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TOP-10 traffic analysis tools
Traffic analysis is not one "counter," but a bunch of tools that closes the data path from click to LTV. Below are 10 classes of solutions, their role in the stack, benchmarks on metrics and where you most often lose the truth.
1) GA4/Web analytics (interface truth base)
Why: behavior on the site/landing pages, funnels, attribution in the interface, fast UTM slices.
What to watch:- CR `click→reg`, `reg→KYC`, `KYC→FTD`; Engagement rate; Conversion lag.
- By UTM: 'source/medium/campaign/content/term', devices and GEO.
- Pros: fast, free/cheap, Explorations (funnel/cohort/path).
- Cons: limited revenue accuracy, incomplete attribution without BigQuery and server-based payment transfer.
- Mini check: User-ID, Consent Mode, server-side event 'deposit _ success', internal traffic exception.
2) MMP (AppsFlyer/Adjust/Singular and analogues)
Why: mobile attribution Web→App/App→Web, SKAN/Privacy Sandbox, postbacks.
What to watch: installations, retargeting, 'click_id↔install_id' bundle, ARPU D7/D30 (if there is integration).
Pros: more resistant to loss of identifiers, the only source for apps.
Cons: paid; need a competent scheme of events and consent.
Mini-check: s2s-integration of deposit/purchases, deeplink/OneLink, probabilistic privacy match.
3) DWH + BI (BigQuery/Redshift + Looker/Power BI/Metabase)
Why: event-level truth, cohorts, LTV/Payback, unity of finance and traffic.
What to watch: cumulative ARPU D1/D7/D30/D90, 2nd-dep rate, NGR, Payback by bundles (UTM, creative, GEO, device).
Pros: flexibility, connects marketing + product + finance.
Cons: requires engineer/analyst, scheme discipline.
Mini-check: showcases' facts _ events', 'dim _ utm', currency/timezone, delay control.
4) Anti-fraud/Traffic quality (device/IP/ASN + behavior)
Why: cut off bots, incident and KUS/deps farm.
Signals: abnormal CTR at zero 'reg→FTD', IP/ASN bursts, night peaks, low Engagement.
Pros: saves budgets and nerves for operators.
Cons: risk of false positives; thresholds and appeals procedure are needed.
Mini-check: velocity-rules, device-fingerprint, source lists, incident log.
5) Log management and monitoring (ELK/Cloud Logging + Grafana)
Why: see raw "wire" traffic: redirects, postbacks, errors, delays.
What to watch: status/latency of postbacks, share of retrays, share of duplicates, discrepancies "operator↔treker."
Pros: Debag, Incidents, SLA Control.
Cons: noisy without alerts and normalization.
Mini-check: correlation by 'click _ id '/' event _ id', delay alerts> 15 minutes.
6) Heatmaps and session playback (Hotjar/Clarity)
Why: understand why the 'click→reg' falls: scrolling, clickability, speed, UX gags.
What to watch: rage-clicks, drop-offs of forms, TTFB/interface stability.
Pros: fast UX insights without developers.
Cons: It's not "selling truth"; do not confuse with revenue.
Mini-check: masking of fields (privacy), sampling by key lands/geo/devices.
7) A/B testing and experimentation (Optimizely/VWO/Kameleoon)
Why: Check prelands/landings/creative packages for CR and Payback.
What to watch: uplift by funnel and ARPU surrogate, time to conversion.
Pros: Managed progress, less controversy.
Cons: sampling power and discipline of statistics are needed.
Mini-check: fix the hypothesis/metrics/threshold, do not stop the test ahead of time.
8) MMM/Extended Level Attribution (Robyn/LightweightMMM/Segment modeling)
Why: see the contribution of channels with incomplete determination (privacy), plan a budget.
What to watch: elasticity, Diminishing returns, what-if on CPM/rates.
Pros: Strategic picture on top of end-to-end attribution.
Cons: Requires long rows and an analytics team.
Mini check: net rows of spend/impressions/conversions, shocks (stocks), seasons, lags.
