Cognitive computing,Big Data Analytics and data driven industrial marketing

The integration of cognitive computing and big data analytics leads to a new paradigm that enables the application of the most sophisticated advances in information and communication technology (ICT) in business, including industry, business to business, and related decision-making process. The same paradigm will lead to several breakthroughs in the subfield of industrial marketing: a field both promising and extremely challenging. This special issue makes a case that cognitive computing and big data are a source of a new competitive advantage that, if properly embraced, will further consolidate industrial marketing management position in the of core the decision-making process of businesses operating locally and globally. In this vein, the value added of this special issue is twofold. On the one hand, this special issue communicates high quality research on big data analytics and data science as it is applied in industrial marketing management; On the other hand, it proposes a multidisciplinary approach to the study of the design, implementation and provision of sophisticated applications and systems necessary for data-driven industrial marketing decisions.     

A dynamic capability view of marketing analytics: Evidence from UK firms

While marketing analytics plays an important role in generating insights from big data to improve marketing decision-making and firm competitiveness, few academic studies have investigated the mechanisms through which it can be used to achieve sustained competitive advantage. To close this gap, this study draws on the dynamic capability view to posit that a firm can attain sustained competitive advantage from its sensing, seizing and reconfiguring capabilities, which are manifested by the use of marketing analytics, marketing decision-making, and product development management. This study also examines the impact of the antecedents of marketing analytics use on marketing related processes. The analysis of a survey of 221 UK firm managers demonstrates: (a) the positive impact of marketing analytics use on both marketing decision-making and product development management; (b) the effect of the latter two on sustained competitive advantage; (c) the indirect effect of data availability on both marketing decision-making and production development management; and (d) the indirect effect of managerial support on marketing decision-making. The research model proposed in this study provides insights into how marketing analytics can be used to achieve sustained competitive advantage.     

Big data analytics for supply chain relationship in banking

This paper reports how a commercial bank in Asia uses big data analytic as a tool to explore the internal B2B data to improve supply chain finance and the efficiency of marketing tactics and campaigns. A case study was conducted by analyzing two types of supply chain relationships: (1) supply chain relationships in the credit reports; (2) e-wiring transactions among supply chain companies. The results show that big data analytics is very useful in terms of improving the commercial banks' marketing and risk management performances. The case study also set a good example for B2B firms seeking to understand how they could leverage big data analytics to differentiate customer solutions, sustain profitability and generate new business values. Theorical and practical implications are also discussed.     

Fostering B2B sales with customer big data analytics

This study focuses on the use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from 417 B2B firms. The study also examines whether analytics culture within a firm moderates these effects. The study finds that the use of customer big data significantly fosters sales growth (i.e. monetary performance outcomes) and enhances the customer relationship performance (non-monetary performance outcomes). However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.     

Analytics in the era of big data: The digital transformations and value creation in industrial marketing

Big data analytics has been a topical area in the past decade. Despite it is emphased as a promising tool for the B2B sectors, there is a short of academic studies about this phenomenon in the industrial markets. Existing big data analytics focuses more on the consumers' marketing aspect, while in fact both the consumers' data and the machine-generated transaction data can be gathered and analysed at the interorganisational level. Subsequently, there is a need to increase the attention on the B2B aspects of big data analytics and the interactions of stakeholders. This paper, therefore, investigates the digital transformation enabled by big data analytics in the industrial markets and provides a conceptual framework. It solicits research articles that provide insights into various industrial contexts of this topic and applied both qualitative and quantitative approaches to identify the big data gathering and applications for value creation.     

How to Build an AI Climate-Driven Service Analytics Capability for Innovation and Performance in Industrial Markets?

AI climate-driven service analytics capability has been anecdotally argued as a viable strategy to enhance service innovation and market performance in B2B markets. While AI climate refers to the shared perceptions of policies, procedures, and practices to support AI initiatives, cognitive service analytics capability refers to the analytical insights driven by AI climate and augmented by both machines and humans to make marketing decisions. However, there is limited knowledge on the antecedents of such analytics capabilities and their overall effects on service innovation and market performance. Drawing on service analytics literature and the microfoundations of dynamic capability theory, this study fills this research gap using in-depth interviews (n = 30) and a survey (n = 276) of service analytics managers within the AI climate in Australia. The findings confirm the five microfoundations of cognitive service analytics capabilities (cognitive technology, cognitive information, cognitive problem solving, cognitive knowledge & skills, cognitive training & development). The findings also highlight the significant mediating effect of service innovation in the relationship between analytics climate and market performance and cognitive service analytics capability and market performance.     

