AI Content Performance Analyst Workbook
A printable workbook for completing the Content Performance Analysis and Experiment Pack across 7 course sections.
Download the AI Content Performance Analyst Workbook
Use this workbook with Build an AI Content Performance Analyst. Complete one numbered section per module and assemble the final Content Performance Analysis and Experiment Pack.
What the workbook contains
- Choose the Decision and Performance Objective
- Build the Content Data Contract
- Define Comparable Performance Measures
- Analyze the Account's Own Content
- Add Competitor or Adjacent Context Carefully
- Turn Findings into Ten Content Experiments
- Test and Operate the Analysis Loop
Each section gives you fields for the decision, evidence, metric rule, owner, test result, limitation, and next action required by its matching lesson. The final pages combine those records into the capstone and acceptance review.
How to use it
Start with one account, one platform, one observation window, and one business decision. Complete the matching workbook section after each lesson rather than filling the document from memory at the end. Keep raw rows, denominators, paid status, observation dates, and missing fields visible. Separate observed patterns from hypotheses. When a metric or comparison test fails, preserve the failed result, record the repair, and rerun the affected case.
Review the completed pack with the person who owns the content decision. A polished analysis is not enough: eligibility rules, missing-data treatment, comparison limits, experiment measures, and stop conditions must agree with the course acceptance criteria.
Important boundaries
The workbook is a planning and testing aid. It does not prove causation, future performance, ranking improvement, production readiness, provider availability, or revenue impact. It does not authorize publishing, paid distribution, data collection, account access, outreach, or deployment. Those actions require current evidence and separate approval.
Use the synthetic fixture only to learn the method. Replace it with permitted real or sanitized data before making a business decision.