Companies that build online marketplaces struggle to solve the dreaded chicken and egg problem—how do you get buyers without sellers and sellers without buyers onto the platform? Many of today’s familiar online marketplaces—like Uber, AirBnB, eBay, Xbox Live, PayPal, OpenTable, and Angie’s List—managed to crack this challenge and reach sustainable scales. In addition to creating useful products for their users, most analysts would agree that these businesses also benefit greatly from positive network effects between their buyers and sellers. That is to say, each additional buyer or seller on the platform generally has a positive effect on his or her counterparty:
- More sellers à more options for quality, price, and convenience for buyers
- More buyers à more opportunities to transact for sellers
Many people are ready to give these companies the benefit of having solved for positive network effects on their platform. In fact, I too fell into this trap until Professor Gautam Ahuja of Ross School of Business (visiting at HBS last year) made me re-examine this belief. Rather, there exists an opportunity to more deeply understand these companies by exploring the network effects across all party interactions. We can gain a better idea of the costs involved in growing the business and what valuable follow-on products should be developed. The basis for this company analysis begins by understanding that multi-sided platforms must inevitably also have multiple network effects. To clarify what this means, it might be easier to first understand what network effects look like on a single-sided platform.
A Single-Sided Network Example: Early Facebook
When Facebook first came on the scene in the mid-2000s, it resembled a single-sided platform. In the time before marketers were peppering the site with ads—weren’t those the days!—users basically joined other users in a system where all users were alike. We can think about Facebook’s network effects by looking at what happened when a single new user joined (an increment of +1 new users).
The incremental user’s content provided other users with more to see and explore. This content came in the form of posts, photos, and music preferences and meshed seamlessly into other existing users’ experiences. An existing user now has more to explore and interact with on the site—like seeing pictures of the new user’s trip to Bali or discovering linked articles the new user shared. Existing users’ content might also get more interactions from the new user in the forms of more likes, comments, and tagged photos. These could all be described as positive effects as they generally enhanced the value for Facebook users.
However, the addition of a new user could make the Facebook experience turn negative as well. The new user might constantly post pictures of their cat and make it difficult for existing users to find the content they really wanted to see in their newsfeed. The new user could also be someone existing users didn’t want on the site, like a coworker or boss, which would make them less inclined to share photos or other content. Quite simply, the network effects induced by one new Facebook user could be positive or negative.
The insight into Facebook’s network effects helps us understand a lot about what followed in the company’s history. Because the effects were predominantly positive, people invited their friends and family to the site virally, reducing expensive marketing and growth costs. The site exploded and reached 1.49 billion monthly active users as of June 2015 and saw 1 billion different users log in on the sam day in August 2015. Facebook subsequently developed products like a filterable newsfeed (no more cat pictures from the new user) and privacy tools to reduce users’ pain points (only share photos with friends or make posts visible to friends “except XYZ” people) that resulted from its rapid growth and negative network effects. These developments could have been predicted by our simple analysis of identifying the network and its positive and negative attributes.
A Multi-Sided Network Example: AirBnB
When thinking about multi-sided networks, the model for analyzing network effects grows more complex. Rather than thinking about how an incremental user will affect the entire network, we should scrutinize who the incremental user is and what network is being affected.
To illustrate this more clearly, let’s consider a two-sided platform like AirBnB. When a new user joins the AirBnB site, we should first consider whether the user is a guest or host. Next, we need to explore how that additional guest or host affects other guests and hosts. For this two-sided network example, there exist 4 possible network effect scenarios:
- How does an incremental guest affect hosts?
- How does an incremental guest affect other guests?
- How does an incremental host affect guests?
- How does an incremental host affect other hosts?
Understanding network effects can quickly get complicated when dealing with multi-sided platform. The number of unique network effects necessary to consider is n2 for each distinct n type of parties on the platform. As we saw with Facebook, network effects can be either positive or negative, complicating our understanding of two-sided platforms even more. When considering positive or negative effects, the interactions that should be examined are 2n2.
