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 and TechCrunch and Pando 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 and 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 was. It’s a fallacious perception. Enterprise investing has always been the norm and will continue to be for quite some time.

*Reposted from Tomasz’s personal blog, which you can read here.

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.

*Reposted from Tomasz’s personal blog, which you can read here.

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

*Reposted from Tomasz’s personal blog, which you can read here.

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 Big Digg Lesson

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.

*Reposted from Tomasz’s personal blog, which you can read here.

Who can gems like these forget 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.

Some of my recent posts like Hiring Your Startup’s First Salesperson and The Most Important Principle of Fund Raising work backwards from constraints to make an argument about how best to think about fund raising and when to start building a sales team.

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.

*Reposted from Tomasz’s personal blog, which you can read here.

Every SaaS business must to decide how to charge for the service. Pricing plans are some of the most difficult decisions to make. Equally important to the price is determining the point at which the customer pays – the conversion point.

There are a five different models that I’ve experienced: up front payment, freemium, limited free trials, money back guarantee. Picking the right one depends on a number of different factors. Below is a table that summarizes these five approaches.

Freemium

Freemium is a strategy for products whose value proposition is simply conveyed and whose value increases with time. Evernote’s value compounds with the data the user enters into the database. Expensify’s utility increases with the number of expense reporters on the system.

Freemium businesses must target markets with very large user bases because the conversion to paid rates vary between 2 to 4%. To drive $50M in annual revenue at that conversion rate and $100/year subscription, you would need 17M users.

Sometimes, the end users are buyers (typically in consumer services). Other times (for enterprise customers), end users are sales prequalifiiers who create a groundswell within an organization to convert to paid. Given enough users of a product in an organization, the enterprise can be upsold to a company wide license. Freemium distribution enables a company to acquire those users inexpensively.

Limited Free Trial

The main difference between freemium and trial products is product complexity: trial products are more complex and need time for the user to gain a deeper understanding of the value. CRM tools like Salesforce and 37Signal’s HighRise both use limited trials. The goal with these marketing mechanisms is to drive customers to explore the product for a brief period of time and then force a conversion. Allow too much time to pass and the customer will forget the value proposition.

These kinds of products tend to require significant user behavior change so these products must market a promise and then use the conversion to paid event to enforce that behavior change (carrot + stick).  Because of the behavior change, these products are marketed to decision makers who select the software for their teams: product managers, tech leads and heads of sales. Many of the tests I’ve seen have indicated shorter trial periods are better: 7 days is better than 14 days is better than 30 days.

Up Front Payment

To pursue up front payment, you need an established brand with a clear value proposition and you sell the product to the end-user. Adobe’s Creative Suite is a canonical example. Customers know what the software can do and if they need it, customers will pay for it. The same is true for AutoCad, MS Office, and operating systems. Most of these products are high cost per seat products. Because they are well-known in the market, they command a price premium.

Money Back Guarantee

For products lacking an established brand, but offering an immediate value proposition and charging a high average seat value, there is no better solution than the money back guarantee. It mitigates the customers commitment phobia but establishes a billing relationship at the outset. This pricing strategy requires contact with a sales or account management team which implies a higher cost of sales. Ideally, higher conversion rates mitigate these costs. In other words, the up front commitment is a sales pre-qualifier. Your sales team will have fewer, higher quality leads. Plus, your sales team will have direct product feedback to share with the product team.  Oracle offers this for many of their products as do Eloqua.

While each product and target market have some unique attributes, consider the time to value, complexity of the product, product complexity and your sales model (freemium vs sales) when you do price your startup’s product.

*Originally posted on Tom’s blog, here.

In his Stanford GSB lectures, Peter Thiel spoke about secrets. But he defined secrets for startups in a different way than one might expect.

A secret is not an unknown. Rather, it’s something just not widely believed to be achievable or feasible. In other words, it’s an insight, a thesis that isn’t widely held. Exploiting that secret should be the aim of every entrepreneur. Leveraging the secret means disruption and ultimately success.

Before you start a company, find this kind of secret – an insight that will help you maximize leverage against your competitors and that doesn’t oppose strength with strength.

How do you do that?

I think about it like Judo. There are two basic tenets of judo:

 

First: never to oppose strength with strength.

Second: maximize leverage.

 

You can’t compete with Google by building a better search engine. Google will put 100x the engineering team, leverage their 1,000x greater click data and out spend you on marketing by 10,000x. Don’t oppose strength with strength. Startups shouldn’t rely on more manpower, bigger ad budgets or data access advantages in fields where there is a large incumbent. During its infancy, Google won with distribution. Most internet companies believed search wasn’t valuable. Google thought differently. They offered to power all the major portals’ search engines and eventually dethroned them. This was Google’s secret.

