“First mover isn’t what’s important — it’s the last mover. Like Microsoft was the last operating system, and Google was the last search engine.”
I hear this refrain more and more in pitches. The thinking goes the last entrant to the market benefit from mistakes made by earlier entrants.
But being the last mover isn't always advantageous. The reality is more nuanced.
When Last Mover Works
Technology, platform or behavioral discontinuities. The last mover is the agent of Clayton Christensen's Innovator's Dilemma embodied. It's the company that pursues an incumbent with faster, better or cheaper solution and in particular a solution that cannibalizes the incumbent's business model typically because of a lower cost structure.
While Google did develop a better ranking algo, Google also had a fundamental cost advantage. Google served queries at more than an order of magnitude less cost than its competitors, who used expensive Oracle database servers, enabled by Google's investment in running its software on commodity hardware.
Zynga leveraged Facebook's Open Connect platform to grow its casual gaming business much, much faster than incumbents Pogo and others. The dramatically lower cost of customer acquisition on Facebook, enabled by a an agreement between the two companies, fueled Zynga's rise.
Instagram, “the last photo sharing service”, took advantage of a behavioral discontinuity, the explosive growth of mobile phone snapshots driven by high quality cameras in smart phones, to threaten Facebook's photo sharing dominance.
Last movers can be a challenged by incumbents when the last movers have no discontinuity to bring to bear, face the network effects of incumbent transactional market places, are challenged by low margin competition, or face significant IP battles.
Priceline, “the last online travel agent” was founded in 1997. Bill Gurley wrote a great post outlining how Priceline won the lion's share of the online travel market by reducing their rake. The larger the market place, the greater the monopoly, the more it can reduce rake to starve competitors. Craigslist's strategy of largely ignoring monetization has prevented thousands of competitive market places from competing with it.
Amazon's ability to operate at basically zero profit prevents ecommerce threats from rising. Compare their revenues to their profit. It's hard for any ecommerce company to raise the capital required to build a competing business when your competition is happy with zero net margin.
Akamai exerts huge pressure on the CDN market by litigating competitors out of business. Here's a quote from a Gartner report: “How to tell when a CDN has arrived: Akamai sues them for patent infringement.”
Survivorship Bias
While it's true that Microsoft and Google eventually came to dominate their industries, it wasn't necessarily because they were last. Of course there were operating system companies and search engines that followed (Jollicloud and Cuill) for example. Instead, Microsoft and Google's market power, their ability to keep their advantage in the market over time by anticipating discontinuities has enabled them to remain top dog — at least for now.
Takeaways for Startups
When looking to take advantage of the last mover advantage, ensure that you can leverage a clear discontinuity in the market.
At today's Under the Radar Consumerization of IT (CoIT), the predominant theme will be antagonism. Friction, dislike, resentment within organizations marks opportunity for consumerization of IT startups.
Taking advantage of this sentiment, Expensify employs a very deliberate marketing tack: “Expense reports that don't suck.” Talk to anyone who uses antiquated expense report systems and they are bound to sigh and complain, frustrated by the experience but resigned to the fact they can't do much about it. Expensify provides those people with a better alternative and, most importantly, empowers them to change the way they work.
This pattern is true for Heroku and developers inside of large companies. It's often much faster to build and deploy a project externally on Heroku than to coordinate with IT to deploy on internal resources. And those developers are much happier for it.
Dropbox and Box and Yammer, all of these companies are allies of the end user. To build internal support support, CoIT companies offer much better products to users, build momentum within an organization and eventually enable users to convince or demand that IT change vendors or policies.
As critical as building the right product for end users, CoIT companies ought to provide the right tools for the IT organizations who are often challenged by end users creating security challenges, demanding better tools, and on the whole pushing the IT team beyond comfort.
Each one of the companies presenting today will have a problem statement that focuses on the antagonism between employees and IT. Frustration is the indicator of opportunity.
Reading through the tech press since the Facebook IPO, you might get the impression venture capitalists are still reeling from that apocalyptic offering, believe no further successes can be had in the consumer web, and so are fleeing the consumer web in droves to pursue enterprise investments.
That's because in the past year or so most major tech publications have swung from focusing on consumer products to enterprise companies. GigaOm made this transition first, now TechCrunch and PandoDaily are following suit.
But the problem with sounding the alarm for the ebbing consumer investment is that just not true.
Consumer investments historically have garnered massively disproportionate press coverage compared to their volume because consumer products are easier for readers to understand than enterprise technology. Consumer products evoke emotional reactions, drive page views, and build business. It's hard to jump for joy or trigger lots of retweets over data center virtualization innovations unless that's really your cup of tea.
