When I spoke with the founders and early employees of famous two-sided marketplaces such as Airbnb and Thumbtack, I consistently heard the same story: acquiring sellers was the focus early on, but it became progressively harder to grow the buyers at a suitable rate. This is a natural progression for two-sided marketplaces, and balancing buyers and sellers is probably one of the most difficult problems in the growth stages of a company.

A two-sided marketplace is any platform where buyers and sellers come together to transact: drivers sell their services on Uber to riders, who act as buyers; hosts sell their room nights on Airbnb to the guests that buy them. In order to get initial traction, the marketplaces need both buyers and sellers to use the platform. But why are sellers more important early on? And how quickly should marketplaces grow buyers compared to sellers?

Onboarding sellers early on is critical because of the frequency of transactions

There are two reasons why marketplaces need to grow the number of sellers more quickly early on:

  1. Since sellers transact more frequently, they are instrumental in creating repeat business early on: Uber drivers give on average 6 rides per day while customers take on average one ride every 8 days (Disclaimer: all numbers are back-calculated from publically available data that are snapshots of different points in time. In addition, these are realized averages that do depend on marketplace supply and demand.). Similarly, Airbnb hosts average one stay every nine days while guests average one stay every two years. Even on Ebay, sellers transact once a week while buyers transact once a month. Startups trying to attain early traction need to dedicate resources to acquiring the users that can provide more transactions in a given time frame.
  2. Idle sellers are less dangerous than idle buyers: Airbnb hosts who have trouble renting out their rooms might choose to post on Craigslist or Homeaway simultaneously, but they are unlikely to leave the platform altogether given the low cost of keeping a posting open. The perception that they might get business once in a while can be enough to motivate them to stay with Airbnb. On the other hand, for a traveler looking for lodging on any given night, she will perform the purchase off-platform if she can’t find a suitable seller on Airbnb. Not only do buyers tend to have more alternatives than sellers because institutional sellers exist (hotels, taxis, etc.), but also buyers are harder to retain because of their less frequent transactions.

Optimal growth in the number of sellers and buyers is determined by customer lifetime value

Optimizing the growth in number of sellers and buyers depends on many factors, including timing and location idiosyncrasies. However, the ballpark numbers can be inferred by looking at retention rates and transaction frequencies.

In a marketplace that fully satisfies sellers and buyers, the following equation holds:

(number buyers) * (buyer lifetime value if all transactions are satisfied) = (number of sellers) * (seller lifetime value if all transactions are satisfied)

 Therefore,

(number of buyers) / (number of sellers) = (seller lifetime value) / (buyer lifetime value)

By the definition of lifetime value (discount rates are ignored for this analysis since it remains consistent for buyers and sellers),

(Lifetime value) = (Gross margin per transaction) * (monthly frequency) / (monthly churn)

The gross margin per transaction is the same for buyers and sellers from the point of view of the marketplace. Therefore,

(number of buyers) / (number of sellers) = (seller frequency) / (buyer frequency) * (buyer churn) / (seller churn)

Interestingly, churn also depends on frequency, though the relationship differs by marketplace. In general, though, the less frequent a party performs a transaction, the higher the likelihood of churn.

What this means for Airbnb and Uber

The frequency of transactions for an Uber driver is much higher than the frequency of transactions for an Uber rider. Similarly, the frequency of transactions for an Airbnb host is much higher than for an Airbnb guest. Looking forward, these companies will have to grow their buyer base much more quickly than their seller base. In addition, as these companies determine their steady-state churn rates on both sides over time, the data could suggest that the growth needs to be more lopsided than before.

This creates an important contrast to the traditional two-sided marketplace: Ebay. Ebay sellers do transact more frequently than buyers, but the ratio is an order of magnitude smaller compared to Uber and Airbnb. Growth in these marketplaces cannot be measured the same way as growth was measured for Ebay.

By: Philip Hu


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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:

AirBnB Network Effects Diagram

 

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:

Uber Network Effect Diagram

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.

Uber Promotion Code

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.

Looking Ahead

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


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Airbnb is a company that most are familiar with. Nevertheless, it is important to summarize here again, the key points of its dramatic rise to understand what the future holds for the company with such a DNA. Its two founders (Brian Chesky and Joe Gebbia) back in 2007 were struggling to pay rent for their San Francisco loft. They struck upon the idea of renting their air mattresses after traditional hotel accommodation was sold out for a big convention in the city. After a series of iterations, guidance from incubators and VCs, Airbnb grew rapidly by emphasizing that its way of providing traveler accommodations provided authentic, local experiences that were unique and differentiated from the conventional staid hotel/motel.

