Journal of Marketing Analytics最新文献

Abstract

We study changes in marketing mix sensitivities in the United States following the Great Recession using weekly scanner data from 2010 representing 16,294 stores (9,900 food stores, 3,185 mass merchandisers, and 3,209 drug stores). We estimate baseline category sales and the dependence of category demand on marketing activities (price, display and feature). We then relate the store-level demand to geodemographic characteristics of stores’ zip codes, accounting for local competitive intensity faced by stores. We find that as the percentage of college educated consumers in a store’s zip code increases, category sales increase and price sensitivity of the store decreases (regardless of store format). Median income of the store’s zip code explains most of the observed variation in estimated price sensitivities across stores. We find that a majority of stores are underpricing cereal, coffee and cola, i.e., pricing lower than what the optimal mark-ups implied by their price elasticities would suggest.

Sellers on online marketplaces such as Amazon.com use a variety of retail and retail media advertising services to improve their brand performance, including awareness, consideration, and revenue. But how can they measure their progress and drive these metrics? For 122,000 brands, we measure Amazon shoppers’ brand awareness, consideration, and purchases and test how they change with ad and retail actions. Furthermore, we compare these brands’ past media mix with the recommended allocation based on the model’s coefficients. We find that new product launches and upper-funnel retail media advertising are particularly effective for small brands. Medium-sized and large brands benefit most from lower-funnel advertising. For the funnel stages, all three metrics benefit from the number of new reviews, % discount, negative keywords, and geo-reach campaigns. These results are robust across different product categories, but we find notable differences in how upper- and middle-funnel ad products succeed in driving sales.

The timing of degree completion for students taking post-secondary courses has been a constant source of angst for administrators wanting the best outcomes for their students. Most methods for predicting student degree completion extensions are completed by analog methods using human effort to analyze data. The majority of data analysis reporting of degree completion extension variables and impacts has, for decades, been done manually. Administrators primarily forecast the factors based on their expertise and intuition to evaluate implications and repercussions. The variables are large, varied, and situational to each individual and complex. We used machine learning (automated processes using predictive algorithms) to predict undergraduate extensions for at least 2 years beyond a standard 4 years to complete a bachelor's degree. The study builds a machine learning-based education understanding XAI model (ED-XAI) to examine students’ dependent and independent variables and accurately predict/explain degree extension. The study utilized Random Forest, Support Vector Machines, and Deep Learning Machine learning algorithms. XAI used Information Fusion, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME) models to explain the findings of the Machine Learning models. The ED-XAI model explained multiple scenarios and discovered variables influencing students’ degree completion linked to their status and funding source. The Random Forest model gave supreme predictive results with 89.1% Mean ROC, 71.6% Overall Precision, 86% Overall Recall, and 71.6% In-class Precision. The educational information system introduced in this study has significant implications for accurate variables reporting and impacts on degree extensions leading to successful degree completions minimally reported in higher education marketing analytics research.

The exponential growth of data-driven decision-making in modern business stimulates the gravity of market intelligence (MI) in both industry and academia. Consequently, the field of MI has been significantly influenced by a substantial body of literature over the past few decades. The study makes a scholarly contribution by carefully analyzing the previous literature and plotting relevant research constituents. Employing a bibliometric lens to 293 articles, we found a notable increase in the number of publications over last few decades. The United Kingdom, followed by the United States, received the most attention as a region in the MI literature; however, the most prolific institution is located in Sweden. Besides, a detailed keyword analysis takes the study to the next level, identifying six key research themes, including ‘strategic decision intelligence,’ ‘marketing and sales,’ ‘entrepreneurial dynamics and emerging markets,’ ‘strategic navigations in dynamic environments,’ ‘holistic product development, research and innovation,’ and ‘information systems and knowledge management,’ and 13 future research questions. The study lends a hand to researchers providing a statistical and visual summary of MI’s scholarly status, future research direction, and marketing analytics practitioners by offering insights on implementing MI in a holistic and efficient marketing decision-support system.

Abstract

The purpose of this research is to test the moderating role of perceived brand globalness (PBG) on consumer unfavourable attitudes and buying intention towards brands associated with a conflicting political belief. Three studies were conducted. Study 1 (N = 228) examines the effect of a brand being associated with a conflicting political belief on attitude and buying intention. Study 2 (N = 212) tests the moderating role of PBG among these relationships. Study 3 (N = 126) tests the moderating role of PBG for a service brand and adds the mediating role of self-congruity. We followed marketing analytics best practices to examine causal effects of manipulated stimuli. We also examined the psychometric characteristics of the data-collection tool and tested direct relationships between variables. Additionally, we used percentile bootstrap confidence intervals, with PROCESS-Macro, to test mediating effects in the theoretical model. The studies include moderating analyses and analysis of variance. The results show that the association of a brand with a conflicting political belief negatively influences perceived self-congruity, which leads to an unfavourable attitude and, in turn, to a lower buying intention. Presenting a brand as global mitigates such unfavourable effects. The moderating effect of PBG is confirmed for a tangible product and for a service brand.

