User Journey Analysis
We see what standard reports miss: real steps, friction points, and moments of truth across the customer journey - from first touch to repeat purchase. We build CJM* We analyze it. Path/Flow*, delve into funnel* cohort*, validate hypotheses with session recordings and A/B tests, then turn findings into CR, LTV, and revenue growth.
Get an audit of the wayWhy Businesses Need to Deal with the User Path
The user journey is rarely linear. People come back, compare, get lost between devices and channels. We combine web events, traffic sources, emails and instant messengers, CRM transactions and payments to reconstruct. User JourneyAnd find the places where conversions and margins are lost, instead of the controversial interpretations, the unified version of the truth in metrics and actions.
The result is a prioritized plan of improvement: fast UX fixes in bottlenecks, proper marketing attribution, meaningful experimentation, which reduces the cost of attraction, speeds up onboarding, increases retention and check — without a bet on “wonder creative.”
Business effects (typical range)
- +15-35% CR* by removing friction in key steps.
- -20-30% to CAC* Prioritizing effective ways.
- +10-25% to retention* and LTV's rise.
- Transparent attribution* We know what really affects the purchase.
Data and tools
We collect every part of the puzzle: frontend and server events, session recordings, heat maps, campaign costs, UTM* and click ID, CRM, and payments. Reports show the journey at user and segment level: by device, source, creative, and region.
Behavior and UX
- Session Replay* Heatmaps* Identify the "traps" of the interface.
- Funnels* with sections: devices, segments, channels, creatives.
- Path/Flow* frequent and abnormal routes, loops, dead ends.
Marketing and attribution
- Normalization UTM* и сшивка sessions на уровне пользователя.
- Attribution*: first/last touch and multi-touch*.
- Import costs and ROI/ROAS calculation by track and segment.
Warehouse and reports
- Warehouse* for raw and aggregated events (e.g. ClickHouse).
- dashboards* in Redash/Metabase/Grafana by role.
- Data Quality*: Allerts on event drop and conversion.
The right collection
- Tracking Plan*: A single pattern of events/properties.
- Server events* for resistance to blockers.
- Consent* and compliance with GDPR/152-FZ.
Typical stack
How the project works
Research and hypotheses
Interviews with marketing, product and support. Quick exploratoryReports, uploads, primal hypotheses about friction points, motives and barriers.
Mapping CJM and Events
Build. CJM*: stages, tasks, emotions, points of contact. Update Tracking Plan*: Event names, properties, identifiers, sources.
Path and funnel analytics
Looking for lead/buyer divergences, loops, dead ends, dumps, device segmentation, region, channel, creativity, page speed.
Experiments and UX Improvements
Preparation of A/B: Hypothesis → Metric → Duration → Audience. Prioritization of the ICEAnd the impact on the unit economy. We roll out quick fixes iteratively.
Dashboards and procedures
Role-oriented dashboard* (CEO/marketing/product) Documenting formulas, update rates, rules of interpretation.
Control of data quality
DQ checks*: event drops, order out-of-orders, schema disruptions, instant messenger attachments, retros and deduplication.
What do you get?
Documents
- CJM* and a map of paths with pain points and "moments of truth."
- Tracking Plan* and the attribution scheme (rules and formulas).
- A roadmap for UX improvements and a package of A/B experiments.
Infrastructure
- Configured event capture system (web + server), roles and accesses.
- dashboards* Roles, alerts and data quality monitoring.
- Integration with CRM/payments/advertising, automatic backups and rotating logs.
Support
- Support of A/B, UX corrections and hypotheses.
- Team training: reading reports, interpreting metrics, making decisions.
- Regular question-answer sessions with metric holders.
Cases and scenarios
E-commerce: the growth of cart conversion
Journey analysis found a loop: product card -> cart -> back to catalog. Session recordings showed lost focus on the promo code and hidden delivery selection. Quick fixes (default fields, hints, progress bar) plus A/B testing increased checkout CR by 22% and reduced payment-step drop-off by 31%.
- +22% to CR checkout
- -31% of refusals to pay
- +12% to the average check (upsell in the address step)
SaaS B2B: Activation and retention
The paths showed that users jumped onboard and immediately went to the Reports section without seeing the "first value." Added a short "guided tour", auto-complete demo data and trigger emails, resulting in +18% to activate D7 and +14 p.p. to keep D30 in paid plans.
