Modeling Consumer Choice among SKUs

1996 ◽  
Vol 33 (4) ◽  
pp. 442-452 ◽  
Author(s):  
Peter S. Fader ◽  
Bruce G. S. Hardie

Most choice models in marketing implicitly assume that the fundamental unit of analysis is the brand. In reality, however, many more of the decisions made by consumers, manufacturers, and retailers occur at the level of the stock-keeping unit (SKU). The authors address a variety of issues involved in defining and using SKUs in a choice model, as well as the unique benefits that arise from doing so. They discuss how a set of discrete attributes (e.g., brand name, package size, type) can be used to characterize a large set of SKUs in a parsimonious manner. They postulate that consumers do not form preferences for each individual SKU, per se, but instead evaluate the underlying attributes that describe each item. The model is shown to be substantially superior to a more traditional framework that does not emphasize the complete use of SKU attribute information. Their analysis also highlights several other benefits associated with the proposed modeling approach, such as the ability to forecast sales for imitative line extensions that enter the market in a future period. Other implications and extensions also are discussed.

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Prof. Amit Shrivastava ◽  
Prof. Sushil Kumar Pare ◽  
Prof (Dr) Saumya Singh

Inadequate is the empirical research on store choice model in view of retail store attributes with endogenous construct of store patronage intention of consumer. Conventional wisdom and social science research-based insights for underpinning the design of store environment established elements such as music, scent, crowding and physical attractiveness of the store. Earlier empirical findings lack on key anterior, which include consumers’ time and effort as well as the psychological costs such as convenient, economical, risk mitigated shopping experience. The premise on which overall effects in our model rests, is that store attributes influence consumers' cognitive process and develop perceptual framework of store choice criteria — namely, convenience, reputation of outlet, branded merchandise (mediated through perceived quality). This research presents a formal test of the linear regression equation model in the context of store choice behaviour, involving one product category. The present paper explores these attributes and their affect on consumer from different socio-economic classes, willingness to purchase and to patronize if these factors are modified. Questioning the earlier conclusions that all attributes aforementioned are equally important in consumer decision making, the current results indicate that consumers place differential significance on each attribute, and the level of significance placed on each attribute varies with different socio economic class. These findings are significantly important to the retail industry as they identify the critical attributes responsible for building consumer choice and patronage among different socio economy classes. This model also paves way for another premise of empirical research, that shoppers might develop category-wise store choice or patronage behaviour model.


2015 ◽  
Vol 7 (2) ◽  
pp. 101-120 ◽  
Author(s):  
Heiko Karle ◽  
Georg Kirchsteiger ◽  
Martin Peitz

We analyze a consumer-choice model with price uncertainty, loss aversion, and expectation-based reference points. The implications of this model are tested in an experiment in which participants have to make a consumption choice between two sandwiches. Participants differ in their reported taste for the two sandwiches and in their degree of loss aversion, which we measure separately. We find that more-loss-averse participants are more likely to opt for the cheaper sandwich, in line with theoretical predictions. The estimates in the model with rational expectations are slightly more significant than those with naïve expectations. (JEL D11, D12, D84, M31)


Author(s):  
Lin He ◽  
Christopher Hoyle ◽  
Wei Chen ◽  
Jiliang Wang ◽  
Bernard Yannou

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. A framework and a step-by-step procedure for implementing consumer choice modeling in UCBD are presented in this work. To implement the proposed approach, methods for common usage identification, data collection, linking performance with usage context, and choice model estimation are developed. For data collection, a method of try-it-out choice experiments is presented. This method is necessary to account for the different choices respondents make conditional on the given usage context, which allows us to examine the influence of product design, customer profile, usage context attributes, and their interactions, on the choice process. Methods of data analysis are used to understand the collected choice data, as well as to understand clusters of similar customers and similar usage contexts. The choice modeling framework, which considers the influence of usage context on both the product performance, choice set and the consumer preferences, is presented as the key element of a quantitative usage context-based design process. In this framework, product performance is modeled as a function of both the product design and the usage context. Additionally, usage context enters into an individual customer’s utility function directly to capture its influence on product preferences. The entire process is illustrated with a case study of the design of a jigsaw.


2014 ◽  
Vol 16 (6) ◽  
pp. 72-76 ◽  
Author(s):  
Umer Shehzad ◽  
◽  
Salman Ahmad ◽  
Kashif Iqbal ◽  
Muhammad Nawaz ◽  
...  

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Kejia Hu ◽  
Jianyou Zhao ◽  
Yuche Chen ◽  
L.D. White

This paper develops a framework to evaluate HEVs, PHEVs and EVs on-road emissions impact, by integrating endogenous vehicle consumer choice model and MOVES-based regional emission transportation model. A case study based on Harris County, Texas data is implemented to examine the on-road emissions under different market penetrations (due to different future energy price) and government policies. The results show different on-road transportation emissions level for Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Total Hydrocarbon (THC). In addition, cost effectiveness of reducing on-road emissions by extending tax credit for plug-in electric vehicles (PEV) is calculated and reported. 


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