Online reviews: Can this destroy your product or strengthen your brand?


Online reviews: Can this destroy your product or strengthen your brand?

Researchers show that online reviews directly interfere in the customer’s product choice and that it’s possible to take advantage of this.

Erivan Ramos
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Jan 12
Photo by Charles Deluvio on Unsplash

Future generations will never know what it’s like to go to a physical store and buy something influenced by a friend indication or just believing in the seller’s product evaluation. What was a common habit — until the emergence of the internet and online reviews — are increasingly being left behind by modern consumers. Nowadays besides the visible growth of online purchases, the reviews of products have an immediate effect on online purchases.

But after all, are online reviews the good or bad guys? Learn more below.

A report published by Podium [1] shows that for 93% of consumers, online reviews have an impact on their purchase decision and also, nearly 60% of consumers look at online reviews at least once a week with 21% of consumers looking at online reviews multiple times per week, that means online reviews have been an integral part of the shopping experience.

Paper published on WEBIST conference [2] suggest that consumers perceive the source of consumer opinion reviews as reliable and less risky and also consumer’s reasons to search for reviews includes motives as risk reduction and quality seeking. For this research, from a business perspective, the increasing popularity of online reviews affects a wide range of management activities and takes control of company information and distributes it to the consumer and suggests that companies should learn more about the features of the review functions and their influence to invest in new marketing concepts.

Proposed model by an article on Journal of Business Research [3] developed a framework to predict the consumer’s purchase intention of durable goods.

Using search patterns which are associated with purchase for a particular attribute, is possible to identify personalized products and publicizing the advertisement or products recommendation to the right customers. That is, using the power of data science, machine learning and big data to maximize the influence of attribute choice and to increase product demand. How?

Helping customers making an effective purchase decision in a shorter time period and consequently, for e-retailers, a personalized recommendation platform that provides appropriate products direct to the relevant customers.

Online review data must be used by companies as a parameter to improve the quality of their services and products. We assume that most reviews indicate that the delivery time is a disadvantage in my product or service, so I should review my entire logistics process for example.

Through the research data, it is possible to identify what the user thinks relevant to the purchase and to direct the marketing campaign for this niche.
The battery is an important feature for purchasing any mobile phone, however, in the case of cameras, the reviews of the battery are not too relevant [3]. So in this case, I know that to improve my smartphone sales, I should invest in marketing that prioritizes the quality of this feature, while for cameras I should focus more in the pixel attributes for example.

Although negative reviews can spread quickly and to an unlimited number of people across multiple platforms ruining companies reputation, the information contained in online reviews is very valuable to both consumers and businesses alike.

If your company is not yet taking advantage of these data, besides wasting time and money, your company is not in line with the modern consumer.

References:

[1] Podium. State of Online Reviews.

2017. <http://learn.podium.com/rs/841-BRM-380/images/2017-SOOR-Infographic.jpg>

[2] Constantinides, E. and Holleschovsky, N.I., 2016, April. Impact of online product reviews on purchasing decisions.

In International Conference on Web Information Systems and Technologies (Vol.

2, pp.

271-278). SCITEPRESS.

[3] Bag, S., Tiwari, M.K. and Chan, F.T., 2019. Predicting the consumer’s purchase intention of durable goods: An attribute-level analysis.

Journal of Business Research, 94, pp.408-419.

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