For the last few years, digital marketers have been playing defense on data privacy — reactively responding to government regulations or privacy-focused OS updates imposed by tech platforms and walled gardens. Every time Google makes an announcement about third-party cookies or Apple anonymizes another user’s behavior, marketing leaders are forced to recalibrate measurement techniques and educate executives on the latest changes in their ability to track individual engagement and assign attribution.

At the same time, the advent of Generative AI has made it possible for marketing teams to achieve personalized marketing at scale by delivering ever-increasing variations of messaging, visuals, and content types. This flood of creative assets across multiple channels demands a comprehensive understanding of performance to optimize complex campaigns — just as the traditional models of attribution are becoming untenable due to increased data privacy.

This is why forward-thinking marketers are turning (or returning) to cohort measurement as a privacy-friendly, future-proofed alternative to traditional attribution.

Cohort measurement involves analyzing groups of users who share common characteristics or experiences over time, rather than tracking individual user behavior. By focusing on aggregate data from these cohorts, marketers can discern patterns, measure campaign performance, and make data-driven decisions without relying on or compromising personally identifiable information (PII).

Marketers can think about cohorts vs individual attribution as comparing two ways to measure the flow of a river:

• Individual attribution: Tracking the path of each leaf as it floats downstream, recording its position and velocity over time.
• Cohort tracking: Setting up a fixed grid in the river and counting the time in between, and the direction of, groups of leaves as they cross boundaries in the grid.

Once you’re able to measure the normal flow of the river, you can measure the impact of “pebbles” you toss into the river — i.e., marketing activities.

Both approaches are valid, and both have their strengths and challenges. But one thing is clear: in the age of consumer privacy and GenAI-powered personalization, marketing teams need to take back control of measurement and achieve a clear understanding of performance across channels, silos, and walled gardens.

This guide will explore the pros and cons of cohort measurement versus the traditional approach of individual tracking and attribution. We’ll delve into why this shift is crucial, how it impacts different stages of the customer lifecycle and the central role that data standards play throughout this new paradigm.

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Shifting Away From Individual Lead Attribution

Con: Moving away from attribution means losing some channel-specific precision

Pro: Gaining a greater breadth of data through end-to-end measurement leads to a more holistic understanding

Traditional attribution focuses on tracking individual user journeys, allowing for micro-optimizations within specific channels. But this approach often creates a trade-off: data tends to remain siloed between channels. Additionally, attribution models can favor high-intent channels like search at the expense of ‘view-through’ channels like video.

Shifting to cohort measurement trades some of that granular precision for a more comprehensive view of marketing performance. Think of the river analogy: instead of tracking each leaf floating downstream, we’re measuring the overall flow of the river.

But to make this work effectively, organizations need to prioritize robust data standards — such as clear taxonomies, consistent naming conventions, thorough metadata tagging, and content IDs. These foundations allow marketing orgs to track content performance across touchpoints, teams, and even third-party agencies and consultants.

This end-to-end approach allows marketers to see performance across all channels, including offline touchpoints, providing a more complete picture of marketing impact. When teams gain this kind of holistic understanding of campaign effectiveness, the lack of individual IDs and log data may not seem like much of a loss.

Rebuilding Campaign Measurements and Reporting

Con: Going back to the beginning of how you measure campaign success can be intimidating

Pro: This is an opportunity for a reset and to reduce reliance on less-than-reliable data

Reimagining how to measure marketing success requires time, resources, and a willingness to embrace uncertainty — for a while. Teams may need retraining, processes will need updating, and there might be resistance from stakeholders comfortable with existing methods. It can feel like starting from scratch, disrupting established workflows and reporting structures.

But the truth is, traditional attribution has never been perfect. It often provides a false sense of precision and falls down in cross-channel measurement. This transition is a chance to break free from flawed attribution models and decrease dependence on unreliable data.

By embracing cohort measurement, marketers regain control over their data and analytics. This approach reduces reliance on third-party cookies and walled garden reporting, which often limit visibility and control. Instead, it empowers teams to build a more robust, privacy-compliant measurement framework that provides a clearer picture of overall marketing effectiveness.

This reset is not just about adapting to current challenges — it’s about future-proofing the approach to campaign measurement and optimization.

Making Room for Cross-Channel Experimentation

Con: Shifting away from after-the-fact observation of attribution

Pro: Leveraging the power of fast, directional data and experimentation

Individual behavior tracking and attribution is all about observing what happened during a conversion and looking backwards. But as attribution becomes more difficult with increasing privacy regulations, marketing and analytics teams have the opportunity to shift from observation to experimentation on a broader scale — conducting careful tests across channels to measure and optimize campaign performance.

Marketing teams have historically been much more comfortable with A/B testing on landing pages or websites that we own and control. Conducting experiments across channels introduces more complexity — but cohort measurement, coupled with robust data standards, makes it possible.

When organizations establish clear taxonomies and consistent naming conventions to track how their content and messaging perform across channels, they can then implement sound experimental design. That includes a broader scope of experimentation — like changes to product messaging or tone across channels — compared to the micro-tweaks to copy or color palettes on a page-by-page or ad-by-ad basis.

Trusting in directional results at the cohort level — what lift in conversion did we see in the test group that saw our new ads, compared to the control group that did not? — allows teams to move faster and make informed decisions more quickly than if they required granular insights into individual behavior.

Blending Cohort Measurement with Individual Tracking Throughout the Customer Lifecycle

Con: Measuring performance around customer acquisition will look different

Pro: Individual tracking and attribution (with consent) can be effective for customer retention and upsells

Bonus pro: Cohort tracking is incredibly impactful around customer acquisition and brand awareness

Adopting cohort measurement doesn’t mean abandoning individual tracking entirely. Instead, it introduces a more nuanced approach across the customer lifecycle.

For existing customers who have provided consent, individual tracking and attribution are still highly effective for measuring and optimizing retention efforts and upsell campaigns.

But cohort measurement excels in areas where individual attribution has always struggled: customer acquisition and brand awareness. By analyzing group behaviors and trends, marketers gain valuable insights into the effectiveness of their upper-funnel activities without relying on individual user data.

Bottom line: there’s a time and place for both attribution tracking and cohort measurement. They can work in tandem throughout the customer lifecycle, providing a comprehensive view of marketing performance from initial brand exposure through to referrals, renewals, and upsells. With this nuanced approach, marketers can leverage the strengths of both methods and adapt their measurement strategies to the specific needs of each stage in the customer journey.

The First Step to Cohort Measurement: Build a Foundation of Data Standards

Across all these pros and cons, a theme emerges: maintaining robust data standards is key. Clear taxonomies, consistent naming conventions, thorough metadata tagging, and universal content IDs aren’t just nice-to-haves in this new age of marketing analytics — they’re essential. These data standards ensure the move away from individual attribution doesn’t equate to moving away from understanding performance and making impactful, data-driven decisions.

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