User journey tracking in Motadata ObserveOps reconstructs each visitor session into an ordered path through your web application, scoring every step on Core Web Vitals and Apdex and linking slow steps to their backend traces.
User journey tracking maps the real path a visitor takes through your web application, one browser session at a time. Instead of guessing which pages people reach and where they drop off, Motadata ObserveOps records each session as a sequence of page views and actions captured directly from the browser, then assembles those sessions into an ordered journey you can inspect step by step.
The Real User Monitoring (RUM) module builds every journey from frontend data only: sessions, page views, user actions, errors, long tasks, and resource timing. That gives you an honest view of what real users did, not what a synthetic script was told to do. Because the data comes from the browser itself, a journey carries the same conditions your visitors experienced, including the device, network, and location behind each session.
RUM reports on the frontend experience only. When a slow step in a journey traces back to a backend service, ObserveOps links that session to its application trace through RUM-APM trace correlation, so the two read as one timeline, while backend tracing itself stays with application performance monitoring.
Journey capture instruments Vue.js, Angular, React, and Next.js applications, so a mixed frontend stack still produces one consistent model of how visitors move through your pages, regardless of which framework rendered a given screen.
Each visit is recorded as a session made up of page views and user actions, so the full path through the application is reconstructed step by step rather than inferred from aggregate page-view counts.
Alongside page views and actions, RUM captures errors, long tasks, and resource timing directly in the browser, rolling resource timing into a load-performance waterfall and frontend rendering into flame charts, so a slow or broken step in a journey carries its own performance and error context instead of a single blended number.
Each step in a journey is measured against Core Web Vitals thresholds: Largest Contentful Paint (LCP) at 2.5 seconds or less, First Contentful Paint (FCP) at 1.8 seconds or less, and Cumulative Layout Shift (CLS) at 0.1 or less, alongside Interaction to Next Paint (INP) at 200 milliseconds or less. An Apdex score, measured against a 2-second satisfaction threshold, adds a single user-satisfaction read on top of the four.
Errors are captured and grouped, so a script failure that recurs across many sessions surfaces as one issue tied to the journey step where it happens, instead of a scatter of identical, unrelated-looking events.
Journeys can be segmented by geography, device, and network, so you can compare how different audiences move through the same pages.
Funnels track how sessions progress across a defined sequence of steps, showing where visitors continue and where they leave, so a multi-page journey can be measured as a single flow.
Individual sessions can be recorded and replayed for session replay that shows exactly what a user did and where a journey broke down.
A severity-based Flame Chart gives a real-time view over LCP and FCP, with no extra telemetry configuration required. The same widget reads backend counters in Application Performance Monitoring, so a frontend view and a backend view read on the same visual scale.
W3C distributed trace context links a RUM session to the backend application trace it triggered, so a journey step that stalls resolves into one timeline instead of two disconnected views. The backend trace itself stays owned by Application Performance Monitoring.
Saved Views in Session Explorer preserve filter combinations, search queries, time ranges, and layout, so a recurring investigation is one click away. Custom Reports support Chart, Grid, Top-N, Counter, and Aggregation formats, using the same report-creation workflow as every other module. The RUM Alert Widget improves the visibility of RUM alerts for a quicker response, Policy View adds severity color indicators and Business Service associations to RUM policy records, and RUM grouping now extends to the alert heatmap. Unified Incident Declaration then carries a RUM policy breach into the same workflow as Log, Flow, Trap, and APM incidents, instead of a separate path.
A browser instrumentation layer collects sessions, page views, actions, errors, long tasks, resources, and Core Web Vitals directly from the user's browser. Vue.js, Angular, React, and Next.js applications are supported. Application registration now follows a redesigned screen with settings navigation aligned to the Application Performance Monitoring registration experience, so bringing a frontend application under journey tracking follows a familiar path for a team that has already registered services in APM.
Sessions are assembled into ordered journeys and measured against Core Web Vitals thresholds for LCP, FCP, CLS, and INP, with an Apdex measure scored against a 2-second threshold and grouped JavaScript errors adding context to each step.
Session Explorer, replay, conversion funnels, and the RUM Flame Chart present each journey, and RUM-APM trace correlation joins a frontend session to its backend trace so both halves of a request read as one timeline.
Journey tracking instruments Vue.js and Angular applications, plus React and Next.js applications added in a later release, so a session-based journey is captured the same way regardless of which of the four frameworks rendered a given page. That consistency matters for a mixed frontend stack: a visitor's path through a page rendered in one framework and a page rendered in another still shows up as one journey model rather than two incompatible sets of data.
Because journeys are captured directly from the browser, that same instrumentation applies wherever a web application is reached from. Segmentation by geography, device, and network then lets you compare how coverage and experience differ across your actual audience, rather than assuming every visitor experiences a journey the same way.
Journeys built from actual sessions replace guesswork about how visitors navigate your application.
Conversion funnel tracking shows the exact step where sessions stop progressing, so investigation starts in the right place.
Segmentation by geography, device, and network reveals which visitors struggle with a journey before you decide what to fix.
RUM-APM trace correlation links a session to its backend application trace, so a stalled journey step does not become a dead end.
Saved Views in Session Explorer preserve filter combinations, search queries, time ranges, and layout, so returning to a known journey is one click away.
Unified Incident Declaration carries a RUM policy breach through the same triage workflow as Log, Flow, Trap, and APM incidents, so a journey-affecting issue does not need a separate escalation process.
A journey is only ever the frontend half of the story, and ObserveOps is built to join it to the other half without blurring which module owns what. RUM owns the browser: sessions, page views, actions, errors, and the journeys built from them. When a step in that journey stalls because of a backend service, RUM-APM trace correlation links the session to its application trace at application performance monitoring, which owns the trace itself.
The same journey data also feeds outward in the other direction. A RUM Performance SLO can be evaluated against multiple metrics from a journey, and that Service Level Objective is owned by the SLO module: it consumes RUM metrics to track a target, but it does not collect backend data of its own. Keeping journey capture, trace ownership, and SLO evaluation in their own modules gives each layer one job, joined at the seams rather than duplicated across all three.
User journey tracking in Motadata ObserveOps turns raw browser activity into the real route your visitors take, page by page and action by action. By building journeys from actual sessions, scoring each step against Core Web Vitals and Apdex, segmenting them by audience, and connecting them to the wider platform through trace correlation and a shared incident workflow, teams can see where experiences break down and act on what real users did rather than what a script predicted. The result is a clear path from a drop-off in a funnel to the session, the step, and the underlying cause behind it, without losing track of which module owns which part of that path.
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