Ecommerce Product Recommendations Example
John — Tech & Outdoors Persona A
Single in his 20s; interested in Samsung electronics, camping gear, tech accessories, and nootropics.
- Recommendations: electronics, backpacks, headlamps
- CTAs: “Quick look”, “Add to cart”
- Higher relevance & AOV
Jennifer — Family & Wellness Persona B
Mother in her 30s; interested in children’s toys, yoga supplies, self-help books, and kitchen items.
- Recommendations: toys, yoga mats, cookwares
- CTAs: “Quick look”, “Add to cart”
- Improved engagement & conversions
John — Tech & Outdoors Persona A
Product listing emphasizes camping gear and tech accessories relevant to his interests.
- Recommended: multitools, electronics, and gadgets
- CTAs: “Quick look”, “Add to cart”
- Higher engagement & AOV
Jennifer — Family & Wellness Persona B
Product listing highlights yoga and family-oriented products matched to her browsing behavior.
- Recommended: yoga mats, toys, home goods
- CTAs: “Quick look”, “Buy now”
- Boosts personalization & conversions
John — Tech & Outdoors Persona A
Shows bundle upsells that pair tools with related accessories.
- Frequently Bought Together (multitool + sheath)
- “Add all to cart” bundle CTA
- Higher AOV via cross-sale
Jennifer — Family & Wellness Persona B
Bundles complementary items (e.g., yoga mat + strap) with a single-click add.
- Smart bundles based on browsing history
- Inline price + savings shown
- Increases cart size & conversions
From Static Grids to Adaptive Recs
Turn every surface into a smart shelf with product recommendations delivered via the website personalization tag/SDK. Carousels and content blocks update in real time to reflect each shopper’s intent, history, and context.
Key Placements (Home, PDP, Cart)
- Homepage: “Recommended for you,” “Trending in your area,” “Recently viewed.”
- PDP: “Similar items,” “Frequently bought together,” “Complete the look.”
- Category/Search: “Top rated in this category,” “Because you liked…”.
- Cart/Checkout: “You may also need…,” low-friction add-ons and refills.
- Empty states/404: rescue rails to keep shoppers exploring.
- Email: rec slots that render at open time (optional) to keep messages fresh.
Signals & Ranking
Models blend behavioral, similarity, and context signals to rank items:
- Behavioral: views, carts, purchases; recency/frequency; session depth.
- Similarity: attributes (brand, color, price band), embeddings, co-view/co-buy.
- Context: geo, device, traffic source, inventory and price.
The result is a personalized, high-intent list for each placement—updated live as shoppers interact.
Business Rules & Pinning
Combine algorithms with merchandising controls: exclude OOS/low-margin SKUs, cap duplicates, promote seasonal collections, or pin hero items for campaigns and launches.
Badges & Social Proof
Add persuasive overlays—ratings, review counts, “Popular now,” “Only 3 left”—using social proof to increase click-through and cart adds.
On-site Promos & Bars
Pair recs with targeted notices (free shipping thresholds, bundle savings) built in the Pop-up & Banner Manager. Trigger by cart value, category interest, or inventory urgency.
A/B Testing & Lift
Test rail logic, badge sets, and layouts against control. Track CTR, add-to-cart rate, revenue per session, and downstream effects (AOV, conversion).
How do the carousels render?
Client-side via the personalization tag/SDK, or server-side where needed. Layouts are theme-agnostic.
Can I keep my merchandising rules?
Yes—layer rules over algorithms: exclude, boost, pin, and schedule by campaign.
What’s required to start?
Product feed (IDs, titles, URLs, price, stock, attributes) and the site tag. Optional: review data and margin bands.
