analytics-insights

What this skill does

# Analytics & Insights

## GA4 "AI Assistant" channel group (added 13 May 2026)

Google Analytics 4 added a new **default channel group called "AI Assistant"** on 13 May 2026 ([GA4 channel groups doc](https://support.google.com/analytics/answer/9164320?hl=en)). When a referrer matches a recognized AI Assistant (ChatGPT, Gemini, Claude, etc.), GA4 automatically:

- Categorizes the session under the **AI Assistant channel group**
- Sets the **Medium dimension to `ai-assistant`**

This is the **attribution-side counterpart** to the new GSC AI Performance Report (rolled out 3 June 2026 — see `/digital-marketing-pro:gsc-ai-performance`). Because the GSC AI report intentionally excludes click data, the GA4 AI Assistant channel is currently the cleanest path to attribute *actual traffic* coming from generative AI surfaces.

**Recommended GA4 setup checks** when onboarding a brand:

1. **Confirm the channel group is live in the property.** Newer GA4 properties get it automatically; older ones may need it to appear after Google's backfill completes. If the brand reports their channel reports look unchanged after 13 May, check explore reports filtered by `sessionDefaultChannelGroup = "AI Assistant"`.
2. **Add the AI Assistant channel to custom reports + dashboards** — for any brand running an AEO program (`/digital-marketing-pro:aeo-geo`, `/digital-marketing-pro:aeo-audit`), the AI Assistant channel trend is now a primary KPI alongside organic search clicks.
3. **Don't merge AI Assistant into "Organic Search" or "Direct".** Some legacy reporting templates roll AI traffic into Direct (because referrers weren't always present) or Organic Search (because answer engines feel "search-like"). Both are misattributions now — the AI Assistant channel is the authoritative bucket.
4. **Reconcile with `aeo-audit` outputs and the GSC AI report.** Three data sources, three different views:
   - `aeo-audit` (synthetic probing) — what AI engines *could* say about the brand
   - GSC AI Performance Report — actual impressions in Google AI Overviews / AI Mode (no clicks)
   - GA4 AI Assistant channel — actual *traffic* from AI assistants (clicks materialized)

   A healthy AEO program shows growth across all three; divergence between them is a diagnostic signal.

## When to Use This Skill

Activate this module when the user's request involves any of the following:

- **KPI Frameworks**: Defining the right metrics and success measures for a business model, campaign, or channel
- **Performance Reporting**: Building weekly, monthly, quarterly, or campaign-specific reporting templates
- **Anomaly Investigation**: Diagnosing sudden drops or spikes in traffic, conversions, or other metrics
- **Competitive Intelligence**: Analyzing competitor strategies, share of voice, positioning, and performance
- **Attribution Modeling**: Determining how credit for conversions is assigned across marketing touchpoints
- **Marketing Mix Modeling (MMM)**: Estimating the impact of each marketing channel on overall business outcomes
- **Incrementality Testing**: Designing experiments to measure the true causal impact of marketing activities
- **Dark Social Measurement**: Tracking and attributing traffic from private sharing channels (DMs, Slack, email forwards)
- **Privacy-First Measurement**: Adapting measurement strategies for a cookieless, privacy-regulated environment
- **Dashboard Design**: Structuring dashboards for different stakeholder audiences

**Trigger phrases**: "KPIs," "metrics," "reporting," "dashboard," "why did traffic drop," "anomaly," "competitor analysis," "competitive intelligence," "attribution," "marketing mix model," "MMM," "incrementality," "lift test," "dark social," "cookieless," "privacy-first," "ROAS," "ROI," "performance," "what happened to our numbers"

## Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. **Check session context** — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. **If you need the full profile**, read: `~/.claude-marketing/brands/{slug}/profile.json`
3. **Apply brand voice** — Formality, energy, humor, authority levels must shape all content tone and word choices
4. **Check compliance** — Auto-apply rules for brand's target_markets and industry using `skills/context-engine/compliance-rules.md`
5. **Reference industry benchmarks** — Consult `skills/context-engine/industry-profiles.md` for the brand's industry
6. **Use platform specs** — Reference `skills/context-engine/platform-specs.md` for character limits and format requirements
7. **Check campaign history** — Run `python campaign-tracker.py --brand {slug} --action list-campaigns` before planning new work
8. **If no brand exists**, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. **Check brand guidelines** — If `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` exists, load and enforce: `restrictions.md` for banned words, restricted claims, and mandatory disclaimers; `channel-styles.md` for channel-specific tone overrides (may differ from base voice); `messaging.md` for approved key messages, taglines, and positioning language; `voice-and-tone.md` for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

## Required Context

Before executing analytics work, gather:

1. **Business Model**: SaaS, e-commerce, lead gen, marketplace, etc. (determines the KPI framework)
2. **Business Maturity**: Startup, growth, scale-up, or enterprise (determines measurement sophistication)
3. **Current Metrics**: What is already being tracked? What tools are in use?
4. **Analytics Stack**: Google Analytics (UA/GA4), ad platforms, CRM, BI tools, CDPs, tag managers
5. **Data Availability**: How much historical data exists? What granularity?
6. **Reporting Audience**: Who receives reports? (Exec/C-suite, marketing team, board, clients)
7. **Known Issues**: Any known data quality problems, tracking gaps, or recent changes?
8. **Geographic Scope**: Single market or multi-market (affects privacy regulations)
9. **Privacy Constraints**: GDPR, CCPA, ATT — what consent mechanisms are in place?
10. **Specific Question**: If investigating an anomaly, what exactly changed and when?

For anomaly investigation, prioritize speed. Ask for the specific metric, timeframe, and any known changes. For strategic measurement work, gather the full context.

## Capabilities

- **KPI Tree Generation per Business Model**: Hierarchical metric frameworks that connect top-level business goals to actionable marketing metrics, customized for SaaS, e-commerce, lead gen, marketplace, subscription, media, and other models
- **Standardized Reporting**: Templates for weekly performance snapshots, monthly strategic reviews, quarterly business reviews, and campaign post-mortems — each designed for different stakeholder audiences
- **Anomaly Detection and Root Cause Diagnosis**: Structured diagnostic framework for investigating sudden metric changes — systematic elimination of causes (tracking issues, external events, algorithm changes, seasonality, competitive actions, internal changes)
- **Competitive Intelligence Framework**: Methodology for monitoring competitor activity across channels (SEO, paid, social, content, PR), estimating competitor spend, and benchmarking performance
- **Marketing Mix Modeling (MMM) Guidance**: Framework for understanding channel-level contribution to business outcomes, including data requirements, model design considerations, and result interpretation
- **Incrementality Test Design**: Experiment design for geo-based lift tests, holdout tests, conversion lift studies, and matched-market tests to measure t