A Guide to the Berlin Tech Ecosystem

The rise of Berlin as an entrepreneurial center is not exactly news, and a number of U.S. VC funds or angel investors have been active there for a while. My overall impression, however, is that many U.S.-based entrepreneurs and investors have only a fuzzy idea about what is going on over there, and arguably in Europe in general – a missed opportunity in my opinion, and perhaps one of the several reasons why Europe is still a largely underserved opportunity in terms of venture capital investment.  The globalization of entrepreneurship has been one of the key trends in our industry.  At this stage, there’s enough evidence of global success stories coming out of Europe that smart U.S. investors should be routinely investing in the area, despite the traditional issues associated with early stage investing in Europe (multiple markets, multiple languages, tax and regulatory issues, different attitudes towards risk/failure, etc.).

From that perspective, I thought it might make sense to share notes (very much in note format) from recent travel to Berlin (among other reasons to speak at Data Days, a fun event) and a number of conversations with Berlin-based entrepreneurs and investors in other contexts.  This is meant to be a “beginner’s guide” for outsiders like me, not the ultimate reference on the topic.

Overall, there’s plenty of reasons to be excited about Berlin.  While some claim that the real action has already moved on to other locations (like Istanbul), the Berlin tech ecosystem is still early, and in many ways trailing other emerging global tech centers like London and NYC, probably by a few years.  The fundamentals, however, are encouraging, particularly considering that the government (very active both in London and NYC) has been largely absent from the development of the Berlin ecosystem, so pretty much everything that has been happening there so far has been driven by raw, grassroots entrepreneurial energy.

Many thanks to Christophe Maire (currently CEO of Txtr), Jess Erickson (General Assembly Berlin), Koen Lenssen (Tengelmann Ventures) and Saskia Ketz (SumAll) for reviewing this post.

Key characteristics

Unique

  • Just like New York or San Francisco are not like the rest of the U.S., Berlin is not like the rest of Germany
  • Artistic (buoyant contemporary art scene), rebellious culture (or “counterculture”), which seems to translate well into design talent
  • Innovative and fun (renowned nightlife)
  • Cheap, large apartments – particularly compared to tech centers like London, NYC or SF where finding affordable housing is an extraordinary challenge. Some say that this won’t last long if Berlin continues on its current trajectory; at the same time, the city is very large and housing supply may exceed demand for a while.
  • Socially open, no class system, left leaning with an openly gay mayor (Klaus Wowereit) from the SPD (social democratic party), in power since 2001.
  • Capital of one of the world’s largest economy, yet due to history, no local industry – local talent does not have many other options
  • Traditional economic centers in Germany such as Munich, Frankfurt and Hamburg lag behind in terms of tech startup activity – which doesn’t mean that those cities do not have interesting companies: for example, Hamburg seems to have a gaming cluster (Bigpoint, Innogames, GoodGames, etc.), and both Facebook and Google have offices there; Munich has West Wing (online shopping club for Home & Living, received $50M of investment in June 2012), Frankfurt/Wiesdaben is active in social entrepreneurship (Hans Reitz/Grameen).  But Berlin seems to have strong gravitational pull; for example, when airbnb arrived in Europe, it set up shop in Hamburg after acquiring German clone Accoleo , but subsequently moved moved all operations to Berlin. Twitter also chose Berlin over other German cities to base its operations.  MTV has been there since 2004 (after moving from Munich).

Immigrant-friendly

  • Part of the European Union (doesn’t solve all labor laws issues, but helps tremendously in terms of recruiting talent from other EU countries)
  • Just about everybody seems to speak English
  • International founders: perhaps ironically, a number of Berlin startups, including some of the most prominent ones, were founded or co-founded by non German nationals, including Souncloud (Swedish founders), Gidsy (Dutch founders), Amen (one American co-founder), Incrediblue (Greek founders), Readmill (Swedish founder), GoEuro (American), rules.io (American), GetYourGuide (Swiss), etc.