Below I’ve illustrated how AirBnB might experience positive and negative network effects across all 4 of its network change scenarios:
An Updated Model for Thinking About Network Effects: Uber
The AirBnB analysis is a useful starting point, but I find it easier to simplify each of the distinct networks into a more manageable characterization. This reduction allows us to quickly understand the dynamics in the networks of a company while maintaining an explainable simplicity. I therefore classify interactions into one of three buckets.
- Collaborators (Positive)—Parties predominantly enhance the experience of other parties in the network. Examples of collaborators include funders on Kickstarter who together to support an idea or product or gamers on xBox live who play together in multiplayer Halo battles. Such relationships encourage users to invite other users to a site, and can lead to organic site growth and lower user acquisition costs.
- Counterparties (Positive)—Parties are involved predominantly in a monetary transaction or exchange that satisfies both sides. Examples buyers and sellers transacting for a deal on Groupon or a mother ordering food via delivery service Sprig. Parties exchange clearly identifiable goods and services, which, when priced at a point such that the transaction clears, creates value for both parties that supports repeat usage and high user lifetime value from multiple transactions.
- Competitors (Negative)—Parties predominantly compete for resources or opportunities. Examples include applicants applying to Y Combinator where only a select number of applicants are accepted or eBay bidders competing against each other for a baseball autographed by Mickey Mantle. In both cases, competition is likely to give users a worse user experience as they might not secure the opportunity or good or end up paying a higher price. This can result in a lowered user experience, unsubscribing, and high sign-up or reactivation expenses.
By using this framework, it helps me understand the operational costs a company is likely facing and what products they might consider developing in the future. For example, if this analysis were applied to ride-sharing start-up Uber, it might look like this:
For Uber’s two-sided platform, a large part of the company’s value comes from solving the most obvious network dynamic: matching drivers with riders and riders with drivers. The company was able gain a foothold in markets like San Francisco because cab companies were not keeping pace to satisfy this counterparty need. As more drivers joined Uber, riders benefitted with greater ride availability and more riding options (uberPOOL, uberX, uberXL, uberTAXI, UberBLACK, uberSUV, uberSELECT, uberPOP, uberBIKE, etc.). As more riders sign up, drivers are more likely to match with a pick-up request and earn money for their services. These services led to increased usage by both riders and drivers as value was realized.
However, this dynamic doesn’t necessarily lend itself to growth. Drivers aren’t actively inviting or converting new riders, and new riders aren’t energetically recruiting new drivers. The first time they usually encounter each other is during an Uber ride, at which point they’re both already on Uber’s platform. While a positive experience for each party—a clean, convenient ride for the passenger and a profitable transaction for the driver—will influence who how often the other party uses Uber in the future, they’re not actively growing the platform.
The story gets more interesting when you look at the network dynamic across the rider <-> rider and driver <-> driver dimensions. For current riders, each additional rider chiefly means increased competition for resources. In Uber’s case, this manifests itself in surge pricing when many people try and use the app at the same time—such as during a rainstorm or Friday rush hour. The experience is painful, and users are upset by either the wait time to find a rider or the total cost for the trip. Similarly, as more drivers join the platform, existing drivers face both increased competition for riders and reduced chances for earning surge prices. If driver’s aren’t able to find riders and drive around unoccupied, this cuts into the driver’s ability to earn for time worked.
By understanding the interplays occurring across its networks, it’s easier to identify and appreciate Uber’s growth, marketing, and development challenges over the past few years. While Uber has benefitted from word-of-mouth marketing for its remarkable service, much of Uber’s recent growth depends on promotions and discounts rather than virality. Because riders and drivers aren’t actively working to sign up other drivers and riders without an incentive, Uber bears the burden of these growth costs itself.
