A Logit Model of Brand Choice Calibrated on Scanner Data

By Peter M. Guadagni, John D. C. Little. 2011

1,237 citations recorded by the source corpus.

Авторы используют мультиномиальную логистическую статистическую модель (multinomial logit model) для прогнозирования выбора потребителями конкретного бренда из нескольких брендов, имеющихся в продаже. Модель опробована и откалибрована на данных о покупках различных брендов упаковок молотого кофе в супермаркетах в течении 32 недель. В качестве независимых переменных, объясняющих выбор покупателей, в числе прочих, используются показатели предпочтения какого-то бренда, предпочтения определенного размера упаковки, присутствие/отсутствие специальных рекламных акций в магазине, регулярная розничная цена, размер скидки при распродаже. Авторы установили, что коэффициенты этих переменных статистически значимы.

Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing

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Citations in the corpus

Minimax-optimal Inference from Partial Rankings (2014, background): “This problem, known as rank aggregation, has received much attention across various disciplines including statistics, psychology, sociology, and computer science, and has found numerous applications including elections, sports, information retrieval, transportation, and marketing [1, 2, 3, 4].”
A multinational examination of the symbolic–instrumental framework of consumer–brand identification (2011, neutral): “Also, the common practice among marketing researchers is to model consumer brand choices based on product attributes and marketing mix (e.g., Guadagni & Little, 1983, and many subsequent extensions).”
Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact (2006, methodology): “Because of this, we can use the multinomial logit model (Ben-Akiva and Lerman 1985, Guadagni and Little 1983 ) to determine the sensitivity of customers to the parameters of the offered products.”
Invited Paper - Learning Models: An Assessment of Progress, Challenges, and New Developments (2011, neutral): “These are a myopic learning model (β = 0), and a reduced form model similar to Guadagni and Little (1983), henceforth GL.”
Customer Metrics and Their Impact on Financial Performance (2006, neutral): “. of marketing variables on such consumer decisions (e.g., Guadagni and Little 1983, Gupta 1988).”
Variational Inference for Large-Scale Models of Discrete Choice (2007, methodology): “Discrete choice models have a long history in statistical analysis, appearing in applications as varied as the analysis of consumer choice data (Guadagni and Little 1983; Fader and Hardie 1996), transportation planning (Theil 1969; McFadden 1974; Ben-Akiva and Lerman 1985), economic demand…”
Understanding Large-Scale Dynamic Purchase Behavior (2020, neutral): “Guadagni and Little (1983), Gupta (1988), Manchanda et al. (1999). These three studies analyzed purchase behavior for only a small subset of the product assortment, considering respectively 8, 11, and 4 alternatives.”