Tot put this into perspective, what do you think is fraction of venture investment dollars last year were in consumer companies?
About 17%.
How about over the last 17 years?
16%.
Here's a chart of NVCA data for that period. Click on it to enlarge it.
So 84% of venture dollars invested over the past 17 years have been invested in enterprise companies. It's clear that enterprise investments are the bread and butter of the venture business. And that trend isn't changing.
This massive swing toward enterprise investing never existed. It's a fallacious perception. Enterprise investing has always been the norm and will continue to be for quite some time.
I started working in ad tech in 2005 and during the past eight years, the ad tech ecosystem has progressively become more sophisticated, competitive and oligopolistic. It's hard to innovate in ad tech. But if you're looking to start a company in the sector, you'll need to amass proprietary data or develop a market place with unassailable liquidity to vie successfully in the market.
A Mental Model for the Ad Ecosystem
The structure of the ad ecosystem, greatly simplified, looks like the image above. On the left, the advertiser supplies dollars that flow to the right. The DSP, demand side platform, uses algorithms to inform an advertiser's media purchases; i.e., which websites and mobile apps will perform best? The advertiser and DSP purchase media on the ad exchange which is an electronic market place where advertisers can buy media algorithmically and in real time, called RTB for real-time bidding. On the other side of the exchange, the publisher uses supply-side platforms to find the best paying advertisers to buy their ad inventory.
There are hundreds of SSPs and DSPs, thousands of advertisers, millions of publishers but only a handful of exchanges: Google's DoubleClick, Facebook's FBX, Yahoo's RightMedia, MoPub, Adap.tv and a few others. These exchanges, like most market places, exert huge network effects because advertisers are attracted to the exchanges with the most inventory selection/liquidity. The exchanges see every transaction and have unparalleled visibility and data access into their respective ecosystems.
Data, data, everywhere
If there's one defining characteristic of online advertising, it's data. Advertisers buy data and license algorithms to find better inventory. Publishers sell their data and license other algorithms to find better advertisers.
In order to compete in an ecosystem of data, a startup has to bring one of three advantages to market: better algorithms to use on the same data as everyone else, better data than anyone else or a market place with the largest volume of ad inventory in a segment.
Better algorithms is the fastest way of getting into market as a startup. Similar to starting a new quant hedge fund, you develop a novel trading strategy that works and sell it to customers. But competition in ad-tech is just like the financial markets - as soon as others see your strategy working, they are likely to copy it. Over time, the marginal advantages of better algos erode. Unless a startup continues to invest heavily in algorithm improvement, it will forever be in a cat-and-mouse game.
Better data: Where algorithms can be conquered, proprietary data is unassailable. With access to richer ad performance data, more detailed user data, more granular conversion funnels, your startup has created a significant barrier to entry. Better data means better results. And if you're the only game in town, then you'll attract big advertising budgets.
Getting access to better data is very challenging. It means finding and partnering with publishers and/or advertisers on an exclusive basis for some period of time. And then leveraging that data to build a successful DSP/SSP/ad network.
Market places in ad tech, as in the rest of the tech industry, are beautiful things. They are natural monopolies, capital efficient and are strategically valuable. Building a new ad tech market place is the most challenging way of entering the market because of the strategic role these products play in the ecosystem.
The most successful startup market places (RightMedia, Adap.tv, BlueKai, MoPub) each took advantage of a discontinuity in the market place (inventory glut, video ads, rich user data, mobile ads) to develop a foothold in the market faster than the incumbents. Over a few years, each of these companies built liquidity into their market places and now are the leaders in their segments.
The Recipes for Success
As the ad tech ecosystem has bloomed, competition has increased dramatically. To best position your new ad tech startup for success and develop a long term advantage, you need to develop leverage by developing proprietary data sources or by creating a market place based on some technology discontinuity. In other words, bring something to market that no one else has and that is difficult to copy.
On the day of Tableau's IPO, a company known for innovating in data visualization, I thought I would share the most impressive HCI concept I've seen in a long time.
In my view, Bret Victor is on the forefront of human computer interaction design. In the first two or three minutes of this video at Stanford, he demonstrates his home-built software that combines data analysis with visualization. It's magnificent and really hard to describe because it's so novel.
Victor uses words to create and manipulate the drawing and it all seems so natural and fluid. The interaction with the software evokes a conversation between two designers over a piece of paper - which is exactly the kind of interaction that makes software seem human and natural. I hope we see an explosion of these types of tools in the future.
When I was a little boy, I watched a cooking show on Sundays called “Jacques Pépin.” Over the course of 30 minutes, Jacques would orchestrate a symphony of raw ingredients into a dish that I yearned to smell and taste. That chicken paillard looked sumptuous.
