Adobe Analytics

Problem: The team runs A/B tests in Convert but can’t clearly see which variation is driving revenue and high-value events inside Adobe Analytics, where leadership expects a single source of truth. Solution: Convert sends experiment and variation names into Adobe Analytics eVars. Revenue, orders, and key events are then segmented by variation directly in Adobe’s standard and custom reports. Outcome: Stakeholders see test impact in the same revenue dashboards they already trust. Winning variations are identified faster, and budget is shifted confidently toward experiences proven to drive higher revenue.

Problem: Global A/B test results look flat, but marketers suspect performance differs by traffic source, campaign, or device. They lack granular visibility across acquisition channels in one place. Solution: Convert experiment and variation data are captured as eVars, allowing Adobe Analytics users to break down any channel, campaign, or device report by test exposure and variation. Outcome: Teams discover that certain variations win strongly for paid search but underperform for email or social. They roll out variations selectively by channel, increasing ROI and reducing negative impact on underperforming segments.

Problem: Experiments on landing or product pages are evaluated only on immediate clicks or form submits, missing long-path behaviors like repeat visits, content depth, or multi-step funnel completion. Solution: With Convert data stored in visit-level eVars, Adobe Analytics attributes all subsequent page views, events, and conversions in the visit to the correct variation, not just the initial interaction. Outcome: Analysts reveal that some variations drive fewer instant signups but more qualified users who complete deeper funnel steps. Optimization decisions shift from short-term metrics to full-journey value.

Problem: CRO results from Convert and Adobe Analytics reports don’t always align, creating mistrust and friction between experimentation and analytics teams over which numbers to believe. Solution: The integration pipes Convert experiment exposure into Adobe Analytics using configured eVars and existing tracking code. Both teams analyze tests in the same Adobe reports and attribution models. Outcome: Discrepancies shrink and conversations move from “whose data is right” to “what should we change.” Experiment results are accepted faster, enabling more frequent rollouts and a stronger testing culture.

Problem: The team wants to move from simple A/B tests to targeted experiences, but they lack insight into how different audience segments respond to variations across behaviors and content. Solution: Convert’s experiment and variation data in Adobe Analytics can be combined with existing segments (loyalty tier, content affinity, geography, device) to analyze performance for each audience slice. Outcome: Marketers identify high-value segments that respond best to specific experiences and build targeted personalization roadmaps. This increases engagement and conversion without broad, risky site-wide changes.

Problem: Complex Adobe Analytics implementations make teams nervous about adding experimentation. They fear broken tracking, misattributed conversions, and unreliable test readouts. Solution: Convert’s integration uses your existing s_code.js or AppMeasurement.js setup and offers sync/async examples. Teams validate eVar population and experiment data flow using Adobe reports and browser debugging tools. Outcome: Analytics owners gain confidence that experiments won’t disrupt core reporting. They approve more tests on more critical journeys, accelerating experimentation without compromising data quality.