Endogeneity of marketing variables in multicategory choice models

A regressor is endogenous if it is correlated with the unobserved residual of a model. Ignoring endogeneity may lead to biased coefficients. We deal with the omitted variable bias that arises if firms set marketing variables considering factors (demand shocks) that researchers do not observe. Whereas publications on sales response or brand choice models frequently take the potential endogeneity of marketing variables into account, multicategory choice models provide a different picture. To consider endogeneity in multicategory choice models, we follow a two-step Gaussian copula approach. The first step corresponds to an individual-level random coefficient version of the multivariate logit model. We analyze yearly shopping data for one specific grocery store, referring to 29 product categories. If the assumption of a Gaussian correlation structure is met, the copula approach indicates the endogeneity of a category-specific marketing variable in about 31% of the categories. The majority of marketing variables rated as endogenous are positively correlated with the omitted variable, implying that ignoring endogeneity leads to an overestimation of the coefficients of the respective marketing variable. Finally, we investigate whether taking endogeneity into account by the copula approach leads to different managerial implications. In this regard, we demonstrate that for our data ignoring endogeneity often suggests a level of marketing activity that is too high

Big data applications in food supply chains

International Conference On Engineering And Computer Science (ICECS) 2022: The use of innovative technology in accelerating problems sustainable development. 13 December 2022, Bandar Lampung City, IndonesiaFood supply chains are characterized by innovation, not only in products but also in processes. This paper aims to identify big data applications in the food and drink sector and present its findings as a state-of-the-art literature review. Academic databases were searched using ‘food’ or ‘drink’ and ‘big data’ keywords. Scholarly publications from 2015 onward are identified and presented in broad categories of demand prediction and retail operations optimization. The review recognized big data applications as a great opportunity for food supply chains. The applications aimed 1) to understand the customer base and inform marketing communications strategy, 2) to predict demand and organize retail operations to meet this demand, and 3) to optimize prices, assortment, and inventories based on demand patterns. Applications in this review focused more on descriptive and predictive analytics than prescriptive analytics, possibly due to the emergent nature of these applications. Descriptive analytics applications focused on capturing data, summarizing the status quo, and developing customer segments which can then be managed using varying marketing strategies. Predictive analytics applications focused on demand prediction with novel approaches proposed by the machine learning community. Prescriptive analytics applications aimed at promotion optimization and pricing for profit maximization. Cognitive analytics applications extracted customer reviews from online stores to inform which products should be marketed in what way. The review offers managerial insights on circumstances where big data analytics could prove beneficial. Managerial implications suggest that data integrators enable big data applications by ensuring the data collected are accurate, timely, and complete to inform descriptive, predictive, and prescriptive analytical models