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Personalization Example

One store, tailored by recommendations.

See how an e-commerce site adapts its product recommendations across home, category, and cart pages — powered by Personyze. Drag the sliders to compare generic vs personalized.

⚡ Before & after — drag to compare

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
Homepage recommendations for Jennifer persona Homepage recommendations for John persona
How it works

Built with Personyze — in three steps.

01

Detect

Personyze tracks each shopper’s browsing, cart, and purchase behavior in real time.

02

Decide

The AI recommendation engine ranks the most relevant products with business rules and pinning.

03

Render

Recommendations update live across home, category, and cart — no page reload, no developer.

Under the hood

How Personyze personalizes this store.

Every change you see is driven by a real Personyze capability. Drag any slider to compare generic vs personalized.

01

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.
02

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.

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
Product listing page recommendations for Jennifer persona Product listing page recommendations for John persona
03

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.

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
PLP cross-sale bundles for Jennifer persona PLP cross-sale bundles for John persona
04

Badges & Social Proof

Add persuasive overlays—ratings, review counts, “Popular now,” “Only 3 left”—using social proof to increase click-through and cart adds.

05

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.

06

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.

Demo — adaptive recommendations across homepage, PDP, cart, and email, powered by real-time shopper signals.

Build your own

Personalize your store like this.

This recommendations example runs entirely on Personyze — AI product recs, behavioral targeting, and A/B testing. Build the same for your store, no code required.

Free to start · No credit card · Setup in minutes