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The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

Challenges of property data in developing markets have been reported by several authors. However, a deep understanding of the actual nature of this phenomenon in developing markets is largely lacking as in-depth studies into the actual nature of data challenge in such markets are scarce in literature. Specifically, the available literature lacks clarity about the actual nature of data challenges that developing markets pose to valuers and how this affects valuation practice. This study provides this understanding with focus on the Lagos property market.

This study utilises a qualitative research approach. A total of 24 valuers were selected using snowballing sampling technique, and in-depth semi-structured interviews were conducted. Data collected were analysed using thematic analysis with the aid of NVivo 12 software.

The study finds that the main data-related challenge in the Lagos property market is the lack of database of market property transactions and not the lack or absence of transaction data as it has been emphasised in previous studies. Other data-related challenges identified include weak property rights institution with attendant transaction costs, underhand dealings among professionals, undocumented charges, undisclosed information, scarcity of data relating to specialised assets and limited access to the subject property and required documents during valuation. Also, the study unbundles the factors responsible for these challenges and how they affect valuation practice.

The study has implication for practice in the sense that the deeper knowledge of data challenges could provide insight into strategy to tackle the challenges.

This study contributes to the body of knowledge by offering a fresh and in-depth perspective to the issue of data challenges in developing markets and how the peculiar nature of the real estate market affects the nature of data challenges. The qualitative approach adopted in this study allowed for a deep enquiry into the phenomenon and resulted into an extended insight into the peculiar nature of data challenges in a typical developing property market.

We propose a simple model consisting of two separated markets: the market for good y and the market for good x. Purchasing information about consumer behavior in the former market helps the monopolist firm, in the latter market, to price-discriminate. Consumers differ in their income and in their level of myopia. Personal data market regulation could both increase consumers' awareness about the treatment of their data and allow them to have their data erased from the data holder. We find that the former aspect of the policy reduces the number of transactions, and hence tends to reduce total surplus, while the second typically boosts willingness to pay of consumers and has positive effects on surplus, provided that the share of high-income consumers is not too high. The overall effect of regulation on total welfare depends on the share of high-income and myopic consumers.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

Regulatory initiatives and related technological shifts have been imposing restrictions on data-driven marketing (DDM) practices. This paper aims to find the main restrictions for DDM and the key management theories applied to investigate the consequences of these restrictions.

The authors conducted a unified bibliometric analysis with 104 publications retrieved from both Scopus and Web of Science, followed by a qualitative, in-depth systematic literature review to identify the management theories in literature and inform a research agenda.

The fragmentation of the research outcomes was overcome by the identification of 3 main clusters and 11 management theories that structured 18 questions for future research.

To the best of the authors’ knowledge, this paper sets for the first time a frontier between almost three decades where DDM evolved with no significative restrictions, grounded on innovations and market autoregulation, and an era where data privacy, anti-trust and competition and data sovereignty regulations converge to impose structural changes, requiring scholars and practitioners to rethink the roles of data at the strategic level of the firm.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

In the past few years, information technology has stimulated several innovations in the business and marketing fields, and advances in the technology are changing the research surrounding those fields. Recently, focusing topics in the management and marketing field are electronic customer relationship management (CRM) and the practical use of marketing data and information technology. The goal of this article is not to provide an all‐inclusive tutorial on CRM but rather to provide fundamental concepts behind CRM and some aspects of the system development process. This article provides a comprehensive review of CRM and marketing data sources, and consider some design concepts for creating an effective CRM system from the viewpoint of practical use of the data sources.

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation.

A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions.

The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization.

From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

Examines computerised marketing information systems and considers the implications of their role and the major characteristics of such systems and discusses the DEMON model in detail. States that a system is a set of components which interact in a certain manner to achieve its goals; an open system is distinguished from a closed one by the fact that interaction exists with its environment; since most organisations must be considered as open systems, their environment becomes a dialectic part of any systems definition. Concludes that computerised marketing information systems will never be able to replace the questioning human mind.