Looker Studio (Formerly Google Data Studio)

Problem: Leadership wants to see how A/B tests impact revenue and key KPIs, but reports live in separate tools and static decks that quickly go out of date. Solution: Convert sends experiment and variation data into Google Analytics, which is then pulled into Google Data Studio alongside revenue, traffic, and engagement metrics. A single executive dashboard visualizes test performance and business impact. Outcome: Executives get a live, trusted view of which experiments drive revenue and growth. Decisions on roadmap, budget, and channel mix become faster and more data‑driven, without analysts rebuilding reports every week.

Problem: Different teams run experiments across regions and products, each reporting results in their own format, making it hard to compare tests or maintain a unified testing narrative. Solution: Convert experiment data flows into GA and is surfaced in standardized Google Data Studio templates. Teams plug into the same experiment dimensions and KPIs, while tailoring views to their own markets or product lines. Outcome: The organization gains a consistent, comparable view of testing performance across teams. Stakeholders trust the data more, cross‑team learnings are easier to spot, and scaling the experimentation program becomes simpler.

Problem: CRO teams can see which variation wins on a single page, but struggle to show how those wins influence downstream metrics like assisted conversions, retention, or multi‑step funnels. Solution: By passing Convert experiment and variation identifiers into GA, marketers can build Data Studio reports that segment funnels, assisted conversions, and user journeys by experiment and variation. Outcome: Teams clearly see how a test on one touchpoint affects the entire journey. This supports smarter prioritization of experiments, stronger business cases for UX changes, and better alignment with lifecycle and retention goals.

Problem: Marketing wants to know if a winning variation performs equally well across paid, organic, and email traffic, but manual slicing of data is time‑consuming and error‑prone. Solution: Convert’s experiment data in GA is combined with channel and campaign dimensions in Data Studio. Interactive dashboards let users filter results by source, medium, campaign, or audience segment in a few clicks. Outcome: Teams uncover channel‑level nuances—like a variation that wins for paid search but underperforms for email. They can roll out changes selectively, optimize campaigns faster, and avoid one‑size‑fits‑all decisions.

Problem: Product managers and stakeholders rely on ad‑hoc updates or slide decks to understand experiment outcomes, leading to misalignment and repeated status requests. Solution: Convert experiment metrics are visualized in Google Data Studio as always‑on reports, showing test status, uplift, confidence, and key KPIs pulled from GA. Stakeholders access live links instead of waiting for manual updates. Outcome: Transparency around the testing roadmap improves, and stakeholders self‑serve answers about performance. PMs and CRO teams spend less time on reporting and more time designing and running impactful experiments.

Problem: Winning variations are chosen mainly on conversion rate, with limited visibility into secondary behaviors like bounce, scroll depth, or engagement that could reveal hidden trade‑offs. Solution: Convert’s variation data in GA is blended with behavioral metrics and events in Data Studio. Analysts build reports that compare variations on both primary conversions and deeper engagement signals. Outcome: Teams detect when a “winner” harms engagement or user experience, and can refine or rollback changes accordingly. This leads to more sustainable wins, better UX, and a more mature experimentation practice.