Journal of Marketing Analytics最新文献

This paper outlines a practical pedagogical framework for integrating AI-driven analytics into marketing education, tailored to equip students for the fast-evolving industry. Central to this approach is an iterative model that adapts teaching strategies to keep pace with technological advancements and industry demands. The framework emphasizes practical application, steering curriculum development towards the inclusion of AI tools like machine learning and predictive analytics, and crafting experiential learning opportunities. A focused examination of current teaching methods reveals gaps and introduces actionable solutions for fostering analytical skills essential for the AI-enhanced marketing landscape. The model not only advocates for a balance between theory and practice but also addresses challenges such as resource accessibility and the necessity of ethical considerations in AI education. By promoting interdisciplinary collaboration and continual curriculum refreshment, the paper positions the model as an essential blueprint for nurturing future marketing professionals capable of leveraging AI analytics for strategic decision-making. The conclusion calls for academia-industry partnerships to further enrich marketing education and underscores the importance of this framework in preparing students for successful careers in AI-driven marketing.

Marketing analytics has an impact on the role of every marketer in some way. However, faculty members often face challenges when recruiting students for courses focused on analytics, and many marketing students tend to avoid quantitative courses due to fear and hesitation. The lack of marketing pedagogy with classroom activities designed to help educators showcase marketing analytics as engaging, interesting, and practical adds to the problem. To meet this need, this paper presents a classroom activity focused on building interest in marketing analytics, designed to be completed in a single-class period in any marketing course by any instructor, regardless of expertise in analytics. Administered in four different marketing courses at three universities, results show that students experienced a significant increase in their awareness of, interest in, and excitement for marketing analytics. Students also experienced a significant decrease in how intimidating they perceive analytics to be. Qualitative comments provide more detail related to fears or hesitations that were lessened by the course activity. Overall, the results support that this activity provided students with a significant increase in their awareness of, interest in, and excitement for marketing analytics.

Marketing interviews are widely used to acquire information on the behaviour, satisfaction, and/or needs of customers. Although online surveys are broadly available, one of the major challenges is to collect high-quality data, which is fundamental for marketing. Since online surveys are mostly unsupervised, the possibility of providing false answers is high, and large numbers of participants do not finish interviews, yet our understanding of the reasons behind this pattern remains unclear. Here, we examined the possible factors influencing response rates and aimed to investigate the impact of technical and demographic information on the probability of interview completion rates of multiple surveys. We applied survival analysis and proportional hazards models to statistically evaluate the associations between the probability of survey completion and the technical and demographic information of the respondents. More complex surveys had lower completion probabilities, although survey completion was increased when respondents used desktop computers and not mobile devices, and when surveys were translated to their native language. Meanwhile, age and gender did not influence completion rates, but the pool of respondents invited to complete the survey did affect completion rates. These findings can be used to improve online surveys to achieve higher completion rates and collect more accurate data.

The Net Promoter Score (NPS) is a popular tool for assessing for-profit organizations’ customer loyalty and future growth to assess customer loyalty and future growth in for-profit organizations. However, its application in higher education has been limited, especially in examining its relationship with the alumni’s desire to give back to the university. To address this gap, we investigate the relationship between NPS and desire of online MBA students’ to give back to their alma mater. We also investigate the factors influencing online MBA students’ college experience and satisfaction, examine how those factors impact their propensity to recommend the academic institution to their friends. Our analysis is based on survey data from 306 online MBA students at a mid-sized state university in the U.S. Upon calculating the NPS, 15 detractors, 58 passives, and 162 promoters were identified and categorized. Results show that classes and financial aid staff were the most influential factors in the likelihood of recommendation. Statistical analysis shows that the promoters, compared to detractors, were 2 to 14 times more likely to give back to their alma mater. Recommendations were provided to assist university administrators.

