Replay UX Audit

Replay UX Audit

When to Use

• A product team wants to understand why users struggle with a specific flow before redesigning it
• Conversion rate on a key funnel step is lower than expected — need to find the friction
• Before a UX research round: use session replays to form hypotheses to test in interviews
• After launch of a new feature: is it being used as intended? Where do users get stuck?
• When a flow has high bounce/abandon rates but no clear error signals
• Quarterly UX review: proactive audit of key flows before they become problems

Core Jobs

1. Define Audit Scope

Clarify the focus before sampling sessions. The audit should target one of:

• Specific user flow: e.g., signup → onboarding → first action (multi-page flow)
• Specific page: e.g., pricing page (single-page behavior analysis)
• Specific feature: e.g., search filters, bulk action tool, data export

Define:

• Entry point (what page/action starts the flow)
• Exit point (what constitutes success vs. abandonment)
• Target user segment (new users? paid users? mobile-only?)
• Time window (last 7 days for recent data, last 30 days for statistical significance)

2. Sample 20-50 Relevant Sessions

Use mcp__Amplitude__list_session_replays to find sessions matching the scope:

Sampling strategy — always mix success and failure:

• ~40% completed sessions (users who finished the flow successfully)
• ~40% abandoned sessions (users who left mid-flow)
• ~20% partial sessions (users who made progress but didn't complete)

This balance is critical: studying only failures shows what's broken, but studying successful sessions reveals what workarounds users invented, which signals non-obvious friction.

Sample size guidance:

• 20 sessions: enough for a quick hypothesis check
• 35 sessions: enough for pattern confidence on a specific page
• 50 sessions: full audit with quantitative friction estimates

3. Watch for Friction Signals

For each session, use mcp__Amplitude__get_session_replay_events to extract the interaction timeline.

The 5 friction signals — what to look for:

Rage Clicks — rapid repeated clicks on the same element

Signal: 3+ clicks within 2 seconds on same element
Meaning: User expects interaction that isn't happening
Examples: disabled button that looks enabled, form submit that silently fails

Dead Clicks — clicks on non-interactive elements

Signal: Click with no page response or state change
Meaning: Element looks clickable but isn't; or link is broken
Examples: text that looks like a link, icon without hover state, grayed-out element

U-Turns — backward navigation immediately after forward navigation

Signal: User goes to Page B then immediately returns to Page A
Meaning: Page B didn't have what they expected, or they made a mistake and noticed immediately
Examples: clicking wrong menu item, seeing an unexpected modal, wrong filter applied

Hesitation — long pause before taking an action (>10s with cursor movement but no click)

Signal: Mouse movement without clicks, sustained time on element
Meaning: User is unsure what to do next; decision anxiety or unclear affordance
Examples: ambiguous CTA labels, multiple similar options, unclear next step

Form Abandonment — user starts form input then leaves

Signal: Focus on form field, then session ends or navigation away
Meaning: Form is too long, too confusing, requires unavailable information, or failed
Examples: unexpected required fields, confusing validation errors, too many steps

4. Build Friction Map

After reviewing all sessions, build a structured map of where friction occurs:

Flow: Checkout → Payment → Confirmation

Step 1: /checkout/cart     [friction: LOW]
  - Clear path forward
  - Minor: some users don't notice "Apply Coupon" link (dead click on cart total)

Step 2: /checkout/shipping [friction: MEDIUM]
  - 23% of sessions show hesitation on "Same as billing" checkbox
  - 12% of sessions show u-turn (back to cart, then forward again)
  - Form validation error on ZIP code triggers rage clicks in 18% of sessions

Step 3: /checkout/payment  [friction: HIGH]
  - Rage clicks on "Submit Payment" in 34% of sessions
  - 41% of abandonment happens at this step
  - Key signal: users click submit, wait 5s, click again (double submit pattern)

Step 4: /checkout/confirmation [friction: NONE]
  - Sessions that reach here show smooth behavior
  - Concern: only 59% of sessions that start checkout reach this step

5. Quantify Friction Points

For each identified friction signal, calculate:

• Frequency: What % of sampled sessions show this friction?
• Recurrence: Do the same users encounter this friction multiple times in one session?
• Correlation with abandonment: What % of sessions with this friction end in abandonment?

Friction: Rage clicks on "Submit Payment"
  - Frequency: 34% of sessions (17/50 sampled)
  - Recurrence: 71% of affected users click 3+ times
  - Abandon correlation: 68% of rage-clicking users abandon the session
  - Estimated impact: 34% × 68% = 23% of checkout sessions lost to this friction

6. Prioritize by Frequency × Severity

Rank issues using a simple matrix:

7. Recommend UX Improvements

For each high-priority friction point, provide a specific recommendation:

Recommendation format:

• Problem: what the user experiences (be concrete — "user clicks Submit 3 times")
• Root cause hypothesis: why this happens (button doesn't disable after click)
• Recommended fix: specific UI change (disable button after first click + show spinner)
• Success metric: how to measure if the fix worked (rage click rate on Submit button)

MCP Tools

• mcp__Amplitude__list_session_replays — search and filter sessions for the audit scope (by page, user segment, time range, completion status)
• mcp__Amplitude__get_session_replays — get session metadata for the sampled set (duration, pages visited, device type)
• mcp__Amplitude__get_session_replay_events — extract the full interaction timeline per session to identify friction signals

Key Concepts

• Friction map: A structured breakdown of where users struggle in a flow, with frequency and severity data
• Rage click: Rapid repeated clicks on the same element — the strongest signal of user frustration in replay data
• Dead click: Click with no UI response — indicates mismatched affordance (element looks interactive but isn't)
• U-turn: Immediate back navigation after forward navigation — signals the destination didn't meet expectations
• Abandon correlation: The percentage of sessions showing a friction signal that end in task abandonment — the key metric for prioritization
• Friction score: Frequency × severity, used to rank issues for the roadmap

Output Format

## UX Audit: <flow or page name>
Sessions reviewed: N (XX successful, XX abandoned, XX partial)
Time window: <date range>

### Friction Map by Flow Step
<table or list of steps with friction level and key signals>

### Top Friction Issues (ranked by priority)

**Issue 1:** <name> [Priority: X/10]
Frequency: XX% of sessions
Signal: <specific behavior observed>
Root cause: <hypothesis>
Fix: <specific recommendation>
Success metric: <how to measure improvement>

**Issue 2:** ...

### Summary Metrics
- Steps with HIGH friction: N
- Estimated sessions lost to friction: XX%
- Top 3 improvement opportunities: <list>

### Recommended Next Actions
1. <highest impact fix — estimate: X% conversion lift>
2. <second fix>
3. <third fix>