Your Solution to Monetizing Your Closet

Your Solution to Monetizing Your Closet

What is it?

VillageLuxe is described as the Airbnb of high-end fashion, where consumers can both lend and rent designer goods from other users. The business focuses on a niche market of fashion-conscious women in NYC and is based on improving the inefficiencies of women’s closets. Fashion-focused women spend considerable amounts of money on clothing, shoes and accessories, however, between uses these ‘investments’ end up sitting in closets underutilized. While many women resort to consignment or donation in order to get rid of clothes they no longer wear, what about the clothes they want to keep, but don’t wear as often? VillageLuxe’s solution: renting.

Renting your clothes allows you to continue to recognize value from already purchased clothing that would otherwise go unused in between wears. This idea of monetizing underutilized assets has been seen with other business models such RelayRides with cars and Airbnb with homes.

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Network of Fashionistas

VillageLuxe users engage in a two-sided network with lenders and borrowers, and most users participate on both sides. As a result, each new user actually adds value to both sides of the network, giving VillageLuxe a unique ability to balance between supply and demand. VillageLuxe goes a step further to encourage this behavior explicitly, subsidizing $10 of a user’s first rental if she also lists an item.

Additionally, users themselves are motivated to spread the word in order to maximize the value of the service. The more fashion-oriented women who sign up, the more rental options users have, creating a positive network effect.

Despite these expansion efforts, the business has been slow to scale, however, this appears to be intentional. VillageLuxe offers a luxury service and is able to maintain exclusivity through an invite-only signup process. While this exclusivity stifles user growth, the expensive nature of the products and the need for maintaining quality validate the company’s decision to use vertical segmentation in order to keep high quality users, thus preserving the value of the platform for everyone.

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Quality is Key

Quality control is something that should be top of mind for VillageLuxe given the issues that similar companies, like Airbnb, have had to date. Asking women to share expensive designer clothes with strangers means asking them to change their behavior. Women need to become comfortable with this new concept, making it critical for VillageLuxe to build users’ trust through a quality experience. VillageLuxe has implemented mandatory reviews and social connections to assist with exactly that.

Uber used mandatory reviews in order to improve the accountability of its user base. VillageLuxe has followed suit, but unlike Uber, VillageLuxe customers’ multi-transaction relationships makes doing so even more critical. VillageLuxe acts as a community for like-minded women to share their fashion investments. The cycle of lending and borrowing means that this week you might borrow a dress from Sarah and next week Sarah might borrow a purse from you. You feel more of a responsibility to return Sarah’s dress in good condition if you know she will be borrowing your items later. And, if you love Sarah’s fashion taste and want to borrow her clothes in the future, it’s in your best interest to be a responsible borrower.

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VillageLuxe’s Future

It will be exciting to follow VillageLuxe’s journey and to see whether the business model is scalable and can maintain the same level of quality and trust as it expands. Surely VillageLuxe will be presented with similar questions that Airbnb faced about how much control the company actually has to manage the users’ experience and whether they can prevent people from abusing the service.

It will also be interesting to see what impact VillageLuxe has on future consumer buying patterns. Will shoppers begin to consider future rental income when making purchases, turning clothing into potential investments? This could open up luxury goods to a new demographic that previously couldn’t afford them.


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The Meta-Search Paradox and Value of Online Aggregators

Priceline is an online hotel booking service with inventory that is also directly available from Marriott, Intercontinental, Hilton, and many others. Priceline receives a commission for every room reserved, a commission that is less than the profit that the hotel reaps from the booking. However, in online search ad auctions,  Priceline’s ads are somehow able to appear higher than the ads of any of the individual hotels. Considering that Priceline makes less money per booking, how is it able to pay more per visitor than the hotels?

AdWords is Google’s search ad marketplace and is the most prevalent ad auction online. It uses a formula to determine ad position based on the following inputs: advertiser cost-per-click (CPC), a “quality score,” and the click through rate (CTR) of the ad for a particular search term. The quality score is based on ad relevance (to the search term), landing page experience, and expected click through rate (CTR).