Since then it has faced numerous challenges to its businesses, which can be classified into four main types. (1) Regulatory challenges – especially in the large, key and symbolic cities of San Francisco and New York City. Housing has become less affordable and many are complaining that Airbnb hosts are making rent less affordable, city councils are seriously considering proposals to rein in Airbnb; (2) Competition from Airbnb clones – companies like onefinestay, Homestay, Oasis Collections, Tujia, all serve a different segment of the market, be it customer’s geography of origin, price segment or destination focus; (3) Traditional competitors like Marriott which partnered with Ian Schrager to revitalize the boutique edition of the hotel chain, or rolling out workspace on demand; (4) appealing to the lucrative customer segment of business travelers, which Airbnb has tried to, but arguably failed to make much inroad so far.

These 4 challenges that it faces are very much the result of how it has chosen to evolve its business model over time – responding instinctually to where it sees the (short-term) profit pools. I argue that how it manages to tackle its main challenges comes down very much to whether it continues on the same path of evolution that it has gone down before in the past, or whether it chooses to switch gear. Switching gear I believe is key to making it a sustainable and successful business, though not one that will necessarily dominate as much as before.

Starting from its humble roots, the company has made some pretty small but significant choices in its business model. Instead of sticking to the legal sublet of rooms and beds, it has chosen to offer the illegal rental of entire apartments, because this is what most travelers are comfortable with and the ticket size is bigger. Despite what it continues to insist in its marketing, it has shifted its competitive advantage and value proposition from one of offering local and community-oriented experiences to one of being able to transact in an environment of trust and reputation – the host is often not around when an entire apartment is rented out. It has tested out full-service cleaning and turnover services, airport car service and it is rumored to be testing out more travel agency like experiences as part of its self-professed push to become an end-to-end hospitality provider. It recently announced plans to curate a selection of properties for business travelers.

This orientation towards growth and more growth is sure to excite the founders and its investors. However, personally it feels like growth that is potentially damaging to its own business in the long run. Here is my prediction of the worst (and quite likely outcome) that will happen if Airbnb continues along its choices:

  1. (1) Airbnb wins the first few battles for the next 5-10 years, meanwhile more illegal sublets of entire apartments happen in NYC and SF, and the situation becomes more egregious. Negative sentiment results in the passing of regulation that results in restricting vacation rentals to a certain % of a year and is heavily enforced primarily by making it Airbnb’s liability to ensure compliance to the law
  2. (2) During this next 5- 10 years, travelers are trained by Airbnb’s trust and reputation system to become more trusting of online transactions in the vacation rental market place. Clones eat into Airbnb’s customer base, especially in the high end
  3. (3) Traditional hotels, already becoming more like hotel management companies, than hotel owners, become more nimble and flexible, favoring smaller properties, putting emphasis in “boutiqueness”, design and experience. Some others feel that vacation rentals are a fad and return back to traditional hotels for their superiority in the hospitality experience (it has got to be better than an entire apartment rental when the host isn’t even there)
  4. (4) Business travelers never really subscribe to the Airbnb model. Companies do not really want to associate themselves with something potentially illegal, business travelers are fully focused on productivity, efficiency and cannot contemplate a 0.001% chance of a reservation cancelled by a host

After regulation becomes more hostile to Airbnb, what is likely to remain are the big brands that have learnt the ropes of being more local, boutique and community oriented. The question then is what it can do to mitigate the possibility of the above? I’d suggest the following:

  1. (a) Stay away from business travel. There’s no product market fit between a management consultant working 9 am to midnight and a slight bit of inefficiency from an authentic experience provided by a local. Traditional hoteliers will probably get a bit of consolation from this since many go after this target market, and consequently will attack Airbnb less (especially on regulation)
  2. (b) Proactively regulate oneself. There are more things that Airbnb can do to conform to local laws and regulations rather than to proclaim that it is championing the cause of small businesses and local citizens of the city. There are many options: limit number of entire apartment rentals in a city, limit the density of entire apartment rentals, limit the % of a year that an apartment can be rented.
  3. (c) Start offering a differentiated brand/sub-brand that caters to different segments of travelers. As consumers become more accustomed to transacting online, there is lower and lower cost for the consumer to multi-home (because risking trust in a transaction becomes less of an issue), while the differentiated product of companies like onefinestay will prevent Airbnb from growing along this dimension.

