June 2, 2025

Measuring Campaign Success Without User-Level Data

Resources
Performance
Privacy-First Marketing – WITHIN

Digital marketing is shifting fast. Privacy regulations like GDPR and CCPA have elevated user privacy expectations. Safari and Firefox already limit cross-site tracking. And in April 2025, Google announced that it will not deprecate third-party cookies in Chrome. Instead, Google confirmed users will retain control over their own cookie settings, and the Privacy Sandbox tools will continue to roll out.

The long-term trend is clear: privacy-first marketing is the new standard. Compliant, ethical measurement isn’t a nice-to-have; it’s a must. Third-party cookies aren’t the future. They’re a shortcut. Smart brands are building better systems now, and WITHIN is helping lead that shift with data-responsible, performance-first strategies.

Embracing First-Party Data Strategies

With third-party tracking on the decline, first-party data becomes a brand’s most valuable asset. This is data collected directly from customers through owned channels, such as websites, apps, and emails, with the user’s consent.

Benefits of First-Party Data

  • Cleaner, More Reliable Measurement: Third-party data often comes with gaps, duplication, and limited context. First-party data is direct and tied to real user actions. It delivers clearer ROI tracking across touchpoints and improves attribution models by reducing reliance on inferred behavior.
  • More Relevant Personalization: With consent-based behavioral and preference data, brands can tailor messaging, offers, and experiences based on actual user interests and intent without crossing privacy lines. This increases engagement and boosts conversion efficiency.
  • Higher Customer Trust and Retention: Transparency builds loyalty. Customers are more willing to share information when it’s clear how it will be used and when value is delivered in return. When done right, first-party data programs can help turn casual shoppers into long-term customers.

Actionable Steps to Build and Use First-Party Data

Grow Email and SMS Lists

  • Use gated content, exclusive offers, or early access to drive sign-ups.
  • Add prominent CTAs across high-traffic pages and media channels.

Launch Loyalty and Referral Programs

  • Reward purchases, engagement, and referrals while capturing data.
  • Use the program as a feedback loop to improve targeting.

Use Interactive Tools to Capture Intent

  • Add quizzes, product finders, or short surveys to collect declared preferences.
  • Capture insights that go beyond clicks—what customers want, need, and expect.

Track On-Site Behavior with GA4

  • Set up events for key actions like product views, sign-ups, and checkouts.
  • Build audience segments based on behavior for smarter retargeting.

Centralize and Activate in a CRM or CDP

  • Unify customer data across channels for a full-funnel view.
  • Trigger automated campaigns based on real-time behaviors.

Build smarter strategies with data you already own. Our Digital Transformation solutions can help.

Rethinking Attribution with Incrementality Testing

When user-level tracking isn’t available, incrementality testing provides a clear, privacy-safe way to measure what’s actually working. This method shows the impact of a campaign by comparing a test group (those exposed to your campaign) with a control group (those who weren’t).

How to Run an Effective Test

  1. Set Up Test vs. Control Group: Split audiences by geo, timing, channel, or holdout groups. Keep variables consistent across both groups outside of exposure.
  2. Use the Right Methodology: Apply a Difference-in-Differences (DiD) model to account for external trends like seasonality or market shifts that could skew results.
  3. Focus on Business Outcomes: Track outcomes like revenue, conversions, or cost per acquisition. Skip vanity metrics. The goal is to quantify incremental performance.
  4. Test, Learn, Optimize: Use findings to refine channel strategy, creative, and spend allocation. Repeat across campaigns.

This is a proven, privacy-safe way to validate marketing impact without relying on cookies.

Measuring Holistically with Media Mix Modeling (MMM)

Media Mix Modeling (MMM) uses historical spend and performance data to estimate the contribution of each marketing channel. Its channel-level and privacy-safe design makes it ideal for marketing without individual tracking cookies. 

How to Build a Strong MMM Strategy

  1. Gather the Right Data: Pull clean historical data across paid, owned, and earned media. Include spend, impressions, conversions, and other key KPIs.
  2. Choose a Modeling Tool: Use open-source tools like Meta’s Robyn or Google’s Lightweight MMM (or its successor, Meridian). 
  3. Run and Refresh the Model Regularly: Rebuild models every quarter or campaign cycle to reflect seasonality, channel shifts, and macro trends.
  4. Apply Learnings to Optimize Budget Mix: Use MMM insights to cut waste, double down on high-performing channels, and plan with confidence.

MMM delivers what cookie-based attribution can’t: a high-level, channel-informed view of what’s driving real business outcomes. For brands serious about scaling in a privacy-first landscape, it’s a must-have.

Unlocking Insights with Clean Rooms and Cohort Analysis

Clean rooms allow brands and platforms to securely collaborate and analyze cross-platform data without exposing personal information. When combined with cohort analysis, they help uncover which customer segments are driving the most value and where to optimize spend.

How to Activate

  1. Choose a Clean Room Partner: Work with platforms like Google Ads Data Hub, Amazon Marketing Cloud, or Meta’s Advanced Analytics Suite. These environments allow secure data matching and analysis across ecosystems.
  2. Build Cohorts Around Business-Relevant Traits: Segment users by factors like engagement level, product interest, or purchase history. The goal is to identify patterns, not individuals.
  3. Translate Insights Into Action: Apply cohort findings to improve creative relevance, fine-tune targeting strategies, and guide media mix allocation. Focus investment where behavior shows intent or high value.

Protecting Privacy with Differential Privacy Techniques

Differential privacy is a method that protects individual data by slightly altering the results of a dataset — for example, by adding small, random variations. These subtle changes help ensure individual data points can’t be traced back to a single person, but still allow accurate analysis of overall trends. It’s a privacy-first way to get insights without exposing anyone’s identity.

How to Integrate It

  1. Use Open-Source Frameworks: Tap into tools like Google’s Differential Privacy project or Apple’s privacy frameworks. These are designed to make implementation easier.
  2. Focus on Group-Level Trends: Shift analysis away from individual-level tracking. Build reports and dashboards that surface behaviors across segments, not users.
  3. Build DP Into Your Measurement Stack: Incorporate differential privacy into internal analytics, dashboards, and reporting. 

Navigating the Future of Marketing Measurement

Despite Google’s recent decision to keep third-party cookies in Chrome, the broader shift toward privacy-focused marketing continues. Brands that adopt privacy-first measurement methods now will be better positioned for long-term growth and compliance.

What You Can Do Now:

  • Audit your current measurement tools for privacy compliance.
  • Pilot one of these privacy-first techniques.
  • Train teams to work with privacy-safe data strategies.
  • Stay adaptable as consumer expectations and global regulations evolve.

Need a partner in your privacy-first journey? WITHIN can help. Let’s Chat.