Tag Archives: reviews

Culture, Conformity and Emotional Suppression in Online Reviews


Paper: “Culture, Conformity and Emotional Suppression in Online Reviews” by Hong et al., 2016

“While Americans say, “the squeaky wheel gets the grease,” the Japanese say, “the nail that stands out gets pounded down.”

In other words, in the States, people who complain the loudest get the most attention while in Japan, people are discouraged to express personal opinions loudly especially if they don’t fit the community expectations. This phenomenon illustrates the differences between individualist (American) and collectivist (Japanese) cultures as defined by Hofstede (2001) and House et al. (2004). But this post is not entirely about cultural differences – it is about their influence on online reviews. Continue reading Culture, Conformity and Emotional Suppression in Online Reviews

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


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:

results

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 Engineering23(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 Applications7(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/

The future of reviewing: Videos!


“59% of customers believe that reviews influence their buying behavior.”

(Xu et. al, 2015)

….You can only imagine the influence reviews have on product sales and what a valuable asset they are to businesses!

Especially for experience goods, the generation of reviews ensures quality and provides social control. Throughout the past weeks, the elective on ‘customer-centric digital commerce’ has shed light on the underlying motivations of users to post reviews, the possibilities to structure them and how (not) to respond to them. We have realized that format and style can have significant effects on consumers’ perceptions and buying behavior.

Continue reading The future of reviewing: Videos!

Fire your sales team, Boost e-WOM participation!


Imagine, you’re on a birthday party without a mobile phone, tablet or laptop but you would like to have some information about a certain experience good because you’re considering a buy. I guess you might ask your friends or family relatives about their findings and opinions. Think again how you’re purchase decision looks like after they share a negative story about that related product….

In contrast with the traditional word of mouth ( face-to-face context ) , consumers use blogs, search engines, internet communities, social media, and consumer review systems to gather information and make informed purchase decisions. Due to the rise of internet and the development of phones, tablets or laptops, traditional word-of-mouth interactions are replaced/substituted by electronic word of mouth. So e-WOM, defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (T. Hennig-Thurau, 2003) is an important source used during the path to purchase or so called customer journey. Upon that, previous research conducted by Bickart and Shindler, show that customers actually pay more attention to the information provided by other customers rather than those of the salesperson or marketers because they have used the product and is considered as more trustworthy.

Understanding the importance of e-WOM, e-commerce sites attempt to encourage their customers to produce more e-WOM because consumer-produced information provides potential customers with a sense of trust. But how can firms (Online retailers), encourage their customers e-WOM participation, what are customers motivations and how does it affects e-Loyalty (customer loyalty in the internet market) ?

Blogpost 1

Research done by Yoo, C.H et al. in order to examine the impact of e-WOM participation on e-loyalty, has shown that both intrinsic and extrinsic motives have an impact on e-WOM participation. Specifically, it was found that internal motivation  most influences customer’s participation (fig 2). Customer’s participation is operationalized as the actual level of involvement and frequency in e-WOM writing and reviews. Customers participation behavior does have a significant impact on formation of Site identification. Site identification can be devided in (1) Personal site identification; the extent to which a customer thinks the image of an online shopping site matches his/ her own image, and (2) Social identification which refers to the identification that a customer feels with respect to interactions, via the e-WOM system, with other customers on the same online shopping site.( C.H, Yoo, 2013)

e-WOM participation behavior enhances social identity among customers. Additionally social identity plays a role in using the e-WOM system. It is for this reason important to maintain an e-WOM system for customers so they can develop a strong social identity on the site through enhanced interaction with other customers.

E-Loyalty

Finally, both personal and social site identifications have a significant influence on customer e-loyalty. Remarking,  that personal identification has a stronger impact on e-Loyalty.

Conclusive, based on the conducted research, when e-WOM is well managed, it was shown that it has positive effects on the  customer evaluation of the company and on intentions to repurchase.

Created by : Luut Willen

References :

Bickart, R.M. Schindler, Internet forums as influential sources of consumer information, Journal of Interactive Marketing 15 (2001) 31–40.

Hennig-Thurau, G. Walsh, Electronic word-of-mouth: motives for and consequences of reading customer articulations on the internet, International Journal of Electronic Commerce 8 (2003) 51–74.

Chul Woo Yoo, G. L. (2013). Exploring the effect of e-WOM participation on e-Loyalty in e-commerce. Decision Support Systems :DDS (2013)

What Makes a Helpful Online Review?