9) CDP и TMS (Segment/mParticle + GTM/server-side GTM)
Why: a single catalog of events/identifiers, data routing in GA4/MMP/BI/advertising networks.
What to watch: completeness of events, consent, quality of identification (match rate).
Pros: less "zoo" scripts, privacy control.
Cons: Cost, requires circuit architecture.
Mini-check: event dictionary, User-ID mapping, server-side container for key events.
10) ETL/Reverse-ETL (Fivetran/Airbyte/Stitch + Hightouch/Census)
Why: drag raw data from ad cabinets/payments to DWH and back - segments to ad platforms.
What to watch: download delays, doubles, field quality, automatic validation tests.
Pros: automation of reporting and activations (LAL, VIP, churn signals).
Cons: expenses for connectors and circuit support.
Mini-check: increment schedule, primary key dedup, integrity tests.
How to build a stack for your stage
Start/Launch (1-3 brands, GEO ≤5)
GA4 + server-side deposit event
BigQuery + Light BI- Hotjar/Clarity for UX
- Simple anti-fraud layer
- GTM (server-side if possible)
Scale-Up (10 + GEO, app shares are growing)
Connect MMP- Strengthen DWH/BI, logs and SLAs
- A/B platform on the prelands/lands
- CDP + ETL, white/black source lists
- Anti-fraud with scoring and appeals
Enterprise (regulated markets)
Full datalake + NGR storefronts/taxes- MMM over end-to-end attribution
- WAF/bot management, SSO/RBAC, log audit
- Incident Processes and Quarterly Schema Audits
Metrics that should converge everywhere
CR: `click→reg`, `reg→KYC`, `KYC→FTD`
Quality: '2nd-dep rate', D7/D30 retention, chargeback rate
Economics: CPA, ARPU_D7/D30/D90, NGR, Payback, ROAS/ROI
Technical health: delay of postbacks,% retrays, p95 latency, discrepancy "operator↔treker"
Frequent mistakes
1. "One tool will solve everything" - no. Need a bunch.
2. No server-side events - money is "lost" in the browser.
3. UTM chaos - disparate reports.
4. Zero anti-fraud - "cheap FTDs" break NGR.
5. Decisions on small samples - scale to statistics is prohibited.
6. Lack of logs and alerts - you see incidents after the fact.
Stack implementation checklist (compressed)
Equipment
- User-ID, Consent Mode, server-side deposits
- DWH + BI: Cohort Showcases/Payback/ARPU
- Redirect/postback logs, delay alerts
- Anti-Fraud Rules and Incident Log
- GTM sGTM, UTM validator, event dictionary
OS
- Statistic Threshold (Clicks/Reg/FTD)
- Weekly retro hypotheses/creatives
- Source whitelists/blacklists
- Appeal and reconciliation procedures with operators
30-60-90 plan
0-30 days - framework
Enable GA4 + server-side 'deposit _ success'; establish UTM order
Raise BigQuery/BI and Basic Storefronts (ARPU/Payback)- Start postback logs and delay alerts> 15 min
- Install a simple anti-fraud layer, connect Hotjar/Clarity
31-60 days - depth
Connect MMP (if there is an app), ETL from offices and payments- Deploy A/B Platform on Prelands/Lands
- Introduce white/black lists, dispute procedure and retro quality
- Standardize Event Dictionary/CDP, server-side GTM
61-90 days - sustainability and strategy
NGR/Retention/LTV Deep Cases, D90 Cohort Reports
Enable MMM Pilot for Media Mix- Load/emergency drills (postbecks/queues), safety audit
- Finalize launch/reconciliation/escalation playbooks
A strong traffic analysis is an orchestra of 10 instrument classes, where everyone knows the part. Collecting browser and server events, DWH + BI for cohorts and revenue, anti-fraud, SLA logs, UX diagnostics, A/B and strategic attribution - together give a managed economy: you quickly find winning bundles, protect margins and scale without surprises.