Assume that the auction formula inputs for a Priceline ad and a Hilton ad on a search query for “hotel room in Boston” were the same – same CTR, same ad quality, etc. It seems impossible for Priceline to be able to outbid Hilton in this case, but paradoxically, they they are able to outbid individual hotels on a huge variety of keyword queries.

Suppose a simplified scenario where there are only 10 hotel providers in Boston: Hilton, Marriott, Starwood, and 7 others. Each of the 10 have 10 rooms available for a particular night that a user is interested in. Imagine a user who clicked the Google Ads of each individual hotel before making a decision. Each of the hotels would have to pay for the user to click their ad, even though 9 out of 10 got no benefit. Thus, each one has only a 10% chance of attracting this person’s business, given that he is committed to buying a hotel room in Boston. If a hotel has to pay Google for 10 ad clicks to make one room sale, those ad payments start to add up and eat into the profit margin of the room sale. In contrast, an aggregator like Priceline who has deals with each of the 10 direct players, will have 100 rooms in its inventory. It has a much higher chance of actually gaining a conversion since the consumer is likely to find exactly what they are looking for on Priceline. The aggregator has a higher conversion rate and therefore it can pay more than a direct advertiser to attract a user to its site, despite a lower profit margin for a sale.

In addition to this structural benefit that aggregators benefit from, users often appreciate aggregators because they simplify the buying process. The CTR of an aggregator and is often higher than those of direct players which, coupled with the higher purchase conversion rate, means that aggregators can bid even less and still maintain the top ad positions in search. Aggregators are valuable to users as they minimize the need to shop around across a highly fragmented industry. It is much easier for the user to compare amenities, prices and location in one place. The breadth of listings gives consumers the further confidence that they are making an informed decision.

Furthermore, aggregators can seem like an independent third party and thus foster trust. A user that visits a hotel’s website directly, isn’t likely to have full trust in the reviews that are displayed since they might be biased. A third party that aggregates review information is likely to be more trustable. This isn’t to say that a third party is necessarily trustworthy, but rather that consumers are more likely to trust their reviews and/or recommendations.

You might ask why the hotels would continue to support this structure and fund a channel that undercuts their margins. The answer primarily lies in the fragmented nature of the marketplace. The aggregators would only be threatened if a large majority of hotels in an area colluded to pull their contracts. It is not in an individual hotel’s best interest to turn off this marketing channel and they are unlikely to be able to come to a deal with their direct competitors. The result of this fragmented structure is that the aggregators continue to thrive online.

This model works in a surprising number of fields. Flights, hotels, rental cars, apartments, home services, online courses, and many others.

In addition to providing deep breadth of listings for their users, aggregators can further differentiate themselves by helping users in the decision-making process. This is becoming especially important as online consumers seek the simplicity associated with being served an answer on what to do by a trustworthy expert. Unfortunately, some companies exploit this user desire, by, for example, promoting products that bring in the highest affiliate commission rather than the ones that are truly the best.

There is an opportunity for new companies in many fields to step up and take their role as quality raters and custom recommendation seriously. For example, the company bestreviews.com is focused on getting real expert input on specific products and recommending products in all categories on the basis of the combination of expert opinions and public reviews. Similarly, users will value eversence.com, the well-researched science-based supplement recommender.

As the online economy continues to grow and evolve, the role of online aggregators will also have to adapt if they want to stay competitive. I believe that the aggregators will further differentiate by creating more value by providing complimentary services such as deep research and trustworthy recommendations.

By: Liza Yermakova


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New restaurants always hope to attract enough eyeballs on Internet and mobilize as many trials as possible within a short period of time after opening. But current online restaurants promotion windows, typically Yelp and Groupon, don’t help a lot, though they attract many target customers of restaurants.