We have all been there; browsing for too long on Tripadvisor.com or Amazon.com trying to find that one review that could be the decisive factor in buying (or not buying) that specific product. But what exactly is it that we are looking for? What makes one review more helpful than another? The article of Mudambi and Schuff (2010) tries to find the answers to these questions by reviewing almost 1600 reviews on Amazon.com throughout several products and product categories.

When browsing online, individuals are presented an increasing amount of customer reviews; these reviews have proven to increase buyers’ trust, aid customer decision making and increase product sales (Mudambi, Schuff & Zhang, 2014). In addition, customer reviews can attract potential visitors and can increase the amount spent on the website.  Hence, retail sites with more helpful reviews hold greater potential to offer value to consumers, sellers as well as the platform hosting the customer reviews.

In order to increase the helpfulness of customer reviews, several websites such as Amazon.com and Yelp.nl ask the question “was this review helpful to you?” and list more helpful reviews more prominently on the product information page.  Mudambi and Schuff (2010: 186) define a helpful review as a “peer-generated product evaluation that facilitates the consumer’s purchase decision process”.

The article distinguishes between two types of goods when looking for products online: search goods and experience goods. Search goods possess attributes that can be measured objectively, whereas the attributes of experience goods are not as easily objectively evaluated, but are rather dependent on taste. Examples of search goods are printers and cameras; examples of experience goods are CD’s and food products.

Past research showed conflicting findings as to whether extreme ratings (rating very negatively or very positively) are more helpful that moderate reviews; some argue that extreme ratings are more influential, whereas others argue that moderate reviews are more credible. Mudambi and Schuff (2010) argue that taste often plays a large role with experience goods as consumers are quite subjective when rating; hence, consumers would value moderate ratings of experience goods more, as they could represent a more objective assessment (H1).

Next, Mudambi and Schuff (2010 scrutinize the review depth of customer reviews. Since longer reviews often include more product details, and more details about the context it was used in, the authors hypothesize that review depth has a positive impact on the helpfulness of the review (H2). Nevertheless, the review-depth of a review might not be equally important for all products. Reviews for experience goods often include unrelated comments or comments so subjective that they are not interesting to the reader. For example, movie reviews often entail elaborate opinions on actors/actresses that are not important for the reader. On the other hand, reviews of search goods are often presented in a fact-based manner as attributes can be objectively measured. As a result, it is argued that review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods (H3).

By evaluating almost 1600 reviews (distributed over 6 products; 3 experience goods and 3 search goods) and excluding the ones that did not get any vote whether it was helpful or not, the researchers were able to confirm all three hypotheses. The article teaches us that there is no one-size-fits-all method as to what makes a reviewhelpful. Experience goods prove to be less helpful with extreme ratings, whereas search goods benefit from in-depth reviews.

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Mudambi, S. & Schuff, D. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly, Vol 34 (1), pp 185-200.

Mudambi, S., Schuff, D. & Zhang, Z. (2014). Why Aren’t the Stars Aligned? An Analysis of Online Review Content and Star Ratings. IEEE Computer Science, 3139 -3147.

Reputation and uncertainty in online markets


One of the main problems which occurs in electronic markets is the information asymmetry between buyers and sellers. Buyers do not have the same information about the products as sellers do. The most important part of information advantage sellers have, has to do with the quality of the product. Sellers know the real quality, while buyers must rely on the description text provided by the seller. A well-known concept in the context of information asymmetry is moral hazard, which occurs when one party carries more risk than the other party. This happens in electronic markets when the buyer needs to complete the payment before the seller sends the product. In this way, the seller carries more risk. One of the most common types of internet fraud has to do with non-existing sellers or sellers who deliver unrepresented goods or even no goods at all. One way to overcome this information asymmetry is to build a relation with the other party. The problem is that for offline transactions this seems to be feasible, but for online transactions building a relationship seems to be harder. Therefore, the online reputation systems are introduced. In this way buyers can rate the behavior of the sellers, so that the more reliable sellers stand out against the unreliable sellers. Rating sellers may help other buyers in their decision process, so the online reputation system is a good example of consumer value creation.