Over the past few weeks, I've been coding in Rails 3 quite a bit, building a collection of tools to be more effective as a VC. And oddly enough, I think back to Jacques Pepin quite often.
The Jacques Pepin of the Rails world is Ryan Bates, creator of RailsCasts. Watching Ryan's shows I've learned how to build AJAX search, deploy an identity management system and code beautiful charts in ten minutes each.
There's something intoxicating, exciting, and liberating about watching someone convince you of your capacity to perform a seemingly challenging feat, whether cooking or coding, by showing you how easy it is to do. It's a testament to the power of video in teaching.
Buttressing Bates' tutorials, StackOverflow has become my essential tech support community. Paste an error message into Google and the first link is bound to contain a fellow coder's struggles and eventual solution to the problem I'm now facing.
The combination of tutorial expert video and support community is immensely powerful because it shares the expertise of a master with many while scaling the support and help needed by a mass audience. I believe this form of teaching will become ubiquitous for this reason. It's already happening in coding with Railscasts and StackOverflow, university education with 2U, gaming with Machinima and many others.
Jacques Pepin never did tell me how I botched that homemade mayonnaise. But there's someone on the web who has watched the same episode, made the same mistake and found a solution who might.
Derek Powazek questions the intrinsic economic viability social networks in his post “What If Social Networks Just Aren’t Profitable?”. It's a logical question to pose in the aftermath of the Digg sale and the wobbly Facebook IPO.
There is one clear lesson from Digg's sale: the technology that powered a once-massive social network is worth about $500,000.
The Atlantic summed up the essence of of social networks' business models brilliantly. It's not the technology that's intrinisically valuable, but the activity on the network that attracts users and advertisers and produces a data by-product. Once users commit to a network, the network must develop a revenue model based upon the content created by the users. In so doing, social networks can generate huge revenues quite profitably.
But these networks don't all go to market the same way. Below is a draft taxonomy of social network revenue models. Comments welcome.
Media Social Networks (MySpace, Facebook, Twitter, Pinterest) - Media networks' primary insert ads into the experiences of potential customers. MySpace generated hundreds of millions of dollars by selling home page take overs and remnant ads. Facebook generates billions by providing more granular targeting across a much larger user base. Leafing through the pages of history, this is no different than the business of phone books: create a listing of people's name and contact details and businesses will soon follow paying for inclusion. To date, ad dollars have formed the largest fraction of social network revenue dollars. Pinterest is a bit different in that advertisers will be able to drive transactions directly from whatever advertisement or sponsorship products Pinterest builds. But this is still a media business, just with a performance oriented advertiser base.
Data By-Product Social Networks (LinkedIn, PatientsLikeMe) - Data by-product social networks offer free services to the main user base but sell some data product to a different customer set. LinkedIn and PatientsLikeMe have cultivated vibrant communication networks. To generate revenue, they collect, filter, serve and sell the data users create to interested parties: recruiters and professional networkers in the first case and pharmaceutical companies in the second case.
Premium Subscription Social Networks (Yammer, AxialMarket, Gerson Lehrman Group, XBox Live, World of Warcraft, Dating Sites) - Sometimes access the community is valuable enough to the end user to warrant charging a monthly subscription. Yammer charges for secure, managed enterprise social networks. Axial collects fees from financial professionals to access user created deal flow. GLG collects fees to access subject matter experts. Gaming networks like XBox Live and World of Warcraft offer matchmaking and game hosting services. Dating sites offer access to a network of candidates for a fee and often will charge for relevant digital goods or rights to communicate with other members.
Pro Bono Social Networks (Chat, Email) - Email hasn't generated much revenue since the days of dial-up when a subscription to Aol included an email address and chat account. Email and chat have since become commodified and are operated at or near a loss, ideally winning user loyalty on behalf of adjacent properties. If you use GMail, you'll likely use Google.com more often.
Freemium Social Networks: (Line) It's hard to argue with Line's strategy of virtual goods and stickers. Line's 150M users spent nearly $60M on these goods in Q1. By leveraging a network effect and capitalizing on its users' desires to express emotion and individuality, Line has grown tremendously.
To Be Determined Social Networks (FourSquare, Tumblr, Quora, Instagram) - For many of the newest social networks, revenue models are still nascent. Discovering the most natural form of monetization within a community is challenging. Some networks never need to find it (Instagram). Others spend years searching for it. Perhaps this group's revenue models will fall into the above categories. Or perhaps they will create a new form of revenue model.
Social networks have only existed for about 7 years. In that time, we have witnessed the growth of a few hegemonies and scores of niche players. We have retrofitted revenue models from previous eras of Internet businesses. But the data quality and density found in social networks are unlike most other computer systems. Networks are still exploring the alchemy of converting social media data to gold. One day, it will be a science.
