Unified event framework
Redefined key events across frontend interactions, product views, checkout steps, and experiment variants.
Headless Commerce · Adobe + ContentSquare + Experimentation
They were shipping wins in CRO without a clean read on revenue impact.
This brand had active CRO and UX programs, but lacked a reliable way to connect behavioral improvements and experiment wins to real business outcomes.
Context
This DTC brand operated on a headless architecture with a modern frontend, a headless CMS, Adobe Analytics, Adobe Target, ContentSquare, GA4, server-side tagging, and BigQuery.
The growth team was active. They were running CRO experiments, improving UX, tuning paid media, and investing in behavior analysis. On the surface, it looked like a sophisticated optimization program.
But under the hood, measurement across behavior, experiments, conversions, and revenue was not coordinated tightly enough to tell the business which changes were actually making money.
The problem
The team was doing the right work. Tests were running. Funnels were being improved. Engagement metrics were moving.
But once results were reviewed closely, the system couldn't answer the one question that mattered: which changes actually drove more revenue?
ContentSquare showed stronger behavior. Adobe reported uneven conversion patterns. GA4 attributed revenue differently. Backend sales didn't always move in proportion to the wins being celebrated in optimization reports.
So the business had experimentation, but not always confidence in what those experiments were worth.
The problem wasn't a lack of testing. It was a lack of connection between experience data and business outcomes.
What was actually broken
The issue wasn't that systems were missing. It was that they were running in parallel.
- UX behavior and revenue were not tightly linked
- Experiment success criteria varied across tools
- Conversion events were not consistently defined
- Client-side collection introduced gaps and inconsistencies
- No shared framework tied experimentation to business impact
Each platform contributed useful data. But without a common structure, the organization couldn't confidently compare behavior, conversion, and revenue together.
The approach
We reframed the challenge from “how do we track more?” to “how do we measure what actually drives revenue?”
Server-side measurement layer
Implemented server-side collection to improve signal reliability and reduce discrepancies between tools.
Behavior-to-revenue mapping
Connected ContentSquare engagement data, Adobe conversion data, and backend revenue into a common measurement view.
Experimentation measurement system
Standardized test definitions, success metrics, and attribution windows so experiment results could be compared consistently.
Warehouse integration
Unified behavior, conversion, and revenue data into BigQuery so analysis could happen in one place instead of across silos.
Measurement governance
Documented definitions, logic, and QA standards so the experimentation layer could scale without drifting.
Implementation
This work cut across experimentation, analytics, and data engineering— not as separate streams, but as one measurement problem.
- Adobe Analytics event restructuring
- ContentSquare alignment with conversion logic
- sGTM deployment for server-side signal consistency
- BigQuery pipeline for unified behavior and revenue analysis
- Experiment tracking standardization across tools and teams
The objective wasn't just cleaner reporting. It was a system that could tell the business which optimizations actually mattered.
Outcome
The shift wasn't just technical. It changed how the business evaluated performance.
Engagement improvements could now be examined in the context of conversion and revenue, rather than being treated as standalone wins. CRO results became financially measurable instead of just directionally promising.
Paid media also benefited from cleaner signals and a better understanding of what happened after the click, allowing budget decisions to be tied more closely to business outcomes.
The team moved from “this test improved engagement” to “this change improved revenue—and we know why.”