In the article by Rice (2012) reputation and uncertainty in online markets are tested in a game setting. The game setting is as follows:

Game sequence

In this game setting there are two groups: buyers and sellers. Buyers have an amount of money which they ‘invest’ in the seller. The seller decides on how much of that investment he will return. Therefore the ‘investment’ can be seen as a price, and the ‘return’ can be seen as a delivered good. Initially the sellers announces how much he will return (e.g. quality of a good). Then, the buyer chooses how much he wants to invest in the seller (e.g. how much does the buyer wants to pay for the product). After that, the seller chooses how much of that money which was invested by the buyer he will return to the buyer (e.g. what quality will he deliver). Finally, the buyer has the opportunity to rate the seller. There is also an uncertainty factor included in the game setting. This provides the uncertainty that the returned amount can be intercepted by the researchers with a chance of 30%. The findings suggest that the occurrence of reputation systems stimulates people to take part in a transaction. Buyers who don’t meet expectations receive poorer ratings, while buyers who exceed expectations receive higher ratings. But when the buyers doubt whether the unmeet expectations are not caused by the seller, fewer poor ratings occur. This is related to the uncertainty factor. It seems that the higher the uncertainty factor, the more the buyers tend to trust the other party. Also, sellers’ positive ratings result in a higher investment of buyers. In some cases, a poor rating is weighted more heavily than a good rating.

I think the article highlights some very interesting aspects of online reputation systems, but I still have one question on my mind after reading this article. Nowadays, a lot of these online reputation systems are extended with visual text instead of just a scale rating. These text boxes mostly have much more details about the transaction experience with a specific seller and have therefore more value. I am wondering whether the findings of this article are also relevant in reputation systems with boxes of text, because these text-based reputation systems seemed to be more popular in the recent years. What do you think?

Sources:

Sarah C. Rice, (2012) Reputation and Uncertainty in Online Markets: An Experimental Study. Information Systems Research. 23(2):436-452.

http://www.scambusters.org/topscams2013-14.html, retrieved 22 April, 2015

http://www.spamlaws.com/internet-fraud-stats.html, retrieved 22 April, 2015

Reviews & Ratings: Consumer online-posting behavior


“Unfiltered feedback from customers is a positive even when it’s negative. A bad or so-so online review can actually help you because it gives customers certainty that the opinion is unbiased.” 

– Source: Gail Goodman, Entrepreneur, 2011

Social media delivers an ultimate platform for customers to broadcast their personal opinions regarding purchased products and services and therefore accelerate word-of-mouth (WOM) or consumer reviews to travel fast. Nearly 63% of consumers are more prone to buy products on a website that has online consumer reviews (iPerceptions, 2011). Online consumers reviews are trusted 12 times more, in comparison with descriptions of the product stated by the manufacturers themselves (eMarketer, February 2010). Companies who provide space for reviews on their websites, have an increase in company sales of nearly 18% (Reevoo). This video below defines how customers can assess online consumer reviews and recommendations while researching and shopping online.

Youtube: “Online Reviews and Recommendations”

Chen et. Al (2011) examined the interactions amongst consumer posting behavior and marketing variables such as product price and quality. An important part of the research was about how such interactions progress as the Internet and consumer review websites draw widespread approval where people use it more often. The study’s new automobile models data comprised of two samples that were gathered from 2001 and from 2008. As an automobile involves thorough searching before making a significant financial decision, these years were seen appropriate. Also more consumers made use of the Internet between 2001 and 2008 when considering purchasing an automobile. A total of 54% of new-automobile consumers made use of the Internet in when buying a car in 2001, reported by Morton, Zettelmeyer and Silva-Risso. According to a report by eMarketer, in 2008 this percentage was increased to nearly 80%. This study included prominent automobile review websites that covered the distinctive sections of the market— leading car enthusiasts (experts) as well as amateur consumers.

91-review-sites-e1415383495712

Motivations for Posting Online Consumer Reviews.

Gaining social approval – self-approval – indicating a level of expertise or social ranking – by demonstrating their superb purchase decisions, are all psychological reasons why consumers post online reviews. It can also be used to state satisfaction or dissatisfaction. Diverse types of customers are driven by distinctive motivations for posting reviews online. The earlier group of Internet users (in the study – year 2001) differs from the second group of Internet users (year 2008) when it comes to the reasoning as to why they post online. The consumers categorized as early group of users (a.k.a. experts – early adaptors of innovation) have high levels of product expertise, making them more likely to be psychologically seeking status and engaging in noticeable consumption. They are seeking to representing know-how and social ranking is particularly significant in the Internet’s early years (2001), as they tend to have high incomes and are more so price insensitive.

