Pendo

Problem: Product teams ship experiments but can’t clearly see how each variation affects adoption of key in-app features, leaving roadmap decisions based on incomplete or siloed data. Solution: Convert sends experiment and variation exposure into Pendo via pendo.track(). Teams then analyze feature usage and adoption funnels in Pendo, segmented by which Convert variation users saw. Outcome: Roadmap and rollout decisions are grounded in clear evidence of which experiences drive feature adoption, reducing guesswork and accelerating time-to-value for new capabilities.

Problem: Analytics in Pendo show overall engagement trends, but don’t distinguish between users exposed to different experiments, masking the true impact of product changes. Solution: The integration records each active Convert experiment and variation as Pendo events. Product analysts build Pendo segments based on experiment exposure to compare behavior across variants. Outcome: Teams uncover which experiences drive deeper engagement, retention, and NPS, enabling more precise targeting, better rollouts, and higher ROI from experimentation.

Problem: A/B tests are often judged on short-term metrics like clicks or signups, with no visibility into whether the winning variation actually improves long-term retention or churn. Solution: Convert pushes variation data into Pendo, where it becomes part of each user’s event history. Teams track cohorts over time in Pendo, segmented by the variation they originally saw. Outcome: Retention, expansion, and churn can be tied back to specific experiments, helping teams favor variations that deliver durable value instead of short-lived metric spikes.

Problem: Onboarding experiments run on key pages, but teams can’t easily see how each variation affects downstream in-app actions like completing setup or discovering core features. Solution: With both scripts installed, Convert logs onboarding experiment exposure into Pendo. Product teams then analyze onboarding completion, time-to-value, and feature discovery by variation. Outcome: Onboarding flows are iterated based on real behavior data, improving activation rates and reducing time-to-first-value for new users across segments and plans.

Problem: Experiments are targeted to specific pages or categories, but their impact on broader product journeys—like upgrade paths or key workflows—remains unclear. Solution: Convert’s flexible targeting controls where experiments run and what gets sent to Pendo. Pendo reports then show journey completion and funnel performance by experiment and variation. Outcome: Teams identify which page-level changes improve entire journeys, not just local metrics, leading to more coherent experiences and higher conversion across the product funnel.

Problem: Product managers working in Pendo daily lack visibility into which experiments users are seeing, forcing them to cross-check tools and slowing analysis. Solution: The JavaScript snippet automatically syncs active Convert experiments into Pendo as custom events, so experiment names and variations are visible alongside standard product analytics. Outcome: Product teams analyze usage, feedback, and guides in full experiment context, speeding insight generation, reducing dependency on data engineering, and improving collaboration with growth teams.