Tag Archives: amazon.com

From e-commerce to social commerce

February 18, 2018 383790dr Leave a comment

A matter of trust

The advancement of Web 2.0 social networks brought new developments to e-commerce. In recent years, e-commerce has transformed to social commerce. Social commerce is a new stream and subset of traditional e-commerce, which combines e-commerce with Web 2.0 social networks.

Social commerce, trust & buying intention

Thanks to social networks consumers can now communicate, rate other products, review others’ opinions, participate in forums, share their experiences and recommend products and services. By bringing the features of Web 2.0 social networks to e-commerce, consumers can support each other in the acquisition of products and services in an online context. This results in more customer-oriented business models where customers can share knowledge, experiences and information about their products and services.

Social commerce has three main constructs that empower customers and increase the sociability of e-commerce:

Forums and communities: Online discussion sites that support information sharing;
Ratings and reviews: Provide comprehensive information about a product for potential customers;
Referrals and recommendations: Unlike brick and mortar stores, in online stores it is not possible to interact with staff, so customers rely more on other customers’ recommendations.

Trust is a central issue in e-commerce. Social commerce has helped to establish more trust in e-commerce platforms. Customers experience higher levels of trust as they can support each other through information exchange. This is because interactions and interconnectivity reduce the perceived risk in online transactions. Reviews, ratings and recommendations can indicate the trustworthiness of an online seller as customers consider reviews from other customers to be more reliable than information from a commercial website.

Hajli (2015) found that the three social commerce constructs significantly positively influence the user’s intention to buy. Trust appeared to be a mediating variable. Social commerce constructs have a positive effect on user’s trust, which in turn positively influences the intention to buy (Figure 1). To arrive at these findings, Hajli (2015) conducted a survey study with four constructs: intention to buy, social commerce constructs, perceived usefulness and trust. A five point Likert-scale was used in the questionnaire. Data was collected at universities in the UK. The final sample consisted of 243 completed and usable questionnaires. Next, Structural Equation Modelling (SEM) was used for data analysis. The hypotheses were tested with the Partial Least Squares (PLS) method. The findings underline that social commerce constructs, like customer reviews, are more likely to increase trust, and in turn increase customers’ intention to buy.

Amazon customer reviews

From a practical perspective, this study encourages online businesses to make a plan for reviews and to manage social networks effectively as it significantly impacts customers’ purchasing decisions. It recommends them to engage with their customers through reviews to develop trust. Other research indeed shows that 91 percent of customers read online reviews and that 84 percent trusts online reviews as much as a personal recommendation (Bloem, 2017) In practice, this implies that not offering customer reviews is similar to ignoring 84 percent of your buying population by not giving them the information they want to support them in their buying decision (DeMers, 2015).

To illustrate, Amazon optimised its business model based on customer reviews and ratings. Customer reviews are one of the most important ranking factors in Amazon’s A9 algorithm. It ranks product search results based on the positivity of customer reviews and rating. (Grosman, 2017)

Fake review problem

A weakness in the study of Hajli (2015) is that it does not consider that information related to the identity of the reviewers influences the perceived trustworthiness of a review. The paper simply finds that more reviews increases trust, which in turn increases the buying intention. However, in reality, it might not be that straight forward anymore with the rise of fake product reviews. Nowadays, it is hard for customers to decide which reviews to trust. There is looming crisis of confidence in online product reviews, which used to be a key factor in customers’ buying decision. (Silverman, 2017) As customers cannot trust reviews anymore, it can be questioned whether the positive relation between the number of reviews, trust and buying decision still holds.