You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance

The potential of big data analytics when it comes to gaining business insights, such as market trends and consumer preferences, has captured the interest of both scholars and business practitioners. However, the extant literature has so far provided limited empirical evidence to demonstrate how big data analytics can create business value. To address this research gap, this paper followed a novel big data analytical approach that involved analysing email archives about product/services demand clusters in a B2B setting. We analysed 621 k emails exchanged between 2009 and 2018. We identified a number of discussion clusters that were considered proxies for the interest buyers expressed in the products/services on offer. These clusters and associated discussion trends were linked to the company's revenues and financial performance, showing good predictive power. In doing this, we have demonstrated how widely available data, such as emails, which all companies have, can be used to underpin new methods for the early identification and monitoring of product demand trends, informing marketing strategies.     

Real-time big data processing for instantaneous marketing decisions: A problematization approach

The collection of big data from different sources such as the internet of things, social media and search engines has created significant opportunities for business-to-business (B2B) industrial marketing organizations to take an analytical view in developing programmatic marketing approaches for online display advertising. Cleansing, processing and analyzing of such large datasets create challenges for marketing organizations — particularly for real-time decision making and comparative implications. Importantly, there is limited research for such interplays. By utilizing a problematization approach, this paper contributes through the exploration of links between big data, programmatic marketing and real-time processing and relevant decision making for B2B industrial marketing organizations that depend on big data-driven marketing or big data-savvy managers. This exploration subsequently encompasses appropriate big data sources and effective batch and real-time processing linked with structured and unstructured datasets that influence relative processing techniques. Consequently, along with directions for future research, the paper develops interdisciplinary dialogues that overlay computer-engineering frameworks such as Apache Storm and Hadoop within B2B marketing viewpoints and their implications for contemporary marketing practices.     

Online content match-making in B2B markets: Application of neural content modeling

Business-to-business (B2B) sellers need to enhance content marketing and analytics in an online environment. The challenge is that sellers have data but do not know how to utilize it. In this study, we develop a neural content model to match the content that B2B sellers are providing with the type of content that buyers are seeking. The model was tested with two experiments using a dataset that combines cookie-based browsing data from 74 B2B seller companies over a period of fourteen months. In total, the data comprises 180 million browsing sessions tracked via 11.44 million cookies from 34,170 buyer companies. In the first experiment, we study the content in the sellers' own channels, and in the second experiment we study paid channels. With these experiments, we illustrate that browsing data can be combined with marketing content data to evaluate and improve the content-marketing efforts of B2B seller firms. Since the development of digital information technologies (DITs) has made the B2B buying process more buyer driven, our neural content modeling approach can be used to create B2B analytics that re-empower the sellers.     

Marketing ecosystem: An outside-in view for sustainable advantage

In the modern business environment, consumers are increasingly influenced by megatrends involving marketplace, technology, socioeconomics, geopolitics, and natural environment. Simultaneously, the data and insights that can inform consumer attitudes and behaviors often reside outside of firms' direct control. Consciously incorporating these interdependent factors into firms' decision-making is essential for adaptability and sustainable profitability.Building on the “outside-in” perspective, we propose that firm strategies should be informed through the lens of the marketing ecosystem that considers the interrelated and dynamic megatrends. By leveraging advances in data and technology, firms can sense-make the marketplace by extracting insights from massive amounts of diverse consumer data with modern-day analytics. By mapping out the megatrends with marketing analytics, firms can 1) more accurately predict consumers' changing preferences and formulate appropriate strategies to engage with them; and 2) become more market-adaptable and competitive in the present and the future.To deliver sustainably compelling value to customers, firms should adopt an ecosystem mindset and cooperate with various stakeholders. A broad-thinking, agile, and humble firm culture can enable the development of more robust outside-in capabilities. We elaborate on the megatrends in the interconnected world of the marketing ecosystem, and propose emerging research directions in each area.     

How social media shapes the fashion industry: The spillover effects between private labels and national brands

Social media has become one of the major industrial marketing channels for companies. Because of the nature of social media, social media marketing produces a strong branding effect for small and medium enterprises (SMEs) in the fashion industry. This study contributes to social media analytics research by exploring the interactions between private labels and national brands in fashion social media and investigating how these interactions influence the popularity and subsequent sales of private labels. Our main findings suggest the presence of large national brands has a positive spillover effect on the popularity of private labels in fashion social media and ultimately influences sales of private label products. The results add to our understanding of the impact of Business-to-Business (B2B) social media marketing on brand competition in the fashion industry.     