How deeply do you consider the impacts of building a public API for your startup? It's no small decision: you could be enabling your disruptor.
APIs are incredibly powerful tools for enabling partners, building ecosystems and engendering success among customers. For example, Salesforce's Force ecosystem, which enables developers to build products atop customer Salesforce installations, increases the value customers derive from Salesforce selling more seats and retaining customers longer. Google's Maps API enables developers to spread Google Maps, building the brand, increasing distribution and all the while improving ranking by sending back user feedback signals.
But for many startups, in particular proprietary data and network based businesses, APIs create one of the most effective ways to sap competitive advantage, enabling viable competitors to emerge.
Twitter released their API broadly to developers who built competing front-ends and was forced to revoke access lest developers disintermediate Twitter's relationship with its users. Facebook's API term evolve constantly in response to perceived threats like Path and MessageMe. LinkedIn's API is much more restrictive to prevent conflicts of interest from arising.
In each of these five cases, the API provider has to consider the API's balance of data trade: the net input of data vs net output: how much data value are you giving away compared to what you receive in return.
In Twitter's case, Twitter provided developers a content stream that would grow developer user bases for clients. But, in the end, Twitter developers couldn't provide enough value back to Twitter to build a case for the API.
Similarly, Facebook revokes access to developers with whom they perceive a negative balance of data. Facebook's social graph is it's most valuable asset - ceding it to others via API would be disastrous. But weaving themselves into the fabric of the web through oAuth and identity increases the net data into Facebook while allying partners across the Internet with Facebook.
LinkedIn's restrictive API is essential because they are a data business. They sell data to interested recruiters. Too lax of an API might enable wily recruiters to skirt payment requirements and ultimately copy the relevant data for themselves.
APIs are great strategic tools. They can reinforce and grow businesses, partner ecosystems and customer value. But improperly deployed APIs can also undermine the business you're building. Ensure your balance of data trade is always overwhelmingly positive before releasing your API.
h/t to David Hammer for helping me think through this post
We're looking for an analyst to join the early stage consumer investment team at Redpoint. This person will help us find and evaluate investments and work closely with the early stage team. If you know anyone who is a recent grad, is passionate about technology and is looking to work hard and learn a ton, please send them our way. Details are below:
Job Specification:
Redpoint Ventures is looking for a full-time analyst to join our investment team. As an analyst you will help us research and develop new investment themes and evaluate investment opportunities in technology startups. The position is full-time and based in Menlo Park.
Since 1999, the Redpoint team has been partnering with visionary founders who create new markets or redefine existing ones. Redpoint invests in seed, early and growth stages, with a focus on companies providing new distribution platforms, next gen media and media distribution business, infrastructure technologies for data processing, and companies focused on enterprise, cloud and mobile.
The ideal candidates for Analysts:
Have an outstanding academic track record.
Are enthusiastic about technology, startups and entrepreneurship.
Are self motivated and driven to succeed.
Exhibit great communication skills.
To apply, please email a copy of your resume to redpoint.analyst@gmail.com. Please put “(Your Name) : Analyst Application” as the subject line. In lieu of a cover letter, please respond to the following questions in the body of your email:
Give us a two or three sentence summary about your background.
Tell us about a specific accomplishment from your background that explains how you fit with our candidate description above.
Tell us about a startup we haven’t invested in yet but should. What do you think is the biggest risk to the investment?
In the summer of 2008, a crisis evolved on my team at Google: social networks had grown exponentially. The AdSense network was flooded with social network page impressions whose poor performance challenged advertisers and Google alike.
During that summer, we formed a tiger team of about four or five key people. We shed our daily responsibilities, relocated our desks to a conference room in a deserted building on the edge of campus where we brainstormed and debated and wrote patents and developed a plan of action. Those few weeks were some of the most productive of my time at Google.
There are many who believe Steve Jobs was a creative genius. I think it would be more accurate to say he was a genius who loved creativity.
Jobs greatest strength may have been clearing the way for creative people to achieve their potential. Segall argues Jobs' relentless defense of creativity made him and his companies great.
Creativity blossoms in environments of mercilessly small teams, honest/direct/brutal feedback, and “no compromises” attitudes. Practically speaking this means deploying small teams on projects and constraining meeting sizes; empowering/trusting these small teams to make bold strides; hiring well; providing clear direction and honest feedback - ultimately enabling faster iteration cycles for better results.
But it doesn't take a crisis to form tiger teams or champion and defend creativity. We can do this things every day. It's about waking up in the morning and asking, “How will I enable greatness today?”
