AI for Marketing Mix Modeling: Foundations of Modern Attribution

मार्केटिंग मिक्स मॉडलिंग दशकों से मौजूद है, लेकिन पिछले कुछ वर्षों में इसे बनाने का तरीका बदल गया है। आधुनिक दृष्टिकोण पारंपरिक अर्थमिति को मशीन लर्निंग के साथ मिश्रित करते हैं, गोपनीयता-अनुकूल डेटा को संभालते हैं, और ऐसे परिणाम उत्पन्न करते हैं जो अतीत की त्रैमासिक रिपोर्टों की तुलना में तेज़ी से अपडेट होते हैं। यह कोर्स आपको एक शांत, संरचित परिचय देता है ताकि आप आत्मविश्वास से MMM के बारे में बात कर सकें, भले ही आपकी टीम कोई भी टूल इस्तेमाल करती हो। You will learn what MMM actually measures, where AI strengthens the approach, and how it sits alongside multi-touch attribution and incrementality testing. The course stays grounded in widely used concepts and respects the realities of marketing organizations. What you'll learn: - Understand the goals of Marketing Mix Modeling and how it differs from last-click attribution - Recognize the data inputs MMM requires, including media spend, sales, and external factors - Explore how AI improves classical MMM through nonlinear effects, adstock decay, and Bayesian priors - Compare MMM with multi-touch attribution and incrementality experiments in the same toolkit - Read model outputs including channel contribution, response curves, and saturation effects - Understand the integration points between MMM, planning tools, and broader analytics stacks The course begins with the goals of marketing measurement, moves through how AI extends classical MMM, and closes with practical realities including data quality, governance, and stakeholder communication. Written exercises help you connect each concept to a real product or brand context. This course is designed for absolute beginners with no econometrics or AI background, including marketing analysts, brand managers, and students of marketing science. No prerequisites are needed. The course explains every concept as it appears and respects how real marketing teams actually plan and report.