Listen to Your Customers: Insights into Brand Image Using Online Consumer-Generated Product Reviews

Gensler, Sonja; Völckner, Franziska; Egger, Marc; Fischbach, Kai; Schoder, Detlef

doi:10.1080/10864415.2016.1061792

Cited by 100 publications

(52 citation statements)

References 70 publications

(131 reference statements)

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“…Text analytics, especially review content characteristics, can enable marketers to quantify unstructured data. Thus, review content attributes can help retailers and manufacturers develop marketing intelligence from online product reviews (Gensler, Völckner, Egger, Fischbach, & Schoder, 2015). This implies that after collecting and monitoring customer reviews, marketers should recognize that reviews that are too lucid or too sentimental would not be considered helpful or genuine by consumers, and so they can eliminate such potentially suspicious reviews from their systems (Larson & Denton, 2014).…”

Section: Managerial Implicationsmentioning

confidence: 99%

Online Review Helpfulness: Role of Qualitative Factors

Agnihotri

Bhattacharya

2016

Psychology and Marketing

125

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Consumers are increasingly reading online reviews before making any purchasing decisions. The significance of online reviews has only grown over the years. Though in the past, scholars have emphasized the impact of quantitative factors (e.g., review ratings) on online reviews, only recently have they begun to explore the role of qualitative aspects of online reviews. Content readability and associated sentiments in text provide two important qualitative cues that influence the helpfulness of online reviews. However, the extant literature has overemphasized the linear association between these aspects and the helpfulness of reviews. Using the elaboration likelihood model and the classic ideal point concept, the current work asserts that after an ideal point is attained, lucid and sentimental reviews diminish in utility (i.e., helpfulness of an online review for consumers decreases). This may happen because consumers are wary of fraudulent reviews. This study proposes that if experienced reviewers give such extreme reviews, then consumers might still draw utility from these reviews. In other words, this study explains the moderating role of reviewer experience, which heuristically influences consumers’ trust of online reviews, thus making even too simplistic or extremely sentimental reviews helpful.

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Section: Managerial Implicationsmentioning

confidence: 99%

Online Review Helpfulness: Role of Qualitative Factors

Agnihotri

Bhattacharya

2016

Psychology and Marketing

125

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“…The fast fashion industry continuously faces challenges in satisfying consumers' desire for new products faster (Cook and Yurchisin, 2017), which results in the need to continuously investigate the market to quickly understand the demand and to respond accordingly. Past studies investigate the role of online consumers' generated contents, in terms of ratings, photographs, reviews that includes personal opinions and recommendations spread throughout social media, online platforms for e-commerce, bookings and products/activities reviews lead to a massive amount of data that marketers might access to improve decision making processes (Chong et al, 2017;Fan et al, 2015;Gensler et al, 2015;Halvorsen et al, 2013;, by impacting competitive and marketing intelligence (Fleisher, 2008;Xu et al, 2011). Social networks such as Pinterest, Twitter, Facebook, etc.…”

Section: Introductionmentioning

confidence: 99%

Making sense of consumers’ tweets

Pantano

Giglio

Dennis

2019

IJRDM

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Purpose Consumers online interactions, posts, rating and ranking, reviews of products/attractions/restaurants and so on lead to a massive amount of data that marketers might access to improve the decision-making process, by impacting the competitive and marketing intelligence. The purpose of this paper is to develop understanding of consumers online-generated contents in terms of positive or negative comments to increase marketing intelligence. Design/methodology/approach The research focuses on the collection of 9,652 tweets referring to three fast fashion retailers of different sizes operating in the UK market, which have been shared among consumers and between consumer and firm, and subsequently evaluated through a sentiment analysis based on machine learning. Findings Findings provide the comparison and contrast of consumers’ response toward the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence. Practical implications The research provides an effective and systemic approach to accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online and investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence. Originality/value To best of the authors’ knowledge, while other authors tried to identify the effect of positive or negative online comments/posts/reviews, the present study is the first one to show how to systematically detect the positive or negative sentiments of shared tweets for improving the marketing intelligence of fast fashion retailers.

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“…Social media has rapidly become a valuable source of opinions (Belbachir and Boughanem, 2018) and recommendations on products and brands (Okazaki et al, 2014;Xiao et al, 2018), and eConsumers increasingly rely on online recommendations given by "who they know" rather than the content produced by the company (Steffes and Burgee, 2009). For this reason, it is vital for companies to monitor how their brands are discussed "in the wild" (Gensler et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, customer information from social media can be collected in real-time and more cost-effectively than with more traditional data collection methods (Salminen, Şengün, Kwak, Jansen, An, Jung, Vieweg and Harrell, 2018). Social media platforms provide access to timely data, making it possible for companies to monitor their online corporate reputations (Gensler et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Classifying online corporate reputation with machine learning: a study in the banking domain

Rantanen

Salminen

Ginter

et al. 2019

INTR

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Purpose User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations. Design/methodology/approach The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data. Findings After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation. Practical implications For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN. Originality/value This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.

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Listen to Your Customers: Insights into Brand Image Using Online Consumer-Generated Product Reviews

Cited by 100 publications

References 70 publications

Online Review Helpfulness: Role of Qualitative Factors

Online Review Helpfulness: Role of Qualitative Factors

Making sense of consumers’ tweets

Classifying online corporate reputation with machine learning: a study in the banking domain

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