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?”
Craigslist's generic market place gave rise to fifty or more single-purpose alternatives some of which are quite valuable companies: AirBnB, Care.com, 99Designs, oDesk, CustomMade, Gazelle.
Each of these startups operate verticalized market places that solve customer problems better than Craigslist's generic platform. In so doing, these startups have grown their observed markets.
The same unbundling trend will happen to social networks.
Generic social networks will continue to succeed. But their most attractive users in the best segments will be systematically poached by verticalized competition.
Today in the US, there are three major generic social networks: Facebook, LinkedIn and Twitter. Personal, professional and news networks. In each case, they serve a vast cross-section of people.
Facebook's dominance is challenged by mobile-first messaging applications. Some of these offer a better product experience for core functions (Instagram, WhatsApp), others offer localized products that feel more native (Line, WeChat) and, others still offer better tools for self-expression (Tumblr) and last others offer more intimate networking (Path, Avocado, Couple).
LinkedIn, host to the world's resumes and workflow tool for recruiters, is challenged by dedicated professional communities offering workflow tools on top of social graphs. SpiceWorks is a community of IT professionals that also provides IT management services. Axial is an online network that helps connect buyers and sellers of private companies. Doximity is a social network for doctors that provides secure collaboration for cases.
Each of these segments represent tremendously valuable audiences whose these products could have been built on LinkedIn but are better served by dedicated tools.
Twitter, the news syndication and discussion component, is challenged by services like Quibb and Yammer which uses smaller social networks to filter and discuss news, in particular professional news.
Make no mistake, each of the major social networks, like Craigslist, can continue to thrive despite unbundled competitors. But many of the most valuable users and products will be built outside of the ecosystem.
Unlike Craigslist, these social networks have far more capitalistic cultures, much larger balance sheets and are on the whole much more acquisitive which in the long term might be their best defense.
Who can forget gems like these word problems from 3rd grade math class?
Q: Jack walked from Santa Clara to Palo Alto. It took 1 hour 25 minutes to walk from Santa Clara to Los Altos. Then it took 25 minutes to walk from Los Altos to Palo Alto. He arrived in Palo Alto at 2:45 P.M. At what time did he leave Santa Clara?
It was during those classes that our mathematics teachers taught us how to work backwards through the problem.
A: You can work backwards from the time Jack reached Palo Alto. Subtract the time it took to walk from Los Altos to Palo Alto. Then subtract the time it took to walk from Santa Clara to Los Altos.
Working backwards is one of the most important skills founders and startups employ because it's a technique to reverse engineer success. It means planting a flag in the ground and asking, “How do we get there with what assets and constraints we have?” This is true for growth, marketing, fund raising, hiring, financial planning, management and many other functions of startups.
At last week's Growth Hacking conference Eliot Schmuckler, responsible for growth at LinkedIn and now VP of Product & Growth at Wealthfront presented his techniques for driving growth. First, he defines the right growth goal, a single most important number.
Second, he determines the growth drivers by working backwards from his goal. How many users do he need when? Which growth channels will provide what fraction of his users? Under which circumstances can his funnels grow to meet the desired targets for growth? How must his team ramp growth to accomplish the goal?
Coincidentally, I met a head of marketing candidate for a portfolio company last week. When I asked him about his marketing methodology, he said, “I measure gross margin by cohort and work backwards from there.” He was embodying Schmuckler's advice.
Working backwards isn't always the best technique. Agile product engineering and management favor iterative development and tend to avoid longer term planning which can be particularly valuable when discovering product market fit and iterating quickly.
But working backwards does work well in cases when longer term plans are required. By identifying goals and constraints we can find reverse engineer solutions within those parameters.
Kenny van Zant drew this diagram for me on white board and I think it's the best visualization of how SMB SaaS freemium business grow. The diagram highlights a few important mechanics of the SMB SaaS business model.
In any given freemium user base, small-office/home-office (1 to 20 employee shops) users tend to be a few times larger in size than true SMB customer (20 to 500 employees). Most of these SOHO customers remain unpaid evangelists. A few of these customers grow into SMBs and become paying customers - they cross the dashed line in this diagram. But most paying users for freemium services are SMBs.
While these mechanics may be well known, there are three counterintuitive things about the SMB SaaS model.
Counterintuitive Point #1: The SMB Market Opportunity is 2x Larger than the SOHO Opportunity
Below is a bar chart showing the number of employees working in businesses by size of employees using data from the US Census. While there are nearly 5M SOHOs in the US and only about 1M SMBs, there are twice as many employees in the SMB segment than in the SOHO segment, 38M in SMB and 21M in SOHO.
For any business charging on a per seat model, this means the SMB market is about twice as large as the SOHO market.
