Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The growing pervasiveness of the Internet has changed the way that consumers shop for goods. Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. While there is a significant body of theory on multi-attribute choice under uncertainty, the literature that examines product reviews has not built on this stream of theory for a variety of reasons. Typically, the impact of product reviews has been incorporated by 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. We provide a text mining technique that allows us to incorporate text in choice and panel data models by decomposing textual reviews into segments, evaluating different product features. We test our approach on a unique dataset collected from Amazon, and demonstrate how it can be used to learn consumers' relative preferences for different product features. The dataset used contains three different groups of products (digital cameras, camcorders, PDAs), associated sales data and consumer review data gathered over a 15-month period. Additionally, we present and discuss two experimental techniques that can be used to alleviate the problem of data sparsity and of omitted variables: the first technique models consumer opinions as elements of a tensor product of independent feature and evaluation spaces and the second technique clusters rare opinions based on pointwise mutual information. The paper concludes by discussing the managerial relevance of this work as a tool for extracting actionable business intelligence from user-generated content

Deriving the Pricing Power of Product Features by Mining Consumer Reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

Understanding the order effect of online review sentiments and product features

Online reviews have played an increasingly important role in the popularity and success of e-commerce (Yin et al. 2014). The decreasing digital divide and ubiquitous internet access, along with the proliferation of smart mobile devices, have resulted in an exponential increase in the online purchase of goods and services. Additionally, customers are encouraged and incentivized to share their personal experiences using the product or service. Such experiences represented on internet platforms are captured through electronic word-of-mouth, typically in the form of online reviews. Prior studies on online reviews have shown that the experience of consumers plays an important role as an information source when potential consumers make purchasing decisions (Luo et al. 2013). Researchers have also revealed that opinion mining and sentiment analysis of online reviews can be used to predict the pricing power (Archak et al. 2011) and sales (Chevalier and Mayzlin 2006; Gu et al. 2012) of the product

The customer is always right: analyzing existing market feedback to improve TVs

Online consumer reviews can be analyzed using an algorithm that quantifies the consumer’s sentiments towards a product as well as the sentiment towards specific features of a product. In turn, the covariance between different features can be analyzed and rated. Our research uses both feature and sentiment analysis to illustrate these correlations and consumer preferences

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Impact of personalized review summaries on buying decisions: An experimental study

This study evaluates the impact of personalization of review summaries on consumers’ cognitive efforts and buying decision. Following an experimental procedure we tested four hypotheses pertaining to online buyers’ decision process. Our results show that personalized review summary significantly reduces the information processing effort and information requirements of those who received personalized review summaries as compared to those who did not. This study thus contributes to e-commerce literature on online buyer behavior and recommender systems strategy