How to pick and monitor your critical user journeys
You don't need to monitor everything. You need to monitor what hurts most when it breaks.
Last updated: February 2026The "revenue + activation + support" framework
Pick journeys that, if broken for 1 hour, would ruin your day. These fall into three categories:
Revenue journeys: checkout, upgrade, payment confirmation. If these break, you're losing money in real-time.
Activation journeys: signup, onboarding, first-time setup. If these break, you're losing trials and future customers.
Support journeys: password reset, team invites, settings changes. If these break, your support queue fills up.
How to map a journey into monitorable steps
Walk through the journey yourself. Note each page load, form submission, and success/error indicator.
For each step, identify the success signal: what should appear on screen if the step worked?
Keep it simple. 4–8 steps per journey is enough. Over-monitoring creates noise.
What to assert (success signals)
Good assertions: "Dashboard text appears," "Success toast visible," "Confirmation URL reached."
Avoid brittle assertions: don't assert on exact pixel positions, rapidly changing content, or elements that vary by A/B test.
Add 2–3 assertions per journey. Don't overcomplicate.
How often to run checks
Start with 10–15 minute intervals. This catches most issues within a reasonable window.
For revenue-critical flows (checkout), consider every 5 minutes.
For less critical flows (settings, team invites), every 30 minutes may be enough.
How to avoid alert fatigue
Require 2 consecutive failures before alerting. This filters out transient network blips.
Route different checks to different channels. Not every failure is "drop everything" urgent.
Include recovery alerts so you know when things are back to normal without manually checking.
Example: turning signup into a monitor
Step 1: Visit /signup. Assert: signup form visible.
Step 2: Fill in email and password. Click "Create account." Assert: no error message appears.
Step 3: Assert: confirmation page or "Welcome" text appears.
Step 4 (optional): Complete first onboarding task. Assert: success indicator visible.
Schedule: every 10 minutes. Alert: Slack #alerts after 2 consecutive failures.
By the numbers
Organizations with higher observability maturity report faster incident detection and resolution compared to less mature peers (correlated with reduced downtime impact).
Gartner, Observability and Monitoring research (observability maturity findings) (2023)Digital experience monitoring (including synthetics/RUM) is commonly used to detect customer-impacting issues that traditional infrastructure metrics miss, especially in multi-step web flows.
Gartner, Digital Experience Monitoring (DEM) market guidance (2024)Engineering teams frequently cite alert fatigue as a top operational pain point, leading to missed or delayed responses when alert volume is high.
PagerDuty, State of Digital Operations / incident response trends (2024)Downtime and performance degradation are consistently linked to lost revenue and increased churn risk for SaaS businesses, making end-to-end monitoring a revenue protection lever.
IDC / industry downtime impact analyses (digital business downtime cost findings) (2023)Real-world examples
Revenue Journey: Upgrade → Stripe Checkout → Plan Activated
Scenario: A SaaS team ships a pricing page update and a new Stripe webhook handler. The UI still redirects to Stripe, but the webhook fails in production due to a missing environment variable, so users pay but don’t get upgraded.
Outcome: A synthetic journey asserts (1) Checkout session created, (2) payment success page reached, (3) plan reflects ‘Pro’ in-app via UI/API. The check fails within minutes, preventing hours of silent revenue leakage and reducing support tickets from “I paid but nothing happened.”
Activation Journey: Signup → Onboarding → First Value Action
Scenario: A feature flag change accidentally hides the primary onboarding CTA for new users only. Existing users are unaffected, so internal dogfooding doesn’t catch it.
Outcome: A synthetic new-user journey (fresh account each run or nightly) validates the onboarding path and asserts the activation event (e.g., ‘first project created’). The team detects the regression quickly, restoring trial-to-activation before a full day of new signups churn.
Auth Journey: Password Reset + Email Delivery
Scenario: Transactional emails start getting throttled due to a misconfigured SPF/DKIM update or provider incident. The app is “up,” but users can’t reset passwords, driving ticket volume and churn.
Outcome: A journey asserts that the reset email arrives within a threshold and that the link successfully sets a new password and logs in. Alerts route to Slack first for single failures and page only after multi-region confirmation, minimizing noise while catching real deliverability incidents.
Core Workflow Journey: Create → Process Background Job → Export
Scenario: A queue backlog or worker crash causes background processing to stall. The UI accepts input, but the job never completes, so users can’t export results.
Outcome: A synthetic flow creates a small test job, polls for completion, and asserts export availability. The team catches worker/queue issues that wouldn’t show up in simple HTTP checks, reducing time-to-detect for ‘app feels stuck’ complaints.
Key insights
1.
Pick journeys that map directly to business outcomes (revenue, activation, retention, support load), not just “important pages.”
2.
Most customer-impacting outages happen in the seams: auth, billing, email, background jobs, third-party APIs, and feature flags—your journeys should cross those seams intentionally.
3.
Assertions should validate outcomes (plan upgraded, record created, email received), not just uptime (200 OK). Outcome-based checks catch silent failures that metrics often miss.
4.
Monitoring frequency is a business decision: run the most critical revenue/auth journeys more often, and control noise with confirmation rules and severity-based routing.
5.
Alert fatigue is best solved by design: journey-level deduplication, multi-region confirmation, and paging only on high-severity failures while logging the rest for trends.
6.
Include at least one ‘support journey’ (password reset, invite acceptance, status page) because these breakages spike tickets and churn even when the core app is technically up.
7.
Treat synthetic checks as living product specs: update them when onboarding, pricing, or auth changes—otherwise you’ll either miss incidents or create flake-driven noise.
Pro tips
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Start with a ‘Journey Scorecard’ spreadsheet: for each candidate journey, score Impact (revenue/churn/support), Frequency (daily users), and Fragility (dependencies). Pick the top 3–5 and write one synthetic per journey.
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Add one assertion per step that proves value (not just visibility): e.g., after “Create project,” assert the project exists via API; after “Upgrade,” assert plan changed and a receipt email arrived.
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Reduce noise on day 1: require 2 consecutive failures or 2-region confirmation before paging, and route lower-severity journeys to Slack-only. Track flake rate monthly and fix the noisiest checks first.
How CheckyWorky compares
vs Datadog Synthetics
Powerful at scale with deep integration into Datadog, but can be heavier to configure and costlier for small teams running many high-frequency multi-step journeys. CheckyWorky’s positioning can emphasize quick setup for 3–5 critical journeys and small-team-friendly alerting defaults.
vs Checkly
Developer-centric and flexible (code-first checks, Playwright). CheckyWorky can differentiate by focusing on guided journey selection (framework + templates tied to revenue/activation) and opinionated alert-fatigue controls for lean teams.
vs UptimeRobot
Great for simple endpoint uptime, but not designed for end-to-end multi-step user journeys with rich assertions (e.g., checkout + webhook + in-app plan change). CheckyWorky emphasizes ‘pretend customer’ workflows and business-outcome assertions.