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This paper illustrates how online databases, available from commercial vendors, can be used as the foundation for developing new business strategies. Emphasis is placed on the use of customer relationship management (CRM) ideas to identify new prospective customers for a high‐tech B2B firm. Specifically, the concept of customer lifetime value was used to evaluate current customers and match their profiles with the profiles of new prospects from the database. Once high‐quality new prospects were identified and prioritized, the company’s salesforce had a much clearer path to follow towards success. The paper contributes to the competitive strategy literature by documenting the successful use of a CRM approach to develop marketing strategies and tactics for a B2B firm that is seeking growth by acquiring new customers.

This study aims to demonstrate how marketing analytics can be used to identify the challenges a B2B company faced in the conversion from a hard-copy print catalog to a digital ordering system. Specifically, an empirical research approach identified the potential issues the company was likely to face in the digitalization of the company’s catalog.

Using the Qualtrics survey platform, a questionnaire was used to obtain a final sample of 332 customers (a 14.02% response rate) on a variety of issues related to the transition from the company’s current printed catalog to a digital catalog ordering system. A variety of data analysis procedures were used to gain insight and highlight potential issues in the move to a digital format.

A variety of potential stumbling blocks were identified that suggest the company should move forward with caution. The data analysis was used to suggest areas that needed to be emphasized in the rollout of the new digital ordering system.

Like all marketing research, this application is limited by the methods used and the data generated by this study. Its implications suggest the potential use of marketing research before an important change in a B2B company’s marketing approach.

This paper provides an approach that can be used by firms considering a change to digitize key components of their marketing assets.

The research contributes to the B2B marketing literature by demonstrating how data-driven marketing analytics can be used to identify potential issues prior to the development of a new digital marketing approach used by B2B firms.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

The availability of online databases provides corporate executives with a valuable set of resources for the development of marketing strategies and tactics. This paper provides an example of how an online “mailing file” can be used to guide a company's efforts to identify new business prospects. As a result, this paper demonstrates how the management of Internet databases, if used properly, can enhance a company's competitive position.

This paper seeks to provide an exploratory empirical study of the variables that are part of the return on trade show information (RTSI) concept, which is based on the use and value of information gathered at a trade show.

The research is designed to explore relationships and identify those variables that are a particularly important part of the RTSI concept. The paper provides an exploratory test of the relationship between a series of variables that are related to the value of information gathered at trade shows. Data were collected from trade show attendees approximately 60 days after the trade show. A multiple regression model was developed that explores the relationship between the dependent variable that focuses on information value and the independent variables on various aspects of information acquisition, information dissemination, and information use.

The final multiple regression model found a significant relationship for several variables and has an adjusted R² value of 0.552. Four significant independent variables were identified – one each in the information use and the shared information categories and two in the information acquisition category. These findings present an interesting picture of how information is used within an organization after it is acquired at a trade show.

The research is limited by the multiple regression model used to explore the relationships in the data. Also, data from only one trade show were used in the model.

This paper focuses on the intangible, longer‐term benefits as important considerations when determining the value of new trade show information to the firm. The evaluation of trade show information also should include these intangible benefits, such as improved interdepartmental relations or interactions as well as discussions with other trade show participants in finding new uses for information that impacts the company's future success, as well as shorter‐term benefits such as booth activity.

The paper offers a unique approach for determining the value of information acquired at trade shows. Though information gathering has been included as an outcome variable in previous trade show studies, no other research has studied the value of this new trade show information to the company.

The current availability of online direct marketing databases provides numerous opportunities for marketing professionals to improve their marketing strategies and tactics. This paper illustrates how online “mailing file” data, formerly known simply as “mailing lists”, can be used to develop high‐quality sales leads. A set of procedures is presented that provides a way for sales managers to guide the company’s salesforce in seeking new customers. A case study is included that demonstrates how a company that competes in a high‐tech business‐to‐business market has developed and used sales leads that are highly targeted. In particular, the paper provides the results of an empirical study that uses a mailing file database as the starting point for developing a market segmentation approach to lead generation. Issues encountered in this type of research are identified, and suggestions are made for handing these issues.

Despite the numerous benefits of digitalization, many business-to-business (B2B) firms have yet to rely on data-driven decision-making, wavering the decision to adopt digital marketing practices. Topical scholarship is scattered across disciplines, schools of thought and methodological approaches, leading to an inability to suggest better management practices. This study aims to review the extant B2B marketing digitalization literature and addresses these concerns.

This paper conducted a systematic literature review of 96 high-quality articles extracted from the Web of Science database. Thereafter, this paper carried out descriptive statistical and content analyses of these articles.

Six primary research streams have been identified, and 16 research propositions have been formulated to comprehensively overview the B2B marketing digitalization landscape. The study delves into the factors and barriers influencing the pace of B2B marketing digitalization, sales lead generation and sales performance. Additionally, it introduces B2B digital value creation frameworks, emphasizing the crucial role of marketing analytics and decision tools in effective B2B marketing. The research also underscores various digitalization strategies aimed at bridging the digitalization gap in B2B companies at both strategic and tactical levels. Finally, the study presents an agenda to stimulate future research on theoretical and managerial topics critical to enriching the field.

This research outlines 16 research propositions that could be further tested to get more detailed insights into the digitalization of B2B marketing. Additionally, practitioners, authorities and researchers in the field may find this review valuable as it provides a comprehensive overview of current research in the domain.

This paper is designed to illustrate how clickstream data, collected from a B2B web site and then analyzed using web analytics software, can be used to evaluate and improve B2B web site performance. A number of issues in the application of clickstream data and web analytics software are to be identified and discussed.

A case study approach is used to present some of the technical issues in the field of web analytics and to demonstrate their value in B2B web site management. Three field experiments, focusing on incorporating ways to discourage shopping‐cart abandonment and the use of two different free‐shipping promotions, were used as the basic research method for collecting the data. Web traffic conversion funnels are used to conduct the analysis and present the findings.

The analysis of clickstream data using web analytics procedures serves as a useful tool in the enhancement of a B2B web site by investigating how visitors move through the web site conversion process and complete their purchase. Improved sales result from each of the three field experiments.

Researchers may use the paper as evidence that web analytics methods can be applied successfully in a B2B application for a technology‐oriented company.

The paper illustrates the use of clickstream data to measure the progression of web site visitors through the conversion process toward purchase.

Insight is provided into the usefulness of web analytics as a framework for performance measurement that is used to drive success for B2B web sites.

The adoption of a model‐building approach to marketing is today inevitable, due to improvements in hardware and software and the increased professionalisation of marketing and its techniques. Aggregate response models are focused upon, particularly the issues of which responses are realistic and should be modelled, how the response can be expressed and how a choice can be made between options available. The traditional model‐building process is described, and the inclusion of correct variables found to be critical, the primary means of doing this being statistical analysis. Simple expressions perform as effectively as more complex ones, and should be used if able to give operationally meaningful results. Cross‐correlation analysis and biased estimation techniques provide good guides to usable variables and their effects.

Uses Resnik and Stern’s content analysis criteria to examine audio and visual information of in‐cinema slide advertisements within one regional market in Australia to determine whether two types of cues are compatible or reinforce one another. Suggests that there was extensive information framing for a narrow set of information cues. States that there were also significant differences in the types of audio and visual cues, which might result in conflicting information being communicated or information overload.