The question, then, is what to leverage. Startups’ advantage is speed which means they can explore new technologies (mobile apps, Node, Meteor, Cassandra, RedShift) which build better products, new distribution channels (mobile and tv app stores, virality, OpenGraph) which reach customers more cost effectively or novel product designs (Path, Dropbox, Expensify).

Many companies are now using distribution as their secret – mobile app stores and Facebook Open Graph enable startups to access hundreds of millions of users in ways that incumbents simply aren’t prepared to leverage. Expensify uses mobile app stores to acquire hundreds of thousands of SMBs in ways that their market’s incumbent, Concur (market cap $3B), simply can’t copy.

Find your secret and you’ll be well on your way to disrupting a huge market.

NB: Thiel does mention other kinds of secrets. For example, PayPal’s financial losses due to fraud which are secrets in the traditional sense. It’s important to think about the judo principles when deciding whether to keep these secret. Simply put, if a competitor can gain leverage by using the secrets you disclose, keep them closely held.

Originally posted on Tom’s blog, here.

When the core teams of a startup work in harmony, they create tremendous leverage for a business. I saw this last week with one startup I work with, Axial, a New York based company enabling private companies to access debt, equity capital and strategic acquirers.

First, the marketing team created a powerful blog post and infographic, just top-notch content marketing. This campaign creates awareness among potential target customers who become curious about the service.

Next, the product and engineering teams refine a product that welcomes users, organizes them into certain key segments and qualifies them for the sales team.

Last, the sales team uses data collected by the product to inform and prioritize the sales processes improving close rates and customer satisfaction.

Although I can’t share the data, I can assure you that startup case studies like this one reaffirm Aristotle’s aphorism that the whole can be greater than the sum of its parts. It’s awesome to see a team working together, building harmony and driving a business forward.

Originally posted on Tom’s blog, here.

Once you have built your product and it’s in the market, there are only three things that matter: distribution (getting the product into users’ hands), engagement (validating that you’ve built the right product and that users are using it), and monetization (making money from those engaged users). Some companies call this the 3 Rs: reach, retention and revenue. Whatever you call it, this is your strategy.

At board meetings, I’ve started categorizing each portfolio company’s roadmap items into these three buckets. It’s a simplification that immediately reveals the priorities of the company at any stage.

Is most of the product development focused on growth or new product features that drive engagement? How have those priorities changed over time? Where are most of our people allocated? Are these priorities the right ones given the stage of the company?

Breaking strategy into these three parts makes it easy – you now have a template, a stencil for describing your strategy. Make a table that looks like this:

Fill it in with the top 2 to 3 initiatives per quarter. You have just built a board-level roadmap that describes your strategy. Explain it to your board, solicit feedback, then hold a town hall and describe it to your team. Then get back to building your company!

*Originally posted by on Tomasz’s personal blog, here

Some of our companies started financing processes in earlier this quarter. At a strategy session with one of our companies, the team and I crafted the outline of the pitch deck. They asked me what questions a venture investor might ask in the initial meeting.

Distilling the investment analysis into a small number of general questions is challenging because of the diversity of businesses we see but, I gave it a try and came up with the following questions I might ask a startup to answer in an initial meeting.

1. [Value prop] What is the problem and is it worth solving? Why is now the right time to solve it?
2. [Team] Does the team have the vision and the wherewithal to build this company?
3. [Go to market] What is the competitive angle (competitive barrier to entry and/or go-to-market) that will enable this company to succeed where others have tried and failed?
4. [Sales effectiveness & product validation] Who does the startup sell to? Which customers have used the product and how have they received it? How much is each customer worth?
5. [Product distribution] How does the company acquire customers cost effectively? What are the unit economics (customer acquisition cost, contribution revenue, and churn rates)?
6. [Revenue model] Does the company have the revenue model to build a big (>$100M annual revenue) business with good margins (gross ~ 50 to 60% / net ~15 to 25%) under reasonable assumptions?
7. [Market size] Can the market enable or bear a $100M revenue Alternatively, is this product in a quickly growing market or riding a disruptive wave?

Other risks including legal risks, technology development risks, value chain implications and so on may also be important. But when building a generic fund-raising deck, answering these questions in your pitch deck will serve as a solid starting point.

It’s important to unite these points with a single theme or story to sell the dream.

*Originally posted by on Tomasz’s personal blog, here.