Detail Investigating dynamic brand choice processes: A comparison of on-line shopping environments and store shopping environments Bibliografi Author: Krishnamurthi, Lakshman (Advisor); Zhang, Jie Topik: BUSINESS ADMINISTRATION; MARKETING|MASS COMMUNICATIONS Bahasa: (EN )    ISBN: 0-599-56647-7     Penerbit: Northwestern University Press     Tahun Terbit: 1999     Jenis: Theses - Dissertation Fulltext: 9953410.pdf (0.0B; 4 download) Abstract In this dissertation we propose a dynamic brand choice model to investigate similarities and differences in consumers' brand choice behavior in an on-line shopping environment and the traditional store shopping environment. The focal research problem is on how to customize promotions in an on-line shopping environment. Customization refers to both targeting and timing. The ultimate goal is to provide an actionable micro-marketing decision algorithm to help managers decide on when to promote which brand to whom. We address the issue by constructing a dynamic brand choice model to investigate how variety seeking and inertia change over time for the same individual and differ across consumers. Based on the proposed model, we derive analytical results on how to customize price promotions under two objectives: (1) to induce a consumer to switch to a brand; (2) to prevent a consumer from switching away from a brand. Two major contributions are made in this research. First, methodologically, our model offers a very flexible way of capturing an individual's time-varying pattern of state dependence (variety seeking or inertia tendency). It overcomes some limitations of previous models in the literature. Second, from a managerial point of view, this work is action-oriented. It offers detailed normative guidelines on how to carry out customized promotions. The algorithms can be built into a decision support system to help managers make just-in-time promotion decisions. Data from Peapod Interactive Inc. are used to study on-line shopping behavior. Data provided by ACNielsen are used to study store shopping behavior. Data of butter and liquid laundry detergent from each source are chosen to calibrate the model. A subset of households in the ACNielsen data is drawn to match households in the Peapod data on two key demographic variables (income and household size) in order to minimize the differences in the two consumer samples. We demonstrate how to implement the customized promotion algorithm using model estimation results from the Peapod data. We also discuss the strategic implications for on-line retailers, store retailers, and consumer product manufacturers in the age of Internet technology. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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