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.     

Growth hacking: Insights on data-driven decision-making from three firms

Theoretical backgroundThe work explores how Big Data analysis can reshape marketing decision-making in B2B sector. Deriving from Data-Driven Decision-Making (DDDM) approach, the Growth Hacking model is employed to investigate the role of cognitive computing and big data analytics in redefining business processes.PurposeThe main objectives of the study are: 1) to assess how a data-driven orientation to the use of big data analytics and cognitive computing can reframe marketing decisions in B2B segment; 2) to explore whether the adoption Growth Hacking can be helpful in exploiting the opportunities offered by big data analytics and cognitive computing in B2B marketing.MethodologyThe paper is based on Action Research (AR) methodology that permits researchers to participate actively in the observation of businesses and to examine how decisions are undertaken and managed over time.ResultsThe main findings allow identifying the most common strategies and tactics employed in three companies operating in different B2B sectors to exploit the opportunities offered by cognitive computing and big data analytics according to a data-driven marketing approach. Based on the application of the Growth Hacking model, the tools of analytics and the main objectives, outcomes and implications on marketing decision-making are revealed.OriginalityThe identification of the main objectives and outcomes produced across the three dimensions of the Growth Hacking model (data analysis, marketing and programming) can help academics and practitioners to understand the main levers to attain marketing goals, such as the enhancement of relationship with customers (CRM), continuous learning and development of new products and potential innovation.     

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.     

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.     

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.     

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.     

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.     

Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence

Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-to-day data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.     

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.     

B2B analytics in the airline market: Harnessing the power of consumer big data

This paper utilizes market-level data to explore the relative performance of individual companies amongst defined competitors. We show the potential of using consumer clickstream data, an important type of big data, to create a new set of B2B analytical frameworks. In the markets where complex interactions between competitors, search intermediaries and consumers create a network, B2B relationships can be inferred from consumer search patterns, and can then be modeled to gauge the online performance. A commercial dataset from ComScore’s US panel of one million users is used to illustrate a new approach to measure and evaluate the online performance of competitors in the US airline market. The methodology and associated performance framework demonstrate the potential for new forms of market intelligence based on the visualization of market networks, online performance calculated from matrix algorithms, the measurement of the impact of search intermediaries, and the identification of latent relationships. This research makes theoretical and empirical contributions to the debate on the use of big data for B2B market analytics. B2B managers can use this approach to extend their network horizon from an egocentric to a network view of competition and map out their competitive landscape from the perspective of the customer.     

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.     

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.     

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.     

Market microstructure in a global B2B network

On‐line marketplaces raise several interesting issues, among them the relevance of location when content is digitized, and the assessment of a supplier's capabilities when buyers worldwide only have electronic contact with sellers. In global B2B on‐line marketplaces, market microstructures, i.e. which firms compete for the same customers, are thus likely to be influenced by how customers value location and firm capabilities in their decisions to do business with different suppliers on‐line. We suggest that both these sets of attributes will continue to matter on‐line—firms possessing similar capabilities, as well as firms that are similar in location by country, time zones or clusters, will compete for business from the same customers. We model the similarity in competitive positions between pairs of firms based on the overlap in their customer networks, using data on actual interactions between supplier and customer banks on an electronic trading system. Using QAP network regression techniques on the 100 largest banks in this industry, we find that similarity in capabilities influences who competes with whom, and that location still matters in a global B2B exchange. Interestingly, location influences who a firm's competitors are, but not where its customers are from. Copyright © 2001 John Wiley & Sons, Ltd.     

Study on the influencing of B2B parasocial relationship on repeat purchase intention in the online purchasing environment: An empirical study of B2B E-commerce platform

With the meteoric progress of digital technology and the advent of network economy, there has been increasing interest in the business model of purchasing with B2B network among B2B e-commerce platforms and customer firms. Based on the perspective of parasocial relationship and dual-process theory, this study constructs a model of cognitive and emotional influences on customer firms' behavior, and analyzes the influence of features of entrepreneur endorser and online purchasing platform on B2B parasocial relationship, and how this relationship can affect repeat purchase intention. Furthermore, this paper examines the moderating effect of trust in the relationship between B2B parasocial relationship and repeat purchase intention. Findings indicate that perceived interactivity and trustworthiness of entrepreneur endorser and the service and product quality of online purchasing platform have a positive impact on B2B parasocial relationship. B2B parasocial relationship has a significant and positive impact on repeat purchase intention, while trust moderates the relationship between B2B parasocial relationship and repeat purchase intention.     

