Microsoft Power BI

Problem: CRO and marketing teams run dozens of tests across sites and funnels, but results live in separate tools. Leadership can’t get a single, trusted view of experiment performance and impact. Solution: Convert sends experiment and variation data through Segment into your warehouse, which Power BI connects to via Postgres. Teams build unified dashboards that surface all tests, KPIs, and winners in one place. Outcome: Stakeholders get a single source of truth for experimentation. Reporting time drops from hours to minutes, and decisions about scaling or killing variants are made faster and with more confidence.

Problem: Standard A/B testing reports stop at clicks or conversions, making it hard to prove how experiments influence revenue, LTV, or downstream product usage tracked in other systems. Solution: Convert variation assignments are stored in your warehouse alongside CRM, billing, and product data. Power BI joins these tables so you can segment revenue, LTV, and retention by experiment and variation. Outcome: Teams identify which variants drive not just more signups, but higher-value customers. Budget and roadmap decisions are based on full-funnel financial impact, not just surface-level conversion lifts.

Problem: Different business units and brands report on experiments in inconsistent ways, using their own tools and metrics. This fragmentation makes cross-team learning and governance nearly impossible. Solution: Convert’s experiment and variation data is centralized via Segment and ETL into a shared warehouse. Power BI models and dashboards standardize KPIs, naming conventions, and reporting views for all tests. Outcome: Experimentation becomes comparable across markets and teams. Best-performing patterns are easier to spot and replicate, and leadership can benchmark experimentation maturity and impact across the organization.

Problem: In-app A/B testing reports often show only high-level averages. Teams can’t easily see how experiments perform for specific segments like channel, geography, device, or product usage cohorts. Solution: Convert passes experiment and variation IDs into the warehouse, where they’re joined with marketing, product, and user attributes. Power BI enables ad-hoc slicing of results by any available dimension. Outcome: Teams uncover segment-specific winners and hidden losers, avoiding one-size-fits-all rollouts. Personalization strategies improve as experiments are tailored to the audiences that actually respond best.

Problem: Executives want to understand the overall ROI of experimentation, but get lost in isolated test reports and spreadsheets that lack context, trends, and clear business narratives. Solution: Convert’s experiment metadata and outcomes are warehoused and visualized in Power BI as portfolio views: test velocity, win rate, impact by funnel stage, and cumulative revenue uplift over time. Outcome: Leadership sees experimentation as a measurable growth lever, not a black box. Funding for CRO programs is easier to justify, and roadmaps are prioritized based on historical impact and opportunity size.

Problem: Teams fear vendor lock-in and rigid analytics stacks. Changing ETL tools or warehouses often breaks experimentation reporting and forces painful re-implementation. Solution: Convert integrates via Segment into any compatible ETL and warehouse. Power BI connects to the warehouse using standard connectors, so you can swap infrastructure without losing experiment visibility. Outcome: Your experimentation analytics stay stable even as your data stack evolves. Data teams retain architectural freedom, while marketers keep continuous, reliable access to test insights in Power BI.