The paper "Significance of Customer Reviews to Business " is a great example of marketing coursework. With the rise of the internet, customer reviews have become an important business tool. Customers use social media, search engines and websites to spread the electronic word of mouth (eWOM) globally in a manner that is not easily forgettable (Filieri, 2015). Research shows that customer reviews are an important influence on customer purchase decisions as well as attracting and retaining the customers. At the moment, there has been an increase in the number of customers shopping online.
As the number of online shops has increased, it has become difficult for the customers to make their purchase decisions based on pictures and product descriptions. This has made them turn into customers reviews which provide vital information on the products and services being sold (Elwalda & Lu, 2014). Customers are able to compare the reviews and obtain information on the experience of what they are about to make a purchase on. Despite the importance of customer reviews, there have been varying viewpoints on their impacts. There have also been varying viewpoints whether online customer reviews offers the best channel compared to the traditional channels.
In addition, online reviews have presented a problem due to bias, manufacturer keeping track of the opinions and numerous reviews received (Forman, Ghose & Wiesenfeld, 2008). This has led to the need for mining and summarising customer reviews. This paper will analyse the varying viewpoints on customer reviews and findings from industry research and academics. The discussion will outline ways in which the current academic research on customer reviews impacts the business.
In addition, the discussion will look at why the companies use mining and summarising of customer reviews and the impacts it has on business. Lastly, the paper will look at the application of customer reviews on Amazon. com and eBay and their use of mining and summarising. Theory Significant customer reviews to business Most of the internet based business models rely heavily on their customers’ feedback. This has been aimed at building loyalty and attracting new customers. Kramer, Guillory & Hancock (2014) claim that most of the online business look at online reviews as a vital marketing tool which is better than the use of TV advertising.
In addition, online reviews have been used as a tool for revenue forecasting. Despite this, its efficiency still remains a controversy. This is due to the fact that the functioning of the online review system is based on voluntary contributions. Online reviews have been seen as a public good since they benefit all consumers and firms. When the reviews are negative, it leads to harmful effect on adoption and diffusion of new products (Elwalda & Lu, 2014).
Research shows that it is possible to transfer emotional states through emotional contagion. This is especially with the rise of social media where a message can spread to a large audience in a short time. This also applies to the use of online reviews. Consumers feelings about a product when expressed online can affect other people behaviours towards it. When a product or service receives a negative review, it is possible for it to affect those reading it (Kramer, Guillory & Hancock, 2014).
Amazon (2011). Customer reviews. Available at: https://www.amazon.com/eBay- Inc/product-reviews/B004SIIBGU (Accessed: 1 February 2017).
Ayeh, J. K., Au, N., & Law, R. (2013). “Do we believe in TripAdvisor?” Examining credibility perceptions and online travelers’ attitude toward using user-generated content. Journal of Travel Research, 52(4), 437-452.
Bhattacharjee, R., & Goel, A. (2005, August). Avoiding ballot stuffing in ebay-like reputation systems. In Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems (pp. 133-137). ACM.
Cabral, L., & Hortacsu, A. (2010). The dynamics of seller reputation: Evidence from eBay. The Journal of Industrial Economics, 58(1), 54-78.
Elwalda, A., & Lu, K. (2014). The influence of online customer reviews on purchase intention: the role of non-numerical factors. In Proceedings of the LCBR European Marketing Conference 2014.
Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313.
Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177). ACM.
Jain, V., Narula, G. S., & Singh, M. (2013). Implementation of data mining in online shopping system using tanagra tool. International Journal of Computer Scienceand Engineering, 2(1), 47-58.
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive- scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.
Lackermair, G., Kailer, D., & Kanmaz, K. (2013). Importance of Online Product Reviews from a Consumer's Perspective. Advances in Economics and Business, 1(1), 1-5.
Leswing, K. (2016). Amazon is banning most reviews that were written in exchange for a free product. Available at: http://www.businessinsider.com/amazon-bans-incentivized- customer-reviews-2016-10 (Accessed: 1 February 2017).
Lupo, J. (2015). Comparing Amazon and eBay feedback mechanisms. Available at: https://www.feedbackfive.com/blog/amazon-feedback-vs-ebay-feedback/ (Accessed: 1 February 2017).
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com. MIS Quarterly Vol. 34 No. 1, pp. 185-200
Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006). The value of reputation on eBay: A controlled experiment. Experimental economics, 9(2), 79-101.
Sun, M. (2012). How does the variance of product ratings matter?. Management Science, 58(4), 696-707.
Wang, B. C., Zhu, W. Y., & Chen, L. J. (2008, December). Improving the Amazon review system by exploiting the credibility and time-decay of public reviews. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 03 (pp. 123-126). IEEE Computer Society.
Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of marketing, 74(2), 133-148.