Mastering Klaviyo Profile Properties for Better Segmentation

Every profile in Klaviyo is a filing cabinet of data about that person. Their name, email, purchase history, browsing behavior, predicted lifetime value — it's all stored as profile properties. And the more properties you have, the more precisely you can segment and personalize.

Most brands use maybe 10% of the data available in their Klaviyo profiles. They send to "everyone who bought" or "everyone who opened recently." That's fine for beginners. But the brands generating 30-40% of revenue from email are segmenting on dozens of properties — product preferences, purchase frequency, price sensitivity, engagement patterns, and custom properties they've built specifically for their business.

Here's how to use profile properties to build segments that actually reflect who your customers are and what they want.

Default Profile Properties

Klaviyo collects these automatically through your Shopify integration:

Contact info: Email, phone number, first name, last name.

Location: City, state/region, country, zip code, timezone. Collected from billing addresses and IP-based geolocation.

Ecommerce data: Total order count, total revenue (historic CLV), average order value, first order date, last order date.

Engagement data: Last email open date, last email click date, last SMS open date. Number of emails received, opened, clicked.

Predictive data (Klaviyo calculates): Predicted next order date, predicted customer lifetime value, predicted gender, churn risk score.

Consent: Email consent, SMS consent, consent timestamps.

These default properties already give you powerful segmentation. But the real advantage comes from custom properties.

Custom Profile Properties

Custom properties are data points YOU add to profiles based on your specific business needs. They don't exist until you create them, and they can store any information relevant to your marketing.

How to add custom properties:

1. Through forms. Any field on your Klaviyo popup or embedded form becomes a custom property. Ask "What's your skin type?" and the answer is stored on their profile as a custom property.

2. Through integrations. Your review platform, loyalty program, or quiz tool can sync data to Klaviyo profiles as custom properties.

3. Through the API. If you have development resources, you can push any data point from your site or app to Klaviyo profiles.

4. Through CSV import. Upload a spreadsheet with email addresses and custom data columns to bulk-update profiles.

5. Through Shopify metafields. Certain Shopify customer metafields sync to Klaviyo as profile properties.

The Custom Properties Every eCommerce Brand Should Collect

Here's the data we set up for every client:

Product preference/category. What category does this person buy from? Skincare vs. haircare. Men's vs. women's. Running vs. yoga. This single property transforms your campaigns from generic to relevant.

How to collect: Post-purchase flow asks "What are you most interested in?" Or derive it from purchase history using Shopify tags or catalog categories.

Skin type / Body type / Preference type. Whatever the primary personalization axis is for your products. Skin type for beauty. Size preference for apparel. Taste profile for food/beverage.

How to collect: Welcome quiz on your site. First interaction asks the question. Answer stored as custom property.

How they found you. Attribution data at the profile level. "How did you hear about us?" with options: Instagram, TikTok, Google, Friend, Podcast, Other.

How to collect: Add to your popup form or post-purchase survey. Invaluable for understanding which acquisition channels produce the best email subscribers.

Birthday / Anniversary. Date properties that power automated celebration flows.

How to collect: Ask in a preference center or a dedicated "birthday club" signup form.

VIP status / Loyalty tier. If you run a loyalty program, sync the tier to Klaviyo. Gold, Silver, Platinum — or whatever your tiers are.

How to collect: Loyalty platform integration (Smile.io, LoyaltyLion, Yotpo Loyalty all integrate with Klaviyo).

Communication preference. Does this person prefer email, SMS, or both? How often do they want to hear from you?

How to collect: Preference center where subscribers self-select frequency and channel.

Using Properties for Segmentation

Custom properties unlock segments you couldn't build with default data alone.

Examples:

"Women who prefer skincare, located in California, who haven't purchased in 60 days, with a predicted CLV above $200."

That's a highly specific segment for a targeted winback campaign with skincare-focused content, possibly referencing California-specific trends or weather.

"Men who joined via TikTok, made 1 purchase, and haven't opened an email in 30 days."

