How to Use Shopify Metafields for Product Recommendations [Complete Guide]

Metafields let you remember what customers tell you and use that information to show them products they’ll actually want to buy. Instead of everyone seeing the same generic recommendations, each customer gets suggestions based on their specific needs-like having a personal shopper who remembers their preferences.

What Are Shopify Metafields?

Metafields are extra data fields you can add to products and customers. Shopify gives you the basics like “product type” and “vendor,” but metafields let you track anything you want: skin type, experience level, dietary restrictions, fitness goals-whatever matters for your products.

Why This Matters

You can remember customer preferences. When someone takes your product quiz or fills out their profile, that information gets saved. Next visit? Your store already knows they have dry skin or prefer decaf coffee.

You can add detailed product attributes. Tag products with specifics like “beginner-friendly,” “vegan,” or “works for small spaces.” Then match those tags to customer preferences.

You can automate customer grouping. Shopify’s customer segments use metafield data, so you can automatically create groups like “dry skin customers” or “advanced fitness enthusiasts” for targeted marketing.

Real Examples

Skincare Store:

Save: customer’s skin type, concerns, experience level
Tag products: which skin types they work for, key ingredients, complexity
Result: Someone with dry skin and anti-aging concerns only sees relevant moisturizers and serums

Fitness Equipment:

Save: customer’s goals, experience, space constraints
Tag products: target muscles, size, difficulty
Result: A beginner in an apartment sees compact starter equipment, not industrial gym machines

Coffee Subscription:

Save: preferred roast, flavor notes, how much they drink
Tag products: roast level, origin, caffeine content
Result: Light roast fans never see dark roast options

How to Set This Up

Plan Your Data First

For customers, track:

Quiz answers and stated preferences
Purchase history patterns
Goals or interests they’ve mentioned

For products, note:

What makes this product special beyond basic specs
Who’s the ideal buyer
What complements it

Create the Fields in Shopify

Settings → Custom data
Add definitions for customers and products
Stick to simple types: text fields or true/false toggles

Sample setup:

Customer: “Skin Type” (text)
Customer: “Experience Level” (text)
Product: “Works for Skin Types” (text)
Product: “Beginner Friendly” (yes/no)

Collect the Information

Use a quiz app or form to ask customers their preferences, then automatically save answers to their metafields. When they browse, pull from those fields to show matching products.

Check out our guide on saving quiz answers to customer metafields for a complete setup.

Building Recommendations

Manual matching: Create simple rules like “if skin type = dry, show products tagged for dry skin.”

Automated scoring: Use apps that compare customer and product metafields, score matches, and display the best fits automatically.

Behavioral learning: Track what people actually buy and update their metafields over time. Someone who consistently buys eco-friendly? Add that to their profile.

Where to Show Recommendations

Product pages: “Customers with your skin type loved these” or “Complete your routine with…”

Collections: Show beginners different products than advanced users, even in the same collection.

Email campaigns: “Based on your dry skin, here are new moisturizers” or “You bought [X], try these similar items.”

Learn how to sync quiz results to Klaviyo for personalized email flows.

Measuring Results

Watch these numbers:

Click rate on recommendations
How many recommended products convert
Customer feedback through surveys or reviews

Common problems:

Recommendations feel off → Check your metafield data for errors and add more specific tags

No one clicks → Move recommendations to more visible spots and improve the design

Too much manual work → Get an automation app to handle updates and matching logic

Best Practices

Start small. Pick 3-5 key preferences instead of trying to track everything right away.

Be consistent. Always use “Beginner” not sometimes “Easy” or “Novice.” Keep your language uniform across all products.

Keep it current. Review monthly—add new attributes as your catalog grows, update customer preferences based on behavior, delete fields you don’t use.

Test different approaches. Try recommendations in various spots on your site. Compare manual rules versus automated scoring. Ask customers what they think.

Helpful Tools

Kiezer creates interactive product quizzes that automatically populate metafields

Shopify Search & Discovery (built-in) handles recommendations using metafield data

ReConvert runs post-purchase surveys that update customer metafields

Custom Fields apps help manage complex metafield setups

You might also need a developer to add metafield-based sections to your theme for personalized grids and smart filters.

The Payoff

When you use metafields well, customers find what they need faster, buy more, and come back. You’ll see higher conversion rates, bigger cart sizes, more loyalty, and fewer returns.

This approach aligns perfectly with zero-party data collection strategies that prioritize customer consent and explicit preferences.

Start with a few key preferences. Stay consistent with your data. Refine based on what actually works. The goal isn’t to track everything—it’s to track the right things that help customers discover products they’ll love.