Split.io

Problem: Product and marketing teams run separate tests in Split and Convert, but results live in silos. No one can see how a new feature rollout interacts with landing page experiments or impacts shared KPIs. Solution: Install Convert and Split SDKs and stream Convert experiment and variation IDs into Split via custom events. Use Split as the shared experimentation layer to view feature flag states and Convert test assignments together. Outcome: Teams see how feature rollouts influence CRO tests and vice versa. Decisions on launches, rollbacks, and scaling are based on a unified view of product and marketing impact, not fragmented reports.

Problem: Marketing optimizes for clicks and signups in Convert, while product teams track activation and retention in Split. It’s hard to prove that CRO wins actually drive deeper product engagement. Solution: Send Convert experiment and variation data into Split as custom events. Analyze downstream product metrics (activation, feature usage, retention) in Split by Convert variation and experiment participation. Outcome: Marketing can show which variants improve long-term product behaviors, not just surface conversions. Budget and roadmap decisions prioritize experiments that move both acquisition and engagement metrics.

Problem: New features are rolled out via Split flags while marketing runs concurrent Convert tests on messaging and UX. Without shared data, teams can’t tell if performance changes are due to the feature or the page variant. Solution: Track Split feature flag exposure alongside Convert experiment assignments in Split. Segment results by both flag state and Convert variation to understand interaction effects between rollout and CRO tests. Outcome: Teams confidently time launches, adjust rollout percentages, and pick winning messages tailored to each feature state. Risk is reduced and feature adoption campaigns become data-driven and coordinated.

Problem: Segmentation is limited to basic user attributes. Teams can’t easily target users based on which features they see in Split and which Convert experiments they’ve participated in. Solution: Standardize experiment participation as events in Split, including Convert experiment and variation IDs. Use Split’s analytics to create segments that combine feature flag states with specific Convert variations. Outcome: Growth and product teams can run highly targeted follow-up tests, messaging, and in-app experiences. Segments become more predictive, improving personalization and the ROI of subsequent experiments.

Problem: Product and marketing report conflicting outcomes for the same initiative—one sees a win, the other a loss. Without a shared data layer, it’s unclear which audience, feature state, or variant drove the discrepancy. Solution: Send all Convert experiment events into Split and align them with feature flag data. Analyze performance by user cohorts that share both flag configuration and Convert variation exposure. Outcome: Teams quickly identify why results diverge—such as a feature only enabled for certain variants or segments. Debates are replaced with evidence, and experiment learnings become reliable and reusable.

Problem: Large organizations struggle to maintain a consistent log of all experiments. Different tools, naming conventions, and tracking methods make governance, QA, and compliance difficult. Solution: Use Split as the central experimentation ledger by streaming all Convert experiment and variation identifiers as standardized events. View and manage Convert-driven events in Split’s Event Types under Admin Settings. Outcome: Experimentation governance improves with a single source of truth across product and marketing tests. Compliance reviews, audits, and cross-team documentation become simpler, reducing risk and duplication of work.