There is a version of DTC growth that worked brilliantly until about 2021. You built a Shopify store, launched Meta campaigns, and the platform handed you targeting precision on a level that felt almost unfair. You could reach people who had just searched for your product category, visited a competitor, or spent time in a demographic sweet spot your brand was built for. Third-party data made early-stage acquisition cheap and scalable.
That era is over.
iOS 14.5 removed the tracking signal from three in four iPhone users. Google is deprecating third-party cookies. Meta's attribution is over-reporting by anywhere from 30 to 100%. The customer data you thought you had access to is shrinking, and the brands still building growth strategies on borrowed data are running out of road. The ones building first-party data infrastructure right now are building a moat that compounds every month.
What First-Party Data Actually Means
First-party data is any information you collect directly from your customers and visitors on properties you own. Your Shopify store. Your email list. Your SMS subscribers. Your loyalty programme. It includes purchase history, browse behaviour, email engagement, product preferences, and communication preferences.
Zero-party data is a subset: information customers proactively share with you, like quiz answers, skin type preferences, dietary goals, or how they heard about you. It is declared rather than observed. Both matter. Zero-party tells you what customers want. First-party shows you what they actually do.
The difference between these and third-party data is ownership. Third-party data is rented from platforms that can change the terms, restrict access, or lose the signal overnight. First-party data belongs to you. It does not degrade when Apple ships an update. It does not disappear when a platform adjusts its attribution window. And because customers consented to share it with you, it is privacy-compliant by design.
75% of brands plan to phase out reliance on third-party data by 2026. The ones who started building their first-party infrastructure two years ago are already seeing the compounding benefit. The ones starting now are catching up. The ones still waiting are making a strategic error.
Why Most DTC Brands Are Collecting Data Wrong
Most DTC operators know they should be collecting first-party data. The problem is how they are doing it. These are the mistakes I see consistently across audits.
Missing the Viewed Product event
The Viewed Product event does not fire automatically in Klaviyo's native Shopify integration. It requires a custom JavaScript snippet added to your product template. Without it, browse abandonment flows cannot trigger. Browse abandonment is one of the highest-converting automations in ecommerce, consistently outperforming abandoned cart flows for cold traffic. I estimate this is missing in roughly half the Klaviyo accounts I review. That is abandoned revenue from people who showed intent and then left.
Duplicate profiles fragmenting segmentation
When customers sign up through Shopify's native checkout and then through a Klaviyo popup, or switch between email and phone number as their identifier, they create duplicate profiles. Each profile holds a partial view of that customer. Your segmentation fires on incomplete data. Your predicted LTV models are wrong. Your suppression logic misses people. Use Klaviyo-powered forms exclusively across your store. Set a merge strategy and audit for duplicates quarterly.
Collecting email without building a preference profile
An email address is a starting point, not a data strategy. The brands generating the highest revenue per subscriber are not the ones with the biggest lists. They are the ones who know what their subscribers bought, what they browsed, what category they care about, and how often they want to hear from them. An email address with no profile context is a broadcast list. An email address with purchase history, browse data, and quiz answers is a segment of one.
No post-purchase data collection
The moment immediately after purchase is the highest-engagement window in the customer relationship. Your customer just handed you money. They are paying attention. Most brands send a generic order confirmation and do nothing else. A two-question post-purchase survey asking how they heard about you and what problem they were trying to solve generates attribution data no platform can give you, and product insight that shapes your next campaign.
The Five Collection Methods That Actually Work
There is no shortage of tactical advice on first-party data collection. Most of it misses the point. The goal is not to collect as much data as possible. The goal is to collect the data that changes what you do. Here are the five methods with the highest return on effort.
Email and SMS popup with a value-led offer
The popup is still the highest-volume first-party data collection tool in ecommerce when executed correctly. The mistake most brands make is treating it as a discount mechanism rather than a value exchange. Your offer should match the motivation of the visitor segment. New visitors respond to percentage discounts. Returning visitors who have not purchased respond to social proof and urgency. Loyalty programme members should see a different offer entirely. Klaviyo's conditional display logic lets you serve different popup variants by visit behaviour, purchase history, and list membership. Use it.
