Matomo

Problem: CRO teams run A/B tests in Convert but must switch tools and manually reconcile data to see how experiments affect traffic, engagement, and conversions tracked in Matomo. Solution: Enable the Matomo integration per experiment in Convert so experiment and variation names are sent as custom variables into Matomo. Both tracking codes work together to log which variation each visitor saw. Outcome: Teams analyze experiment impact directly in Matomo alongside existing KPIs, eliminating spreadsheet merges and tool-hopping. This speeds up decision-making and increases trust in test results.

Problem: Marketers know a winning variation overall may not perform equally across traffic sources, geos, or devices, but lack a clean way to slice A/B test results by these Matomo segments. Solution: Convert passes experiment and variation data into Matomo as custom variables, allowing analysts to apply Matomo’s native segments (e.g., campaign, country, device) to each variation’s traffic. Outcome: Teams uncover segment-specific winners and losers, refine targeting, and prioritize rollouts for high-value audiences, leading to more nuanced experimentation strategies and higher ROI per test.

Problem: CRO teams optimize for on-page metrics, but revenue, lead quality, or offline conversions are tracked deeper in Matomo, making it hard to attribute those outcomes to specific test variations. Solution: By logging experiment and variation as Matomo custom variables, every downstream event and goal in Matomo can be filtered by test exposure, even beyond the initial test page. Outcome: Businesses see which variations drive not just clicks but qualified leads, repeat visits, and revenue, enabling them to prioritize changes that impact real business outcomes, not vanity metrics.

Problem: Analytics teams worry that adding A/B test tracking will overwrite or conflict with existing Matomo custom variables used for other reporting and compliance needs. Solution: In Convert, users choose the exact Matomo custom variable index reserved for experiments, and can anonymize experiment and variation names to align with privacy policies. Outcome: Experiment data is added cleanly to Matomo without breaking current reports or governance. Teams gain CRO visibility while maintaining data integrity and regulatory compliance.

Problem: Some organizations require strict control over tracking scripts and don’t want auto-injected code that might conflict with custom Matomo implementations or tag managers. Solution: Developers can implement the Matomo integration manually by adding the provided JavaScript function to push Convert experiment and variation data into Matomo’s custom variables. Outcome: Technical teams maintain their preferred tracking architecture while still unlocking experiment-level analytics in Matomo, satisfying both governance requirements and CRO needs.

Problem: Experimentation data lives in Convert, while behavior analysis and dashboards live in Matomo, making it hard for stakeholders to get a single, trusted view of CRO performance. Solution: With Convert experiments connected to Matomo, stakeholders see experiment exposure directly in Matomo dashboards and reports, and can build custom views around test activity. Outcome: Matomo becomes the central hub for understanding how tests shape user journeys and KPIs, improving visibility for leadership and aligning teams around a shared source of truth.