This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining.Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

http://cbafiles.unl.edu/public/cbainternal/facStaffUploads/AdvancedDataMiningTechniques.pdf

Advanced Data Mining Techniques

The Role of Big Data and Predictive Analytics in Retailing

An Experimental Approach for Analyzing Customer Demand with an Application to a Web-Based Information Service

In the real world, buyer demand for a product can depend directly and indirectly on the marketing efforts of “other products” in different categories. The authors offer a behavioral rationale for the existence of the effects of “other products’” marketing efforts and propose a taxonomy of possible intercategory relationships. The discussion enables the authors to identify several promising new research directions.

/pdf/product-complements-and-substitutes-in-the-real-world-the-3ykuxxnk50.pdf

Product Complements and Substitutes in the Real World: The Relevance of “Other Products”

An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data

Analysis of cross category dependence in market basket selection

Modeling aggregate market response is a core issue in marketing research. In this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, Multiplicative Competitive Interaction (MCI) models. Forecasts are compared in two separate studies: (1) the Information Resources Inc. (IRI) coffee dataset from Marion, IN and (2) the A. C. Nielsen catsup dataset from Sioux Falls, SD. Our results suggest neural networks are a useful substitute for MCI models when there are too few observations available to estimate a fully-extended MCI model. Implications are discussed.

/pdf/mining-sales-data-using-a-neural-network-model-of-market-wrsl278vq9.pdf

E. Ann Furr Petersen

Papers

Analysis of cross category dependence in market basket selection

Mining sales data using a neural network model of market response