Increasingly, customers pay careful attention to reviews, e.g. looking for reviews with a Verified Purchase tag. Nearly 66.3 percent of Amazon reviews are five-star ratings, which is highly unrealistic. Reviews on Amazon are a key factor when making a purchasing decision and without reviews it is difficult for online retailers to gain sales. In an attempt to boost sales, retailers offer reviewers free or discounted samples in return for a positive customer review. So, it is no surprise that 96 percent of paid reviews on Amazon is rated four- or five-star. (Cipriani, 2016)

Source credibility

Many authors have investigated the positive impact of online reviews on sales of products and services. However, given the importance of source credibility, I believe more research is needed on trustworthiness of reviewers as an important construct. The source credibility theory explains how a recommendation persuasiveness is affected by the perceived credibility of its source. Actually, customers accept reviews depending on the perceived trustworthiness of the reviewer, which consequently impacts the buying decision. Reviewer trustworthiness is therefore an important moderating variable that positively moderates the impact of review-based online reputation. (Banerjee, Bhattacharyya, & Bose, 2017)

Concluding, instead of solely increasing the number of (positive) customer reviews, online retailers should also build a good review-based online reputation that encourages and identifies top trustworthy reviewers and that ranks reviews based on reviewer trustworthiness.

This post was inspired by: Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191

References:

Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness. Decision Support Systems, 17-26.

Bloem, C. (2017, July 31). 84 Percent of People Trust Online Reviews As Much As Friends. Here’s How to Manage What They See. Opgehaald van Inc.: https://www.inc.com/craig-bloem/84-percent-of-people-trust-online-reviews-as-much-.html

Cipriani, J. (2016, March 14). Why You Shouldn’t Trust All Amazon Reviews. Opgehaald van Fortune: http://fortune.com/2016/03/14/paid-amazon-reviews/

DeMers, J. (2015, December 28). How Important Are Customer Reviews For Online Marketing? Opgehaald van Forbes: https://www.forbes.com/sites/jaysondemers/2015/12/28/how-important-are-customer-reviews-for-online-marketing/#35eccc711928

Grosman, L. (2017, February 28). Five Tips To Improve Your Ranking On Amazon. Opgehaald van Forbes: https://www.forbes.com/sites/forbescommunicationscouncil/2017/02/28/five-tips-to-improve-your-ranking-on-amazon/#3079c5f89fed

Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191.

Silverman, D. (2017, April 20). A Matter of Trust: Amazon Declares War on Fake Product Reviews. Opgehaald van Clavis Insight: https://www.clavisinsight.com/blog/matter-trust-amazon-declares-war-fake-product-reviews

Articles

Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

February 28, 2017 tessazander Leave a comment

We have all been there, scrolling through all the reviews before we buy something. You want to see all of this user-generated content, since you are afraid you will regret the wrong choice (Tsekouras, 2017). Also, this information overload leads to being less satisfied, less confident and more confused (Park & Lee, 2009). You could look at the average rating of the product, however these are often bimodal distributed and therefore less helpful (Zhang & Pavlou, 2009). How can you feel confident that you have seen all the important reviews, without having to read all of them?

This is what Ghose & Ipeirotis (2011) studied.

The authors looked at data from Amazon over a period of 15 months to study the impact of reviews on products sales and perceived usefulness. They looked at audio and video players (144 products), digital cameras (109 products) and DVDs (158 products) and their reviews.

The paper identified multiple features that affect product sales and helpfulness, by incorporating two streams of research. First, the information within the review is relevant. Second, reviewer attributes might influence consumer response.

What did they find?

An explanatory study found that the following factors are important:

Thus, perceived helpfulness does not necessarily lead to higher product sales.

They also performed a predictive model, which showed the importance of reviewer-related, subjectivity and readability features on predicting the impact of reviews. Furthermore, the predictive model showed that the predictions were less accurate for experience goods, like DVDs, in comparison to search goods, such as electronics.

What are the managerial implications?

Amazon currently uses ‘spotlight reviews’, which displays the most important reviews. However, it requires enough votes on reviews before a ‘spotlight review’ is determined. The predictive model is able to overcome this limitation, since it is possible to immediately identify reviews that are expected to be helpful for consumers and display them first.

