Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

Chong, Alain Yee‐Loong; Ch’ng, Eugene; Liu, Martin; Li, Boying

doi:10.1080/00207543.2015.1066519

Cited by 161 publications

(113 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1. It is worth mentioning here that there are papers that completely ignore textual contents of online customer reviews and only utilize variables such as number of reviews and star ratings [3,9]. Interestingly, one of the results in Ref.…”

Section: Literature Reviewmentioning

confidence: 97%

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

Suryadi

Kim

2018

Journal of Mechanical Design

View full text Add to dashboard Cite

In the buying decision process, online reviews become an important source of information. They become the basis of evaluating alternatives before making purchase decision. This paper proposes a methodology to reveal one of the hidden alternative evaluation processes by identifying the relation between the observable online customer reviews and sales rank. This methodology applies a combined approach of word embedding (word2vec) and X-means clustering, which produces product-feature words. It is followed by identifying sentiment words and their intensity, determining connection of words from dependency tree, and finally relating variables from the reviews to the sales rank of a product by a regression model. The methodology is applied to two data sets of wearable technology and laptop products. As implied by the high predicted R-squared values, the models are generalizable into new data sets. Among the interesting findings are the statements of problems or issues of a product are related to better sales rank, and many product features that are mentioned in the review title are significantly related to sales rank. For product designers, the significant variables in the regression models suggest the possible product features to be improved.

show abstract

Section: Literature Reviewmentioning

confidence: 97%

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

Suryadi

Kim

2018

Journal of Mechanical Design

View full text Add to dashboard Cite

show abstract

“…Substituting equation (17) into optimal strategy (11) and (12) can obtain the optimal decision of the enterprise. At the same time, with the undetermined coefficient (14), the profits of M, R and I can be obtained. In addition, it should be noted that in order to ensure M 's normal wholesale activities, it is necessary to ensure w N > 0, that is (Φ − βK)G N > βξ > 0, and as G N > 0, so Φ > βK.…”

Section: Proof Of Propositionmentioning

confidence: 99%

“…In recent years, new digital marketing channels based on e-commerce and social media have emerged, data has exploded, and more and more companies are beginning to recognize the value of big data [9][10][11][12]. It is pointed out that the effective use of big data can help companies make better decisions [13,14]; data analysis companies and third-party Internet service platforms came into being, which also revolutionized closed-loop supply chain management. More and more companies choose to cooperate with third-party Internet service platforms in order to obtain higher-quality user information and understand and predict customer needs more accurately [15].…”

Section: Introductionmentioning

confidence: 99%

Research on Collaborative Management Strategies of Closed-Loop Supply Chain under the Influence of Big-Data Marketing and Reference Price Effect

2020

Sustainability

View full text Add to dashboard Cite

This paper integrates the Internet service platform with big-data marketing into the dynamic closed-loop supply chain system as an independent game subject. Considering the reference price effect of consumers, this work constructs differential games among manufacturer, retailer and Internet service platform under three business models of independent, collaborative production and collaborative marketing. Using Bellman’s continuous dynamic programming theory, this work obtains the optimal feedback strategies of price and big-data marketing effort, brand goodwill, return rate of used products and corporate profits under the three business models. Comparing the three scenarios and analyzing the sensitivity of key exogenous parameters, it can be found that the involvement of Internet service platform has a crucial impact on the sustainable profitability of supply chain enterprises. Considering the reference price effect of consumers, enterprises should adopt different strategic alliances in different periods, which can also gain new development momentum in the context of data-driven marketing, achieve the improvement of the triple-bottom line of closed-loop supply chain and even reach a win-win situation for supply chain enterprises.

show abstract

“…While big data has created new challenges (e.g., data growth, infrastructure, governance/policy, integration, compliance/regulation or visualization), it offers an opportunity for data scientists to develop predictive analytic techniques and discover unknown dependencies across various data sources both within and exogenous to the supply chain (Rozados & Tjahjono, 2014). Common examples are traffic analytics for transport (Khazaei, Zareian, Veleda, & Litoiu, 2016), Internet of Things (O'Leary, 2013), consumer review analytics and prediction (Chong, Ch'ng, Liu, & Li, 2015), health care (Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014), and social media analysis (Manovich, 2012). As discussed in the following section, sentiment analysis operates using big data, but specifically it addresses the emotions or sentiments in online posts, that is, consumers' feelings expressed within their online reviews.…”

Section: Figure 1 Supply Chain Showing Flow Of Information and Matermentioning

confidence: 99%

Think exogenous to excel: alternative supply chain data to improve transparency and decisions

Wood

Reiners

Srivastava

2016

International Journal of Logistics Research and Applications

View full text Add to dashboard Cite

Efficient decisions along the supply chain have traditionally demanded sophisticated information sharing processes. Even with decades of research on theoretical and practical developments on integrating systems and stakeholders, in practice, we still seem to struggle to achieve full transparency and mitigate inefficiency challenges. We explore the emerging sentiment analysis technique to augment sales and operations planning (S&OP) with currently unavailable exogenous information. Even though sentiment analysis has gained traction, a comprehensive application in supply chains has not yet been attempted. Relevant topics are reviewed to allow an examination of the key relationships in a process framework, grounded in dual-process and bullwhip effect theory. Our proposed conceptual framework extends our conception of sentiment analysis integration to improve supply chain decisions and performance. The framework addresses managers interested in developing additional analytical capabilities and researchers to initiate further empirical research on the potential held by sentiment analysis in supply chain research.

show abstract

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

Cited by 161 publications

References 52 publications

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

Research on Collaborative Management Strategies of Closed-Loop Supply Chain under the Influence of Big-Data Marketing and Reference Price Effect

Think exogenous to excel: alternative supply chain data to improve transparency and decisions

Contact Info

Product

Resources

About