Analytics Insights
Analyze marketing performance.
Marketing teams lose hours to ad-hoc, inconsistent analytics insights work — Analyze marketing performance. Use when: KPI frameworks, attribution modeling, anomaly investigation, measurement strategy. This playbook turns the process into a repeatable, brand-aware workflow.
Who it's for: marketing analysts, growth analysts, BI leads
Example
"Run /analytics-insights for our brand" → Analytics Insights workflow output with brand context, structured inputs captured, process steps executed, and a complete deliverable ready for review.
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Analytics Insights
# Analytics & Insights
## 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 performanceGet new playbooks like this one
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What This Does
Analyze marketing performance.
Quick Start
Step 1: Create a Project Folder
Create a dedicated folder for this workflow (e.g. ~/marketing/analytics-insights).
Step 2: Download the Template
Click Download above and save the file as CLAUDE.md in that folder.
Step 3: Run the Workflow
Open the folder in Claude Code and describe your goal. Claude will prompt you for any missing inputs, follow the structured process, and produce a complete deliverable.
Inputs You'll Need
Claude will prompt for the brand context, objective, and any asset references needed to produce the deliverable.
How It Works
Primary Workflow: Measurement Framework & Reporting
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Business Context & Goal Alignment
- Classify the business model and maturity stage
- Identify the north star metric (the single metric most tied to business value)
- Map business goals to marketing objectives to tactical metrics (KPI tree)
- Determine reporting audience and their decision-making needs
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KPI Tree Construction
- Start with the top-level business goal (revenue, growth, profitability)
- Break into marketing contribution metrics (marketing-sourced revenue, CAC, LTV)
- Decompose into channel-level metrics (channel CPA, ROAS, conversion rate)
- Add leading indicators (traffic, engagement, pipeline, MQLs)
- For each KPI, define:
- Definition: Exactly how it is calculated (no ambiguity)
- Source: Where the data comes from
- Benchmark: Target or industry benchmark
- Cadence: How often it is reviewed
- Owner: Who is responsible for this metric
- Limit the framework to 15-25 KPIs total — more causes metric fatigue and diluted focus
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Reporting Template Design
- Weekly Snapshot (for marketing team):
- Key metrics vs. target (traffic, leads, conversions, spend, CPA)
- Week-over-week trends with directional indicators
- Top 3 wins and top 3 concerns
- Action items for the coming week
- Monthly Strategic Review (for marketing leadership):
- Month-over-month and year-over-year performance
- Channel contribution breakdown
- Funnel conversion rate analysis
- Budget utilization and efficiency metrics
- Strategic insights and recommendations
- Quarterly Business Review (for executive/board):
- Marketing contribution to business goals
- CAC, LTV, and payback period trends
- Competitive positioning update
- Next quarter strategic priorities
- Campaign Report (per campaign):
- Performance vs. pre-defined KPIs
- Channel-by-channel analysis
- Creative and audience performance
- Learnings and recommendations
- Weekly Snapshot (for marketing team):
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Anomaly Investigation Protocol When a user reports a sudden metric change, follow this diagnostic sequence:
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Step 1: Verify the Data
- Is the tracking code still firing correctly?
- Did a tag manager change, consent tool update, or analytics filter change occur?
- Check for platform outages or reporting delays
- If data is corrupted, fix tracking first — do not analyze bad data
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Step 2: Define the Anomaly Precisely
- Which metric changed? By how much? Over what time period?
- Is it all traffic or a specific segment (channel, device, geography, page)?
- Did it happen suddenly or gradually?
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Step 3: Check External Factors
- Google algorithm update (check SEMrush Sensor, MozCast)
- Industry news or seasonal patterns
- Competitor activity changes
- Platform policy or feature changes
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Step 4: Check Internal Factors
- Website changes (deployments, URL changes, redirects)
- Content changes (published, removed, or modified)
- Campaign changes (launched, paused, budget shifted)
- Technical issues (site speed, server errors, mobile rendering)
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Step 5: Isolate and Diagnose
- Cross-reference the anomaly with the identified factors
- Determine the most likely root cause
- Estimate the impact and expected recovery timeline
- Recommend corrective actions
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Privacy-First Measurement Architecture
- Audit current measurement for privacy compliance gaps
- Design a measurement stack that works without third-party cookies:
- Server-side tracking for owned touchpoints
- First-party data enrichment strategy
- Privacy-compliant consent management
- Platform-native conversion APIs (Meta CAPI, Google Enhanced Conversions)
- Modeled conversions for attribution gaps
- Marketing mix modeling for channel-level effectiveness
- Incrementality testing for causal validation
- Create a transition plan from current state to privacy-first architecture
- Account for iOS ATT impact on iOS-heavy audience segments
What You Get
A complete, brand-aligned deliverable ready to review, share, or hand off to execution.