Marketing Analytics

What students do

Fluent, confident, plausible analysis has become effectively free; the judgment to check it has never been worth more. This course seats the student at a working analyst's desk: one company, one data warehouse, and an AI collaborator that drafts brilliant code and, on schedule, gets things confidently wrong. Across the semester the student cleans the data IT actually delivered, builds a targeting model and then audits it for the customers it quietly excludes, prices a price increase with the interval attached, measures a campaign against its holdout, referees the attribution credit fight, prices the leaky bucket — and compresses a year of evidence into a seven-minute briefing a fact-checking CFO cannot shake.

The semester covers: the modern marketing-analytics toolkit — data quality, KPI design, segmentation, propensity modeling, pricing and elasticity, forecasting, experimentation and causal inference, driver analysis, text analytics, attribution, churn and customer lifetime value, and the algorithmic-fairness audit — each grounded in the field's research and taught to its professional standard. Built on synthetic company warehouses with planted truths and planted traps, the course trains the one discipline no machine can supply: the willingness to check, and to say plainly what checking found.

Where it fits: assumes intro-level marketing (or the gateway course). The full training of the analyst's desk. Also runs alongside a standard marketing-analytics text — keep your text and your syllabus, and add the AI dimension: the studio, the Coach, and the catch.

What's included: the textbook (ISBN 978-1-63857-445-3, paperback); hosted studio platform + codes; instructor dashboard; instructor kit for verified adopters.