Counterintuitive Point #2: SOHO Customers are Just As Important as SMBs
Despite the typically lower conversion rates and smaller business opportunity of SOHOs, SOHOs are critical in SMB SaaS success. They are the word-of-mouth marketing force that blogs and tweets and drives mobile app store volume to boost chart rankings and tells their friends about this great free product they are using to solve their problem.
Counterintuitive Point #3: Freemium Businesses Reverse the Hunter and Farmer Sales Roles
In traditional software businesses, the enterprise sales team hunts. They acquire leads, cold call, and push customers to convert. Their counterparts, the inside sales team, nurture and cultivate these relationships to prevent churn and grow revenue through up-sell.
Freemium businesses reverse the roles. The inside sales team fields the freemium leads from free user base, make the cold calls and push the customers to convert. The enterprise sales team looks for multiple teams within the same company paying separately, builds a relationship and grows revenue through up-sell, helping their customers grow.
A Different Animal
While SMB SaaS business may seem like traditional software businesses scaled down, they aren't. The SMB and SOHO markets require a wholly new marketing approach that may seem counterintuitive at first, but when implemented well, can build very large businesses.
To many entrepreneurs, hiring the first salesperson is a mystery. When should I do it? How much should I pay this person? How do I structure the work?
The great part about sales teams and sales departments is that they quantitative - sales teams thrive on numbers. At the most fundamental level, sales productivity has to exceed costs.
So let's answer the question of when to hire an salesperson by understanding the financial mechanics of a sales team. When building a sales team, there are three things to consider:
The costs of the sales person - salary and performance pay
The output of that salesperson - sales productivity
The inputs required to make a salesperson successful - lead volumes
Let's discuss each point in order.
Costs
Assume it costs $70k annually to hire an inside sales person: $30k in base salary, $30k in on-target earnings/performance pay (or OTE) and $10k in benefits. This means at the very least, your first sales person must close $70k in business for you to break-even on the hire.
But in the early days of a sales team it's typical to see a sales-quota-to-earnings ratio is about 1:2 or 1:3. As a sales team and product matures and price increases, this ratio can grow further. I'll use 1:2 or $140k annual quota for this example.
Outputs: Sales Productivity
Sales productivity has two components: average deal size and deal velocity.
Our hypothetical salesperson must sell $140k of products each year or about $12k per month. There are many ways of accomplishing this goal. At one extreme, our salesperson could close one $140k deal each year. At another, she might close twenty-four $500 deals each month. Any permutation in between meets the quota requirement.
Selling a $140k contract is a very different sale to a very different customer from a $500 contract. Most enterprises won't buy $140k worth of software over the phone from your inside sales person. They want to meet someone, build a relationship and trust and negotiate a contract. You'll need a field or outside sales person for that and they fetch salaries of $250k+!
Additionally, pursuing a few very large contracts introduces huge variance in sales forecasting. It's called elephant hunting for a reason - high risk, high reward.
As a startup with presumably constrained finances, the ideal first sales person produces predictable and consistent sales. This is better for performance measurement (understanding how well the sales person is doing) and cash flow management (ensuring sales is filling the coffers early and often).
As a result in addition to a quota, sales managers often prescribe a sales velocity or the number of deals closed within a period. Sales velocity is dependent on your product's price and the total number of customer contacts a salesperson can make in a month.
At best, the average salesperson has the time to convert 60 leads to customers. Assume a sales person works 20 days per month for 9 hours per day. Assume each sales call takes 45 minutes of time, 15 minutes of preparation/followup and each sale requires 3 calls (introduction, product demo and close) and voila - 60 leads.
But not all leads convert. Typical conversion rates for inside sales teams are roughly 20 to 30%. Of the 60 leads, only 15 should convert to customers. Those 15 customers need to produce about $12k in monthly quota to pay for our sales person which implies a pricing floor of $800 per month or $9600 per year.
Unless your product can fetch that price, a sales team structured in the way outlined above would be unprofitable.
Freemium businesses convert about 2 to 4% of new user accounts into paying customers. More traditional enterprise sales tend to convert 10 to 15% of leads (people signing up on the home page asking for information).
A freemium business needs to generate about 3000 users that could pay $800 per month to gin up 60 qualified leads each month. And a traditional enterprise software startup needs to create 400 signups. Until your startup is filling the top of the sales pipeline with roughly these numbers, you risk hiring a salesperson too early.
Other things to consider
There are many other factors to consider when laying the foundation for your sales team including customer churn rates, customer costs to serve (account management), payback periods, contract requirements, sales person experience, internal time allocation and so on.
But when your startup's software can command a high enough price and when your lead volumes reach a certain threshold, you should have the confidence to hire your first salesperson.