Perspective: The Innovation Dilemma: How to Innovate When the Market Is Mature

Most companies have ambitious growth goals. The trouble is there are only so many sources of market growth. Markets in many countries and industries are mature and increasingly commoditized; achieving growth in market share is expensive; and acquisitions often do not work. For most companies, product development means line extensions, improvements, and product modifications, and only serves to maintain market share. Markets aren't growing, so firms increasingly compete for a piece of a shrinking pie by introducing one insignificant new product after another. The launch of a truly differentiated new product in mature markets is rare these days. As a result, development portfolios have become decidedly less innovative since the mid‐1990s, and R&D productivity is down. The answer is bold innovation—breakthrough products, services and solutions that create growth engines for the future. This means larger‐scope and more systems‐oriented solutions and service packages. Examples such as Apple's iPod are often cited. (Note that Apple did not invent the MP3 player, nor was this opportunity in a blue ocean; in fact there were 43 competitors when Apple launched!) What Apple did succeed in was in identifying an attractive strategic arena (MP3s) where it could leverage its strengths to its advantage and then to develop a solution that solved users’ problems. The result—an easy‐to‐use, easy‐to‐download MP3 system, which also happened to be “cool.” Our benchmarking studies reveal that five vectors must be in place to undertake this type of innovation to yield bolder and more imaginative development projects. First, develop a bold innovation strategy that focuses your business on the right strategic arenas that promise to be engines of real growth. Most businesses focus their efforts in the wrong areas—on flat markets, mature technologies, and tired product categories. Break out of this box towards more promising strategic arenas with extreme opportunities. Next, foster a climate and culture that promotes bolder innovation. Leadership is vital to success. If senior management does not have the appetite for these big concepts, then all your efforts and systems will fail. Senior management plays a vital role here in promoting an innovative climate in your business. Next, create “big ideas” for integrated product‐service solutions. The best methods for generating breakthrough new product ideas are identified in this paper. Then drive these “big concepts” to market quickly via a systematic and disciplined idea‐to‐launch system designed for major innovation initiatives. Just because these projects are imaginative and bold is no reason to throw discipline out the window. In fact, quite the reverse is true. Finally build a solid business case and focus on the winners. Most innovation teams don't get the facts, and consequently build weak business cases; the result is that many worthwhile innovations don't get the support they need to be commercialized. It's essential to do the front‐end homework, and so build a compelling business case. Then make the right investment decisions—evaluating “big concepts” for development when little information is available. Note that financial models don't work well when it comes to evaluating major innovations, because the data are often wrong. But other methods can be used to make these tough go/kill decisions. Illustrations and examples are provided from many industries and companies to show how to implement these five vectors.     

How to design good experiments in marketing: Types,examples, and methods

In this article, we present key tenets of good experimental design and provide some practical considerations for industrial marketing researchers. We first discuss how experiments have the ability to assess causal claims. Next, we provide an experimental taxonomy table, which brings out the value and limitations of different types of experiments and maps the various goals of business marketing research within each category. Here, we pay particular attention to field experiments since they provide experimental realism by measuring respondents' actual behavior. We also provide a thorough discussion on important practical issues such as questions on experimental design, sample size, and how to involve business organizations in the implementing steps. The paper concludes by stressing the importance of combining data types (e.g., field plus laboratory experiments) and by offering methodological advice on how to analyze experimental data in marketing.     

Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach

Over the past few decades research has predominantly focused on the technical aspects and theoretical challenges of Artificial Intelligence (AI). With the deluge of data and the increase in processing power, businesses are now facing the challenge of how to deploy AI that generates business value. In this direction, there is still nascent research on how AI can be leveraged in for B2B operations, and particularly marketing. To address this gap, this study draws on the dynamic capabilities view of the firm and specifically on the micro-foundations approach and builds on three selected case studies of large organizations in Norway that use AI for B2B marketing purposes. The study identifies a number of AI-specific micro-foundations of dynamic capabilities, essentially highlighting how organizations can use AI to manage B2B marketing operations in dynamic and uncertain environments. This study also identified several key cross-cutting elements emerging from the data, demonstrating how some key concepts are inter-related and how they affect overall business value.     

The transition from product to service in business markets: An agenda for academic inquiry

In recent years, industrial manufacturers around the world have deployed growing efforts in developing services in addition to their traditional product business in order to secure long-term growth and to remain competitive in the marketplace. Against this background, the present article introduces the reader to this special issue. It first recalls key aspects of the emerging service-dominant logic of marketing and examines how it relates to the business marketing field. It then illustrates the challenges faced by top executives of industrial companies in the transition from a product-centric to a service-centric business perspective through an interview with the managing director of ThyssenKrupp Service AG. After discussing how the articles included in this special issue advance the extant literature on industrial services marketing, the article finally develops a number of directions for future research on services in business markets.     

A big data driven framework for demand-driven forecasting with effects of marketing-mix variables

This study aims to investigate the contributions of promotional marketing activities, historical demand and other factors to predict, and develop a big data-driven fuzzy classifier-based framework, also called “demand-driven forecasting,” that can shape, sense and respond to real customer demands. The availability of timely information about future customer needs is a key success factor for any business. For profit maximization, manufacturers want to sense demand signals and shape future demands using price, sales, promotion and others economic factors so that they can fulfil customer's orders immediately. However, most demand forecasting systems offer limited insight to manufacturers as they fail to capture contemporary market trends, product seasonality and the impact of forecasting on the magnitude of the bullwhip effect. This paper aims to improve the accuracy of demand forecasts. In order to achieve this, a back-propagation neural network-based model is trained by fuzzy inputs and compared with benchmark forecasting methods on a time series data, by using historical demand and sales data in combination with advertising effectiveness, expenditure, promotions, and marketing events data. A statistical analysis is conducted, and the experiments show that the method used in the proposed framework outperforms in optimality, efficiency and other statistical metrics. Finally, some invaluable insights for managers are presented to improve the forecast accuracy of fuzzy neural networks, develop marketing plans for products and discuss their implications in several fields.