Marketing Analytics Overview

Principles

Definition

Marketing analytics is a systematic approach that employs quantitative methods and empirical data to measure, manage, and analyze marketing performance. It investigates the impact of marketing initiatives on customer behavior, campaign outcomes, and business objectives, providing actionable insights for optimization and strategic planning within a research or operational context.

Ontological type

Core Methods

Data Sources

Applications

Key developments

Key Figures

Measurement and Process Modeling era

Eric von Hippel [1] was a prominent scholar during this era affiliated with the Massachusetts Institute of Technology [3] and the École Polytechnique Fédérale de Lausanne [4]. His key contribution was the Lead Users concept, exemplified by the 1986 paper Lead Users: A Source of Novel Product Concepts [7], which demonstrated how early insights from lead users could generate novel product concepts and inform marketing research under sparse data conditions. V. Srinivasan [2] was a leading figure associated with Stanford University [5] and the University of Pennsylvania [6] during this period. His contributions include Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice [8], Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models [9], and An Approach for Tracking Within-Segment Shifts in Market Shares [10], which collectively advanced measurement-grounded marketing analytics by enabling more flexible modeling of preferences, diffusion, and market dynamics under incomplete data.

Value-Centric Marketing Analytics era

V. Kumar [1] stands as a leading figure in value-centric marketing analytics during the Value-Centric Marketing Analytics era, with affiliations at University of Virginia [3] and University of Connecticut [4]. His contributions include Measuring Marketing Productivity [7], A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy [8], and Balancing Acquisition and Retention Resources to Maximize Customer Profitability [9], collectively advancing CLV-based resource allocation and long-horizon profitability metrics. Pedro Domingos [2] is a prominent figure in this era, with affiliations at Rockefeller University [5] and University of Washington [6]. Mining the network value of customers [10] articulated how customer networks contribute to overall value, signaling a shift toward network-aware CLV modeling that enriched value-centric analytics for long-term profitability.

AI-Enabled Marketing Automation era

Marko Sarstedt [1] is a prominent scholar associated with Ludwig-Maximilians-Universität München [3] and the University of Newcastle Australia [4] during the AI-Enabled Marketing Automation era. His contributions in this era include the assessment of the use of partial least squares structural equation modeling in marketing research [7] and Rethinking some of the rethinking of partial least squares [8], which advanced methodological rigor and accessibility of PLS-SEM for marketing analytics. Christian M. Ringle [2] is associated with James Cook University [5] and Universität Hamburg [6] during the AI-Enabled Marketing Automation era. As a co-author on An assessment of the use of partial least squares structural equation modeling in marketing research [7] and Rethinking some of the rethinking of partial least squares [8], he helped advance PLS-SEM adoption, improving interpretability, governance, and scalability of marketing analytics in AI-driven settings.