Much remains to be seen in terms of what other initiatives Airbnb has up its sleeves. The abundance of accommodation options certainly makes it exciting for travelers. But if the sharing economy companies are to make these options permanently available, moderating their growth in favor of more sustainable practices might be a better choice for the long run.

By: Aaron Foo


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Payment platforms for online and mobile businesses – current and future landscape

While most of the press around mobile payments has focused around B2C solutions such as Square and LevelUp, online payment platforms such as Zuora, Stripe and Braintree are also seeing robust growth, riding the tremendous growth in e-commerce and P2P mobile payments. Example client include Uber, LivingSocial and AirBnB (Braintree), box.net and zendesk (Zuora) and shopify and edmodo (Stripe).  Such platforms have a few key elements that all competitors in this space try to replicate:

  • A robust, reliable and scalable solution
  • Simple developer-friendly APIs
  • Excellent customer service
  • Quick (and in some cases “instant”) set up, including setting up a new merchant account for the client

A robust, reliable and scalable solution

Accepting payments for a new high-growth startup can be a very painful process if attempted on your own, but companies such as Braintree, Stripe and Zuora attempt to simplify the process as much as possible. The goal is to provide a solution that scales as your startup scales – from facilitating 100 transactions a week to a 1000 transactions a minute, all the while providing a reliable, secure and affordable service. Key to this space is supporting both desktop and mobile transactions, as a growing number of e-transactions occur on mobile devices. A number of startups also make international expansion a very early priority, as they attempt to be the first-mover in several markets. Payment providers try to stay one step ahead of the curve by expanding internationally and having a deep understanding of foreign legal and financial frameworks.

Simple developer-friendly APIs

Stripe is perhaps the best poster-child for having developer-friendly payment APIs. Stripe boasts having APIs “that get of your way” and also pioneered the “instant” setup features that were replicated by Braintree – which allow you to get started with a payment solution in under a day. The key here is to have API wrappers for various languages such as Ruby, PHP, Python and many more to make it incredibly easy to get started and integrate with your service.

Excellent customer service

Braintree seems to be leading here, and promises to always have a real person answer a customer service call. Customer service is key in this business, which is based on having reliable, trustworthy service with quick turnarounds if something goes wrong. Parts of the payments process remain tedious and high-touch. For example, setting up a new merchant can often involve multiple long-threads between the payment-solution provider and the client, where the payment-solution provider acts as the middleman (and underwriter) between the client and the bank. The client wants to have the account set up as soon as possible, while the bank wants to make sure that a proper risk assessment as done – companies like Braintree try to simplify the process by having excellent customer service and quick turnaround times.

Instant set-up

Now that Stripe and Braintree have instant setup (by eliminating the waiting period for a new merchant account or underwriting approval), startups can have a quick headstart in facilitating e-commerce transactions.  Through this process, companies such as Braintree also get more insight about the client’s business model and growth plans, and try to ensure that clients’ accounts are never frozen or shut down because of unanticipated activity.

Disruption and future landscape

While there are certainly scale benefits to serving many clients, I do not see any network effects associated with providing online payments. However, this could change as some of these providers attempt to get into the mobile P2P payments space, such as Braintree’s acquisition of Venmo.

On the other hand, the companies in this space are addressing an unmet need. For many high-growth startups, solutions such as PayPal, authorize.net are too expensive, slow, outdated and too hard to integrate with. I see solutions by Braintree and Stripe taking away a lot of business from PayPal. Switching costs are also high – it is usually hard to migrate customer payment information from one platform to another.

Although payment providers are seeing tremendous growth just because of the amount of growth in e-commerce and online/mobile transactions, all these solutions (except for Braintree’s Venmo business) are still reliant on the infrastructure provided by the credit-card networks. All the startups in this space seem to be playing the puppy-dog strategy – posing as small players who are friendly with the credit-card networks and are doing little to disintermediate them.

Competition is tough in the payment space, and more and more players (both large and small) are getting into this space everyday. Braintree, Stripe and Zuora seem to have carved out a niche, but need to remain innovative and competitive to stay relevant going forward. I’m looking forward to seeing many more innovate solutions come out of these companies to make payments for young, high-growth startups even easier.