Yelp is the biggest restaurant review companies in U.S. People go to a restaurant and try the food, then some of them will give ratings as well as detail reviews to this restaurant, from the taste of the dishes to the environment of the restaurant. With the accumulation of the review numbers, potential customers of a certain restaurant can refer to these reviews and get a whole picture of this restaurant. These reviews effectively help them to make decisions and also help the restaurant to attract more new customers if it received lots of high ratings and good reviews. However, Yelp might be not a good eco-system for new restaurants. Considering Yelp as a restaurant markets, those restaurants with lots of favorite comments and high ratings always attract more eyeball and relatively easier to transfer those eyeballs to real trials. And the more people tried, the more will offer ratings and reviews. This virtuous circle helps those established restaurants to be more and more popular. On the other hand, a new restaurant, because lacking reviews and comments, is hard to attract enough eyeballs and mobilize trials. With very little trial, customers who are going to offer ratings and reviews are few. Therefore, new restaurants are hardly to promote themselves on Yelp. They have no way to outperform the established restaurants or even buried by them on Yelp.

Groupon seems to offer a good marketplace for new restaurants to do promotion deals. With very attractive high discount deals, Groupon tends to motivate its customers to try the deals with very low cost. It makes sense for new restaurants, which want to attract customers as quickly as possible after open, to offer those deals to Groupon. But are the customers attracted by Groupon good customers for the restaurants? Think about who are the people who always use Groupon: people who seek for cheap things rather than people who try new things. The purpose for new restaurants to attract as many customers as possible at the beginning is to build the buzz and build the loyalty after customers’ trial. But Groupon customers only care about the deal, not the restaurant. When they finish a cheap dinner, they are going to seek for another one offered by another new restaurant in Groupon. This makes the promotion spending a waste of money. Meanwhile, it is hard for high-quality new restaurants to distinguish from bad one, or even a negative impact on them, because they might not be able to offer a deal as cheap as those low-quality restaurants.

With that said, there is a business opportunity to build a new website that helps new restaurants to promote. If one can find a way to accumulate many effective reviews very quickly and attract high quality target customers, it will attract high-quality new restaurants and high-quality customers and finally becomes a platform for all new businesses and people who would like to try new services.


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It’s a common scenario.  The weekend is here and you’re looking to try a new restaurant.  The problem is that you don’t know which restaurant to try, and you certainly don’t want to pick the wrong one.  Why we attach so much buyer’s remorse to this indisputably low risk decision is a good question, yet it can’t be denied that we do.

Driven by the quest for a guilt free restaurant choice, you head for today’s natural starting point – a quick review of Yelp.  Maybe you also run a quick Google search for “best restaurants in MyCity,” but that search likely brings you back to Yelp anyways.  So, you dig in.

What do you find?  Well, a search for restaurants in Cambridge returns over 40 restaurants with at least a 4-star rating.  Not much distinction there, so you begin to click through a few.  Lots of “I love it” and “best ever’s” are scattered across the occasional “I hate it.”  Still, tough to glean much from that.  Absent the restaurant name on top, the reviews are all essentially interchangeable and are all either incredibly positive or incredibly negative.  Maybe if you focus on Sam L., you’ll find better advice.  Sam L. kind of looks like you in his ¼ inch x ¼ inch photo, and he may even be wearing the same shirt that you own.  He must have similar dining tastes.  Unfortunately though, Sam L. seems to base his star rating more on the perceived attitude the hostess gave him rather than the actual quality of the meal.  No help there.  (click on the “Real Actors Read Yelp” link below for more brilliance from Yelp reviews)

Eventually, you realize that you’re not getting what you’re looking for and leave frustrated.

This frustration is something that I’ve experienced often, and I believe it’s experienced by many as online reviews begin to inundate consumer web-based research.  Without claiming to have the full solution, I’d suggest that there are 3 areas of improvement that can provide better reviews to consumers and possibly even propel some interesting innovation in the online review space.  I’ll continue to use the restaurant example, but I believe that these improvement areas are present in a number of other online review services (e.g. TripAdvisor/travel review)…

Making reviews more relevant to me (and to you):  Relevance is an essential dimension to the online experience, and it continues to be addressed across numerous fronts (e.g. search, advertising, deals).  This is an area where you see the big players taking the leading role.  From Facebook likes to Google filtering search results based on your web history, a significant amount of attention (and dollars) has been spent on trying to make the web more personally meaningful to the individual.

