Nowadays, computer-based decision support is a firm component of the operational and strategic management in many companies. In marketing planning appropriate systems can be used, e.g., for new product development, for advertising media design, as well as for the systematic directing of the sales force. In market research the efficient preparation, execution, and analysis of large data collections are hardly possible without sufficient computer support. In retailing particularly the wide spread of point-of-sale scanning and the associated availability of huge amounts of data manifest the necessity of appropriate research efforts, e.g. in the fields of data warehousing and data mining. Furthermore, the increasing importance of e-commerce, particularly in connection with the Web 2.0, offers new and complex challenges. The mentioned topics are the subject of current research and teaching to a different degree.
A further topic of our current research concerns the development of computer-based preference measurement methods for new product planning and the improvement of existing products. Special challenges for preference measurement result from the increasing complexity of new products and the also increasing heterogeneity of many markets. Currently, among other things, we work on adaptive preference measurement approaches, which reduce the time and effort on the part of the respondents and ease the consideration of complex products. Furthermore, we investigate the decision behavior in connection with computer-based preference measurements by means of eyetracking.Furthermore, we focus on the systematic analysis of online customer reviews using econometric methods, e.g. for uncovering hidden preference patterns.
Efficient and effective sales planning is a crucial factor for successful business management. Objectives of quantitative sales planning are particularly the profit optimized alignment of sales territories or the corresponding determination of the sales force design. The main focus of our research is on the comparative examination and discussion of popular algorithms. Further research investigates whether an adaption of already successfully applied methods from different topics, e.g. machine learning, can be used in the context of sales planning. Beyond, the increasing globalization of markets and the resulting reorganization of sales management are taken into account within the framework of quantitative sales planning.
The ability to identify and to react in time on up-and-coming chances in the business environment is an indispensable factor for the future success of an organization. Our project deals with the development of autonomous systems which support the information seeking activities of managers on a daily basis. This includes the modeling of human information seeking processes and the transfer to autonomous systems, which carry out the effective and efficient search and information retrieval of weak signals covered by various textual information in a given information environment, particularly documents available on the Internet. Our research combines approaches from various areas, such as information retrieval, data and text mining, machine learning, operations research, and management science, to support managers in their competitive and business intelligence duties and responsibilities.
Machine learning is a promising research discipline that gains an increasing importance in numerous application areas. The underlying principle of learning from examples is implemented in several classes of methods, e.g. neural networks or support vector machines, and may thus be used for knowledge discovery in marketing. Meaningful applications arise from point-of-sale scanner data analysis or customer classification in sales management for instance.
The competitor’s reactions to changing market conditions are of substantial relevance for the planning of marketing measures. Since the competitive behavior is characterized by the simultaneous use of different marketing instruments the modeling of multiple discrete choices is of particular importance. Our research activities were focused on the development of methods for analyzing and anticipating competitive reactions.
Data bases in market research, particularly those resulting from standardized surveys, are rarely complete. Therefore, the accurate handling of missing values is of substantial importance for the validity of the attained results. The main focus of our research efforts was concentrated on the development of suitable approaches for those missing values which are neither missing completely at random (“MCAR”) nor missing at random (“MAR”).
This project focused on the development of a generally accepted framework for the production of computer-based decision support systems for the marketing management of small and medium-sized enterprises. Following a transdisciplinary approach we were bundling current case studies and research results of different knowledge domains in a goal-oriented way.
Please see Completed Projects