For example, Uber offers money to users (riders and drivers) who sign up new riders and use a unique promotion code (mine is below—feel free to join using it!). Additionally, Uber will give new riders one or more free rides upon joining. To sign up new drivers, Uber offers drivers a $500 bonus after completing their 20th rider, $500 dollars for signing up a Lyft driver, and fixed hourly income guarantees to ensure new drivers realize monetary gain immediately—with promotions often varying city by city. These acquisition costs can be large for a company looking to scale globally and help explain why Uber has raised massive amounts of cash to grow operations in areas like China, India, and other parts of Asia. In addition to building and operations costs, a lot of that money will likely go to signing up riders and drivers through aggressive promotions and discounts and launching citywide marketing campaigns. Given Uber rider’s lifetime value from its positive counterparty interactions, such costs can likely be easily justified.
On the product development side of the business, Uber’s network effects can explain a lot of what the company has focused on building. Paramount to the experience is maintaining a positive rider <-> driver and driver <-> dynamic. Anything that facilitates a quality service has taken precedent in the pipeline to protect the company’s advantages and keep users using the app. Such developments include credit card scanning for easier payment, license plate information to help riders identify drivers, written explanations for 3 star or below reviews to protect drivers’ reputations, and optimal route maps to make sure the most cost-effective route is taken.
Uber has worked to tackle negative network effects inherent to its business as well. Uber launched a fare split feature that aims to make the riding experience more collaborative and less competitive, easily allowing users pay each other and receive ride receipts. Additionally, a feature was added to surge that allows users to be notified once surge has dropped below as certain level, decreasing the pain from increased competition over resources. Finally, uberPOOL matches different rider pairs so that a each group receives a lower fare for carpooling with the other party. These features all subtly aim to shift the experience from competitive to collaborative.
In this post, I hope that I’ve helped lay out a new model to help understand network effects for multi-sided online marketplaces. By identifying all the distinct networks that exist and then understanding the interplay of people in those networks (collaborators, counterparties, competitors), one can develop a valuable tool for understanding much about an online business. This insight can be used to analyze a company’s growth and marketing costs—will users sign other users up or do they need to deploy resources? It can also give vision into a business’s product development priorities—what networks need to be protected with better products and which networks need pain points addressed?
By: Ry Sullivan
Posted by Leo Brown on Oct 23, 2015 | Tags: Amazon, crowdsourcing, mturk, network effects | 0 comments
Amazon Mechanical Turk (MTurk) has provided inspiration for strongly-worded blog posts since its inception in 2005. I will focus here on the specific question of how MTurk will persist in light of dissatisfaction among so many network participants.
Amazon launched MTurk to “crowdsource” Human Intelligence Tasks (HITs) that are relatively easy, or at least possible, for a human to complete, but challenging or impossible for a computer to complete reliably. HITs might include determining whether there is a barber shop in an image or identifying the mood of a song.
More recently, MTurk has played an important role in social science research as a convenient hive of inexpensive survey participants that, according to some studies, are sufficiently representative and reliable.
MTurk is named after an 18th century ruse in which a chess master was hidden inside a contraption–a Mechanical Turk–claimed to be a chess-playing machine.
The structure of MTurk is a classic two-sided network consisting of Requesters and Workers. Requesters set prices for completion of HITs, and Workers may accept HITs after reviewing them. Workers are not penalized for not completing HITs, but receive no compensation for incomplete HITs.
Both Workers and Requesters benefit from the size of the MTurk network. Workers benefit from having many Requesters who provide many HITs from which to choose (increasing demand for labor and availability of work), and Requesters benefit from an abundance of Workers to ensure competition for HITs, driving prices down and increasing speed of HIT completion. MTurk provides value to all parties by monetizing underutilized assets–in this case, human labor–by reducing labor-market friction (transportation, recruitment, etc.) through the efficiency of a technology platform. Just like Airbnb allows someone to monetize their “underutilized” home, MTurk allows Workers to monetize their “underutilized” ability to complete HITs.
This summer, MTurk changed the structure of its commissions (the amount it charges a Requester per HIT) amid complaints from both Workers and Requesters. From the MTurk blog, “These changes will help allow Amazon to continue growing the Amazon Mechanical Turk marketplace and innovating on behalf of our customers.”