Solid supply of technical talent

  • For now at least, there’s much less of a talent war than in other tech hubs, particularly SF and NYC, but some say it is changing fast.
  • Strong German work ethic (one of the reasons for an overall low employee turnover, together with restrictive laws around employment termination and the German social structure in general).
  • Solid universities, although fairly theoretical: Humboldt, Technical University, Freie Universiat.  Berlin also has two of Germany’s top art and design universities: UdK and Weißensee
  • Geographically close to cheap talent: Leipzig, Warsaw, etc.
  • There’s some history of large tech companies like Siemens having a strong presence in Berlin, but by the sound of it, at this stage startups are the only game it town in terms of recruiting top technical talent

Increasingly solid community infrastructure

  • Lots of co-working spaces: Agora Collective, Ahoy, Betahaus, ClubOffice, Co.Up, LaunchCo, Mobilesuite, Raumstation, St Oberholz, Tante Renate, etc – some spaces are essentially cafes, others are actual office spaces with scheduled activities.
  • More on the way: The Factory
  • Several accelerator programs: StartupBootcamp Berlin, Mcube, Hubraum, Project A, YouIsNow, Berlin Startup Academy
  • Education: General Assembly now offers enterpreneurship courses in collaboration with BetaHaus
  • Press: Berlin has its own industry blogs, including Venture Village and Silicon Allee.  Techcrunch (largely Mike Butcher) covers extensively Berlin startups.

Still early:

  • While it’s been growing for a few years now, the tech ecosystem still generally feels early, compared to London or NYC
  • Berlin was always known for hacker culture (chaos computer club) leading to cutting edge outfits in 90’s (art+com, convergence, gate5).
  • Started with the Samwer brothers – was a mixed blessing for Berlin.  While starting successful businesses, they perfected the art of the startup copycat (with the resulting bad reputation).  Examples: StudiVZ (Facebook clone), Citydeal (Groupon clone, assets acquired by Groupon),  Zalando (originally a Zappos clone, has expanded beyond shoes since), Wimdu (airbnb clone)
  • Now produces original startups (readmill, researchgate, goEuro, etc.), with an increasing number with global relevance and aspirations (SoundCloud)
  • Berlin seems still mostly focused on consumer internet companies (with a good amount of mobile plays), with comparatively fewer B2B or enterprise startups; it is not a full ecosystem yet from that perspective, with some exceptions like cloudControl (European PaaS provider)
  • While there is a fair distribution of startups across the stages (from seed to late stage), overall the ecosystem is too recent to have a very solid track record of exits.  Some examples:  ImmoScout (sold to DT for €450m), Idealo (a comparison shopping site, acquired by Axel Springer in 2006), gate5 (today “nokia Maps” employing 800 people in Berlin), StudiVZ (sold to one of its investors, Georg von Holtzbrinck Publishing Group, for €85 million in 2007), MyphotoBook (sold to Holtzbrink), Nugg.ad (provider of predictive behavioral targeting solutions for digital advertising, acquired by Deutsche Post in 2010 for €50M), KaufDa (local search and local promotion search, acquired by Axel Springer), Zanox (acquired by Springer for €250m), Casacanda (online shopping club for daily design inspirations, acquired by Fab.com in February 2012 in an all-stock deal valuing the company at around $10M), Brands4Friends (sold to eBay for €150m, etc.)
  • Sounds like the government hasn’t quite caught on – in starck contrast to London or NYC.  The success of the Berlin ecosystem seems to have been entirely driven by a grassroot, community effort.
  • There’s less VC money available than in other places like NYC (or to some extent London); companies tend to be very capital efficient and tend to monetize early .
  • One point people disagreed about: some say the ecosystem is still a bit  “clique-ish”, the community hasn’t yet completely geled as a full integrated, selflessly supportive environment (at least compared to SF or NYC) –others disagreed, citing Berlin’s community as one of its key assets.
  • Successful entrepreneurs are starting to give back to the ecosystem in the form of active angel investments.  Some notable examples: Dario Suter, Fabian Heilemann, Christophe Gras, Christophe Maire, Christian Vollman, Marco Börries, Martin Sinner, Lukasz Gadowski, Michael Brehm, etc.
  • Rocket Internet (Samwer brothers) plays have alone raised close to 1bn in 2012 alone and breeds large number of 2nd generation entrepreneurs with international experience. Employs overall 10,000 people in Berlin alone.
  • Team Europe, project-A and other Incubators breed ambitious international companies (Madvertise, Delivery hero, Sponsor pay)

Notable Startups

  • Soundcloud: world’s leading social sound platform
  • Amen: the place for creating and sharing opinions about the extra ordinary things in life.
  • Gidsy: community marketplace for authentic experiences
  • 6wunderkinder: multi-platform productivity solutions for individuals, groups and businesses
  • Wooga: third largest social games developer in the world
  • Delivery Hero:  global network of online food ordering marketplaces
  • Zalando: largest European  eCommerce group (including shoes)
  • 9flats (peer to peer apartment rentals)
  • Lieferheld: platform for ordering and paying food online
  • EyeEm: smart photo-sharing application for smartphones
  • Txtr: distributed eBooks platform
  • Readmill: social and shareable reading platform
  • Researchgate: community for researcher and scientists
  • MoviePilot: promotion platform for movies (DFJ esprit)
  • Monoqi.com: highly curated commerce
  • Other companies/products that came back in conversations: HelloFresh, Trademob, Travis-Cl, Fort Rabbit, Cobot.

