Web and Digital Analytics AI Prompts

Identify conversion rate optimization (CRO) opportunities across this website. Analytics data: {{analytics_data}} Heatmap and session recording data: {{heatmap_data}} (if available) Key conversion goal: {{conversion_goal}} 1. Funnel visualization: Map the steps from landing to conversion: - Step 1: landing page entry - Step 2: [next step] - ... - Final step: conversion complete - Drop-off rate at each step - Identify the single biggest drop-off: this is the priority for CRO 2. Page-level analysis for priority pages: For each high-traffic, high-drop-off page: - Exit rate: what % leave the site from this page? - Time on page: are users engaging or bouncing quickly? - Scroll depth: how far down the page do users scroll? - Click distribution (from heatmap): are users clicking the right elements? 3. Form analysis (if applicable): - Form abandonment rate: started but not submitted - Which form field has the highest abandonment rate? (Indicates friction or required information concerns) - Time to complete the form - Error rate per field 4. Mobile vs desktop conversion gap: - Mobile conversion rate vs desktop conversion rate - If mobile is significantly lower: mobile UX is a priority - Page speed on mobile: Core Web Vitals for mobile specifically 5. Traffic source conversion rate comparison: - Conversion rate by acquisition channel - If paid traffic converts much lower than organic: landing page relevance may be poor - Are paid campaign landing pages dedicated pages or generic product pages? 6. Prioritized CRO test backlog: - Generate 10 specific test hypotheses - Each with: page, element to test, hypothesis, expected lift, effort (Low/Medium/High) - Score by ICE and prioritize Return: funnel drop-off analysis, page-level insights, form analysis, mobile gap, source conversion comparison, and prioritized CRO test backlog.

Audit the Google Analytics 4 event tracking implementation for completeness and data quality. GA4 property: {{property}} Business goals: {{goals}} Current events tracked: {{events_list}} 1. Key events audit: For each business goal, is the corresponding key event being tracked? - Lead generation: form_submit event with form_id, form_type parameters - E-commerce: purchase event with transaction_id, value, currency, items array - Engagement: video_start, video_complete, scroll depth (75% minimum), file_download - Account actions: sign_up, login, subscription_start, subscription_cancel - Content: outbound_click, internal_search, search_results_viewed 2. Data quality checks: - Are event parameters consistently named? (purchase vs Purchase vs PURCHASE = three separate events) - Are revenue events double-counting? (Both client-side and server-side firing) - Are null values appearing in required parameters? (item_id = null in purchase events) - Are session and user counts plausible given actual traffic? 3. Conversion tracking verification: - Test each key event in DebugView: fires at the right moment, with correct parameters? - Compare GA4 conversions to CRM / payment processor records: within 10% variance? - Are conversions cross-device (GA4 uses Google Signals)? Is cross-device linking enabled? 4. Audience building for remarketing: - Are the right events configured as key events for audience building? - Recommended audiences: all visitors, product viewers, cart abandoners, past purchasers, high-value customers 5. Data retention settings: - Event data retention: set to 14 months (not the default 2 months for comparative analysis) - User data: review for GDPR/CCPA compliance 6. Missing event recommendations: Based on the audit, list the top 5 events that are missing or misconfigured, with: - Event name and parameters - Implementation priority - Business value of tracking this event Return: key events audit table, data quality findings, conversion verification results, and top 5 missing event recommendations.

Audit the marketing analytics stack for this organization and identify gaps, redundancies, and improvement opportunities. Current tools: {{tools_list}} Data flows: {{data_flows}} Team capability: {{team_capability}} 1. Analytics stack layers: Map the current stack against these layers: - Data collection: (pixels, SDKs, server-side tagging, webhooks) - Data transport: (tag management, event streaming, APIs) - Data storage: (data warehouse, CDP, CRM, platform-native storage) - Data transformation: (dbt, Fivetran, custom ETL) - Analytics and reporting: (BI tool, platform dashboards, spreadsheets) - Activation: (email platform, ad platforms, personalization engine) 2. Data quality assessment per layer: - Collection: are all key events tracked? Are there data gaps? - Storage: is there a single source of truth or multiple conflicting sources? - Transformation: is business logic documented and version-controlled? - Reporting: do different teams use different definitions for the same metric? 3. Redundancy identification: - Are multiple tools doing the same job? (Two CDPs, two email platforms) - Can any tools be consolidated without loss of capability? - What is the total annual cost of the current stack? 4. Critical gaps: - Multi-touch attribution: is there a cross-channel attribution solution beyond platform-reported ROAS? - Customer identity resolution: can you link the same person across devices and channels? - Offline-to-online: is offline (store, call center) data connected to digital behavior? - Incrementality measurement: is there any program to measure true causal marketing impact? 5. Priority improvements: - Top 3 gaps with highest impact on marketing decision quality - For each: recommended solution, estimated implementation effort, expected ROI 6. Data governance: - Is there a marketing data dictionary? (Agreed definitions for all metrics) - Who owns each data source and is responsible for its quality? Return: stack layer map, quality assessment, redundancy analysis, critical gaps, priority improvements, and governance recommendations.

Analyze website traffic data and identify key trends and opportunities. GA4 or analytics data: {{analytics_data}} Time period: {{period}} Business goal: {{goal}} 1. Traffic overview: - Total sessions, users, and page views - YoY and MoM trend for each - Sessions per user: how often do users return? - Average session duration and pages per session 2. Traffic source breakdown: - Sessions by channel: organic search, paid search, direct, referral, social, email, other - % of sessions by channel (channel mix) - YoY change in each channel's share: which channels are growing or shrinking? - Are we over-reliant on any single channel (> 40% from one source = concentration risk)? 3. Engagement quality by channel: - Bounce rate (or engagement rate in GA4) by channel - Session duration by channel - Pages per session by channel - Conversion rate by channel - Which channel drives the most engaged visitors? The least engaged? 4. Landing page analysis: - Top 20 landing pages by entry sessions - Bounce/engagement rate per landing page - Landing pages with high traffic but low engagement: content-traffic mismatch 5. Device and geography: - Mobile vs desktop vs tablet: sessions, engagement rate, conversion rate - Mobile share trend: is mobile growing? Does mobile convert at a lower rate? - Top geographies by traffic: any unexpected sources or gaps? 6. Conversion funnel from traffic: - Traffic to lead/sign-up/purchase conversion rate overall and by channel - Which pages are the top conversion exit points? Return: traffic overview, channel mix analysis, engagement quality table, landing page insights, and conversion funnel summary.