The commission increase is one, but not the only, reason why the current structure of MTurk invites competition from upstart competitors. Wages for Workers were very low even before the commission increase, which constrains demand for HITs and drives wages even lower. Further, Workers have little recourse when Requesters provide misleading Task instructions or time estimates. So a competing service that offers more protections could be an attractive alternative. (But wouldn’t Requesters avoid a competing service that protected Workers from abuse? Stay with me.)
Requesters also see diminishing benefits of MTurk. The commission increase directly reduces the ability of social science researchers (an increasing proportion of Requesters) to use MTurk as a tool to collect survey respondents, given that research funds tend to be fixed. Some researchers have decided to reduce the wages they offer, or simply recruit fewer respondents, eroding the quality of MTurk for Workers.
Requesters face another type of challenge in addition to the commission increase: the inherent value of MTurk to social science survey research may be declining. This issue is more fundamentally erosive of the MTurk value proposition than commission increases and less easily remedied (though not impossible to remedy). The quality of MTurk-generated survey research is threatened by the trend toward “super users” who are skillful at answering the same types of questions; social networks such as mturkforum.com or reddit where Workers share tips on answers and strategies; and the lack of enforcement of basic standards of social science research that ensure quality data as well as ethical practice.
Other networks are forming that provide some benefits that MTurk does not. Sticky Crowd touts better rates than MTurk; sites like peopleperhour and Upwork focus on more highly skilled tasks. Notably, a popular Requester, Crowdsource, has insourced and now uses its own platform to accomplish the same end as it did with MTurk.
It seems clear that the technology of MTurk is nothing special, at least not in 2015. (That is to say, it is replicable.) MTurk may be the largest player in the space presently, but because multi-homing costs are low for both Requesters and Workers (it is convenient for either type of user to sign up for multiple services), an upstart competitor could lure Workers and Requesters by providing a superior product with better Worker protections and lower commissions. While Worker protections could force Requesters to pay more for HIT completion, it would ultimately result in a stronger, more sustainable network that would benefit both Requesters as well as Workers. Better Worker conditions would attract a more representative and reliable pool of Workers, as well as establishing accountability among all Requesters and rooting out the rotten apples that spoil the bunch.
Perhaps the reality is that survey responses as cheap as MTurk initially provided cannot yield good data in the long run and were merely the result of the platform’s novelty. So, a company that provides an MTurk-type platform at high quality could ultimately be the Facebook to MTurk’s MySpace.
Why, then, does Amazon maintain MTurk in its current format? Perhaps it is at least slightly profitable. Or the strategy is (ostensibly) frugal, if not directly profitable: if Amazon continues to use MTurk to recruit Workers for its own HITs, which was the original reason for MTurk’s existence, it makes sense to open platform for other Requesters to recruit Workers, thereby increasing Amazon the supply of Workers.
But if Amazon does not cultivate and improve the MTurk network to keep both Requesters and Workers happy, the current strategy will prove penny wise and pound foolish, and we may see the day when nobody wants to use Mechanical Turk anymore, leaving Amazon to outsource its own HITs to a competitor.
2. Buhrmester M, Kwang T, and Gosling SD. Amazon’s Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data? Perspectives on Psychological Science 6(1) 3–5, 2011. http://datacolada.org/wp-content/uploads/2014/04/Burhmester-Kwang-Gosling-2011.pdf
By: Leo Brown
Have you ever written a fake review on Yelp, Amazon or the App store to help promote a friend’s restaurant, new product or app? Did you think that it was’nt a big deal and that your comment wouldn’t hurt anybody? If so, now is the time to reflect and change your habits.
What is at stake:
There are two reasons explaining why fake reviews are dangerous for online businesses, whose models rely on trustworthy assessments of products and/or services:
1. Fake reviews kill transparency: by providing fake comments, one essentially removes any element of honesty and trust, which is at the cornerstone of ecommerce businesses. Take Amazon for instance. A fake product review creates friction in the supposedly seamless transaction process by providing false pieces of information. Amazon ends up with unaware customers, who might just end up purchasing subpar products.