That said, there have also been much smaller players that have found positive traction in building recommendation engines of higher relevance.  Stamped (recently bought out by Yahoo in a talent acquisition) sought to allow people to ‘stamp’ things that they liked and share them across their network of friends in a more convenient way than Facebook likes.

However, current efforts within online reviews continue to be primarily focused around associating relevance to things preferred by your circle of friends.  While this is a great first step, I believe that it fails to provide adequate results.  First, your friends have divergent interests, and in the case of the restaurant example, your dining preferences will almost certainly better match a complete stranger (found in a massive dataset much larger than you network of friends) than your best buddy.  The key is figuring who that complete stranger perfect match is.  I believe that big data is a far better solution for an online reviews 2.0 world where preferences can be matched to truly statistically significant comparison sets and deliver much more meaningful reviews and recommendations.

Identifying the proper target and offering objectivity: For the far majority of Amazon reviews, identifying the target of the rating is easy.  It’s the product being highlighted.  However, when rating more multi-dimensional experiences (such as dining or travel), it’s much more difficult to discern what is actually being reviewed and rated.  Sam L. above based his restaurant rating on an interaction with the hostess.  Some may view this as helpful, while others might prefer a more objective review of the quality of the food.  The inherent subjectivity of which dimensions matter most can quickly erode the integrity of the overall rating system.

Just this past week, Yelp released an update to their platform hoping to address this concern more directly.  Yelp is adding menu pages to the restaurants that rate specific dishes while also providing user submitted photos of dishes.  It’s a positive step towards better associating objective ratings with more cleanly defined targets.

Providing more nuanced rankings:  When 40+ restaurants in Cambridge are rated as 4-stars or better, it can be argued that none of those restaurants are actually rated at all.  The consumer is simply unable to make a reasonable distinction between them based on the ratings alone.  Much of this clustering occurs from the polarizing catalyst that encourages consumers to submit reviews in the first place.  Feedback is typically generated from an overwhelming experience, either positive or negative.  The more subtle critiques and comments often don’t inspire the effort to submit a review at all.

One possible way to accomplish more nuanced ratings would be to better segment like-restaurants and force rank the restaurants within each segment across a more normal distribution.  This may generate more enlightening ratings by highlighting the best and the worst pricey restaurants in Cambridge, for example.  Current results tend to skew all ratings for pricey restaurants towards the highest stars, but a force rank would allow the ratings to show the best of the best, and the worst of the best in this instance.

 

References:

Real Actors Read Yelp
http://www.youtube.com/watch?v=QEdXhH97Z7E

Yahoo Buys Mobile-App Maker Stamped
http://online.wsj.com/article/SB10001424052970204076204578078842424741154.html?mod=googlenews_wsj

Ex-Googlers Launch iPhone App for Tapping Into Friends’ Reviews
http://mashable.com/2011/11/25/stamped-iphone-recommendation-app/

Yelp adds menus, makes them mouthwatering (or revolting) with food photos
http://venturebeat.com/2012/10/30/Yelp-menus/


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Online reviews are a great tool to facilitate our decision making and have massively gained in popularity over the last years. Getting feedback about the cleanness of a hotel room, the quality of service in a restaurant or user experiences with a new laptop has never been so easy. Reviews reduce our search cost, convey information that we would otherwise not get or highlight issues, we did not think about reading the glorified manufacturer’s product description. I recently bought a mixer online and based my decision on product reviews on Amazon, videos on youtube and online comparison sites. In class we discussed, how reviews are the most powerful way to create trust and reduce risk for SaferTaxi and airbnb. Many positive comments on a taxi driver or potential tenant indicate that this person might be trustworthy. Yelp and other review sites have built massive businesses around providing these services.

