Building a Personalized Product Recommender in Shopify Liquid

Shopify's built-in recommendations.products is a good baseline. Apps that promise "AI personalization" usually deliver slow scripts and ugly carousels. The middle ground — a Liquid + Section Rendering API recommender powered by your own signals — is where the lift lives.

Signals you actually have

• Current cart contents.
• Customer order history (customer.orders).
• Product metafields (occasion, material, line, complement_to).
• Browsing session (via cookie or local storage).

The architecture

1. PDP renders a placeholder section.
2. Section Rendering API fetches a personalized section async, passing the customer ID and current product.
3. The section uses Liquid logic against metafields and order history to pick complements.
4. Cache aggressively at the section level.

{%- comment -%} sections/recommended-for-you.liquid {%- endcomment -%}
{%- liquid
  assign current = product
  assign suggestions = ''

  if customer
    for order in customer.orders limit: 5
      for line in order.line_items
        assign suggestions = suggestions | append: line.product.id | append: ','
      endfor
    endfor
  endif

  assign complement_ids = current.metafields.custom.complement_products.value
-%}

Why this beats most apps

• Native speed. Renders inside your existing critical path or async via Section Rendering API.
• No black box. You control the rules, so merchandising can adjust them.
• No monthly fee. It's your code.
• AI-ready. Add an LLM call server-side later if you want — same architecture.

When to use a real personalization platform

If you're running 1000+ orders/day and have rich first-party data, platforms like Rebuy, Nosto, or Klaviyo's on-site personalization can outperform pure Liquid. Below that, Liquid wins on ROI.

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We build personalization, PDP, and merchandising features on Shopify and Shopify Plus. Get a quote.