Modeling Emerging Marketing Opportunities in Potential Demand Prediction during Acute Crises using Statistical Learning Methods

Engineering notes

Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This research aims to develop a concept of “potential demand,” a flexible driving variable, to evaluate the role of emerging marketing opportunities during acute crises, taking the COVID-19 pandemic as an example. It fills a research gap in that it addresses the structural shortcomings of the traditional marketing literature that reduces disruptions to risk management. It shifts marketing thought to an integrated approach which views the components of the enhanced marketing mix as a flexible system to promote organizational sustainability. The research was descriptive, analytic and inferential with Purposive sampling and respondents (N = 800). Data were analyzed using exploratory factor analysis and regression models, using advanced software packages as SPSS, Python in Google Colab environment. The measurement model had a high internal consistency and efficiency (Cronbach's alpha coefficient 0.937). The inference analysis results showed that there were positive relationships that were significant. Among the factors, the highest was the "changing consumer behavior", followed by "digital marketing activities", "product availability" and "trust of the customers". The model illustrates the technological paradox with the adoption of technology: it has only a marginal direct effect, as the flexible mix is a product of technology, not its driver of demand. The paper concludes that bold operations changes and re-engineering of offerings are needed in order to seize market opportunities and predict potential demand. It suggests the need for consumer-friendly adaptive pricing and logistics policies that are easily scalable. The literature review contributes to the research by providing background information and context to link the dynamics of digital transformation and strategic marketing flexibility in the effective management of the new consumers' gaps.

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