However, businesses have also understood how valuable or damaging reviews can be to their success and started to actively game the system. Here are three reasons why you should be extremely careful using online reviews.

Up to 30% of all online reviews are fake! (Weise, 2011) Research has shown that for a broad variety of products, a large share of reviews are not posted by genuine users, who have actually used the product or service and want to share their honest feedback. For example some company employees and paid “freelancers” write about amazing experiences with the intent of misleading potential customers. Because of the scale of this issue, the US Federal Trade Commission has passed a guideline that requires online reviewers to disclose any affiliation with the respective manufacturer or service provider. Along these lines the UK government has ruled that TripAdvisor is no longer allowed to advertise “honest, real or trusted” reviews from “real travelers”, because the company cannot guarantee that the posts are not fraudulent (Mayzlin, Dover, & Chevalier, 2012). However, these guidelines are hard to enforce. Companies like Microsoft, Google, Yelp and TripAdvisor are very concerned about deceptive reviews and have sponsored research on computer algorithms to detect and remove such posts.

Reviews are systematically skewed! Why does Yelp actively discourage businesses from asking for reviews? They do it because there is a strong selection bias (Yelp, 2010). Companies have an incentive to only ask satisfied customers for feedback and sometimes even give away merchandise for 5-star ratings. This generally inflates reviews and skews the picture (Streitfeld, 2012). Small hotels are particularly active in review fraud. Research shows that they are 10% more likely to have top ratings on TripAdvisor –open to every user- than on Expedia, where the reviewer had to actually book and pay for a stay before writing a review. It also points out that large hotels are systematically more likely to receive some very bad feedback if they are located near a small independent hotel. The authors attribute this to active discretization and show that the effect is stronger on more liberal review sites like Tripadvisor (Mayzlin, Dover, & Chevalier, 2012).

We are bad at detecting fraudulent reviews! Although we think we can spot the deceptive reviews, the chances of getting it right and not being fooled are very low. Even if we know that one of two postings is fake, on average we only get it right 60% of the time, which is hardly much better than flipping a coin (Ott, Choi, Cardie, & Hancock, 2012). Automated algorithms perform with up to 90% accuracy. For your next hotel stay, try out www.reviewskeptic.com. This tool helps you identify fraudulent reviews based on research at Cornell University.

Reviews are still a great way of making better purchase decisions with limited time and exposure to the actual product or service. However, we should not blindly trust them and know about the pitfalls. As a general rule of thumb,

(1) reviews from sites that require proof of past usage,

(2) posts from users, who have written about multiple brands over a longer time and

(3) testimonials without extreme notions (e.g. excessive use of superlatives or opinions diverging vastly from others) without certain keywords (e.g. husband)

are more likely to be legitimate ones. For more detailed tips I recommend the Cornell research.

Bibliography

Mayzlin, D., Dover, Y., & Chevalier, J. (13. August 2012). Promotional Reviews: An Empirical Investigation of Online Review Manipulation. Retreived from: Social Science Research Network: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2128860

Ott, M., Choi, Y., Cardie, C., & Hancock, J. (2012). Finding Deceptive Opinion Spamby Any Stretch of the Imagination. Ithaca, NY: Cornell University.

Streitfeld, D. (26. January 2012). For $2 a Star, an Online Retailer Gets 5-Star Product Reviews. Retrieved from: The New York Times: http://www.nytimes.com/2012/01/27/technology/for-2-a-star-a-retailer-gets-5-star-reviews.html?_r=2&hp&

Weise, K. (29. September 2011). A Lie Detector Test for Online Reviewers. Retrieved from: BloombergBusinessweek: http://www.businessweek.com/magazine/a-lie-detector-test-for-online-reviewers-09292011.html

Yelp. (13. August 2010). Yelp WEB LOG. Retreived from: Don’t ask for reviews:

http://officialblog.yelp.com/2010/08/dont-ask-for-reviews.html

 


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