This study explores the UGC of two international sports events related to a country to analyse: (1) the way that two international sports events-related UGC vary across different social media, (2) the types of UGC, and (3) the differences in the social media use in different countries. Using the word Spain and hashtags of XLI Marathon Valencia 2022 and Davis Cup 2022, 16,820 posts were obtained. Using frequencies, χ² tests, sentiment analysis, and content analysis, results show (1) the prominent use of social media in both events, (2) different profiles between non-professionals and professionals, (3) predominant negative UGC in more professional content, and (4) the sentiment geolocation. Based on these results, it is possible to recommend considering the potential of each social network, analysing the profile of those who are active in the masses and trying to explain and transform those negative comments that can damage not only the image of the event but also of the country where it is held. Additionally, it is essential to underline the importance of rigorous and systematic social media study.

Adoption of digital novel technologies (DNT) is an important phenomenon faced by managers that is attracting emergent research attention. Although previous research establishes that DNT in general enhance business performance, more recent theorizing suggests that this may not always be the case. Notwithstanding, researchers have paid little attention to contingency conditions that may affect the strength and nature of the relationship between DNT and marketing performance. By examining organizational factors that moderate the DNT–performance relationship, this study explores types of firm for which DNT are particularly beneficial. Based on survey data from firms and empirical analyses, findings indicate that firms pursuing agility and digital business transformation strategies are in a favorable position to achieve better performance when adopting DNT. Additionally, the DNT–performance link is stronger for smaller, product-based firms, regardless from B2B/B2C sectors. The study sheds light on the vision of Industry 4.0 transition with implications for academics. Executives evaluating the adequacy of DNT adoption should consider certain contingencies advanced in this research.

Marketing is one of the areas where large language models (LLMs) such as ChatGPT have found practical applications. This study examines marketing prompts—text inputs created by marketers to guide LLMs in generating desired outputs. By combining insights from the marketing literature and the latest research on LLMs, the study develops a conceptual framework around three key features of marketing prompts: prompt domain (the specific marketing actions that the prompts target), prompt appeal (the intended output of the prompts being informative or emotional), and prompt format (the intended output of the prompts being generic or contextual). The study collected hundreds of marketing prompt templates shared on X (formerly Twitter) and analyzed them using a combination of natural language processing techniques and descriptive statistics. The findings indicate that the prompt templates target a wide range of marketing domains—about 16 altogether. Likewise, the findings indicate that most of the marketing prompts are designed to generate informative output (as opposed to emotionally engaging output). Further, the findings indicate that the marketing prompts are designed to generate a balanced mix of generic and contextual output. The study further finds that the use of prompt appeal and prompt format differs by prompt domain.

The present study introduces a conceptual framework to explore sales professionals’ use of artificial intelligence (AI) in the sales process. The author explores AI’s impact and its relationships with specific outcomes within the sales process. The study first explores the embryonic artificial intelligence literature on sales to measure sales professionals’ perceptions of AI by conducting a content analysis. Based on the results, 79 studies were found on AI and sales, with only 13 specifically looking at the business-to-business sales process. Given the newness of AI, this is a dire need to dive deeper into the use of AI in the B2B sales process. A content analysis from the scant literature and data from 62 sales professionals was performed to conceptually develop a framework proposing AI’s impact on several outcomes: sales process effectiveness, administrative efficiency, and performance with customers.

Abstract

The brand purpose is a relevant new concept considered and applied by companies. However, the existing research is primarily isolated case studies, highlighting evidence of its application as a generator of value and brand identity. No studies still identify the value of a brand purpose, and there is no theory or concept around this point. In this sense, this research aims to map the theory related to the value of a brand purpose from data analyzed systematically that could reveal the related phenomenon and its relations. An exploratory study of an inductive nature was carried out using Grounded Theory with specialists (practitioners) in marketing. The Atlas. Ti 23 software was selected to carry out the analyses in this study. After completing the open, axial, and selective coding phases, the core phenomenon was identified and entitled brand purpose equity (BPE). Four subcategories of BPE were classified: activists, inspirational, differentiators, and identity creators, suggesting that BPE is a multidimensional construct. This is the first study that offers an exploratory conceptual framework to conceptualize and create a theoretical basis for the value of brand purpose. In this sense, this research paves the way for building the brand purpose value theory and a measurement prototype scale, in addition to allowing the identification of antecedents and consequences of the brand purpose and its impacts on brand equity, representing advances and fundamentals for the development of models and empirical evidence of improvement in this area.