On the other hand, it is useful for manufacturers, since they are able to modify future versions of the product or the marketing strategy, based on the reviews that affected sales most.

The main strength of this paper is that it has relevant managerial implications for both consumers and manufacturers, since it studied both the effect on sales and on helpfulness for consumers.

Would the findings be similar on different websites?

Probably, findings will be similar for other retailers of electronics, therefore Coolblue and Mediamarkt could benefit. On the other hand, book reviews on Bol.com are not expected to have as much benefit from the model, since they are experience goods, similar to DVDs.

Not as straightforward, are the implications for clothing retailers. However, I expect these retailers will not benefit as much from the model, since often there is no overload of reviews on clothing websites and therefore there is no need to reduce the information.

References

Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498-1512.

Hu, N., Zhang, J. and Pavlou, P.A. (2009). Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52(10), pp.144-147.

Park, D. H., & Lee, J. (2009). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386-398.

Tsekouras, D. (2017). Customer centric digital commerce: Personalization & Product Recommendations [PowerPoint slide]. Retrieved from Blackboard.

Feature image retrieved from: Enzer, J. (2016, August 17). How to reward product reviews and supercharge your e-commerce business. Retrieved from: http://blog.swellrewards.com/2016/08/how-to-reward-product-reviews-and-supercharge-your-e-commerce-business/

Articles, Opinion, Student Ideas

Recommendation networks and the long tail of e-commerce

February 16, 2017 373396fk Leave a comment

Nowadays, we almost can’t imagine online shopping without recommendations systems. Popular electronic commerce websites like Amazon, Bol.com, Asos.com and so on all have a section with products they personally recommend to their customers. This is often displayed as: ‘You may also like…’ showing multiple products related to the ones you have recently viewed.

Integrating social networks like Facebook and Instagram into the world of electronic commerce is on the up and can contribute to the personalized recommendation systems of online retailers. In this way, customers get personalized recommendations based on what friends in their networks bought. This makes the less popular products, which customer normally not have looked for, more visible and stimulates consumers to buy products that they normally wouldn’t have found. These products are known as ‘the Long Tail’ products and are often presented as ‘Customers like you also bought…’.

To put it differently, if consumers get e-commerce recommendations based on their networks, the level of awareness for less popular products will increase. This means that the distribution of revenue and demand is influenced and shifts more towards a long tail distribution and away from selling primarily the most popular products. Simply by peer-based recommendations, customers will buy more and different products than they would normally have.

Research done by Oestreicher-Singer & Sundararjan (2012), investigates the impact of peer-based recommendations on the demand and revenue distribution. They research the influence of network-based recommendations on the online sales of 250.000 books from online retailer Amazon.com. The research shows that by recommending books based on what friends in customers’ networks bought, the distribution of demand and revenue is highly influenced. The researchers focused on the top 20% most popular and top 20% most unpopular products.

Categories of unpopular books that were displayed based on peer-recommendations experienced a 50% increase in revenue whilst the commonly unpopular books experienced a 15% decrease in revenue. This meant that the unpopular books suddenly became more visible to customers which led to an 50% increase in sales.

That all sounds quite impressive, but one could not say that this 50% increase was only caused by the visibility of products through recommendations. Different other contemporary factors also contribute to the redistribution of demand and revenue of consumers. Lower search costs and higher product variety for instance, have a great influence on the long tail of e-commerce.

All things considered, e-commerce is highly influenced by the power of social networks. The influence of recommendation networks positively affects to phenomenon of the long tail demand. Selling less of more rather than more of less is going to characterize the e-commerce demand curve in the future. The implementation of ‘what other customers like you bought’ will continue to impact our online shopping behavior. If companies implement the right recommendation systems to influence consumer demand, the opportunities are endless!

Sources:

Anderson, C. 2006. The Long Tail: Why the future of Business Is Selling Less of More. Hyperion Press.

