Ingo received a Doctorate from UTS, a Master’s degree in Economics from the University of Iowa and a Bachelor’s degree from the University of California, Berkeley. Ingo has six years experience in manufacturing management from working at a large electronics firm. He has also many years experience in using data mining for marketing research. He worked with clients as both a consultant and trainer in the use of non-parametric classification software for target marketing. Ingo has worked comprehensively with relational databases and ERP data systems to extract information that may have not been known a priori, especially in the area of credit fraud and data interactions.
Ingo was the recipient of the GSB Excellence in Teaching award for Spring 2008
American Statistical Association
Can supervise: YES
Data Mining, Choice Modeling, Non-parametric Analytics, Target Marketing, Localized and non-linear models, Missing Data Imputation, Econometrics, Fraud detection.
Marketing Foundations (Undergraduate)
Marketing Research (Undergraduate)
Business Statistics (Undergraduate)
Analytical Techniques for Decision Making (Postgraduate)
Economics for Management (Postgraduate)
Bentrott, I 2013, 'Using multivariate adaptive regression splines (MARS) to find interactions of socio-demographics that model individual differences in Australian farmers purchase behavior', Proceedings of International Choice Modelling Conference 2013, International Choice Modelling Conference, Open Conference Systems, Sydney, Australia, pp. 1-43.View/Download from: UTS OPUS
Socio-demographics play a major role in accounting for preference heterogeneity and market segmentation in discrete choice models. The use of demographic segments to account for heterogeneity in choice models has been proposed by Ben-Akiva & Lerman (1985) and complex models such as random coefficients logit have been used to account for unobserved differences in preferences. To enter demographics into a repeated choice stated preference model, they must be interacted but, due to the complexity of finding and modelling socio-demographic interactions (McLelland & Judd 1993), the interactions are often restricted to simple terms that act global over the data space. We use MARS to overcome the difficulties associated with detecting and integrating socio-demographic interactions in localized areas of the data space. In our study, heterogeneity that exists amongst farmers can be accounted for by localized interactions of the observable demographics with the experimentally designed choice attributes using basis function found by MARS. The MARS basis functions are hybrid into a conditional logit model that outperforms a hybrid of the MARS basis functions in a random coefficients logit.
Kwak, K, Bentrott, I, Gudergan, S, Louviere, JJ & Wang, PZ 2009, 'How to Identify Potential Attribute by Covariate Interactions in Discrete Choice Models?', INFORMS Marketing Science Conference, University of Michigan, Ann Arbor, Michigan, USA.
Bentrott, I, Kolyshkina, I & Kwak, K 2009, 'Use of Non-parametric Methods to Improve Efficiency of a Marketing Mix Model in a Commercial Setting', INFORMS Marketing Science Conference, University of Michigan, Ann Arbor, Michigan, USA.
Bentrott, I, Wang, PZ, Waller, DS & Galloway, J 2006, 'Cloud seeding, rainfall and persistence: A reanalysis of cloud seeding data 1955-1971', AMOS 2006 Newcastle, NSW 13th National Conference: Climate, Water and Sustainability, The Australian Meteorological and Oceanographic Society - AMOS 2006 Newcastle, NSW 13th National Conference: Climate, Water and Sustainability, The Australian Meteorological and Oceanographic Society, Newcastle, Australia.