UTM & GA4 Governance for Social
Define UTM conventions per platform and a GA4 report set to attribute social properly. Add a checklist for QA before posting.
Author: Inspire Search Corp.
Category: Analytics | Model: gpt-5-thinking
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Showing results for "ga4"
Mobile vs Desktop Behavior Gap
Goal: compare mobile vs desktop behavior. Data: GA4 device category segmentation. Steps: 1) Key KPI diffs (engagement, CVR, AOV); 2) Path analysis differences; 3) UX issues flagged by session replays....
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Robot/Scraper Detection from Server Logs
Goal: detect robots/scrapers from logs. Data: server logs (CDN/WAF), GA4 anomalies. Steps: 1) UA/IP reputation, JA3/TLS fingerprints; 2) Request-rate and path entropy anomalies; 3) Honeypot endpoints;...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Search Behavior Insights (Site Search Logs)
Goal: mine internal site search for intent gaps. Data: GA4 view_search_results and search_term params. Steps: 1) Top queries with zero-results; 2) CTR to result pages; 3) Query clusters using embeddin...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Content Engagement: Scroll & Heatmap Synthesis
Goal: synthesize scroll depth with heatmap insights. Data: GA4 scroll events, heatmap tool exports (e.g., Hotjar). Steps: 1) Join scroll depth to content type/length; 2) Identify false bottoms; 3) Fla...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Attribution: Markov vs Shapley vs Rule-Based
Goal: compare attribution approaches. Data: multi-touch journeys (GA4 pathing + ad click logs). Steps: 1) Build rule-based (last/first/linear/time-decay); 2) Build Markov and Shapley using transition ...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
LTV Modeling with RFM and Pareto/NBD
Goal: model LTV with RFM and Pareto/NBD. Data: orders table + GA4 user_id mapping. Steps: 1) Derive Recency, Frequency, Monetary; 2) Fit Pareto/NBD (BG/NBD) and Gamma-Gamma for monetary; 3) Validate w...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Cohort Retention & Power-User Curve
Goal: analyze cohort retention and power-user curve. Data: GA4 users and events, subscription logs if applicable. Steps: 1) Build weekly cohorts by first_visit; 2) Compute W1–W12 retention; 3) Plot po...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Funnel Drop-off Diagnosis (GA4 + BigQuery)
Goal: diagnose funnel drop-offs for {GOAL_EVENT}. Data: GA4 BigQuery events with event_params. Steps: 1) Construct funnel steps; 2) Use event timestamps to compute step conversion; 3) Segment by devic...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Sessionization & Bot Filtering Playbook
Goal: define sessionization robustly and filter bots. Data: raw web logs + GA4 BigQuery. Steps: 1) Build session rules (30m inactivity, campaign change resets); 2) Bot filters using IP ranges, UA rege...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking
Traffic Source Quality & UTM Hygiene Audit
Goal: audit source/medium quality and UTM hygiene for {START_DATE}–{END_DATE}. Data: GA4 BigQuery export `{PROJECT}.{DATASET}.events_*`, ad platform logs, server-side UTMs. Steps: 1) Classify sessions...
Author: Tsubasa Kato
Category: Web Analytics | Model: GPT-5 Thinking