New Relic

Problem: Marketing sees a lift in conversions from an A/B test, but engineering can’t tell whether the winning variation is also increasing load times or error rates, risking long‑term UX degradation. Solution: Convert passes experiment and variation names into New Relic as custom attributes. Teams filter performance dashboards by “Exp Name” and “Var Name” to compare latency, errors, and throughput per variation. Outcome: You ship only winners that are both high‑converting and performant. CRO, product, and engineering align on decisions backed by a single view of business KPIs and technical health.

Problem: New UI or feature tests occasionally introduce heavy scripts or third‑party tags. Performance dips appear in New Relic, but it’s unclear which experiment or variation is responsible. Solution: Using Convert’s integration, each session in New Relic is tagged with the active experiment and variation. Engineers segment slow transactions and JS errors by variation to pinpoint problematic experiences. Outcome: Teams quickly isolate and roll back underperforming variants before they impact a large audience. Performance regressions are tied directly to specific tests, reducing MTTR and protecting Core Web Vitals.

Problem: Engineering invests in performance optimizations, but it’s hard to prove how much faster pages actually influence conversions and revenue across different experiments. Solution: Convert sends experiment context into New Relic, where teams correlate variation‑level conversion metrics with performance indicators like page load time and Apdex scores. Outcome: You can demonstrate that a faster variation not only improves UX metrics but also lifts revenue. This evidence justifies further investment in performance‑driven experimentation roadmaps.

Problem: A/B tests look healthy in analytics, but certain regions, browsers, or devices report spikes in errors or timeouts. Without experiment context, debugging is slow and guesswork‑heavy. Solution: With Convert attributes in New Relic, teams cross‑filter by geography, device, browser, and “Var Name” to see which combinations break. Custom attributes highlight exactly which variant misbehaves for whom. Outcome: Localized issues are identified and fixed quickly, avoiding blanket test shutdowns. You maintain experimentation velocity while protecting critical segments from broken or degraded experiences.

Problem: Marketing tracks test results in Convert, while DevOps monitors uptime and errors in New Relic. Each team has partial context, leading to misaligned priorities and fragmented reporting. Solution: Convert’s experiment and variation data becomes a shared dimension in New Relic Insights. Both teams build unified dashboards that show conversions, performance, and reliability per variation. Outcome: Stakeholders share a common, real‑time view of how each experiment affects both business and technical KPIs. Decisions on rollouts, canary releases, and feature flags become faster and less contentious.

Problem: Product wants to test bold changes (checkout flows, pricing, personalization), but engineering worries about stability and can’t easily monitor risk at the variation level. Solution: By tagging sessions with Convert experiment attributes in New Relic, teams set alerts and SLOs scoped to specific variations, watching error rates, response times, and resource usage per test. Outcome: High‑impact experiments run with clear guardrails. Risky variants are automatically flagged or throttled when they breach performance thresholds, enabling aggressive testing without compromising reliability.