Conversely, the Internet has advanced and developed over this period, and it has appealed to a bigger population of types of consumers. Where, in 2001 it used to be a select group of Internet users who would post reviews, the Internet usage and online consumer review sites of today have become more mainstream. The Late adopters (2008) cultivate to be more no-nonsense and price focused compared to early adopters.

Marketing variables – effect on consumer online-posting behavior

Marketing variables indeed have an influence on consumer online-posting behavior. In the early stages of the Internet (2001) the price of products had negative relationship but premium- brand image has a positive relationship with the number of online consumer postings; differently, product quality has a U-shaped relationship with the number of online consumer postings. These different relationships are likely to be driven by early adopters of Internet usage.

The Internet infiltrates to mass consumers online, who are more inclined to be price sensitive as well as value driven.

Though certain marketing variables can lead to a big number of consumers engaging in online posting activities, these consumers do not automatically give higher ratings. The study shows that mass consumers lean towards posting online consumer reviews at higher as well as lower purchase price levels. In contrast to posting online consumer reviews primarily at lower price levels, which happened frequently during the early stages of Internet usage. The Internet has been accepted more by mass consumers online, where they express (dis)liking a product or service. This motivation of sharing reviews has become more important compared to sharing expertise of social status.

In conclusion, this research showed that the connections between marketing variables and consumer online-posting behavior are distinctive at the early phases compared to mature phases when it comes to Internet usage. High prices increase the overall consumer review ratings, which may be good news for a firm’s pricing decision. They found that the search for status is a core driver behind consumer-review behavior, predominantly in the early Internet stage. In market where it is difficult to assess quality, costly to assess quality, and where heterogeneous tastes are important factors when choosing a purchase, customers are occupied in all-encompassing decision-making. These conditions make it more likely for consumers to request external opinions online, before they make a decision on what they will be purchasing.

References:

Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media: How online consumer reviews evolve. Journal of Interactive Marketing25(2), 85-94.

Charlton, G. (2012) “Ecommerce Consumer Reviews: Why You Need Them and How to Use Them.” Econsultancy.com

Featured image: http://splumber.com/wp-content/uploads/2014/12/Plumbing-Online-reviews-1030×574.jpg

Social influence bias in online reviews


(This academic blog post is based on Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651.)

During our course we learn that there are four functions of customer value creation: recommend & develop products, compose & co-brand products, sell products & digital distribution and P2P support & product evaluations. In this post I want to focus on the fourth function, namely product evaluations.

When consumers make an online purchase decisions, they tend to rely on online reviews generated by other consumers. Consumers regard them as more persuasive than traditional advertisement from marketers and companies, and reports from third party consumer report companies. This is because online reviews focus more on experience than on technical specifications (Lu et al., 2014). Industry reports state that 61% of consumers consult online reviews before making a new purchase (Cheung et al., 2012).

So we know that consumers base their buying decision on online reviews. Muchnik et al. (2013) research if online reviews accurately represent individual opinions about the quality of a product or service. They suspect that social influence create irrational herding effects, where users follow the decisions of prior users. This can lead to suboptimal decisions and a thereby disrupt the wisdom of the crowds. If that is the case, it means that online reviews could easily be manipulated and disturb our decision behaviour.

To research the social influence bias on individual rating behaviour Muchnik et al. (2013) did a large-scale randomized experiment in a news aggregation web site. They find that negative social influence were corrected by other users by giving a positive rating, so there is no significant herding effect there. However, they did find evidence for herding effects by positive social influence. Positive social influence increased the likelihood of giving a positive rating by 32%. Overall, this increased the final ratings by 25% on average.

An important theoretical contribution of this article is that it confirms prior hypotheses on a tendency towards positive ratings, which makes these results more generalizable. This applies to all different kinds of users (e.g. frequent or infrequent voters) that could be distinguished in the experiment. Future research will need to research about the mechanisms that drive individual and aggregate ratings.

Managerial implications can be interesting for companies who want to use reviews as a marketing tool. If they can up vote positive reviews it can lead to herding effects and thereby positively increase sales. Taking the findings of this article in mind, would you be more critical about online reviews? Or are they too important for your decision making process?

