Marketing models of consumer heterogeneity

Allenby, Greg M.; Rossi, Peter E.

doi:10.1016/s0304-4076(98)00055-4

Cited by 624 publications

(390 citation statements)

References 20 publications

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“…This relation provides a diagnostic tool (Allenby and Rossi 1999). If the average of the sampled customers' conditional taste distributions is similar to the estimated population distribution, the model is correctly specified and accurately estimated.…”

Section: Average Conditional Distributionmentioning

confidence: 99%

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Discrete Choice Methods with Simulation

Train¹

2003

3,816

2,450

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Section: Average Conditional Distributionmentioning

confidence: 99%

“…The model of DeSarbo et al (1995) is a special case of this more general method. Bayesian procedures have been also developed to perform this inference within discrete choice models (Rossi et al 1996;Allenby and Rossi 1999). We describe the Bayesian methods in Chapter 12.…”

Section: Introductionmentioning

confidence: 99%

Discrete Choice Methods with Simulation

Train¹

2003

3,816

2,450

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“…Unlike in other disciplines where researchers are typically interested in estimating averages (and for whom individual level heterogeneity is a nuisance to be controlled for), Marketers place a premium on the analysis, inference and understanding of individual level heterogeneity in any sample (Allenby and Rossi 1998). This allows marketers to then efficiently group individuals or households into a manageable number of segments and thereafter design effective segmentspecific marketing campaigns and interventions; or individual-level ones if cost effective.…”

Section: Hierarchical Modeling and Household Or Individual Level Paramentioning

confidence: 99%

The Role of Big Data and Predictive Analytics in Retailing

Bradlow

Gangwar

Kopalle

et al. 2017

Journal of Retailing

366

181

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The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions -data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.2

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“…The customer's price sensitivity is an important aspect in consumer and marketing research (Allenby & Rossi, 1999) and has attracted increased interest in the past decade, especially in Europe. The increasing success of hard discounters (e.g.…”

Section: Cultural Ergonomicsmentioning

confidence: 99%

The influence of socio-cultural background and product value in usability testing

Sonderegger

Sauer

2013

Applied Ergonomics

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This article examines the influence of socio-cultural background and product value on different outcomes of usability tests. A study was conducted in two different socio-cultural regions, Switzerland and East Germany, which differed in a number of aspects (e.g. economic power, price sensitivity and culture). Product value (high vs. low) was varied by manipulating the price of the product. Sixty-four test participants were asked to carry out five typical user tasks in the context of coffee machine usage, measuring performance, perceived usability, and emotion. The results showed that in Switzerland, high-value products were rated higher in usability than low-value products whereas in East Germany, high-value products were evaluated lower in usability. A similar interaction effect of socio-cultural background and product value was observed for user emotion. Implications are that the outcomes of usability tests do not allow for a simple transfer across cultures and that the mediating influence of perceived product value needs to be taken into consideration.

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Marketing models of consumer heterogeneity

Cited by 624 publications

References 20 publications

Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation

The Role of Big Data and Predictive Analytics in Retailing

The influence of socio-cultural background and product value in usability testing

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