Post-purchase survey
Two to three questions. Delivered in the order confirmation email, timed 24 hours post-delivery. Ask how they heard about you, what problem they were solving, and what nearly stopped them from buying. The first question gives you attribution data your ad platform cannot provide. The second tells you what your product actually means to customers, which is almost always different from what your marketing says. The third identifies the friction your next cohort will face. A 15% survey completion rate on 500 monthly orders gives you 75 data points per month that no platform can match.
Quiz funnel for zero-party preference data
Quiz funnels work because they offer personalised recommendations in exchange for declared preferences. The data collected, whether that is skin type, fitness goal, dietary restriction, or home environment, maps directly to product recommendations and Klaviyo profile properties. Tag each respondent with their quiz answers. Use those tags to trigger personalised flows, segment campaigns, and suppress irrelevant offers. Dr. Squatch, Prose, and Function of Beauty built meaningful portions of their growth on quiz-driven data collection. The infrastructure is accessible to brands at any scale through tools like Octane AI and TypeForm connected to Klaviyo.
Loyalty programme enrolment
Loyalty programmes collect first-party data as a byproduct of participation. A customer who joins your programme provides their email, their purchase history becomes structured data in your system, their engagement with rewards gives you intent signals, and their referral behaviour identifies your highest-value advocates. The data benefit is separate from the retention benefit, though both compound. If you are running a Klaviyo-connected loyalty programme through LoyaltyLion or Smile.io, make sure the enrolment data, points balance, and tier status are syncing as profile properties. Those properties are the foundation of your most personalised segments.
Viewed Product event with browse abandonment
Adding the Klaviyo Viewed Product snippet to your Shopify theme is a one-time technical task that unlocks a permanent revenue stream. Once the event fires, you can build browse abandonment flows that target visitors who viewed a product but did not add to cart. These flows consistently convert at 5 to 8% open-to-purchase rates because they are triggered by demonstrated intent. The event also feeds into Klaviyo's predictive models, improving the accuracy of churn risk scores and next expected order dates across your entire subscriber base.
How to Activate First-Party Data in Klaviyo
Collecting data is the easy part. Most brands have more first-party data than they are using. The gap is activation. Here is the framework I use across client accounts.
Audit your profile property coverage
In Klaviyo, go to Profiles and filter by your top 1,000 subscribers by engagement. Check what profile properties are populated: purchase count, last order date, product category preference, quiz answers, LTV, predicted LTV. For most brands, the majority of profiles have an email address and an order count. That is it. Every missing property is a personalisation opportunity you are not using. The audit tells you where your collection gaps are before you build anything new.
Map data to segments
Build segments that actually mean something operationally. Not just 'purchased in the last 90 days'. Build segments that combine behaviour with preference: customers who bought your top product in the last 60 days and engaged with your last three emails. Customers who completed the quiz and selected preference A but have not purchased. Customers with predicted LTV above your 75th percentile who have not bought in 45 days. These segments are only possible with first-party data, and they are the difference between email that converts and email that gets ignored.
Replace broadcast campaigns with triggered flows
The best use of first-party data is not smarter campaigns. It is replacing campaigns with flows entirely where behaviour makes it possible. Browse abandonment, post-purchase, win-back, predictive churn, and replenishment flows all trigger from first-party behavioural data. Every hour your customer receives a triggered email based on their own behaviour instead of a scheduled broadcast is an hour your email programme is working without you. The brands generating 40-45% of total revenue from email are not sending more campaigns. They have more flows.
Feed first-party data back into paid channels
Your first-party data does not only live in Klaviyo. Sync your Klaviyo segments to Meta Custom Audiences and Google Customer Match. Upload your email list of high-LTV customers and build Lookalike Audiences from that signal. Exclude your existing customers from cold prospecting campaigns so you are not paying to re-acquire people who already know you. First-party data synced to paid channels is how brands continue scaling acquisition efficiently even as third-party signals degrade.
