Product recommendations drive 35% of Amazon's revenue. The same intelligence is now available to independent Indian stores through LetBuyy's recommendation engine — no ML team required.
How Recommendations Work
LetBuyy's recommendation engine uses collaborative filtering: "buyers who bought this product also bought these products." With enough order data (typically 500+ orders), the recommendations become accurate and personalized.
Types of Recommendations
- "You may also like" — products similar to what's being viewed (product page)
- "Complete the look" — complementary products (fashion, home decor)
- "Frequently bought together" — bundling opportunity (product page + cart)
- "Recently viewed" — re-engagement for browsers
- "Based on your history" — personalized for returning buyers
- "Trending in this category" — social proof for new visitors
Placement Strategy
Place recommendations at high-intent moments: below the add-to-cart button (highest converting placement), in the cart before checkout, in the post-purchase confirmation email, and on the homepage for returning visitors.
Manual Override
For low-volume stores or new product launches, use LetBuyy's manual "featured products" override while the AI accumulates enough data. Switch to AI mode once you have 500+ orders.