Collect the right signals
Capture client errors, resource load times, and API failures with user impact context: device, network type, geo, and journey stage. Without this, you can't tell if a spike is real or a handful of poor connections.
Sample intelligently to cover key journeys without flooding storage. Keep 100% of checkouts and login flows, but downsample lower-value pages.
Normalize error names and URLs so you can correlate RUM with backend traces and synthetics quickly.
Correlate with synthetics
Compare RUM data to Watch.Dog synthetics to spot gaps or regional issues. If synthetics are green but users hurt, you're likely facing third-party script issues or CDN edges misbehaving.
Alert when client-side errors spike without matching server errors. That signal usually points to front-end deploys, ad/tag slowdowns, or browser compatibility gaps.
Use waterfall traces and Core Web Vitals to pinpoint where latency accumulates before customers abandon sessions.
Feed back into product
Share client uptime dashboards with product teams so they own regressions. Make ownership clear per route so fixes do not stall.
Use telemetry to prioritize front-end resilience fixes and caching. Address the worst offenders: long JavaScript tasks, unhandled promise rejections, and third-party SDK failures.
Publish status updates that mention client-side impact when only certain browsers or ISPs are affected.
Act quickly on regressions
Enable feature flags for risky front-end changes so you can roll back without redeploying. Pair them with RUM alerts that prove the rollback worked.
During incidents, show client and server metrics side by side in the war room so you don't chase the wrong layer.