Predicting Enterprise Marketing Decision Making with Intelligent Data-Driven Approaches

Jiufan Wang; Qi Xin; Yuning Liu; Junliang Wang; Tianyi Yang

doi:10.5281/zenodo.11357252

Authors

Jiufan Wang Independent Researcher
Qi Xin University of Pittsburgh
Yuning Liu Seattle University
Junliang Wang Johns Hopkins University
Tianyi Yang University of Connecticut

DOI:

https://doi.org/10.5281/zenodo.11357252

Keywords:

New Media Advertising, Audience Attention, Data-driven User Profiling, Marketing Decisions

Abstract

In order to improve the marketing effect of new media, traditional enterprises should increase the proportion of new media advertising and pay attention to the flow of audience's attention. On the basis of integrating data and information, advertising should conform to the changing trend of the media environment and increase the proportion of new media, so as to understand the audience's consumption habits of media use and formulate a reasonable media mix plan. Promote the integration of traditional media and new media to obtain higher publicity results at the lowest cost. This article delves into the key role of data-driven user profiling in marketing decisions. By analyzing user behavior, preferences, and needs, marketing teams are able to more accurately determine target markets, product positioning, and differentiation strategies. In addition, data-driven user profiles can guide the development of marketing and communication strategies, and enable enterprises to implement personalized marketing and customer relationship management. This personalized marketing and service experience helps to improve user loyalty and satisfaction, which in turn enhances the market competitiveness of enterprises. Overall, data-driven profiling plays an integral role in helping businesses achieve their continued growth and profitability goals.

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Author Biographies

Jiufan Wang, Independent Researcher

Independent Researcher, William & Mary , Williamsburge, VA, USA.

Qi Xin, University of Pittsburgh

Management Information Systems, University of Pittsburgh, Pittsburgh, PA, USA.

Yuning Liu, Seattle University

Business Analytics, Seattle University, Washington, USA.

Junliang Wang, Johns Hopkins University

International Economics, Johns Hopkins University , USA.

Tianyi Yang, University of Connecticut

Financial Risk Management, University of Connecticut, Stamford CT, USA.

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