Angels, seed investors, founder collectives or micro VCs

  • Rocket Internet (Dealstreet, Wimdu, GlossyBox, Westwing, 21Diamonds, payleven)
  • Team Europe (Delivery Hero, Lieferheld, Madvertise)
  • Christophe Maire: Swiss serial entrepreneur, currently CEO of Txtr, investor in soundcloud, Amen, EyeEm, LoopCam, Appaware, PhoneDeck
  • Klaus Hommels: Swiss leading European angel investor
  • Ashton Kutcher: investor in Amen, Gidsy, Soundcloud
  • Martin Sinner: MD at Idealo and active angel

VCs

  • US funds: Highland (Wooga, getyourguide), Union Square Ventures (Soundcloud), Kleiner Perkins (Soundcloud), GGV (Soundcloud), RedPoint Ventures (9flats), JP Morgan (Zalando), kinnevik (RocketInternet), Benchmark, FounderFund (Researchgate), Spark Capital (getyourguide)
  • International funds: Index Ventures (Soundcloud, Gidsy), Atomico (6Wunderkinder), Balderton (Wooga), DST Global (Zalando), Kite Ventures (Delivery Hero, Lieferheld), ru-Net (Lieferheld), Sunstone Capital (Amen, Gidsy), Wellington Partners (EyeEm, Readmill), e.Ventures (9flats, kaufDa, Dealstreet), Passion Capital (EyeEm), Mangrove Capital Partners, Partech, b-to-v Partners, etc.
  • German funds: Earlybird, HV Holtzbrinck, IBB (ClipKit, cloudControl), Earlybird Ventures, BMP Media Investors, Tengelmann Ventures, Dumont Venture, High Tech Gründerfonds (a public private partnership with 10+ corporate investors, currently investing out of a €293.5 million fund), GMPVC (German Media Pool, invested in 9flats).
  • Corporates: BDMI, T-Venture (ClipKit, 9flats, 6Wunderkinder).

Joseph Turian, Sqrrl, Infochimps and MemSQL

The December NYC Data Business Meetup was focused on big data infrastructure companies, with the co-founders of Sqrrl, Infochimps and MemSQL presenting to a full house.  We started the evening with a presentation by prominent data scientist Joseph Turian.

The slides are here: Joseph TurianSqrrlInfochimps and MemSQL.

Here are the videos:

Intro

 

Joseph Turian, “How to do AI in 2013”

 

Oren A. Falkowitz, Co-Founder & CEO, Sqrrl

 

Dhruv Bansal, Co-Founder & Chief Science Officer, Infochimps

 

Eric Frenkiel, Co-Founder & CEO, MemSQL

 

And here are a few pics (photo credit: Shivon Zilis):

 

Recorded Future, Lex Machina, DataMarket and numberFire

The November NYC Data Business Meetup was focused on “vertical-specific” applications of big data – startups leveraging the big data stack to offer new solutions to specific industries, such as finance and government (Recorded Future), the legal industry (Lex Machina), energy (DataMarket, although it offers data sets for other industries as well) and sports (numberFire).

The slides are here: Recorded FutureLex MachinaDataMarket and numberFire.

Here are the videos:

Christopher Ahlberg, CEO, Recorded Future:

 

Josh Becker, CEO, Lex Machina:

 

Hjálmar Gíslason, CEO, DataMarket:

 

Nik Bonaddio, CEO, numberFire:

 

Panel discussion:

 

Some pics:

IA Ventures, Accel, Data Collective, Precog and CCS at the NYC Data Business Meetup

Here are the videos from the NYC Data Business Meetup that was held on October 23, 2012, in order of appearance:

Jeff Carr, COO, Precog

 

Max Yankelevich, co-founder, CrowdComputing Systems

 

Roger Ehrenberg, Founder and Managing Partner, IA Ventures; Ping Li, General PartnerAccel Partners; Matt Ocko, Co-Founder and Partner, Data Collective (from left to right):

 

A chart of the big data ecosystem, take 2

So here we are again.  My colleague Shivon and I had made a first attempt at making sense of the rapidly evolving big data ecosystem back in June.  Based on some very helpful feedback from readers of this blog and others, a number of additional meetings with interesting startups and more in depth research, we’ve come up with this second version.

