Authors: Authors: Dr. Pallavi Deshpande, Dr. Amruta Shyam Kumar, and Dr. Joe Lopez

Editor: Dr. Jairaj Sasane

ISBN: 978-81-17787-17-5

DOI: https://doi.org/10.59646/873

Date of Publication: October 30, 2026

Preface

The contemporary marketplace has undergone an irreversible structural transformation. Intuition, long regarded as the primary compass of brand builders, is no longer sufficient on its own to guide strategic marketing decisions. As consumer touchpoints fragment across digital ecosystems and physical channels, organizations generate unprecedented volumes of transactional, behavioral, and perceptual data. This textbook serves as an end-to-end framework for students, scholars, and marketing practitioners navigating data-driven decision-making. Structured systematically across ten unified modules, the volume mirrors the maturity curve of an analytical enterprise:

• Foundations & Architecture (Units I & II): Establishes the taxonomy of descriptive, diagnostic, predictive, and prescriptive analytics. It grounds readers in modern data pipelines, covering first-, second-, and third-party data collection, governance, compliance mandates (GDPR, CCPA, and India’s DPDP Act), and the art of translating raw database architectures into operational visualizations using Power BI, Tableau, and Excel.
• Customer Understanding & Statistical Rigor (Units III & IV): Deconstructs individual-level consumer dynamics through behavioral segmentation, RFM modeling, Customer Acquisition Cost (CAC), and discounted Customer Lifetime Value (CLV). It anchors these business applications in inferential statistics, detailing hypothesis testing, ANOVA, multivariate regression, and hands-on execution across SPSS, R, and Python.
• Channel & Commercial Performance (Units V, VI, & VII): Explores digital ecosystems, touchpoint mechanics, algorithmic attribution models, conversion rate optimization, and A/B testing. It pairs digital telemetry with social listening, natural language sentiment processing, and influencer ROI, before moving into offline and transactional commercial analytics.
• Predictive Modeling & Artificial Intelligence (Units VIII & IX): Transitions from historical audits to forward-looking intelligence. Readers are guided through supervised and unsupervised machine learning—including decision trees, random forests, and propensity scoring—alongside enterprise deployments of Generative AI, prompt engineering, dynamic personalization, and algorithmic bias mitigation.
• Strategic Integration & Executive Governance (Unit X): Synthesizes operational insights into boardroom leadership, focusing on Marketing Mix Modeling (MMM), ROMI optimization, real-time executive dashboarding, and empirical case studies drawn from both high-growth Indian enterprises and multinational corporations.

Every chapter balances mathematical derivations and computational scripts with strategic business evaluation. Theoretical concepts are reinforced through worked numerical problems, code snippets, and clear performance indicators, ensuring readers develop technical proficiency without losing sight of bottom-line impact.