Winning with Data - Bien Frank

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won the right to lead the series A, the first institutional round, would invite the firm that lost the opportunity to invest in the next one. However, this quid pro quo environment evaporated when the sums of money flooding the industry treated stiffer and stiffer competition from new and existing venture capital firms.

      The secular increase in competition has continued over the last 20 years as the scale of technology companies has skyrocketed. Google is now worth nearly $500 billion. Facebook is worth $250 billion. And we venture capitalists chase the next one. The competition drives firms and partners within those firms to develop competitive advantages, and in our business that means information asymmetries, and that means data and relationships. The firm that finds the next breakout company first will often win the right to invest in that business.

      There are many different means for venture capital firms to establish that information asymmetry. Some of them develop unique relationships with key angel investors, individuals who invest in very early-stage companies, with just two founders and a dream. Other firms rely on strong relationships with universities and professors who refer standout students to investors. Yet others specialize, focusing on financial services technologies or consumer subscription businesses. At Redpoint, we have tried to develop an information asymmetry using data. That initiative started almost a decade ago.

      I started at Redpoint, a venture capital firm headquartered on storied Sand Hill Road in Menlo Park, in 2008. During my first week, I remember receiving a thick envelope in the mail from the National Venture Capital Association (NVCA). The envelope contained the NVCA's directory, a thick tome listing all the different venture capitalists across the country. They numbered more than 5,000. Looking out of my office over the Santa Cruz Mountains, I despaired; how would I ever differentiate myself in such a competitive industry? “What would Doriot do?,” I wondered.

      I was very fortunate to work closely with three of the six Redpoint founders, Geoff Yang, Tim Haley, and Jeff Brody, three preeminent venture capitalists who financed billion-dollar businesses like Netflix, Juniper Networks, and HomeAway from their earliest days, and advised those businesses as they transformed huge industries. Over the next few years, they mentored me extensively, and boy did I need it.

      As I started to attend board meetings with these senior partners, I began to realize how little I actually knew about startup management. Sure, I could help them with their Google advertising strategies. But founders would ask questions like “How much should I pay a VP of sales?” or “What is a reasonable cost per click on Google?” or “How fast will the business have to grow to be able to raise the next round of capital?” I was at a complete loss to answer these questions. I hoped no one in the room noted my silence.

      But I knew, from my days at Google, this data must exist somewhere. So, each time a founder asked me a question about his business, be it revenue per employee benchmarks or marketing efficiencies compared to publicly traded companies, I searched for data.

      Once, I found a data set containing startup IPO data dating back to the very earliest days of venture capital that Jay Ritter, a professor at the University of Florida, collected. Startups were surprisingly willing to share their internal data in surveys – anonymously, of course. So, I surveyed them. Friends working at investment banks showed me how to access the data reported by publicly traded companies.

      Armed with those data sets and others, I began to answer the questions posed by founders, using the basic statistics ideas I studied in college. The data proved useful to a few of the CEOs I knew, and they asked me if they could share the data. Of course, I agreed. And one of them in particular suggested publishing the results on a blog.

      I bought the tomtunguz.com domain, selected a simple blogging layout, and began to write. I jumped when 15 people read my first post. Fifteen daily readers grew to 100. One sunny summer day, I watched as my Google Analytics account reported 1,000 people had visited tomtunguz.com. In disbelief, I called my wife. All those hours spent on nights and weekends writing were finally showing some promise. That night we celebrated with some champagne.

      Over the spumante, my wife asked which topics garnered the most interest. I didn't know the answer. So, I began to study the factors that attracted readers: title length, the number of subheadings, the presence of images, voice and tone, time of day to publish, and many others. I learned quite a bit.

      I have 48 seconds with a reader. No pretty images, no witty title, no amount of social media validation from influencers will entice the reader to linger. Tweets sent at 8:54 to 8:59 a.m. Pacific Time generate 25 percent more views than those sent a few minutes after 9 a.m. But e-mail subscribers prefer to read content around 10 a.m., a nice midmorning break. Would e-mail readers like to read posts after lunch?, I wondered. A two-week experiment showed they most certainly did not! Open rates fell in half.

      As I had done before, I published most of my findings and readers contributed experimental ideas. Over time, this iterative effort grew readership to more than 100,000 readers per month and more than 200,000 social media followers.

      But what did all this content marketing ultimately create for Redpoint? A bit of a brand boost, perhaps. Could I justify investing five hours each week to this effort, especially in an industry where the most sought-after startups can raise capital in just a day or two?

      At about the same time, I read Aaron Ross's book Predictable Revenue, which describes Salesforce's processes and tools for growing from zero to more than $6 billion in revenue. The former director of corporate sales, Aaron described Salesforce's process of finding potential customers, educating them through sales efforts, and cajoling them through the sales funnel into a satisfied, paying customer. The heart of this software process was, naturally, Salesforce's software, which catalogued the journey of all the potential buyers.

      Predictable Revenue inspired me to create a sales funnel from my blog. Read by many startup founders, the blog generated leads – startups in which Redpoint might want to invest. If I could consistently and quickly identify those readers, I might be able to grow Redpoint's network of great entrepreneurs and pinpoint the next great business idea. I decided to call it Scour.

      Here's how the system works. I write a blog post. That post is distributed on the web page and through e-mail, social media channels, and some other websites. This content marketing engages a broad network of people. Some of those readers elect to fortify their relationship with the content by electing to receive blog posts by e-mail.

      Scour captures their e-mail address in a database. Using that e-mail address, Scour determines who the reader is by looking across the Internet: Where do they work, do they belong to a startup that could be a good fit for Redpoint, whom do we know in common, are they influential in a particular sphere like open-source software or consumer product design? This research process concludes by prioritizing a list of people to meet for us to build our network and find new startups.

      Unlike the late 1990s, when the startup ecosystem encompassed perhaps 1,000 founders, today more than 4,000 technology businesses are financed each year. And, again in contrast to the previous era, today those 4,000 businesses leave digital footprints all over the Internet.

      Two young computer science students might launch an experimental mobile application for iPhones. The app's success is recorded by Apple. The data is freely available for anyone to download and analyze.

      As founders recruit a team, they open requisitions on job boards all over the Internet. One of the founders might decide to blog in order to build an audience of like-minded people who might eventually work for the business and also generate early demand for the product they are building. Twitter accounts, LinkedIn profiles, Facebook interactions, comments in public forums, job listings – with enough data, we have found it possible to identify very early stage startups with promise consistently.

      Consequently, we have built data infrastructure to aggregate all these signals scattered across the Internet. We store them in a cloud database and continue to grow the size of that database in the hope that all this data will eventually help us find the next great business before anyone else. With this repository of information, we can experiment and explore investment hypotheses.

      Some firms like First Round Capital publish

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