The use of information technology and decision support concepts at the operational business level were slow to take hold in the 20th century. In 2010, the authors documented the evolution and current state of the field of business intelligence and analytics (BIA). In the last decade, however, through the resurgence and mainstream use of artificial intelligence, machine learning algorithms, the development of inexpensive cloud-based mass storage, and the internet-of-things, business intelligence has evolved into data science. In this chapter, the authors trace this evolution across the diverse areas of data science and identify extremely useful advancements and best practices in the field.

The pharmaceutical firms’ detailing and sampling effort of a new drug play an essential role for the success of the new drug launch. Their sales reps want to meet the physicians to introduce their new drug and provide samples but that meeting has to be most effective due to high costs associated with detailing and sampling. This study fills the gap in the literature in the study of pharmaceutical firm’s detailing and sampling effort by examining the variables and their influence on the physicians’ interest to meet the sales rep and on the number of samples they need. The estimation results provide the managerial implication to the pharmaceutical firms’ detailing and sampling efforts. They further provide predicted probability of doctor’s interest and the predicted number of samples for individual targeting. A future study can be applied to other kinds of drugs.

This paper discusses dynamics and differences of business models in the car-sharing industry and focuses on open innovation as the trigger of diverse business models among Uber in the U.S., DiDi Chuxing in China, and KakaoT in Korea. We seek to answer the following two questions: What creates the differences in the business models of the car-sharing industry? Do the differences in open innovation motivate the diversity of business models among Uber, DiDi Chuxing, and KakaoT? We incorporated participatory observation, interviews, and semi-structured questionnaire methods in our study. We used two-step participatory observation and interview methods, hence carrying out observation and interviews two times by different researchers with Uber drivers in the U.S., DiDi-Chuxing drivers in Beijing, and KakaoT taxi drivers in Korea to confirm the interview and participatory observation results. First, business models of the car-sharing firms Uber, DiDi-Chuxing, and KakaoT are not fixed but rather are dynamically changing. Second, business models of car-sharing firms are the result of interaction with government regulations, the taxi industry, public transportation, and the automotive car industry. Third, open innovation strategies of car-sharing firms determine the contents and dynamics of car-sharing business models, such as the revenue business model, responsibility business model, and system business model upon interaction with four agencies.

The application of predictive analytics in higher education has increasingly gained acceptance and interest over the years. In this study, a predictive model is developed to map students’ non-cognitive skills against their class performance. Our predictive analytics model identified the non-cognitive skills that predicted new students’ class performance based on the dataset collected early in the semester. Based on the predictive analytics results, tailored teaching to improve students’ non-cognitive skills was offered in a required class designed for undergraduate business students. The improvement in the average final semester grade for students in the tailored-taught classes based on our predicted analytics approach was 9%, which was higher than that of the class grade taught without the approach. The study finding also demonstrates a long-term, sustainable positive effect to the students with the predictive analytics approach.

We investigate the impact of early adoption of an innovative analytics approach on organizational analytics maturity and sustainability. With the sales operation planning involving the accurate determination of physician detailing frequency, multiple product sequencing, nonlinear promotional response functions and achievement of the right level of share of voice (SOV), an analytical approach was developed by integrating domain knowledge, neural network (NN)’s pattern-recognition capability and nonlinear mathematical programming to address these challenges. A pharmaceutical company headquartered in the U.S. championed this initial research in 2005 and became the first major firm to implement the recommendations. The company improved its profitability by 12% when piloted to a sales district with 481 physicians; then it launched this approach nationally. In 2014, the firm again gave us its data, performance of the analytical approach and access to key stakeholders to better understand the changes in the pharmaceutical sales operations landscape, the firm’s analytics maturity and sustainability of analytics. Results suggest that being the early adopter of innovation doubled the firm’s technology utilization from 2005 to 2014, as well as doubling the firm’s ability to continuously improve the sales operations process; it outperformed the standard industry practice by 23%. Moreover, the infusion of analytics from the corporate office to sales, improvement in management commitment to analytics, increased communications for continuous process improvement and the successes from this approach has created the environment for sustainable organizational growth in analytics.

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.

Pharmaceutical companies have traditionally marketed their products through a combination of several channels: sales details to physicians, direct-to-consumer advertising, professional medical journal advertising, sponsorship of meetings and events and e-promotion. With an impending patent cliff and subsequent loss in revenue, the industry must depend on, among many factors, recently launched products to offset the revenue loss. Coupled with increased generic competition, companies must evaluate the return on investment of their marketing dollars. This paper analyzes the effectiveness of traditional marketing methods, both industry-wide and for recently launched products, using the latest Business Intelligent methods. The dataset used in this paper is a sample of prescription, promotional, competitive, and product data from SDI Health. The analysis in this paper reveals that traditional marketing methods have a decreasing level of impact with the number of prescriptions dispensed, and describes new potential channels for marketing, as well as collecting and analyzing data to aid the industry improve its resource utilization.

