Yandex Metrica

Problem: CRO teams run experiments in Convert but analyze traffic and conversions in Yandex Metrica, forcing them to manually reconcile results across tools and reports. Solution: The integration passes Convert experiment and variation names into Metrica as user or session parameters, so exposure data appears in standard Content reports and existing dashboards. Outcome: Teams see experiment impact on traffic, engagement, and conversions in one place, speeding up analysis, reducing reporting errors, and aligning stakeholders on a single source of truth.

Problem: Winning variations are often chosen on conversion rate alone, without understanding how they change user behavior, scroll depth, or navigation paths. Solution: With Convert data in Metrica, analysts segment all behavior reports by experiment and variation, exploring click maps, session quality, and paths for each test group. Outcome: Teams uncover why a variation wins or loses, design better follow‑up tests, and build stronger hypotheses based on real behavioral patterns instead of just topline metrics.

Problem: Ecommerce and content sites test changes on specific categories or page types but struggle to attribute performance shifts to the right experiment or template. Solution: Convert sends experiment and variation parameters into Metrica, allowing segmentation of standard reports by test name across categories, templates, and URLs. Outcome: Marketers clearly see which experiments drive lifts for each section of the site, prioritize high‑impact templates, and avoid misattributing gains to seasonality or campaigns.

Problem: Different user cohorts (new vs returning, geo, traffic source) respond differently to experiments, but this nuance is lost when test data lives outside core analytics. Solution: By combining Convert experiment parameters with Metrica’s native segments (device, region, source, etc.), teams build granular cohort views for each variation. Outcome: They identify where a variation over‑ or under‑performs, roll out changes only to responsive cohorts, and design targeted experiments that maximize overall revenue and UX gains.

Problem: Stakeholders want simple, consistent reports in the analytics tool they already know, not separate A/B testing dashboards and exports. Solution: Experiment and variation data from Convert appears directly in Yandex Metrica’s standard and custom reports, so analysts build familiar dashboards that include test performance. Outcome: Reporting becomes faster and more self‑serve, leadership sees experiment impact in existing views, and CRO programs gain visibility without extra tooling or manual slide decks.

Problem: Misconfigured tracking between A/B tests and analytics leads to missing or unreliable data, often discovered only after a test has run for weeks. Solution: The integration uses a lightweight code modification plus Metrica’s debug tools and `_ym_debug=1` parameter to validate that experiment and variation parameters fire correctly. Outcome: Teams catch implementation issues early, trust their test data, and avoid wasting traffic on invalid experiments, increasing the reliability and velocity of their CRO roadmap.