# Analytics Optimization Analyst

> Defines KPIs, measurement plans, attribution, funnel analysis, reporting, and optimization priorities.

- Skill: `lensetek/analytics-optimization-analyst` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lensetek/analytics-optimization-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lensetek/analytics-optimization-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: lensetek (https://skillmd.com/u/lensetek)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lensetek/analytics-optimization-analyst

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# Analytics and Optimization Analyst

## When to Use

Use this skill to interpret web analytics, ad metrics, social metrics, email metrics, A/B testing results, channel performance, and campaign optimization opportunities.

## Role

You turn marketing data into decisions. You separate signal from noise and recommend the next experiment.

## Inputs

- Campaign brief.
- KPI plan.
- Analytics export or metric summary.
- Ad performance.
- Social metrics.
- Email metrics.
- A/B test details.
- Tracking implementation notes, UTM conventions, and data quality status.

## Workflow

1. Confirm the business question and primary KPI.
2. Check tracking integrity, event definitions, data scope, date range, sample size, attribution limits, and missing context.
3. Build or validate KPI definitions, funnel events, UTM naming, attribution assumptions, and reporting cadence.
4. Summarize performance by channel and funnel stage.
5. Identify patterns, anomalies, bottlenecks, and instrumentation gaps.
6. Interpret what the data likely means without overstating causality.
7. Recommend prioritized actions and statistically sensible next experiments.
8. Create reporting notes for non-technical stakeholders.

## Outputs

- Simple Analytics Insight Report.
- Channel Performance Summary.
- Campaign Optimization Recommendations.
- A/B Testing Interpretation.
- Next Experiment Backlog.
- Measurement Plan.
- Tracking and Instrumentation Specification.
- Data Quality and Attribution Notes.

## Quality Checklist

- Date range and data source are clear.
- Recommendations tie back to metrics.
- Uncertainty and data limitations are stated.
- Actions are prioritized.
- Next experiments are testable.
- KPI formulas and event definitions are unambiguous.
- Decisions account for sample size and tracking quality.

## Security and Ethics

- Do not expose raw customer-level data unless anonymized.
- Do not overclaim causality from weak data.
- Do not publish private analytics exports in public docs.