Tag Archives: E-Commerce

Finding your way in a review rollecoaster: review analysis


You know that feeling, trying to find that one, perfect coffee machine on Amazon by scrolling through tons of reviews to find the one best suited to your needs?  Think about it. If we as individual consumers are having difficulties browsing through the reviews to find those that add value, imagine the difficulty companies must have in analysing those large amounts of reviews.

The above explained difficulty occurs mainly due to what we call the ‘4 V’s of Data’: volume, variety, velocity and veracity (Salehan and Kim, 2015). That’s where the paper ‘predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics’ by Salehan and Kim (2015) comes in. The paper looks at the predictors of both readership and helpfulness of online consumer reviews (OCR). Using different techniques the paper aims to create an approach that can be adopted by companies to develop automated systems for sorting and classifying large amounts of OCR. Sounds exciting, doesn’t it?! Let’s have a look at how this works.

What this paper is about

Whereas previous literature focusses at the factors that determine the perceived helpfulness of a review, this paper takes a step back. It starts by considering the factors that determine the likelihood of a consumer paying attention to a review in the first place, since without reading a review you cannot determine its helpfulness. Hence the research questions are as follows:

Research Question 1: Which factors determine the likelihood of a consumer paying attention to a review?
Research Question 2: Which factors determine the perceived helpfulness of a review?

In order to answer the  research questions, the paper looks at a sample of 2616 Amazon reviews and considers several factors they believe may impact review readership, helpfulness, or both. Readership is measured as the total number of votes (helpful and not helpful), whereas helpfulness is measured as the proportion of helpful votes out of total votes. Since I see no reason to bore you with detailed methodologies, I made a quick and easy to follow overview of the different factors the paper considers using a random Amazon review as an example:

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Findings

  • Longevity is measured as the number of days since the review was created. It has a positive effect on readership, meaning older reviews are more likely to be read. Whereas this may sound counterintuitive, this could simply occur due to the way in which Amazon sorts the reviews, since by default users view reviews with most helpful votes first, unless they change the setting to viewing the most recent review first.
  • Review – & title sentiment is measured by conducting sentiment analysis on the review content, which scores a review depending on how emotional the content is (either positive or negative). Both have a small, negative effect on helpfulness, which indicates that consumers perceive emotional content to be less rational and therefore less useful. These findings are somewhat different from previous research, which showed that reviews carrying a strong negative sentiment have a stronger impact on buyer behaviour than positive or neutral reviews.
  • Title length has a small, negative effect on readership meaning that a reviews with longer titles are less likely to be read.
  • Review length has a large, positive effect on both readership and helpfulness, meaning that longer reviews are read more and receive more helpfulness votes on average.

All above outlined findings are statistically significant. Whereas previous research focussed mainly on numerical rating and length of the review, this paper looks at the textual information the review contained. This means that the practical implementations are high. For example, the paper suggests that companies may use sentiment data to analyse large amounts of OCR which are constantly produced on the Internet. The paper also showed the importance of the title: make it short and not too emotional. This is something e-commerce companies can guide their customers in when writing a review.

Discussion

In my opinion, a large limitation of this paper is that they use the number of ‘total votes’ as the number of times a review was read. I don’t know about you, but I certainly don’t hit the vote button every time I read a review. Hence I think using a different methodology might be better. For example you, could track customers as they move over a page, note how long they spend at the review and count the review to be ‘read’ if the this time was anywhere between e.g. 20 and 50 seconds (since you don’t want to count people that simply left the page open).

How about in practice?

This sounds great, but are there actually companies out there using similar approaches to make the life of their customers easier? A company that does this very well is Coolblue. Their aim is to be the most customer centric company of the Netherlands (Coolblue, 2018) and hence they go even further than described in the paper. Their product page contains an overview of the pros and cons for the product to allow for an easy overview. Whether these pros and cons come from frequently placed customer reviews isn’t clear. Moreover, they ask customers to fill in the pros and cons, so that customers looking to buy don’t need to read through long, unstructured sentences. Lastly, they use the review helpfulness to rank reviews according to relevance.

Sources

Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.

Coolblue, 2018. Yearbook 2017, Accessed via http://nieuws.coolblue.nl/jaarboek-2017/

From e-commerce to social commerce


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.

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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)

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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

Recommendation networks and the long tail of e-commerce


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

M-Commerce – Creating new opportunities?


Introduction

Mobile commerce is growing rapidly and at a faster pace than e-commerce (Brohan, 2016). Currently, mobile commerce accounts for one third of total e-commerce sales. This percentage is expected to exceed the 50% mark soon. In other words, mobile commerce optimization is not a competitive advantage anymore, but a competitive imperative for companies (Roggio, 2016). Due to mobile commerce, you can buy everything you want, whenever you want. What would you buy via your mobile device? Do you have any wishes on your shopping list?

Continue reading M-Commerce – Creating new opportunities?

Kaskus: Does Recommendation Agent Really Matter?


We are going to discuss a bad example of recommendation agent (RA) used by an Indonesian e-commerce/ online forum platform, Kaskus. You would immediately think that this platform performs badly these days, but you cannot be more wrong. Recently, Kaskus announced that the site has achieved 600,000,000 page views every month and 40,000,000 users registered to the site which certify Kaskus to be the biggest online forum in Indonesia (Lukman, 2014) and Indonesia’s #1 website in 2013 (Redwing, 2013).

Kaskus started as an online bulletKaskus Logoin board forum for gamer communities (Wee, 2012). When the traffic picked up, the users saw the opportunity to sell their things in the forum and thus e-commerce threads were popping up everywhere. Seeing how many e-commerce threads were created, Kaskus established a sub-forum called Kaskus Jual Beli (KJB) or Kaskus Selling and Buying especially for e-commerce threads.

For a long period of time, KJB has no recommendation agent what so ever to assist the buyers when purchasing something. What it had was merely content filtering system which was very inefficient because KJB had too many thread posts. Recently, however, KJB started to implement rule based preference elicitation to its existing RA system.  This new type of RA is arguably effective because there are many different types of item sold. For example, if we want to buy a puppy (Yes, real puppy!) then the condition rule such as “new” or “second” will not be valid anymore.

Kaskus RA

Continue reading Kaskus: Does Recommendation Agent Really Matter?