Understanding market agility for new product success with big data analytics

The complexity that characterises the dynamic nature of the various environmental factors makes it very compelling for firms to be capable of addressing the changing customers' needs. The current study examines the role of big data in new product success. We develop a qualitative research with case study approach to look at this. Specifically, we look at multiple cases to get in-depth understanding of customer agility for new product success with big data analytics. The findings of the study provide insight into the role of customer agility in new product success. This study unpacks the interconnectedness of the effective use of data aggregation tools, the effectiveness of data analysis tools and customer agility. It also explores the link between all of these factors and new product success. The study is reasonably telling in that it shows that the effective use of data aggregation and data analysis tools results in customer agility which in itself explains how an organisation senses and responds speedily to opportunities for innovation in the competitive marketing environment. The current study provides significant theoretical contributions by providing evidence for the role of big data analytics, big data aggregation tools, customer agility, organisational slack and environmental turbulence in new product success.     

Customer involvement in big data analytics and its impact on B2B innovation

Extant literature assumes that customers mainly serve as passive data providers and that firms take responsibility for big data analytics. In line with a current trend in real-world practice, this research, based on the open innovation literature, challenges this assumption and argues that customers can have more engagement in big data analytics. The authors distinguish two constructs: Customer as Data Provider (CDP) and Customer as Data Analyst (CDA). The former is consistent with the mainstream view that customers serve as the data source. The latter, on the other hand, sheds light on an active role customers play in big data analytics – that is, customers participate in a co-creation process where they acquire, analyze and act on big data. Using survey data of 148 Business-to-Business (B2B) innovation projects, the authors find that both types of customer involvement facilitate B2B product innovation. Furthermore, the authors examine moderation effects of customer need tacitness and customer need diversity. Results show that customer need tacitness negatively moderates the relationship between CDP and new product performance while customer need diversity yields a positive moderation effect. Customer need tacitness is also found to positively moderate the relationship between CDA and new product performance.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

Role of big data and social media analytics for business to business sustainability: A participatory web context

The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.     

An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance

This study examines the effect of big data powered artificial intelligence on customer knowledge creation, user knowledge creation and external market knowledge creation to better understand its impact on B2B marketing rational decision making to influence firm performance. The theoretical model is grounded in Knowledge Management Theory (KMT) and the primary data was collected from B2B companies functioning in the South African mining industry. Findings point out that big data powered artificial intelligence and the path customer knowledge creation is significant. Secondly, big data powered artificial intelligence and the path user knowledge creation is significant. Thirdly, big data powered artificial intelligence and the path external market knowledge creation is significant. It was observed that customer knowledge creation, user knowledge creation and external market knowledge creation have significant effect on the B2B marketing-rational decision making. Finally, the path B2B marketing rational decision making has a significant effect on firm performance.     

Linking big data analytical intelligence to customer relationship management performance

This study investigates the driving forces of a firm's assimilation of big data analytical intelligence (BDAI) and how the assimilation of BDAI improve customer relationship management (CRM) performance. Drawing on the resource-based view, this study argues that a firm's data-driven culture and the competitive pressure it faces in the industry motivate a firm's assimilation of BDAI. As a firm resource, BDAI enables an organization to develop superior mass-customization capability, which in turn positively influences its CRM performance. In addition, this study proposes that a firm's marketing capability can moderate the impact of BDAI assimilation on its mass-customization capability. Using survey data collected from 147 business-to-business companies, this study finds support for most of the hypotheses. The findings of this study uncover compelling insights about the dynamics involved in the process of using BDAI to improve CRM performance.     

Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research

The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.     

B2B brands on Twitter: Engaging users with a varying combination of social media content objectives,strategies, and tactics

The objective of this research is to increase understanding about B2B company-led user engagement on social media content. Building on hierarchy-of-effects (HoE) theory, we explore how the world’s leading B2B companies use content objectives (why), strategies (how), and tactics (what) on Twitter. We first integrate B2B advertising and social media research on companies’ content objectives, strategies, and tactics. Then, using qualitative analyses, we examine the existence of objectives, strategies, and tactics in the most engaging tweets (N = 365) of the worlds’ ten leading B2B brands, covering five industries, in 2017. Finally, we quantitatively examine how the use of diverse objectives and strategies differs between the most engaging tweets (N = 318) and least engaging tweets (N = 229) of the companies in 2018. The companies use objectives, strategies and tactics that relate to creating awareness, knowledge and trust, interest, and liking in the majority of their most and least engaging tweets, and express preference, conviction and purchase aspects much less. Differences exist in general, industry-wise, and company-wise. The study is a rare attempt to integrate the extant B2B advertising and social media research, and compare the most and least engaging B2B social media content.