Unfolding large-scale marketing data

• Published in: International journal of research in marketing Vol. 27; no. 2; pp. 119 - 132
• Main Authors: Ho, Ying, Chung, Yuho, Lau, Kin-nam
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2010; Elsevier; Elsevier Sequoia S.A
• Subjects: Brand preferences; Consumer behavior; Correlation analysis; Degenerate solution; Information visualization; Konsumentenverhalten; Markenimage; Market positioning; Marketing; Multidimensional unfolding; Network data; Parameter estimation; Skalenertrag; Social networks; Studies; Multidimensional unfolding; Degenerate solution; Information visualization; Network data
• ISSN: 0167-8116; 1873-8001
• DOI: 10.1016/j.ijresmar.2009.12.009

Marketers use multidimensional unfolding to understand the relationship between customer preferences and product positioning through a joint display of customers and brands on a map. In today's information age, unfolding marketing data is challenging, as marketing data can be large in size and high-dimensional in nature. Moreover, the unfolding model is always subject to the curse of the degeneracy problem. We propose a new approach to unfold customer-by-brand transaction data and customer-by-customer network data in a reduced space. The proposed approach can recover the true configuration with reasonable accuracy, is scalable in terms of the number of estimated parameters, and can produce non-degenerate solution. We compare its performance with existing approaches by simulation experiments and real data analyses with interesting results.