2. Fake reviews limit network effects: Amazon’s value proposition is offering an independent and neutral third party platform to sellers and buyers willing to do business with one another. If that platform becomes crooked and biased, and if sellers can effectively push their products with paid reviews, one can easily imagine online shoppers leaving the platform for better sources of information. Fewer shoppers, fewer sellers, fewer transactions, and in the end, a weakened market place platform with no network effects.
The Amazon police:
In an effort to cut down the risks associated with such threats, major online businesses that rely on ratings and reviews to operate have pulled out the big guns. Amazon is the best example:
Since April 2015, Amazon has launched a very aggressive campaign against fake review providers. It started by filing suit against the operators of buyazonreviews.com, buyamazonreviews.com, bayreviews.net and buyreviewsnow.com. Before these sites were taken down, they allowed any interested Amazon seller to buy fake 4-5 star customer reviews in order to boost sales. As for the sellers who commissioned fake reviews, Amazon banned them as well.
Yesterday marked the second step in Amazon’s crackdown of fake reviews and in its fight against those who create a poor ecosystem. This time, Amazon went directly at those using Fiverr.com to buy and sell Amazon reviews. In a nutshell, Fiverr is an online marketplace that allows users to offer small tasks and services for USD 5. Services include writing, editing, or programming, among others. With the help of Fiverr, Amazon spotted over 1,100 fraudulent individuals and sued them all with the hope that this will send a strong signal to those who try to play around its terms of service.
What more can be done:
With that said, small businesses do not have Amazon’s strike force and it is often too expensive and inconvenient for them to file suits. How can these smaller players combat fake reviews? I see a few ways smaller players can fight fraud from flourishing:
- Make the review writing process more demanding by asking a series of personal questions that can help identify writers. This can help remove robots from writing the reviews.
- On one hand, manually filter reviews and remove fake looking ones. This is time consuming and not perfect though. On the other hand, create algorithms that detect fake reviews.
- Identify dishonest sellers, writers and tag them publicly as such on their profile. Run a zero tolerance policy for everyone to see. Yelp, through its consumer alerts program, along with TripAdvisor among others, are using this technique for instance.
By: Edouard Delvaux
In a recent study, it’s been estimated that fantasy football could cost businesses $13.4 billion in lost productivity. Note, that estimate only accounts for fantasy football and does not include losses from other fantasy sports, such as fantasy basketball, fantasy baseball, and even fantasy golf. And soon, that number will rise even further as more individuals become engaged with fantasy sports (Exhibit 1).
Exhibit 1. The number of fantasy football users is projected to grow at a 10% CAGR between 2014 and 2024.
One of the big reasons for this growth has been driven by innovative platforms (e.g., FanDuel) that allow users to engage with and possibly earn money from professional sports games on a daily basis. This has not always been the case.
Historically (though still to this day), users would draft fantasy teams for an entire season. If these users are anything like me, they typically spend a few days conducting mock drafts, thinking through different strategies, ranking players, etc. to create the perfect team, with hopes of winning the league championship and making a solid payout.
Unfortunately, that hard work in some cases could be instantaneously meaningless. Given that football is such a violent sport, in a split second things can change. A couple years ago, I had what many of my league members considered one of the best fantasy drafts. I was certain that I would at least make the playoffs. That changed immediately when two of my best players got injured. Now, instead of watching a number of random and sometimes boring NFL games (e.g., Jaguars vs. Browns – sorry (for multiple reasons) to those fans who read this), I just watched my Houston Texans play. The excitement of watching my fantasy team compete, praying in some cases for my players to play in rare overtime games to pad their stats, was gone… until applications like FanDuel (and DraftKings) came into the picture.
FanDuel creates value for fantasy players by allowing users to play and gamble against their friends and even random individuals on a daily basis. As opposed to drafting a set of players you are stuck with for the entire season, a user picks players based on a salary cap system. For example, you might have a $25,000 budget and need to fill in a team of 1 QB, 2 WRs, 2 RBs, 1 TE, 1 K, and 1 DEF with that money. The better players cost more (e.g., Aaron Rodgers may cost $10,000, and the remaining $15,000 is distributed between the rest of your team). In this case, even if I were to lose on a given day – whether through injury or bad play by my team – I’m still engaged in the football season because I could have an entirely different team the following day. Plus, I’d have a chance of winning money every day – versus waiting 16 weeks to do so. And in exchange for managing this whole platform, FanDuel takes in around a 10-13% rake (percentage of entry fees) in each competition or league.