References

Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461-470. Available: http://dx.doi.org/10.1016/j.dss.2012.06.008

Lu, X., Li, Y., Zhang, Z., & Rai, B. (2014). CONSUMER LEARNING EMBEDDED IN ELECTRONIC WORD OF MOUTH. Journal of Electronic Commerce Research, 15(4).

Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651

Why do people fill in reviews on online platforms?


With the new Internet technologies, traditional word-of mouth communication has been extended to electronic media, such as online discussion forums, electronic bulletin board systems, newsgroups, blogs, review sites, and social networking sites. Everyone can share their opinion and experience related to products with complete strangers who are socially and geographically dispersed this new form of word of mouth, known as electronic word of mouth (eWOM). This research is about eWOM which has become an important factor in shaping consumer purchase behavior. In early research is found that information provided on consumer opinion sites is much more influential among consumers nowadays.

For instance, eMarketer revealed that 61% of consumers consulted online reviews, blogs and other kinds of online customer feedback before purchasing a new product or service. In addition, 80% of those who plan to make a purchase online will seek out online consumer reviews before making their purchase decision (Infogroup Inc, 2009). Some consumers even reported that they are willing to pay at least 20% more for services receiving an “Excellent”, or 5-star, rating than for the same service receiving a “Good”, or 4-star rating (Comscore Inc, 2007).

Cheung et al. (2012) stated that we do not fully understand why consumers spread positive eWOM in online consumer-opinion platforms. Among the few existing publications, eWOM behavior is primarily explained from individual rational perspective with the emphasis on cost and benefit. Consumer participation in online consumer-opinion platforms depends a lot on interactions with other consumers. But why do people participate and are what stimulates consumers eWOM intentions?

The following variables were defined in this research as influencers of consumers’ eWOM intentions: Reputation, Reciprocity, Sense of Belonging, Enjoyment of helping, Moral Obligation and Knowledge Self-Efficacy. To test their theoretical framework they conducted a research using a sample of online consumer-opinion platform users from OpenRice.com. In total they collected 203 usable questionnaires.

sd

After this study three variables were found significant: Reputation, Sense of belonging and Enjoyment of Helping. Sense of belonging had relatively the most impact on consumers’ eWOM intention. The result is consistent with previous eWOM marketing literature, where sense of belonging is an essential ingredient that creates loyalty and citizenship in a group. Also enjoyment of helping others is crucial in affecting consumers’ eWOM intention. Intentions to write about dining experiences in OpenRice.com demonstrate enjoyment of helping others. Consumers can benefit other community members through helping them with their purchasing decisions. Reputation is a small factor affecting consumers’ eWOM intention. This can be explained by some consumers want to be viewed as an expert by a large group of consumers.

The results of this research can be practical relevant in different ways. Online consumer-opinion platform could allow consumers to create their own personal profile to create a stronger sense of belonging to the group. Also platforms could apply reputation tracking mechanisms, so ‘’experts’’ can be found more easily. And last, the platform could provide a mechanism for contributors so readers can show their appreciation for the received reviews, like a chat.

References
– Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218-225.

– ComScore Inc., Online consumer-generated reviews have significant impact on offline purchase, http://www.comscore.com/Press_Events/Press_Releases/2007/11/Online_Consumer_Reviews_Impact_Offline_Purchasing_Behavior2007.

-eMarketer.com., Online review sway shoppers, http://www.emarketer.com/Article.aspx?R=10064042008Last accessed.

– Marketingonline.nl, http://www.marketingonline.nl/nieuws/word-mouth-marketing-blijft-last-houden-van-roi-issues

Darahkubiru: Behind a Community Platform


Started as a platform to virtually connect with other denim enthusiasts in Indonesia, Darahkubiru has gained popularity as a trustworthy source to gather many denim-related product reviews. Later on the owner decided to start a company based on the website.

logo_darahkubiru-fancy

Since 2009, Darahkubiru has been providing many interesting articles regarding denim and other fashion products, ranging from interviews with local brand owners to a proper product treatment. The primary purpose of this website is simply to attract Indonesian youngster to the world of denim as a lifestyle instead of solely about fashion statement (www.darahkubiru.com). After being online for several months and dozens of positive feedback given by its readers, the owner released a forum section in the website to better accommodate the readers communication with fellow denim geek. In the forum, registered users are able to exchange views, comments, and passion about certain product. Within 5 years, the website managed to attract a staggering 16,312 users.

Then how do they gain profit??

Continue reading Darahkubiru: Behind a Community Platform