The value co-destruction process has been heavily studied in recent years. However, most studies have focused on the value of co-destruction's emotional and behavioral consequences without considering its psychological impact on consumer behavior. This study bridges this research gap by examining consumer alienation as a fundamental psychological state that underpins the value co-destruction process. Its primary objective is to delve into the significance of alienation within retail banking, uncovering its underlying causes and consequences. Furthermore, the study examines how the external locus of control affects the behavior of alienated consumers. The data were collected from 211 French retail bank users. Structural Equation Modeling was used to test the hypotheses. The study showed that consumers’ discontent, perceived differential treatment, and financial distress lead to value destruction and influence consumer alienation toward retail banking service providers. A high level of alienation leads to payment delinquency, negative WOM, and switching intentions to internet-only banks. The external locus of control moderates these relationships. By identifying the antecedents and consequences of consumer alienation in retail banking, this study provides practical advice to retail banking providers on retroactively identifying alienated consumers. This study provides ideas on restoring lost value, retaining customers, and preventing them from switching to internet-only banks. Our research enriches the Service-Dominant Logic theory by exploring the role of consumer alienation on the value co-destruction process. It enhances the understanding of consumer alienation in retail banking.

Abstract

Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

The use of Voice Assistance Systems based on Artificial Intelligence (VASAI) -such as Siri, Alexa, and many others- is becoming more and more popular; however, studies on this topic are still scarce. One of the topics that has only been tangentially addressed is the impact of brand-related issues (such as brand credibility and brand loyalty) on customer satisfaction and continued use intention of VASAI. The present study addresses this topic by postulating a structural model for its evaluation. The author's structural model also examines the influence of system quality constructs (system stability, system agility, and anthropomorphism), and information quality constructs (information exhaustiveness or information up-to-datedness) as independent variables. The researcher uses a questionnaire, based on previous literature, which is administered to a sample of 651 participants. The proposed structural model is evaluated by applying PLS-SEM analysis. The results show that brand credibility influences the constructs of customer satisfaction (β = 0.289/p-value < 0.001) and continued use intention (β = 0.304/p-value < 0.001). Similarly, the findings indicate that brand loyalty has a moderating effect on the relationships between brand credibility and consumer satisfaction, on the one hand, and brand credibility and continued use intention, on the other. In view of the results, the author concludes that some brand-related constructs have an impact on customer satisfaction and intention to continue using VASAI, indicating the critical importance of brand management for the success and future development of these technologies.

Abstract

Fashion brands including luxury brands are embracing TikTok to access young consumers, but there is a notable absence of research on how luxury fashion brands can leverage TikTok. Video analytics is crucial for understanding marketing communications via TikTok, a video-based social media platform. This study aims to examine how luxury brands establish their presence and effectively attract and engage with young consumers on TikTok through social media video analytics. A multiple case study approach was employed on the selected four luxury fashion brands. Data were collected from the selected brands’ official accounts, endorsed users’ accounts, and related hashtag links on TikTok. A three-stage content analysis of social media video analytics was conducted. The common and customized strategies employed across the selected brands on TikTok were identified, respectively. The findings revealed that young consumers prefer high-quality videos regarding branding messages, branded challenges, and influencers-led branded content. A consumer-brand engagement framework was proposed based on the data analysis. This research contributes to understanding how TikTok benefits the fashion industry and offers theoretical and practical insights for fashion brands to better harness TikTok. This study represents a pioneering endeavor in exploring social media video analytics, contributing to the advancement of marketing analytics literature.

This study aims to analyze the impact of consumption values on the adoption of NBG’s mobile banking services and the role of customers’ motivation and app adoption in this process. The study reveals users’ attitudes and behavior towards mobile banking by establishing significant connections between selected UTAUT constructs. Based on a critical case of the m-banking services of a leading Greek financial institution, we gathered 418 full-answered questionnaires from a convenience sample of 654 customers to test 18 research hypotheses. The research results confirm that the three selected UTAUT constructs, are antecedents of behavioral intention, supporting the initial assumption. This study analyzes the factors that drive the adoption and usage of mobile banking apps, providing valuable insights for marketing academia and managers.