In addition to creating value for players like myself, FanDuel has been able to create value for major sports leagues, such as the NBA and the NFL. As mentioned earlier, fantasy sports have led to users watching more games (this goes back to my Jaguars versus Browns example), in hopes that their players perform well, which leads to more advertising revenue for the leagues.
Now, it’s clear that FanDuel has been creating a lot of value for users and leagues, however when thinking about the platform, I would argue that it’s not actually that complicated to develop such an application. With technology these days, I could probably hire a few coders pretty easily replicate this business in weeks. So what’s stopping me? Network effects and a mobilization strategy fueled by over $360 MM in VC and angel funding.
FanDuel experiences direct network effects, which entices more users to join its platform. As more users join, players are able to play in competitions with larger pot sizes (since the pot is created by the users), bet on different games, play in large or small buy-in leagues, etc. As more of my friends joined, I found myself wanting to play as well, since I could now join their league, compete with them on a daily basis and have a chance at winning some money. These direct network effects have helped lead to the over 1 MM active users (as of Q4, 2014) on FanDuel (Exhibit 2), however the company has not just passively growth through direct network effects. On the contrary, it has spent millions on advertising (over $20 MM for 7,500 TV airings since August 1, 2015), while also engaging in a mobilization strategy has been focused on harnessing virality and sparking interest via referral payments and subsidies.
Exhibit 2. FanDuel active user growth between 2013-2014.
FanDuel has been very creative with its referral process. Contrary to the common, “If you refer a friend, we’ll give you both $10,” approach many companies use, FanDuel has created a new structure that allows users to “[Make] A Living From FanDuel Referrals.” As illustrated via the dashboard in Exhibit 3, users can earn a share of their friends’ monthly net revenues (MNR, calculated as approximately 10% of their total entry fees) each month. For example, for every dollar up to $1,000 of MNR, the referrer earns 20% of the referee’s MNR. So if my collective group of referred friends put in $1,000 worth of entry fees this upcoming month, I’d capture ~$200 in referral bonuses for that month. Further, this bonus increases up to 35%, assuming an MNR of $5,000. It’s a pretty great deal for the referer, and the potential to gain a large sum of money each month further incentivizes the referer to promote FanDuel to friends who will be active users. This in turn helps the company continue to grow its user base and grow its competition pools, which help fuel the direct network effects.
Exhibit 3. FanDuel Referral Program
In addition to the referral program, FanDuel is subsidizing early adopters. These days, if flip to ESPN, it seems that the odds are high you’ll run into a FanDuel commercial, and in these advertisements, FanDuel is providing deposit bonus codes that will give users a 100% bonus match on any deposit amount up to $200. With that said, the company has been smart in its subsidy strategy, as it prevents bonus fraud and helps maximize its number of active users. For each real money game played, the “pending bonus” (e.g., the $200 match) is converted to real cash at a rate of 4% per entry fee. For example, if a user plays a game for $100, $4 of the bonus offered become unlocked. So theoretically, to fully receive the $200 bonus, $5,000 worth of betting must have occurred. When doing the match, it’s clear that the subsidy is not as attractive as it immediately sounds, but FanDuel seems to believe that if you try their product and experience the excitement it brings (and see the huge pools of money available), you just might stick in for the long-term, while receiving a decent bonus as well.
In a highly competitive market (namely driven by DraftKings and possibly ESPN and Yahoo! in the future), capturing users and direct network effects will be very important, and for individuals wanting to enter this space, it’ll be very hard to compete against the huge funding and network FanDuel (and DraftKings) has created. With that said, it’ll be interesting is to see how FanDuel and its competitors focus on locking in the users they’ve spent recruiting, given the low multi-homing and switching costs.
By: Aryan Sameri
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.
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.
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.
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.