Add AI Product Recommendations to Your Online Store
Amazon's 'customers also bought' feature drives 35% of its revenue. You can do the same thing. AI recommendation engines analyze browsing behavior and purchase history to show each customer the products they're most likely to buy — turning one-item orders into three-item orders.
Tools You'll Need
| Tool | What It Does | Cost | Link |
|---|---|---|---|
| Shopify | E-commerce platform with built-in AI product recommendations and 'related products' features | From $39/month | Get it → |
| LimeSpot | AI-powered personalized product recommendation engine for e-commerce stores | From $18/month | Get it → |
The Walkthrough
Step 1: Enable Built-In Recommendations
What to do: If you’re on Shopify, activate the built-in “Related Products” and “You May Also Like” sections on product pages and cart pages. If your platform doesn’t have native recommendations, install LimeSpot to add AI-powered recommendation widgets.
Why you’re doing it: 35% of Amazon’s revenue comes from recommendations. You won’t match Amazon, but even a 10% increase in average order value from cross-sells adds up fast. The AI does the merchandising work your team doesn’t have time for.
What to expect: 30 minutes. Recommendations start appearing immediately.
Step 2: Set Up Recommendation Types
What to do: Configure different recommendation types for different pages: “Frequently Bought Together” on product pages, “You May Also Like” on cart pages, “Recently Viewed” for returning visitors, and “Trending Now” on your homepage. AI selects products for each based on real customer behavior.
Why you’re doing it: Different recommendation types serve different purposes. Product page cross-sells increase order size. Cart page suggestions capture impulse buys. Homepage trending items drive discovery. Each type contributes to revenue differently.
What to expect: 30 minutes to configure. The AI optimizes which products appear as it collects more data.
Step 3: Add Recommendations to Email
What to do: Include personalized product recommendations in your transactional and marketing emails. Post-purchase emails should show complementary products. Win-back emails should show products the customer browsed but didn’t buy. Klaviyo and Mailchimp both support dynamic product recommendation blocks.
Why you’re doing it: Recommendations in email get 2–5x higher click rates than generic product listings. Showing someone the exact product they viewed yesterday in an email feels like mind reading. It’s just data.
What to expect: 30 minutes to add recommendation blocks to existing email templates.
Step 4: Track and Optimize
What to do: Monitor recommendation performance weekly: click-through rate, conversion rate, and revenue attributed to recommendations. Most platforms show this in a dedicated analytics dashboard. Test different recommendation placements and types to find what converts best.
Why you’re doing it: Recommendations that nobody clicks need to be repositioned or reconfigured. Recommendations that convert well should be expanded to more touchpoints. Data tells you where the money is.
What to expect: 10 minutes per week. Most stores see measurable AOV increases within 30 days.
Confidence Level
This workflow is Beta — Based on Best Available Knowledge. AI product recommendations are proven at every scale from startups to Amazon. Effectiveness increases with traffic and product catalog size.