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Abundant consumer data has made decision-making more complicated, rather than simple, for marketers. This raises an important question about which variables in the data contain reliable information for retailers to predict future consumer purchase value (CPV) to guide strategic decisions. The authors address this question by exploring the variables "distinctive choice of brand country of origin" (DBCOO) and "country of origin diversity" (COO diversity) as analytical tools to extract insights from consumer purchase data. Building on signaling theory, the authors theorize and empirically test that DBCOO and COO diversity in a consumer's purchase history can signal, and therefore help predict, CPV. Moreover, the authors explore high-involvement product categories and purchase frequency as boundary conditions to develop a comprehensive framework of COO signals as strategic analytical tools. They find that DBCOO in a consumer's purchase history indeed increases CPV and that this relationship is enhanced for high-involvement product categories but moderated curvilinearly by purchase frequency. Moreover, they find that the COO diversity–CPV link is positive but interacts negatively with both moderators. This allows retailers to successfully distinguish high- from low-CPV consumers and thus enables them to manage the marketing mix and resources more effectively. [ABSTRACT FROM AUTHOR]

To obtain a competitive advantage in the e-commerce presale mode, an e-tailer needs to design new products favored by consumers and consider the demand matching and supply chain services to achieve better operational results. This paper develops a data-driven optimization model based on consumer analytics and bilevel multiobjective programming to provide practical solutions for the e-commerce presale mode. At the upper level, the e-tailer, as the leader, identifies consumer preferences by quantifying product popularity through consumer analytics. Then, the e-tailer optimizes the product popularity to select the products suitable for presale and formulates the production plan. At the lower level, the logistics enterprise, as the follower, formulates the distribution plan based on the leader's decision. Because consumer analytics are utilized and the model has a bilevel structure, a data-driven optimization method is proposed to conduct simulations for the proposed model. The model uses the multiple objectives binary particle swarm optimization with multiple social structures (MOBGLNPSO) and bilevel multiobjective particle swarm optimization with multiple social structures (Bi-MOGLNPSO). The results analysis and sensitivity analysis verify that the proposed model and method can improve the demand matching and operational efficiency of the e-commerce supply chain and uncover managerial implications for both the e-tailers and logistic enterprises. [ABSTRACT FROM AUTHOR]

This study considers and defines the relationship between the policies the gas companies and gas distributors use in order to promote and encourage its customers to behave responsibly and maintain a sustainable consumption even in the highly debated activity sector, energy. Starting from the definition of sustainability, "meeting today's needs without sacrificing the ability of future generations to meet their own needs" (Sustainable Development Goals, 2030 Agenda, 2015), also known as sustainable development, companies underline and support the importance of such a development policy. Corporate Social Responsibility is an aspect of corporate governance through which companies have initiated a range of socially responsible actions that can be quantified in terms of sustainability and sustainable performance. These actions will be analyzed and researched during the article. The indicators will be measured and the main results highlighted. The essential role these companies have in the energy field in Europe, together with innovative sustainable development policies, help to avoid, reduce or control the harmful impact of industrial activities on the environment and population.

Like many healthcare organizations, Baylor Scott & White Health (BSWH) is awash with data. Often, this data is used in siloed departments to monitor safety and quality, make local business decisions, and motivate staff to improve processes to achieve sustained excellence and market share. As margins get thinner and competition from various disrupters increases, organizations have tried to improve the patient experience to remain viable as part of a calculated strategy. Nevertheless, these entities have struggled to focus limited resources for sustained improvement in patient experience. This article details how a large Texas-based healthcare system "operationalized" The Beryl Institute's Experience Framework via a multidisciplinary data approach. "Key gaps" that negatively impact the patient experience were identified using 99 data elements from common, readily available sources. Demonstrating the interconnected nature of the data has proven to be essential in engaging leaders to view the patient experience as an essential component to providing quality care. This crucial support from senior leaders drives efforts to safety, quality, and experience. A plan for how this approach can be implemented in any organization is shared, along with a discussion on sustainability, the use of these tools in an organization's improvement journey, and how it can help create higher-performing care teams. Limitations and future opportunities for enhancements to the approach are also provided. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.

Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.

This study considers and defines the relationship between the policies the gas companies and gas distributors use in order to promote and encourage its customers to behave responsibly and maintain a sustainable consumption even in the highly debated activity sector, energy. Starting from the definition of sustainability, "meeting today's needs without sacrificing the ability of future generations to meet their own needs" (Sustainable Development Goals, 2030 Agenda, 2015), also known as sustainable development, companies underline and support the importance of such a development policy. Corporate Social Responsibility is an aspect of corporate governance through which companies have initiated a range of socially responsible actions that can be quantified in terms of sustainability and sustainable performance. These actions will be analyzed and researched during the article. The indicators will be measured and the main results highlighted. The essential role these companies have in the energy field in Europe, together with innovative sustainable development policies, help to avoid, reduce or control the harmful impact of industrial activities on the environment and population. [ABSTRACT FROM AUTHOR]

Discovering needs, habits and consumer behavior is the primary task of marketing analytics. It is necessary to integrate marketing and analytical skills with IT skills. Such knowledge integration allows access to data (structured and unstructured), their analysis and finding out information about the opinions, attitudes, needs and behavior of customers. In the paper is set the hypothesis that software tools can collect data (messages) from social networks, analyze the content of messages and get to know the attitudes of customers about a product, service, tourist destination with the ultimate goal of improving customer relations. Experimental results are based on the analysis of the content of social network Facebook by using the package and function R language. This language showed a satisfactory application and development power in analysis of textual data on social networks for marketing analytics.