Fintech: Attribution and offline closures
Stitching web sessions, call tracking, and CRM showed that last-click attribution understated content traffic. We moved to user-level attribution and adjusted the budget. CAC fell by 21%, ROAS rose by 28% with the same lead volume.
Checklists and methods
Quality control of the track
- Unity of user identifiers on the web + server.
- UTM normalization and session deduplication.
- Coverage of events at all stages of CJM.
- Agreed definitions of CR, LTV, CAC, ROAS.
Experiments
- Hypothesis → metric → minimal effect → duration.
- Segmentation: Devices, New/Returns, Sources.
- Post-test analysis: retension, check, cross-effects.
JTBD and Quality Data
- Short surveys on critical steps of the way.
- Разбор записей sessions по сценариям «задача-трение».
- Barriers classification: content, form, delivery, trust.
Productivity and the way
- Connecting Web Vitals to step-CR in funnels.
- Alerts for speed degradation at narrow steps.
- Monitoring the availability of payment providers.
| Stage of the road | Friction signals | Action | Metrics. |
|---|---|---|---|
| Catalogue → card | High bounce, low CTR cards | Relevant filters, snippets, quick previews | CTR, Time to Product, Scroll-depth |
| Card to basket | Loops, returns, clicks on inactive zones | Clear CTAs, availability, shipping tips | CR to Cart, Rage-clicks, Errors |
| The basket | Long forms, field errors | Autofill, Validation, Payment Methods | CR Checkout, Error rate, Abandonment |
| Payment → replay | Weak activation, low ret. | Onboarding, "first value," triggers | Activation D7, Retention D30, LTV |
FAQ
How much data is needed for sustainable conclusions?
For first insights, 2-4 weeks of traffic; for stable cohorts and seasonal effects, 8-12 weeks, we'll tailor the period to your funnel and volume.
What if some of the traffic is being cut by blockers?
Key events are duplicated from the server, using first-level domains, retros and deduplication, and losses are minimized without distorting the metrics.
Can I count offline sales and calls?
Integrating CRM and telephony, stitching sessions and deals, adding offline events to the funnel and attribution.
How do you rule out “beautiful but useless” dashboards?
For each metric, you specify the source, formula, refresh rate, SLA, and the person responsible. Any graph should answer a specific question and have a threshold for action.
Timeline and format of work?
A baseline project takes 5-10 business days: audit -> CJM/Tracking Plan -> journey collection -> UX/A/B -> dashboards -> operating rules. Next comes support and development.
Decoding of terms
- User JourneyThe sequence of user steps before and after the target action.
- CJM: map of the client path (stages, tasks, emotions, barriers, points of contact).
- Events: user actions (view, click, enter, purchase, etc.).
- Funnels: Sequences of steps with conversion between stages.
- Path/Flowfrequent and abnormal routes, loops and dead ends.
- Cohort analysis: metrics by group, united by time/sign.
- Session Replay: воспроизведение sessions для анализа UX и багов.
- Heatmaps: heat maps of clicks, scroll, attention.
- UTM: Campaign parameters for identifying sources.
- AttributionDistribution of channel contribution to conversion.
- Multi-touch: Multi-channel attribution models.
- DWH: Data storage for analytics.
- dashboards: interactive chart panels and metrics.
- Tracking Plan: a formal list of events/properties and rules of sending.
- Server events: sending key events from the backend.
- Data Quality: control of completeness and correctness of data.
- CR: Conversion Rate - Conversion Rate.
- CAC: Cost of attracting a customer.
- LTV: lifetime value of the customer.
- Insights: evidence-based conclusions and recommendations.
- Exploratory: exploratory analysis of data for hypotheses.
- ICEPrioritization (Impact, Confidence, Ease)
- Retention: retaining users over time.
- KPI: Key performance indicators.
- Consent: User consent to data processing and cookies.
Готовы увидеть реальный путь ваших users?
We will audit current data collection, build the journey map, and show where money is leaking. You will get a concrete plan: which steps to fix, which experiments to launch, and which metrics will grow first.
Request an audit and work plan