Call for Papers - Special Issue: Predicting and Optimizing Marketing Performance in Dynamic Markets

Our world is turbulent: ecological, social, economic, and business environments change continuously and sometimes rapidly. Consumers have changing preferences, learn from experiences and interactions with other consumers, build (or lose) trust in brands, and adopt new technologies. Firms innovate and introduce new products, grow, fail, merge with other firms, and respond to competition. Exogenous events such as dramatic changes in economic conditions (e.g., stock market crash of 2008), regulations (e.g., GDPR in Europe, US-Chinese import tariffs), man-made crises (e.g., VW diesel emissions scandal), as well as natural disasters (e.g., Hurricane Katrina in the US or the COVID19 pandemic), cause major shifts in markets. Market turbulence has not only immediate effects on manufacturing, distribution, product portfolios, pricing, competition, and consumer behavior but has long-term strategic consequences for firms having to anticipate and address the uncertainty caused by market dynamics. To address dynamics and shifts in markets, firms need to be able to analyze and predict changes and their impact on market behaviors and reallocate resources accordingly. To this end modern technologies (e.g., Internet of Things, mobile and GPS technologies, virtual and other digital environments) enable firms to faster identify market shifts and better track and capitalize on dynamics.

The special issue focuses on novel marketing data and modern methodologies from OR, statistics, econometrics, and/or computer science that help companies respond to market dynamics and market shifts more efficiently. In particular, we encourage new research incorporating big, high velocity, or unstructured data, as well as further development of models and methods that account for dynamics (e.g., dynamic linear models, vector autoregression, or hidden Markov models) and market shifts (e.g., regime switching models), uncertainty (e.g., Bayesian learning, dynamic discrete choice models, dynamic programming), as well as heterogeneity (e.g., Bayesian hierarchical modeling). We encourage research that involves large scale applications that have direct practical relevance in business practice in industries such as retailing, transportation, inventory management, entertainment and tourism. Boundary spanning applications of machine learning combined with more classical approaches from marketing and OR, are of great interest to the special issue. The specific topics of interest include but are not limited to:

•    Dynamic multimedia budget allocation 
•    Product-harm crisis
•    Consumer learning and behavior shifts
•    Dynamic pricing
•    Brand marketing policy shifts
•    Dynamic customer relationship management
•    Response dynamics in direct marketing 
•    Brand and corporate equity development
•    Marketing and dynamic competition
•    Advertising new products or events (e.g., movies)
•    Dynamics of viral marketing
•    Demand under dynamic uncertainty
•    Real-time recommendations
•    New product innovation and introduction

Submission Guidelines and Review Process: Papers must be submitted at under the category “S.I. Predicting and Optimizing Marketing Performance in Dynamic Markets” by December 31, 2021. All papers submitted to this special issue should report original work and make a contribution to the journal OR Spectrum by using a quantitative research paradigm and OR methodologies. According to the aims of OR Spectrum, high quality papers are wanted that match the scope of the journal, show rigor in applying state-of-the-art OR techniques, and promise to have an impact on future work of the OR community.
Papers will be screened by the Editor-in-Chief and one special issue editor. If the paper is deemed to be of sufficient quality, it will be reviewed according to the standards of OR Spectrum by at least two experienced reviewers. We will adopt a rapid and fair review process striving to provide reviews within three months of submission. Accepted papers will be available online prior to the publication of the special issue.

Special Issue Editors

Friederike Paetz, TU Clausthal, Germany,

Daniel Guhl, HU Berlin, Germany,

Udo Wagner, University of Vienna, Austria,

Michel Wedel, University of Maryland, United States,