The Chapter 1 for marketing analytics: what the field is, where data comes from, how to think about variables and measurement, the four types of analytics, the methods landscape, and the critical-thinking habits that separate signal from noise.
Topics and managerial frameworks covered in Introduction to Marketing Analytics:
Students manipulate these interactive visual simulations directly inside the chapter reading before answering gated check-in questions:
Students classify 8 marketing scenarios into the four analytics levels: Descriptive, Diagnostic, Predictive, Prescriptive. Instant per-item feedback + final score.
Teaches: the analytics hierarchy is about the *type of question*, not the complexity of the tool. Students consistently confuse diagnostic and predictive, and this exercise surfaces that confusion.
Students read 6 marketing scenarios and pick the technique family that best addresses the question. Each scenario has one best answer plus an explanation of *why* that technique fits.
Teaches: the skill of translating a business problem into an analytical approach — the #1 skill that separates working analysts from students who know statistics but can't apply them.
This chapter connects directly to these full MktPraxis analytical apps (with pre-loaded business case scenarios, CSV upload, and reproducible Python export):
Explore a single variable's distribution, outliers, and summary stats. Always start here.
Pre-loaded Business Scenarios: Influencer Campaign Performance Analysis · Customer Satisfaction Survey Analysis · E-commerce Transaction Analysis · Email Marketing Campaign Analysis · Customer Lifetime Value Analysis · Social Media Post Analytics · A/B Test Results — Messy Data Challenge! · PetPost Subscriber Health Analysis