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.     

Managing B2B customer churn,retention and profitability

It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics.     

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.     

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.     

From transactions to journeys and beyond: The evolution of B2B buying process modeling

Consultants and pundits assert that the business-to-business (B2B) buying process has changed markedly in recent years due to the emergence of online, digital applications and software. Recognizing that impactful, and truly innovative future research is perhaps best created when built on the foundation of past science, we review the arc of B2B buying process modeling from 1956 to the present. Our goals with this research are to: 1. capture the genealogy and evolution of thinking across the years in terms of foundation theories, reasoning approach, types of models, factors researched, and journals in which articles were published, 2. identify the thematic inflection points in the research stream that have led to the current conceptualizations, and 3. suggest a research agenda for the future. We discovered that academic understanding of the B2B buying process has progressed in waves featuring seven themes – transactions, situations, influences, responses, relationships, networks and journeys. Looking to the future, we recommend that scholars examine five areas of research: the impact of technology, modes of customer and supplier interaction, decision-making approaches, tensions between internal and external communities, and B2B marketing analytics.     

Towards a framework of customer value assessment in B2B markets: An exploratory study

This paper examines the key processes and activities of customer value assessment in business-to-business (B2B) markets. Given that an increasing number of B2B firms are providing combinations of products and services, or integrated solutions, the present study examines customer value assessment from the solution supplier's perspective. Specifically, based on an exploratory field study and in-depth interviews with 18 managers in three different firms, the present study identifies five key processes (i.e., value potential identification, baseline assessment, performance evaluation, long-term value realization, and systematic data management) and 11 related activities involved in customer value assessment in B2B markets, and integrates them into a managerially grounded framework. The findings from this study contribute to the literature on customer value and solution research, and provide useful insights for managers on how to assess the value delivered by their offerings to customers.     

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.     

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.     

Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry

E-commerce has provided new opportunities for both businesses and consumers to easily share information, find and buy a product, increasing the ease of movement from one company to another as well as to increase the risk of churn. In this study we develop a churn prediction model tailored for B2B e-commerce industry by testing the forecasting capability of a new model, the support vector machine (SVM) based on the AUC parameter-selection technique (SVMauc). The predictive performance of SVMauc is benchmarked to logistic regression, neural network and classic support vector machine. Our study shows that the parameter optimization procedure plays an important role in the predictive performance and the SVMauc points out good generalization performance when applied to noisy, imbalance and nonlinear marketing data outperforming the other methods. Thus, our findings confirm that the data-driven approach to churn prediction and the development of retention strategies outperforms commonly used managerial heuristics in B2B e-commerce industry.     

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.     

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.     

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.     

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.     

Social media: Influencing customer satisfaction in B2B sales

Social media have changed how buyers and sellers interact, and increased involvement through social media may yield positive results for sales organizations if salespeople utilize it in facilitating their behaviors. Through the perspective of value creation, we test the mediating effects of salesperson information communication behaviors between social media use and customer satisfaction. Using salesperson-reported data, within a B2B context, we empirically test a model using structural equation modeling. Salesperson's use of social media is found to impact information communication behaviors, which enhance salesperson responsiveness and customer satisfaction. Also, salesperson responsiveness is found to have a positive relationship with customer satisfaction. Findings suggest that social media plays an important role in communicating information to customers, but as an antecedent enhancing salesperson behaviors to increase customer satisfaction rather than a direct factor. This encourages managers to carefully assess goals related to social media use of their sales force.     

Product radicalness and firm performance in B2B marketing: A moderated mediation model

There has been ambiguity and controversy in establishing the links between the introduction of radical innovations and firm performance. While radical innovations create customer value and grow product sales, they are also fraught with uncertainty due to customer resistance to innovative products and significant costs associated with commercialization. This research aims to explain the contrarian findings between radical innovations and firm performance in a business-to-business (B2B) context by examining two mediating variables – new product advantage and customer unfamiliarity. Using a multi-informant approach, the authors collected survey data from a sample of 170 Spanish B2B firms engaged in new product development, provided by 357 managers. The authors find that, while new product advantage positively mediates the relationship between product radicalness and firm performance, customer unfamiliarity has a negative mediation effect on this relationship. Furthermore, the authors examine the moderated mediation effect by industry type, manufacturing vs. service, and find that it moderates the mediation of customer unfamiliarity: The negative impact of product radicalness on customer unfamiliarity is greater for manufacturing firms than for service firms. With these findings, the authors discuss implications for development and marketing of radical innovations and how those implications facilitate firm performance in the B2B context.     

