More answers

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How do I design an A/B test for cart abandonment solutions?

Should I cap stacking between referral and first-order discounts?

Resources

Shopify Upselling & Cross-Selling

Shopify Conversion Rate: The Complete Resource Hub

Shopify Holiday & Seasonal Campaign Strategies