Any pharmaceutical company relying heavily on its sales force to detail multiple products knows the importance of optimizing a short time window to detail its products to physicians effectively, in the right sequence. With the trend toward decreasing detailing time that is now averaging less than a minute, the optimization of this period is critical to success, especially in today's challenging selling environment. This paper develops a knowledge-based approach that integrates domain experts' knowledge of the definition of promotional responsiveness with a hybrid model of neural networks and a nonlinear program to accurately determine the physician detail equivalent (PDE) weights that reflect the weighted sequence of detail and portfolio size while identifying the physicians who are responsive to details. The output from this approach drives physician detailing planning, as well as planning for market share of detailing volume, which is known as share of voice (SOV) planning. Results based on six months of implementation indicate that the knowledge-based approach performs significantly better than the traditional approach by more than 12% in profit.

Higher education often lags behind industry in the adoption of new or emerging technologies. As competition increases among colleges and universities for a diminishing supply of prospective students, the need to adopt the principles of business intelligence becomes increasingly more important. Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models. A decision tree analysis, a neural network analysis, and a multiple regression analysis were conducted to predict each student’s grade point average (GPA) at the end of the first year of academic study. Numerous geodemographic variables were analyzed to develop the models to predict the target variable. The overall performance of the models developed in the analysis was evaluated by using the average square error (ASE). The three models had similar ASE values, which indicated that any of the models could be used for the intended purpose. Suggestions for future analysis include expansion of the scope of the study to include more student-centric variables and to evaluate GPA at other student levels.

In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

The service sector of the US economy has been gaining importance. As the service sector evolves, the study of service supply chain starts to gain attention. In this study, we conduct an exploratory review on the studies of manufacturing and service supply chains. We focus on the studies that explore the differences and commonalities between manufacturing and service supply chains. We combine operations management literature with supply chain studies in order to provide an interdisciplinary framework that brings up both the operational and strategic views on the management commonalities and differences between the two types of supply chains.

Most pharmaceutical companies that rely heavily on their sales force for success do not fully understand the effect of details made in previous quarters have on the current quarter, which is also known as the carryover effect. This paper proposes an expert system that utilizes neural networks with nonlinear programming to accurately derive the carryover effect at the customer level. Results suggest that using this adaptive and easy-to-implement expert system helped a firm increase its sales by 3.4% while reducing its sales force expenditure by 8.9%, compared to the control group. The implications of this approach are considered.

With the growing popularity of micromarketing strategies, more companies are tailoring their marketing efforts to meet individual consumer need based on analysis of consumer-level data. However, there are two major obstacles in the way to effective micromarketing strategies: (1) data limitation at the consumer-level making it difficult to construct robust and accurate promotional response function and (2) misplaced usage of a resource allocation approach effective in a data-rich environment to data-challenged environment. These obstacles lead to suboptimal marketing resource allocation decisions. This paper presents a knowledge-based approach specifically designed for effectively allocating marketing resources in the micromarketing environment. A team of domain experts provide knowledge in refining and imputing data to overcome data limitation issues, as well as identifying consumer-level constraints to strengthen the integer programming model's performance. Results indicate that this approach is transparent to all levels of management, adaptable to changes in environment, and easy to implement. In addition, there is tremendous potential for improving not only profitability but also growing the company's intellectual capital. When this approach is applied to a random sales territory, it outperforms the traditional method by more than 19% in profit.

With the growing popularity of consumer-centric sales call strategies, more companies are tailoring their efforts to meet individual consumer need based on analysis of consumer-level data and incentivizing sales representatives to follow the call plan with a top-down instruction. However, due to data limitation issues at the consumer level and corporate headquarters` over-reliance on sophisticated call planning approaches without adequately leveraging domain knowledge, sales forces often find themselves in a challenging situation to increase its effectiveness. This paper demonstrates the importance of leveraging domain expertise in developing a call strategy at the consumer level by introducing an innovative knowledge-based approach and comparing its performance against technologically more sophisticated existing approach that ignores the domain knowledge. The performance comparison is based on implementation of knowledge-based approach derived call strategy to 30 randomly selected consumers of the sponsoring pharmaceutical firm versus the control group. This research concludes with a consideration for managerial implications and areas of future research.