Determinants of social media adoption by B2B organizations

This study contributes to the current dearth of knowledge on the potential of social media as a marketing tool in industrial settings, by focusing on factors that determine social media adoption by B2B organizations. A conceptual model, which draws on the technology acceptance model and resource-based theory, is developed and tested using quantitative data from B2B organizations in the UK. Findings suggest that perceived usefulness of social media within B2B organizational contexts is determined by image, perceived ease of use and perceived barriers. Additionally, the results show that adoption of social media is significantly affected by organizational innovativeness and perceived usefulness. The moderating role of organizational innovativeness is also tested but no support is found. The findings of the study are further validated via nine qualitative interviews with B2B senior managers, yielding additional interesting and in-depth insights into the drivers of social media adoption by B2B organizations.     

Navigating the demands of increasing customer participation through firm and individual job resources

Many firms are increasing the amount of customer participation required in B2B sales in efforts to improve firm performance. Unfortunately, little is known regarding how increasing customer participation expectations effects the firm's salespeople. To address this issue, using the job demands-resources model, this study examines how increases in customer participation influence salesperson burnout and salesperson investment in resources, while accounting for the job resources of job autonomy and belief in innate selling ability. The potential moderating effects of competitive intensity are also captured. The findings, based upon a survey of 210 B2B salespeople, indicate that increasing customer participation does not increase salesperson burnout, but increases investments in resources aimed to increase salesperson professional development. Further, greater job autonomy was found to decrease salesperson burnout and increase investment in resources, with the latter being moderated by competitive intensity. Belief in innate selling ability, in contrast, was found to increase burnout and decrease investment in resources by salespeople, with the latter being moderated by competitive intensity. This study highlights the multiple positive and negative effects of increasing customer participation in B2B selling, providing new insights for how firms can set policies to enhance salesperson well-being and effectiveness in a B2B setting.     

Does corporate social responsibility matter even in the B2B market?: Effect of B2B CSR on customer trust

This study attempts to understand how Corporate Social Responsibility (CSR) positively influences the quality of business relationship in the business-to-business market. The purpose of this article is to suggest the CSR model in the B2B context. First, this study discerns two dimensions of firms' CSR activities based on the previous studies in B2B area - Business CSR and Altruistic CSR. Furthermore, we tried to investigate the CSR activities affecting the result of the development of business relationships (economic and non-economic factors) and customer trust as a relationship performance in the B2B market. Managerial implications and limitation of the study were also discussed.     

What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics

Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system.     

SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices

Development of small and medium enterprises (SMEs) is a key approach to achieving economic growth in the Middle East and successful adoption of technology is vital for SMEs' success and continuity. Artificial intelligence (AI) is part of a new generation of technologies that can facilitate competitive advantage but currently there is a lack of evidence regarding AI applications in relation to B2B SMEs in Middle East countries. Therefore, this study empirically examines antecedents to, and consequences of, successful acceptance of AI practices by B2B SMEs in Saudi Arabia. A conceptual model based on the technology-organisation-environment framework is developed which considers the impact of AI enablers and AI readiness on the acceptance of AI practices, and the impact of this on relational governance, performance, and SMEs' AI-based business customer interaction. The conceptual model was tested using structural equation modelling of survey data collected from B2B SMEs (n = 392). The results showed that, of the AI enablers, acceptance of AI practices was significantly influenced by both technology roadmapping and attitude but not professional expertise. Of the AI readiness variables, acceptance of AI practices was significantly influenced by infrastructure and awareness but not technicality. The acceptance of AI practices was found to significantly affect AI-enabled relational governance and performance, and SME's business customer AI-based interaction. This study provides a broader base for theoretical and practical understanding of issues related to AI practices in SMEs and the B2B sector in general.