Some thoughts:

  • It’s still a work in progress (and will presumably always be, that’s the nature of the beast)
  • It’s even more crowded than the first time around, which reflects the incredible vitality of the big data space
  • We’ve created some new subcategories such as NoSQL/NewSQL and analytics services (reflecting the reality that, for the time being, the last mile of data analysis is very much performed by humans)
  • We have the occasional company that appears in different categories (Infochimps or Autonomy for example)
  • We have learned more about companies that were already on the first version of the chart, and have positioned them differently.  For example, Metamarkets now falls in the “Cross Infrastructure/Analytics” category as they offer a stack that includes a data store (Druid), predictive analytics and visualization.  Another example is Collective[i] – they have built  an entire proprietary big data stack from the ground up, that includes infrastructure, analytics and applications – making the company a rare example of an “Application Service Provider”.

Our goal is to continue updating this chart from time to time, and perhaps make it evolve visually, as we’ve probably reached the limits of what we can reasonably fit on one slide.  It was suggested that we try to visually distinguish on premise offerings vs. cloud based solutions, which we may try to do.

To enlarge, click on the arrows at the bottom right of the chart.

Comments, thoughts, questions? Please add to the comments section.

10Gen, Mortar, Datadog & Rick Smolan at the NYC Data Meetup

Here are the videos and some pictures (scroll down) of the NYC Data Business Meetup held on September 25, 2012

In order of appearance:

1) Rick Smolan told us about his fascinating new project, the “Human Face of Big Data” – see the NY Times coverage here: http://nyti.ms/TO5MDd.

 

2) Mortar (presenter: K Young, CEO). Mortar (www.mortardata.com) provides a platform-as-a-service for Hadoop.  They take care of all of the necessary infrastructure (via AWS) and allow any software engineer to run jobs on Hadoop using Apache Pig and Python without special training.

 

3)  Datadog (presenter: Alexis Le Quoc, co-founder). Datadog (www.datadoghq.com) is a service for IT, Operations and Development teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.  Datadog helps software developers and web ops understand their IT Data by putting it all in context.

 

4) We finished with a fireside chat with Dwight Merriman, CEO and co-founder, 10Gen. 10Gen (www.10gen.com) develops MongoDB, and offers production support, training, and consulting for the open source database. Dwight is one of the original authors of MongoDB. In 1995, Dwight co-founded DoubleClick (acquired by Google for $3.1 billion) and served as its CTO for ten years. Dwight was the architect of the DoubleClick ad serving infrastructure, DART, which serves tens of billions of ads per day. Dwight is co-founder, Chairman, and the original architect of Panther Express (now part of CDNetworks), a content distribution network (CDN) technology that serves hundreds of thousands of objects per second. Dwight is also a co-founder and investor in BusinessInsider.com and Gilt Groupe.

 

Enterprise Tech Panel in NYC

Mark Birch has a good summary of a recent panel organized by the NYC Enterprise Tech Meetup (which also has a video of the panel on its site, unfortunately with poor audio quality).  In addition to Mark, the panel featured David Aronoff (General Partner, Flybridge Capital Partners), Jeanne Sullivan (General Partner, StarVest Partners), Raju Rishi (Venture Partner, Sigma Partners) and myself.  Many thanks to Jonathan Lehr, the organizer of the event, for putting it together. Couple of pics below and also one here (I know! Panel pics are just so exciting!).

One key takeaway for me is that the NYC area used to have a pretty vibrant enterprise tech scene (with Computer Associates, etc.) in the eighties and up until the mid-nineties (before my time), which makes the relative dearth of enterprise tech startups in NYC over the last dozen years somewhat odd.  I’m excited to see a whole new wave of NYC startups rising to prominence, including 10Gen, Opera Solutions, Enterproid, Nodejitsu, AppFirst, Datadog, Mortar, etc.

Facebook’s troubles are everyone’s problem

Like many people in our industry, I have been watching with growing discomfort Facebook getting beaten up on the public markets, including yesterday’s new $17.729 low. Regardless of the reason (the company remained private during its phase of hypergrowth, nobody reading the S-1, etc.) and leaving aside the borderline silly hunt for a culprit (Zuck is immature, the CFO badly overestimated demand, NASDAQ botched the IPO, etc.), the point is that the darling of an entire industry, the most prominent of all emerging internet franchises, the one that epitomized social media at its most powerful, ended up colliding with, and so far losing to, the cold cynicism of public markets.

