kai-analytics

name	kai-analytics
description	Analytics and attribution setup — tracking plan, UTM conventions, dashboard design, and attribution model selection. Use when "analytics setup", "attribution", "tracking plan", "UTM", "marketing analytics", "dashboard setup", "measurement strategy", "how do I track", "which metrics", or any request to set up or improve marketing measurement and attribution.

kai-analytics — Analytics & Attribution Setup

Design a complete marketing measurement system: tracking plan, UTM conventions, attribution model, and dashboard specifications.

Phase 0: Load Product Context

Check if MARKETING.md exists in the project root (same directory as CLAUDE.md, README.md, package.json).

If it exists: Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.

If it does NOT exist: Auto-explore the codebase to create it in the project root (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create MARKETING.md using the template from /kai-email-system. Present draft to user for confirmation.

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References

Load these files as context before starting:

• E:\Dev2\kai-cmo-harness-work\knowledge\playbooks\analytics-attribution.md
• E:\Dev2\kai-cmo-harness-work\knowledge\playbooks\technical-marketing-tracking.md
• E:\Dev2\kai-cmo-harness-work\knowledge\playbooks\saas-metrics-guide.md

Phase 1 — Discovery

1. Read from MARKETING.md. Only ask about things not covered there:
   • Business model and primary conversion event (signup, purchase, demo request)
   • Current analytics tools (GA4, Mixpanel, PostHog, Amplitude, etc.)
   • Ad platforms in use (Meta, Google, LinkedIn, TikTok, etc.)
   • CRM or email tool (HubSpot, Salesforce, Loops, etc.)
   • Current tracking status (nothing, partial, broken, outdated)
   • Key questions they need answered ("where do customers come from?", "which ads work?")
   • Sales cycle type (self-serve, sales-assisted, enterprise)
2. Identify the measurement maturity level:
   • Level 0: No tracking beyond platform defaults
   • Level 1: Basic GA4 + ad platform pixels
   • Level 2: UTMs + event tracking + basic attribution
   • Level 3: Multi-touch attribution + cohort analysis + LTV tracking

Phase 2 — Analysis

Measurement Gap Audit

1. Map the current customer journey: first touch -> conversion -> retention.
2. Identify blind spots — where do you lose visibility?
3. Flag conflicting data sources (ad platform vs. GA4 discrepancies).
4. Assess data quality: are events firing correctly? Are UTMs consistent?

Attribution Model Selection

Recommend the right model based on their business:

Model	Best For	Limitation
Last-click	Short sales cycles, ecommerce	Ignores awareness channels
First-click	Brand-heavy businesses	Ignores nurture channels
Linear	Multi-channel, even contribution	Oversimplifies
Time-decay	Long sales cycles, B2B	Complex to implement
Position-based (U-shaped)	Most B2B SaaS	Requires multi-touch data
Data-driven (GA4)	High-volume businesses	Needs 600+ conversions/month

Attribution Caveats

State caveats beside every attribution recommendation:

• Platform dashboards optimize for their own pixel, identity graph, attribution window, and modeled conversions.
• GA4, CRM, payment, and ad-platform revenue will disagree when UTMs, consent mode, offline conversions, refunds, or sales-cycle delays differ.
• Last-click is useful for capture channels, not proof that awareness or nurture did nothing.
• Multi-touch models describe observed journeys; they do not prove incrementality without holdouts, geo tests, lift studies, or matched-market tests.
• Use directional attribution for budget conversations until event QA, UTM hygiene, consent coverage, and CRM joins are verified.

Phase 3 — Produce

Build these deliverables:

Tracking Plan

Structured event taxonomy:

Event Name	Trigger	Properties	Tool
page_view	Every page load	url, referrer, utm_*	GA4
signup_started	Form opened	source, plan_type	GA4 + Product
signup_completed	Account created	method, plan, value	GA4 + Product + CRM
[custom events per business]

UTM Convention Guide

Standardized naming rules:

• utm_source: platform name, lowercase (google, meta, linkedin)
• utm_medium: traffic type (cpc, email, social, organic, referral)
• utm_campaign: campaign name with date prefix (2026-03_spring-launch)
• utm_content: ad variant or content identifier (cta-v1, hero-image-b)
• utm_term: keyword or targeting (only for paid search)

Include a UTM builder template and naming convention doc.

Dashboard Specifications

Design dashboards for three audiences:

Executive Dashboard (weekly glance)

• Revenue attributed by channel
• Blended CAC trend
• Conversion rate by stage
• Top 5 performing campaigns

Marketing Ops Dashboard (daily operations)

• Traffic by source/medium
• Conversion funnel with drop-off rates
• UTM campaign performance table
• Ad spend vs. revenue by platform

Channel-Specific Dashboards (per platform)

• Platform metrics (CTR, CPC, ROAS)
• Audience segment performance
• Creative/copy variant performance
• Budget pacing

Pixel & Tag Setup Guide

• Which pixels/tags to install per platform
• Implementation method (GTM, direct, server-side)
• Consent management requirements (GDPR/CCPA)
• Testing and validation steps

Phase 4 — Output

1. Deliver the tracking plan as a structured table.
2. Deliver the UTM convention guide as a reference document.
3. Deliver dashboard specs with metric definitions and data sources.
4. Provide an implementation checklist ordered by priority.
5. Include a "data quality audit" checklist for monthly review.

Constraints

• Never recommend tracking that violates GDPR or CCPA without consent mechanisms.
• Always include a consent management note for EU/CA audiences.
• UTM conventions must be consistent — no mixed case, no spaces, no special characters.
• Dashboard metrics must have clear definitions (e.g., "conversion rate = signups / unique visitors").
• Attribution model must match the user's data volume — don't recommend data-driven with 50 conversions/month.
• All event names must follow a consistent naming convention (snake_case preferred).