Recording sessions and behavioral analysis

1) Introduction

Recording sessions is a reconstruction of user interactions with the interface (clicks, movements, scrolling, input, errors, UI state), synchronized with the video track of the screen and event consoles. Behavioral analysis turns a raw stream of events into insights: where people get lost, angry, challenge and why.

The goal: to find friction points faster, reduce Time to Value and increase the conversion of key actions (registration, deposit, game launch, participation in the tournament).

2) When it's especially useful (iGaming scenarios)

Onboarding and KYC: Understand where users get stuck on confirmation steps.
Cash (deposit/withdrawal): validation errors, incomprehensible commissions/limits, cancellation at the last step.
Catalog/search for the game: non-obvious filters, "false clicks" on the cards, confusion between the demo and the real launch.
Tournaments and promotions: reading the rules, clicks on prizes, misunderstanding the conditions.
Mobile scenarios: hit-area, floating element overlaps, bad network behavior.

3) What exactly to fix

UI events: clicks, taps, scrolling, pointing (desktop), focus/blur.
Component states are 'disabled', 'loading', 'error', 'success', sticky, and floating blocks.
Errors and exceptions: front validators, API responses, timeouts, network failures.
Transitions and failures: change of route, closure of models, rollback to the previous step.
Technical context: device, OS, browser, port size, lags (CLS/LCP/INP).

4) Behavioral analysis metrics

Success Rate by task (whether the user has reached the target action).
Time on Task/TTV - time to value/step completion.
FMC (First Meaningful Click) - the first significant click on the goal.
Rage Click Rate - ≥3 clicks in 1-2 seconds at one point.
Dead Click Share - clicks without consequences (no transition/event).
Error Rate - error rate (validation/HTTP/exceptions).
Backtrack Rate - the proportion of returns to the previous flow step.
Abandonment @ Step - care at a specific step (at the checkout, KYC, onboarding).
Scroll Depth p50/p90 - viewing depth up to STA/rules/forms.

Associate them with business metrics: registration/deposit conversion, retention, LTV proxy.

5) Sampling and representativeness

Basic sample: 10-30% of traffic on key screens; 100% - on critical errors and rare scenarios.
Segments: new/returning, VIP, geo, channels (organic/paid/referral), devices.
Noise filters: bots, extreme scrolling speeds, background tabs, playback without interaction.
Periods: last 7/28 days + before/after release windows.

6) Annotations and Workflow

• Problem: "Dead Clicks 22% on "Megaways" badge of the game card."
• Cause hypothesis: "The badge is visually similar to a filter button."
• Solution: "Make the badge a non-clickable style or add a filtering action."
• Expected effect: "− 50% dead clicks, + 8-12% FMC game launch."
• Priority: P1 (blocks the key path )/P2/P3.
• Acceptance criteria: clear metric thresholds.

7) Privacy and compliance

Input masking: email fields, maps, documents, chats - hide characters and selectors entirely.
PII/finance: do not write down values; tokenizing identifiers; anonymizing IP.
Cookie/Consent: respect 'DNT', show consent banner (opt-in/opt-out), separate policies for records/heat cards.
Access and auditing: who watches the recordings and why; browsing logs; shelf life (e.g. 30-90 days).
Right to delete: clear user sessions on demand (DSAR).
Security: encryption in storage and during transmission; export restriction.

8) Technical implementation (recommendations)

Слой данных (data layer): `ui_click`, `ui_error`, `ui_state_change`, `route_change`, `network_error`, `experiment_variant`.
Stable selectors: 'data-session-zone', 'data-component-id'; avoid "fragile" CSS chains.
Gluing with A/B: keep 'session _ id' and 'variant' (without PII) - for comparison by branches.
Performance: Batch events, limit FPS recordings, use adaptive sampling when degraded.
Mobile features: accounting for a virtual keyboard, recycling of a download, gestures (swipe, pull to refresh).
Network diagnostics: log RTT, timeouts, cancellations - often it is the network that "breaks" UX.

9) Analytic patterns (what to look for)

Early care after intro/banner - P1 not visible/not obvious.
Cyclic returns between two steps (A↔B) - content/validation unclear.
A series of form errors - weak microcopies, bad examples, strict masks.
Focus on non-target areas (long cursor freeze frames) - hierarchy and contrast are broken.
Missing hit-area - too small goals, overlaps (sticky/floating).
Failed before/after releases - Error Rate/Abandonment @ Step surge.

10) Behavioral analysis dashboard (minimum)

Session Overview: sample volume, share of mobile/desktop, split by channel.
Funnel Playback: Flowe steps with a click on "watch sample sessions" for each break.
Rage/Dead Trends: Dynamics by Page Type and Segment.
Error Heat: Top Error Map (Validation/API) with Record Reference.
Time to Value: median/quantiles for key tasks.
Release Compare (before/after): delta metrics and jump links to representative records.

11) Integration with heat maps and quality methods

Triangulation: recording sessions (why) + heat maps (where) + funnel metrics (how many).
Interviews/surveys: Use clips from recordings as incentives for "why did you do that? ».
Support/tickets: Associate session IDs with tickets for quick diagnosis.

12) A/B and causal analysis

For each hypothesis, fix the target UX metrics (Rage/Dead/Backtrack) and business metrics (conversion, TTV).
Compare records on A/B branches: where the attention path changes, the number of errors and failures decreases.
Avoid "watching a couple of clips → making a conclusion": use representative samples and confidence intervals.

13) Roles and Process

UX researcher: formulates questions, plans a sample, annotates patterns.
Product/analyst: connects with business KPIs, prioritizes tasks.
Designer/Frontend: implements edits, monitors component states.
QA/Support: adds cases to regression, transfers user complaints to backlog.
Weekly analysis: 30-60 minutes, 5-10 clips, 3-5 tasks with P1 priority.

14) Anti-patterns

Watch records without a goal and plan → burnout, lack of results.
Draw conclusions on "bright" single cases.
Ignore privacy and masking.
Mix mobile/desktop into one output.
Make diagnoses without checking before/after or A/B.
"Cult of Clip": Clip as presentation decoration, not proof of hypothesis.

15) Acceptance Criteria for "after review records" tasks

Problem, hypothesis, solution, expected effect and metrics are described.
Thresholds are set (e.g. Rage Click Rate ↓ up to <1.5%).
Enabled check in release window (before/after) + selective revision of records.
Updated hierarchy guide (if reasons are priority/contrast).
Completed accessibility checklists (focus styles, hit-area, contrast).

16) Short checklist before start

1. Have a goal and a list of key scenarios?
2. Are masking, user consent, and storage configured?
3. Sample and segments defined?
4. Zone labeling and consistency selectors ready?
5. Bundle with A/B and funnel - included?
6. Annotation and prioritization format defined?
7. Prepared dashboard with Rage/Dead/Error/TTV trends?

17) TL; DR

Recording sessions - "microscope" for UX: shows real frictions and behavioral patterns. Do this safely (masking, consent), systemically (sample, segments, annotations), causally (A/B, before/after), and productively (metrics → tasks → effect). The result is less noise, faster to value, higher conversion.