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Deriving the Pricing Power of Product Features by Mining Consumer Reviews

Archak, Nikolay ; Ghose, Anindya ; Ipeirotis, Panagiotis G.

Management science, 2011-08, Vol.57 (8), p.1485-1509 [Periódico revisado por pares]

Hanover, MD: INFORMS

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Título:
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
Autor: Archak, Nikolay ; Ghose, Anindya ; Ipeirotis, Panagiotis G.
Assuntos: Applied sciences ; Artificial intelligence ; Bayesian analysis ; Bayesian learning ; Camcorders ; Computer science; control theory; systems ; Consumer economics ; Consumer goods ; Consumer preferences ; consumer reviews ; Consumers ; Data mining ; Data processing. List processing. Character string processing ; Datasets ; Digital cameras ; discrete choice ; Econometrics ; Economic impact ; Economic models ; Electronic cameras ; Electronic commerce ; electronic markets ; Exact sciences and technology ; Firm modelling ; Forecasts and trends ; Information sources ; Information systems. Data bases ; Mail-order industry ; Management science ; Marketing ; Memory organisation. Data processing ; Operational research and scientific management ; Operational research. Management science ; opinion mining ; Pricing ; Pricing power ; Product choice ; Product quality ; Product reviews ; Ratings & rankings ; Semantics ; Sentiment analysis ; Software ; Speech and sound recognition and synthesis. Linguistics ; Studies ; Text analytics ; text mining ; User generated content
É parte de: Management science, 2011-08, Vol.57 (8), p.1485-1509
Descrição: Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique data set from Amazon containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers' relative preferences for different product features and also how text can be used for predictive modeling of future changes in sales. This paper was accepted by Ramayya Krishnan, information systems.
Editor: Hanover, MD: INFORMS
Idioma: Inglês

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Deriving the Pricing Power of Product Features by Mining Consumer Reviews

Archak, Nikolay ; Ghose, Anindya ; Ipeirotis, Panagiotis G.

Hanover, MD: INFORMS

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