Build an AI Content Performance Analyst

Build a repeatable content analysis workflow that handles missing data honestly, identifies supported patterns, and turns one defined dataset into ten measurable content experiments.

Choose the Decision and Performance Objective

Write an analysis charter with account, platform, objective, decision, audience, window, exclusions, and owner.

Choose the Decision and Performance Objective (14 min)

Build the Content Data Contract

Create a posts input schema, data dictionary, validation rules, and synthetic fixture label.

Build the Content Data Contract (14 min)

Define Comparable Performance Measures

Write the metric rubric, rate formulas, eligibility rules, and confidence anchors.

Define Comparable Performance Measures (14 min)

Analyze the Account's Own Content

Produce an own-account findings ledger linked to specific rows and aggregate evidence.

Analyze the Account's Own Content (14 min)

Add Competitor or Adjacent Context Carefully

Create a comparison ledger that states visible evidence, unavailable fields, and permitted conclusions.

Add Competitor or Adjacent Context Carefully (14 min)

Turn Findings into Ten Content Experiments

Create a prioritized ten-item plan containing repeat, adapt, stop, and test decisions.

Turn Findings into Ten Content Experiments (14 min)

Test and Operate the Analysis Loop

Run deterministic fixture tests, record repairs, and finish the capstone report and operating cadence.

Test and Operate the Analysis Loop (14 min)