The ripple effects are starting to be felt throughout the industry, as they trickle down the ecosystem. Evernote for example announced last week that it was going to delay its IPO until at least 2015. Further down the chain, anecdotal evidence around me seems to indicate that VCs are being increasingly cautious and picky when evaluating consumer internet plays (broadly defined: social, local, mobile, etc.), particularly when there is not a very apparent business model in place or in the works. I’m not sure if it’s the end of an era just yet, but the Facebook IPO that was supposed to create a brand new virtuous circle (Facebook buying a bunch of startups, and Facebook millionaires angel investing in hundreds of others) is so far proving to have the exact opposite effect on the industry.

While nobody is thrilled about what is happening, I hear two types of “rationalizations” around me:
  • It’s not entirely bad that things cool off a little bit — there are just too many startups being created in those consumer areas, too much angel and VC money floating around, valuations that don’t make sense, not enough technical talent to support the whole thing, etc
  • As an industry, we’ll all be fine because things have been heating up on the enterprise tech side. Public markets have been much more accepting of the enterprise tech plays (Splunk, ServiceNow, Palo Alto Networks all did very well in their IPOs). For every Instagram, there seems to be a Nicira type acquisition. Box.net and its young CEO are the object of the type of hype (and investor funding) typically reserved to successful consumer plays. The combination of the cloud and big data trends has many commentators excited. Some see the beginning of a 20 year cycle of innovation in enterprise IT.
The first point is complex and would deserve its own post (hopefully sometime soon). As to the second point, as much I’m a big fan of enterprise tech and agree that it’s probably where the action is going to be in the next few years, the above fails to reassure me, as I just don’t see an enterprise tech boom developing independently from a strong consumer internet sector, long term:
  • Enterprise tech does not have the gravitational pull of the consumer internet. Because you can touch it, feel it, experience it, everyone can relate to the consumer internet. And because it is very visible, the young entrepreneurs who succeeded at it not only made fortunes in a short amount of time, but became pop culture icons in the process (complete with movies, Gap ads, etc.). Rightly or wrongly, this has created all the excitement around tech that we now take for granted. Some of it perhaps led to unwanted attention (not sure that, as an industry, we need Justin Bieber to be angel investing, as much as I’m a fan…), but arguably this has drawn into the industry a lot of talent and money that has lifted all boats. What happens when this interest subsides? Enterprise tech sorely lacks sex appeal: it is complicated, obscure, behind the scenes. You pretty much can’t be an outsider to the tech industry and come up with a good idea. If exciting consumer tech projects stop being funded, will Wall Street techies still continue to migrate to startups? Will hundreds of thousands of people will still feel an urge to learn to code? Will the broader public still care about tech?
  • Consumer internet companies have been driving, or at least influencing, innovation in enterprise IT over the last few years – whether it is actual technology (Amazon pioneering cloud computing, Google/Yahoo/Facebook/LinkedIn being driving forces in big data, etc.), the way enterprise tech is consumed by employees (social enterprise, Bring Your Own Device, etc.) or the way it has been sold to enterprises (freemium plays that bypass the CIO). What happens to this phenomenon, long term, if consumer internet is no longer driving innovation?
  • Consumer internet companies have proven to be great early customers of enterprise tech startups. Of course, you could argue that this “startups selling to other startups” does not make sense because at the end of the day it’s all funded by VC money. But the reality is that every enterprise tech startup needs early customers, and many consumer internet startups have proven to be more willing to use new, bleeding edge technologies – the hope being that, once an enterprise tech startup has a few success stories with startups under its belt, it makes it easier to “graduate” to Fortune 500 companies. When the internet bubble burst in the early 2000s, consumer tech companies went down first, but enterprise tech startups soon followed, because many of them essentially lost a chunk of their customer base. Could the same phenomenon occur today?
The stabilization of the Facebook stock price is hugely important for our entire industry. Tech blogs, true to form, are overall wildly optimistic and blame it on Wall Street “not getting it” (Techcrunch covered yesterday’s new low as a buying opportunity), but smart entrepreneurs, VCs and involved parties seem to be doing everything they can to stop the blood bath (see Fab’s Jason Goldberg post here, for example), as they fully measure the severity of the issue.

Continuuity, Sailthru & Visual Revenue at the NYC Data Meetup

Here are the some videos, slides and pics from the most recent NYC Data Business Meetup.  The videos are unfortunately not of the greatest quality, but are good enough to watch.

Also, note to self: make sure that our audience of 200+ sits closer to the stage, so that the room doesn’t look tragically empty on camera (rookie mistake)!