This tells you: TikTok-acquired customers might not be email-responsive. Maybe they need SMS. Or maybe the content isn't relevant to the TikTok demographic. The segment reveals the insight.

"Customers with oily skin type who bought the moisturizer but NOT the cleanser."

Cross-sell opportunity. Send a campaign featuring the cleanser, positioned as the perfect companion to their moisturizer, specifically for oily skin. That's personalization that converts.

Organizing Your Properties

As you collect more properties, organization becomes important. Klaviyo doesn't have folders for properties, so you need a naming convention.

Prefix by source:

• form_skin_type (collected via form)
• quiz_result (collected via quiz)
• loyalty_tier (synced from loyalty app)
• pref_frequency (preference center)
• calc_segment_score (calculated by you)

Or prefix by category:

• demo_birthday (demographic)
• demo_gender
• pref_category (preference)
• pref_channel
• biz_vip_status (business logic)
• biz_acquisition_source

The naming convention doesn't matter as long as it's consistent and everyone on your team understands it.

Profile Properties for Flow Logic

Properties don't just power segments — they power conditional logic within flows.

Welcome series example: A conditional split in your welcome flow checks the "product_preference" property. If it's "skincare," send the skincare-focused welcome email. If it's "haircare," send the haircare version. Same flow, different content, powered by a profile property collected on signup.

Abandoned cart example: Split based on "loyalty_tier." VIP customers get a cart recovery email with "As a VIP, enjoy free express shipping on this order." Standard customers get the generic reminder. Different treatment, same trigger.

Winback example: Split based on "last_category_purchased." Someone who last bought coffee beans gets a winback email featuring new bean varieties. Someone who last bought equipment gets an email about accessories. The relevance of the winback message determines whether they actually come back.

Maintaining Data Quality

Profile properties are only useful if the data is clean.

Problem 1: Outdated data. Someone's skin type might change. Their preferences might shift. Birthday data doesn't change, but preference data can.

Solution: Periodically re-ask preference questions. An annual "Update your preferences" email with a link to your preference center. Or ask preference questions in post-purchase flows (their most recent answer overwrites the old one).

Problem 2: Inconsistent formatting. If "skincare" is stored as "Skincare," "SKINCARE," "skin care," and "Skin Care" across different profiles, your segments won't work correctly. Klaviyo properties are case-sensitive for some operations.

Solution: Use dropdown options in forms (not free text) so the values are always consistent. If importing via CSV, clean the data before import.

Problem 3: Missing data. Not every profile will have every property filled in. Build your segments to handle nulls. "Product preference IS skincare" will only match profiles where that property exists and equals skincare. Profiles without the property are excluded automatically.

Solution: For critical properties, run campaigns specifically designed to collect missing data. "Help us personalize your experience — take this 30-second quiz." Target profiles where the key property is blank.

Advanced: Calculated Properties

Some useful properties don't come from collection — they come from calculation.

Days since last purchase. Calculated from the "Last Order Date" property. Use this for time-based segmentation without creating date-math segments every time.

Customer lifecycle stage. A property you update based on behavior: "new_subscriber," "first_buyer," "repeat_buyer," "vip," "at_risk," "churned." Update it via flow logic or API based on purchase events.

Engagement score. A numeric score based on opens, clicks, purchases, and site visits. Higher score = more engaged. Use it for send-time optimization and frequency decisions.

These calculated properties require either API development or a tool that automates the calculation. But they enable powerful, simple segmentation: "Show me everyone with an engagement score above 80" is easier to work with than rebuilding complex multi-condition segments every time.

What To Do Right Now

Open Klaviyo. Go to a random customer profile. Look at the properties tab. Count how many custom properties have data. If it's fewer than 5, you're under-collecting.

Next step: Add one question to your popup form. "What are you most interested in?" with 3-4 category options. That single field populates a property that powers segmentation across every campaign and flow you send.

If you want help building a complete data strategy in Klaviyo — custom properties, quiz integration, profile enrichment, and the segments that use them — book a call with our team. We'll build the data foundation that makes every email more relevant.