Brynjolfsson, E., Hu, Y., and Smith, M.D. 2006. From Nichees to Riches: Anotomy of the Long Tail. Management Review 47(4) 67-71.

Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly

Articles

Electronic Word of Mouth and Amazon.com

April 2, 2015 annedegroot123 Leave a comment

Figure 1: ‘Understanding’ online star ratings

Amblee and Bui (2011) have researched the effect of electronic word of mouth (eWOM) on the sales of digital microproducts. They studied amazon.com ‘shorts’: short stories (e-books) that are made available for a set price of 49 cents. They classify these shorts as digital microproducts.

This article focusses on their study on de effect of social commerce on product reputation and sales (hypothesis 1). Amblee and Bui (2011) studied this effect from three different perspectives: Valence (how positive or negative a rating is), the presence of a rating versus no rating and volume (thus the amount of ratings). Interestingly, they do not find a significant correlation between valence of the rating and sales. Amblee and Bui (2011) suggest that this might be due to the generally positive ratings, and thus a low variance between positive and negative ratings. Second, they also find that the presence of a rating is a good predictor of higher sales, as compared to no ratings. Furthermore, they also find a significant correlation between the volume of ratings and the volume of sales.

In their discussion, Amblee and Bui (2011) propose a better scoring system which allows users to score the e-book on different dimensions such as content, writing style and so on. While this suggestion might lead to a bigger variance between ratings, I wonder if it would have a positive effect on sales in the end. Amblee and Bui (2011) point out that the majority of past research on valence suggests that valence is not a reliable predictor of sales. However, by including more ratings to fill in like Amblee and Bui (2011) suggest, you might achieve a negative effect: that customers are no longer willing to fill in the rating. And the importance of the presence of the rating, and moreover the volume is exactly what is found to be so important to spark sales by Amblee and Bui (2011).

Fast forward to 2015. Did Amazon.com change the rating system? I went to Amazon.com and I filtered on short stories and on kindle editions. This is what I found:

Figure 2: Average customer review of shorts on Amazon.com, 2015

Figure 3: Layout of customer reviews of shorts on Amazon.com, 2015.

This shows that the great majority of the ratings is still very high (roughly 84% of the ratings are 4 stars or more). However, it seems like Amazon has added some space for customers to motivate their rating. Furthermore customers can also identify reviews as helpful, and Amazon shows me the most helpful reviews first. By using this system, Amazon.com leaves it up to its customers if they want to motivate their rating (and spend more time rating a book) or not. What do you think, has the system improved? Do you think it will lead to more sales? Please comment below!

References:

Amblee, N. and Bui, T. (2011) ‘Harnessing the influence of social proof in online shopping: The effect of electronic word of mouth on sales of digital microproducts’, International Journal of Electronic Commerce, Vol. 16, No. 2, pages 91-113, DOI 10.2753
Featured image: GTP Headlines, accessed 31-03-2015, http://news.gtp.gr/2014/11/25/amazon-com-launch-travel-service-enter-the-world-online-booking/
Figure 1: quora.com, accessed 01-04-2015 http://qph.is.quoracdn.net/main-qimg-80cd8d435bc1eed5a48d8732857f5aa7?convert_to_webp=true
Figure 2: Amazon.com, accessed 31-03-2015, http://www.amazon.com/s/ref=sr_nr_p_n_feature_browse-b_2?fst=as%3Aoff&rh=n%3A283155%2Cn%3A%211000%2Cn%3A17%2Cn%3A10300%2Cn%3A10307%2Cp_n_feature_browse-bin%3A618073011&bbn=10307&ie=UTF8&qid=1427828706&rnid=618072011
Figure 3: Amazon.com, accessed 31-03-2015,http://www.amazon.com/When-Fall-Love-Blue-Series-ebook/product-reviews/B00HE1PEZO/ref=cm_cr_dp_see_all_summary?ie=UTF8&showViewpoints=1&sortBy=byRankDescending