In order of appearance:

1) Todd Papaioannou, CEO, Continnuuity, a stealth big data startup, based in Palo Alto, CA and backed by Andreessen Horowitz, Battery Ventures, Data Collective and a number of high profile angels. Todd was previously Chief Cloud Architect for Yahoo.

2) Neil Capel, CEO, and Daniel Krasner, Chief Data Scientist, Sailthru, a New York based startup backed by RRE, AOL Ventures, Lerer Ventures, DFJ Gotham, Thrive Capital, Metamorphic, etc.  Sailthru provides fully automated, 1:1 email and onsite recommendations using a unique behavioral targeting platform. Sailthru helps brands cut through the clutter and build trust with their customers by recognizing and acting upon their individual interests. Sailthru’s technology creates individual user profiles associated with each person’s email address and online behavior. Sailthru’s algorithms gauge each individual user’s intent and match appropriate content and frequency of email communications such that every email is tailored to the unique user. That means they send as many permutations of an email as there are recipients. All simultaneously, all automated and all in real time.

Sailthru’s slides (PDF)

3) Dennis R. Mortensen, CEO and Jeroen Janssens, Data Scientist,Visual Revenue, a New York based startup backed by Lerer Ventures, SV Angel, IA Ventures and Softbank. Visual Revenue increases front page performance for online media organizations.  Their platform provides Editors with actionable, real-time recommendations on what content to place in what position right now and for how long. Visual Revenue’s predictive analytics technology allows media organizations to proactively manage the cost of exposing a piece of content on a front page, whilst maximizing the return they expect from promoting it.

Visual Revenue’s slides

4) Panel discussion and Q&A with the audience

 

Data-driven venture capital

I have been very intrigued by the recent emergence of “data driven” firms, aiming to use data to reinvent venture capital.

While they certainly review various data points and metrics before deciding to invest in a startup, as of today venture capital investors largely operate based on “pattern recognition” – the general idea being that, once you’ve heard thousands of pitches, sat on many boards and carefully studied industries for years, you become better than most at predicting who will make a strong founder/CEO, what business model will work and eventually, which startup will end up being a home run.  The trouble is, the model doesn’t always work, far from it, and many VCs end up making the wrong bets, resulting in disappointing overall industry results.  Could VCs be just like the baseball scouts described in Moneyball, who think they can spot future superstars because they’ve seen so many of them before, but end up being beaten by a cold, objective, statistics-based approach?

Enter several firms trying to do things differently:

  • Google Ventures has created various data-driven algorithms that inform their investment decisions – see the team discussing the concept at last year’s Web 2.0 Summit here.
  • Correlation Ventures raised $165M earlier this year for its first fund, which was reportedly oversubscribed (a rarity for a new fund).  Correlation says it has built the “world’s largest, most comprehensive database of U.S. venture capital financings”, which covers “the vast majority of venture financings that took place over the past two decades, tracking everything from key financing terms, investors, boards of directors, management backgrounds, industry sector dynamics and outcomes”.  Based on this data, Correlation has developed predictive analytics models which it uses to guide its investment decisions – as a result, it can make decisions very quickly (less than two weeks) and doesn’t require additional due diligence.
  • Just earlier this week, E.ventures (which results from the relaunch of BV Capital) also emphasized its own data-driven approach to investment decisions

Since I’m a big fan of anything data-driven (decisions, product, companies), the concept resonates strongly with me.  Predictive analytics have been successfully used in various industries, from retail to insurance to consumer finance.  Other asset classes are highly data driven – fundamental and technical analysis drive billions of dollars of trade; hedge fund quants spend their lives building complex models to price and trade securities; high-frequency trading bypasses human decision making altogether and invests gigantic amounts of money based solely on data.  In this world where everything gets quantified, why should venture capital be an exception?

However, as much as I like the idea, I believe venture capital doesn’t lend itself very well to a model-heavy, quasi “black box” approach.  The creation of a reliable, systematic predictive model is a particularly challenging task when you consider the following obstacles:

  • A relatively sparse data set: while by definition there’s not much data about early stage startups, you could argue that that amount is constantly increasing, as everything is moving online, and everything online can be measured.  You could also argue that, if you could have access to all historical data from all VC firms in the country, and efficiently normalize it, you would end up with a lot of data.  But still that amount of data would pale in comparison to what’s available to public market investors – Bloomberg processes up to 45 billion “ticks” (change in the price of a security)… daily.
  • Limited intermediary feedback points: Before getting to a final outcome (game lost or won), baseball is full of small binary outcomes (a player hits the ball or he doesn’t).  Similarly, in market finance, the eventual success of strategy can typically be broken down in many different points with binary outcomes (you make money or you don’t).  In venture capital, before getting to a final outcome (a startup has a liquidity event), it’s unclear how many of those intermediary, measurable points you get, that can enable you to build models – perhaps a few (the startup’s next round is an “up round” or a “down/flat round”) but certainly nothing compared to the above examples.
  • Extended time horizon: in baseball, the rules of the game do not change from game to game, or season to season.  In venture capital, the “game” can last for years, because investments are highly illiquid.  During that time, pretty much anything can change – regulatory framework, unforeseen disruptive forces in the industry, etc.

In addition, it would be interesting to see how startups react in the long run to investors who are interested in them mostly because they scored well on a model, as opposed to spending extended time getting to know them.  Unlike public stock markets, venture capital fundraising is a two-way dance, and startups often pick their investors as much as their investors pick them.

However, while I have my doubts about using data models as valid predictors of the overall success of an early stage startup, my guess is that there are still plenty of interesting insights to be gleaned from the data, and that forward-thinking VC firms could gain a competitive advantage by actively crunching it  – my sense is that very few firms have done so at this stage.

Interestingly, there are some good data sources and emerging technologies out there that could be leveraged as a first step, without engaging into a massive data gathering or technology development effort:

  • Public (and/or free) sources:  Crunchbase is a great source of data.  There are many directions you could go with mining it – as an example, see what Opani (an early stage NYC big data company) came up with here. I bumped into Semgel, a web app that has taken a stab at instantly gathering and analyzing Crunchbase data.  The Crunchbase data could be augmented with data from marketplaces such as Factual.  See also this intriguing article about pre-money valuations of startups (typically not information that’s disclosed) could possibly be mined from publicly available Delaware certificates of incorporation and similar documents in other states.
  • Private Databases: There a few interesting databases that collect and organize more complex information flows around private companies such as CB Insights (which also offers a data-driven tracking tool called Mosaic)
  • Technologies: In addition to the various open-source big data tools, there are some technologies/companies that could be leveraged to mine VC industry data, including for example Quid, co-founded by the talented Sean Gourley – “understanding co-investment relationships and deriving investment strategies” is one the challenges they address.

If anyone is aware of other efforts around crunching data relevant to VCs, or other ways VCs have been used a heavily data-driven approach, I’d love to hear about it in the comments.

Some thoughts on Brewster

Three days in, Brewster, the new personalized address book, has become an instant classic for me.  Perhaps I lucked out, but I didn’t experience much of the delay in processing my contacts that many others reported – I had to wait about 90 minutes which, while not ideal, was fine.  Everything since then seems to have been working like a charm  – the de-duplication and reconciliation of contacts across social networks, in particular, was beautifully done, and that’s not a trivial data problem.

I have always liked the concept of a personalized, always current address book.  In a way, it is sort of like the old Plaxo idea, which was probably before its time.  There were various startups that tried to fix the address book, including Sensobi (that eventually was acquired by GroupMe).  The next iteration of the social concept that I’m aware of is Everyme – at least in the initial vision the founders had for it when they were at Y Combinator in the Summer of 2011.  I was a bit bummed when it pivoted (or evolved) to become a private social network.

I really like that Brewster came out of the gate very “feature-rich”.  While I’m all for MVPs and generally agree that “if you are not embarrassed by the first version of your product, you’ve launched too late”, for something like this, I think the founder(s) made the right call to wait until the product was ready before launching.  At this stage of the game, anything that sounds like yet another hyped up app, and asks me to connect all my social networks when I first log in, etc. had better deliver some real value quickly for me to give it a real chance, and that was the case here.  As the founder Steve Greenwood  has apparently been mulling over this concept for many years, the temptation to release early must have been strong, particularly as it sounds like several startups are working on related concepts, including for example FullContact, but from my user’s standpoint, it was well worth it.

A few other aspects of the product (and its launch) that I like:

– I like that Brewster was clearly thought through as a data product – while the “Favorites” tab has an emotional and aesthetically pleasing aspect to it (depending on how attractive one’s friends are, at least…), the rest of the app is very data-centric: the “Lists” tabs has some interesting automatic categorization (I have 171 friends who are ‘Managing Director”, apparently, does that mean I’m old?), while the “Search” tab is awesome, with good suggested searches and the ability to uncover all sorts of interesting common interests across my contact list.