What This Looks Like in Practice
A supplements brand I worked with had 42,000 Klaviyo subscribers and was generating 18% of revenue from email. Their flows were basic: welcome series, abandoned cart, post-purchase. No browse abandonment because the Viewed Product event was not firing. No segmentation by product category because quiz data was not syncing to profile properties. No predictive win-back because they had not turned on Klaviyo's predictive features.
We spent three weeks on data infrastructure. Added the Viewed Product snippet, fixed the quiz-to-profile sync, merged duplicate profiles, and built four new segments based on purchase behaviour and quiz answers. Then we built browse abandonment, predictive win-back, and a category-specific post-purchase upsell flow.
Email revenue went from 18% to 34% of total revenue within 60 days. No new subscribers. No bigger campaigns. The same list, activated properly.
The Compounding Advantage
Here is what makes first-party data different from any other growth lever: it compounds. Every new subscriber adds to your list. Every purchase adds to your behavioural data. Every quiz completion adds to your preference profile. Every month you have this infrastructure, you know more about your customers than you did the month before, and that knowledge makes every email, every campaign, and every product decision more precise.
Third-party data does the opposite. It erodes. The platforms keep changing the rules, and every change degrades the signal you have been renting. The brands building on first-party data are building something that gets more valuable over time. The brands building on third-party data are building on a foundation that is being pulled out from under them.
The window to build this advantage is now. The brands with two years of first-party data infrastructure already have a compounding head start. The gap between them and brands that have not started is growing every month. This is not a 2027 priority. It is a this-quarter priority.
Free Growth Audit
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Book Your AuditFrequently asked questions
What is first-party data for DTC brands?
First-party data is information you collect directly from your customers and visitors on properties you own: your Shopify store, your email list, your SMS list, your loyalty programme. It includes purchase history, browse behaviour, email engagement, product preferences, and anything customers voluntarily share through forms or quizzes. Unlike third-party data, you own it outright, it is privacy-compliant by design, and it does not degrade when platforms change their tracking rules.
Why is third-party data no longer reliable for DTC brands?
Apple's AppTrackingTransparency framework reduced the tracking signal available to Meta and other ad platforms. Opt-in rates for tracking sit around 25%, meaning three in four iPhone users are invisible to your pixel. On top of this, Google's third-party cookie deprecation is removing cross-site tracking for non-Apple browsers. Ad platforms are over-reporting conversions by 30-100%, and the targeting precision that made early-stage DTC growth efficient no longer works the same way.
What is zero-party data and how is it different from first-party data?
Zero-party data is information customers voluntarily share with you: quiz answers, product preferences, dietary goals, communication preferences. First-party data is behavioural information you observe on your own properties: pages viewed, emails opened, products clicked, purchase frequency. Zero-party data gives you declared intent. First-party data gives you actual behaviour. The strongest DTC data strategies collect both and combine them in Klaviyo profiles.
How do I fix the Viewed Product event missing in Klaviyo?
The Viewed Product event is not tracked by Klaviyo's native Shopify integration by default. You need to add a custom code snippet to your Shopify theme's product page template. Without it, browse abandonment flows cannot fire. Add the Klaviyo JavaScript snippet to your theme.liquid or product template, then verify it is firing via Klaviyo's Activity Feed before building the flow.
What first-party data collection methods generate the highest ROI?
The five highest-ROI methods are: email and SMS popup with a value-led offer, post-purchase survey capturing attribution and product insight, quiz funnel collecting zero-party preference data, loyalty programme enrolment capturing repeat buyer signals, and Viewed Product event tracking enabling browse abandonment flows. Each method feeds profile data that improves segmentation, flow targeting, and campaign personalisation.
How should DTC brands use first-party data in Klaviyo?
Use first-party data in Klaviyo to build behaviour-based segments, trigger flows from actual customer actions rather than schedules, personalise campaign content by product category preference, suppress unengaged profiles to protect deliverability, and feed high-LTV customer segments back into Meta and Google for Lookalike Audience targeting. The goal is to replace broadcast marketing with behaviour-triggered, preference-matched communication built from real data.
About the author
Caner Veli built Liquiproof from zero to 3,000+ global retailers in under 6 years. He now helps DTC and CPG brands fix broken growth engines and scale 2x-15x in 90 days.