 


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Over the summer I had the opportunity to work for both a booming start up – Airbnb – fighting its German copycat – Wimdu – and the dark side of the force in Rocket Internet’s version of Zappos in Brazil: Dafiti.

 I was therefore able to observe first hand how well the Samwer brother’s cloning method (basically pouring marketing dollars into the business) was effective when 18 months after it’s launch, JP Morgan invested 45M$ in Dafiti’s e-commerce business1.

 Simultaneously, barely a year after raising a huge initial round of funding (90M$)2, rumours of Wimdu closing its London and Paris offices reached my ears (the information has not yet been confirmed by RI). I couldn’t help meditate about the reasons why a similar method worked in the case of Dafiti and not Wimdu.

On paper, Wimdu had everything  to become a long-lasting competitor to Airbnb:

·      Platform is exactly the same

·      Marketing message is identical to Airbnb’s (“Travel like a human” vs. “Travel like a local”)

·      Value proposition to both guests and hosts are extremely similar (free listings and 3% commission for the host, 6-12% commission for the guests on Airbnb vs. 15% on Wimdu)

·      Wimdu had local offices in most major markets before Airbnb (Paris, London)

·      Both Wimdu and Airbnb’s pockets were as deep (90M$ vs. 120M$ funding)

Therefore what can explain this first-round knock-out?

1. The nature of the market called for a clear winner

It seems as though Rocket Internet bet big on this one. Indeed, the peer-to-peer industry for rooms / apartments has all the criteria to be a “winner-takes-all” market:

·      High positive network effects: the value of being a part of the “community” increases with the number of users for both hosts (increased demand / revenue) and travellers (competition driven prices on a given location, wider range of destinations). In addition, due to the trust factor involved in peer-to-peer transactions, the incumbent (Airbnb in this case) benefits from the higher number of past transactions (reviews of hosts and guests) thus strengthening these network effects.

·      High complexity to manage a listing on several marketplaces: although travellers are only one click away from another marketplace, hosts have extremely high multi-homing costs from managing a listing on two platforms (i.e. complexity to sync calendars and track bookings, etc…).

It is therefore not surprising to see one of the two emerge as the clear winner but what triggered such a quick victory?

2. Love mattered more than size

The two companies chose very different strategies when allocating their resources. Wimdu spent millions on “recruiting” listings3 via their sales force and travellers via online marketing to make up for being the underdog. In the meanwhile, Airbnb focused on its customer service and its community of users.

Unfortunately for Wimdu, in this industry, the viral coefficient is probably higher than anywhere else:

·      Every new host is a potential future traveller and every happy traveller is a potential new host

·      Additionally, the highly social nature of travelling (who likes to travel alone? who can refrain from “telling all about their vacation” the day they get home?) increases the importance of generating positive word-of-mouth

By focusing on customer service (24/7 customer service, 17 languages served, 90% of calls answered in 90 seconds,), Airbnb grew its community organically (in France, Airbnb had 4,000 listings before even having an office and a French website!). On the other hand, Wimdu let multiple incidents occur without reacting4=;

Additionally, Airbnb understood early on that with such a huge part of their business relying on “trust”, reaching out to their community of hosts was a key component of their success. By organizing offline events where hosts could “put a face on Airbnb” and enabling them to interact between each other, they created an offline community that powered their growth (in France, the number of listings doubled in the 4 months following the first offline meetups).  

Ironically, Airbnb could almost have been called a Zappos culture copycat for embracing Tony Hsieh’s perspective on customer service.

For all those romantics out there, it is a great story to think that in this day and age, “doing business like a human” can still overcome the aggressive strength of marketing dollars.  The question is: will Rocket Internet learn the lessons from this as they launch a Pinterest copycat (called “Pinspire”) in Asia5 ?


 

  1.   http://venturevillage.eu/dafiti-jp-morgan#
  2. http://www.tnooz.com/2011/06/15/news/wimdu-captures-mammoth-90m-funding-round-for-apartment-rental-push/
  3.  http://www.forbes.com/sites/sethporges/2012/04/27/is-this-airbnb-knock-off-google-stalking-potential-hosts/
  4. http://www.trustpilot.co.uk/review/wimdu.co.uk
  5. http://venturevillage.eu/copy-paste-and-pin-the-samwer-brothers-launch-pinspire

 

 


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