– While everything is automated, I like the fact that the product made me work manually to create my list of “favorites”.  That actually increased my personal investment into the product, and makes me less likely to discard it.

–  I really like that Brewster did not use any of the tired “virality” tricks that have become so common place.  No automatic posting on my Facebook newsfeed; no “Sent using my Brewster address book” tag line in emails, etc.

– I was impressed with the email I got to announce that my account was ready, personalized with pictures of some of my key contacts – great way of delivering a unique experience before I even started using the product in earnest.

The data privacy issue (and the fairly dramatic reactions to it) are of course a concern. I’m actually surprised that I don’t care more about it, personally — I guess I have gone pretty far down the path of accepting some privacy risk (as long as it’s not banking information), in return for getting a lot of value from the product, which I feel is the case here.  But obviously many people will feel differently, and this could sink the company entirely, if not properly addressed.

One functionality that I don’t find as impressive, at least as of now, is the “Updates” section — what it has surfaced so far (birthdays essentially) is not particularly interesting.  What would be really cool, eventually, would be an integration with Newsle, to get news about your friends.  Oh wait, add to this an integration with Cue, as well.  All built in natively into my iPhone address book and calendar.  Ok, so, maybe that’s a bit much to ask.  In the meantime, Brewster is already one of the most interesting apps I have seen in a long time.

A chart of the big data ecosystem

My colleague Shivon Zilis has been obsessed with the Terry Kawaja chart of the advertising ecosystem for a while, and a few weeks ago she came up with the great idea of creating a similar one for the big data ecosystem.  Initially, we were going to do this as an internal exercise to make sure we understood every part of the ecosystem, but we figured it would be fun to “open source” the project and get people’s thoughts and input.

So here is our first attempt.

A few things became apparent very quickly:

1) Many companies don’t fall neatly into a specific category

2) There’s only so many companies we can fit on the chart — subcategories as NoSQL or advertising applications, for example, would almost deserve their own chart.

3) The ecosystem is evolving so quickly that we’re going to need to update the chart often – companies evolve (e.g., Infochimps), large vendors make aggressive moves in the space (VMWare with Serengeti and the Citas acquisition)

What do you think? (click on the bottom right to expand)

There are many roads to success: The Buddy Media example

It’s been a few days now since their acquisition was formally announced, and I continue to be fascinated by the Buddy Media story.  But what fascinates me is less the company itself and all the things that make it great –  and instead the fact that its success tests the conventional wisdom of what makes a venture successful.  Rightly or wrongly, investors, prospective employees, the press, and anyone who tries to predict the highly unpredictable fate of startups, tend to default to some common assumptions about what’s going to work and what isn’t.  The Buddy Media story challenges that conventional wisdom in some interesting ways:

1.  NYC is not a good place to start an enterprise software company

It’s a bit ironic that, for all the talk about NYC being a media and eCommerce hub, the largest acquisition in five years would be an enterprise software company.

2.  It takes forever to build a successful enterprise software company.

It took Buddy Media less than 5 years from start to success, including an initial pivot.  

3.  To build a successful enterprise software company, you need technical co-founders, or at least a technical CEO

Buddy Media’s CEO is a serial entrepreneur with two degrees in journalism.  Buddy Media’s COO is a serial entrepreneur with a background in business development and marketing and a degree in economics.  The other co-founder and Chief Strategy Officer is a digital branding and marketing expert with a degree in Broadcasting and Mass Media.  

4.  Selling to marketers and advertisers is a really tough business.

Fortune 500 marketers and advertising agencies are indeed a tough audience – long sales cycles, often low budgets, a preference for homegrown solutions, a reluctance to buy what others in the industry purchase: not easy.   But the Buddy Media success shows that it can be done, with the right execution: build the best product in your category, focus on sales, make friends in the right places, hire some key people from agencies, and work really hard.

5.  Be really careful with strategic money

Buddy Media took a strategic investment from advertising leader WPP, which ended up substantially accelerating their business.

6. Service companies can’t become product companies

After an initial pivot, Buddy Media had to turn themselves into a service company to survive the 2008 economic recession.  James Altucher has a really interesting post on Techcrunch that describes this phase.  Somehow, they were able to gradually build a product offering.  

7.  The best founders are young and single

Two of the co-founders of Buddy Media are married.  On top of that, they have three children.  While there are famous examples of homeruns started by married founders (Cisco, VMware, etc.), in my experience, behind closed doors most investors think it’s a terribly risky idea.   The Buddy Media story shows that where there is will there is a way: founders with family obligations can still endure the rollercoaster lifestyle of the startup world.