comScore

Problem: Experiment performance is evaluated in Convert, while traffic, engagement, and audience KPIs live in comScore, forcing teams to stitch spreadsheets and reconcile numbers manually. Solution: Convert tags every visitor hit with exp_id and var_id via comScore custom labels, so test exposure is visible directly inside comScore Digital Analytix alongside existing metrics and dashboards. Outcome: Teams get a single source of truth for tests and analytics, reduce manual reporting work, and can compare variation performance against trusted comScore KPIs in one place to make faster, aligned decisions.

Problem: Analysts can’t easily see how users who saw a specific experiment behave over time versus those who didn’t, limiting insight into long‑term impact and audience differences. Solution: Using Convert’s experiment and variation labels in comScore, analysts build segments based on exp_id and var_id to compare behavior, retention, and content consumption across exposed and non‑exposed groups. Outcome: The business uncovers which experiences drive deeper engagement and loyalty, identifies high‑value audience segments, and prioritizes rolling out winning variations to the users who benefit most.

Problem: Merchandising and content teams run tests on specific site sections (e.g., product categories, news verticals) but can’t see granular impact on those areas within comScore reports. Solution: Convert passes experiment and variation IDs into comScore measurement URLs for targeted tests, aligning them with existing page and category structures in Digital Analytix reporting. Outcome: Stakeholders see exactly how each variation influences traffic, engagement, and conversions by category, enabling more precise optimization of key sections and better allocation of content or inventory.

Problem: Revenue, ad impressions, and viewability metrics are tracked in comScore, while A/B test results sit in Convert, making it hard to attribute monetization changes to specific variations. Solution: By enriching comScore hits with Convert’s exp_id and var_id, teams correlate variation exposure with monetization KPIs such as ad views, completion rates, and revenue per visit directly in comScore. Outcome: Decision‑makers clearly see which experiences maximize revenue and ad performance, improving test prioritization and enabling confident rollouts of variations that demonstrably lift monetization.

Problem: Enterprise stakeholders rely on standardized comScore dashboards and are reluctant to adopt separate tools or exports just to understand A/B test performance. Solution: The integration feeds Convert experiment data into comScore’s existing reporting layer via custom labels, so analysts can add test dimensions to current dashboards and scheduled reports. Outcome: Experiment insights flow into the same executive views used for broader digital performance, increasing visibility of the testing program, reducing friction, and speeding up buy‑in for new experiments.

Problem: Longitudinal analysis of how a variation affects user behavior over weeks or across devices is difficult when test data and analytics live in separate systems. Solution: Convert’s experiment and variation IDs are stored with every comScore hit, allowing analysts to build cohorts and trend reports in Digital Analytix that follow users across sessions and properties. Outcome: Teams understand the sustained impact of tests on user journeys and multi‑device behavior, leading to more strategic, evidence‑based decisions about which experiences to scale and where to iterate next.