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VC: Find Companies with Data

  1. Short Description:
    1. Most VC sourcing strategies are about generating referrals or inbound interest. Instead, this approach is to find companies using data before they find you. Using data, you identify which companies are growing or appear promising. Data sources may include: tracking web traffic, app downloads, or purchase data from credit cards. A more sophisticated approach is creating a private panel of internet users who share their internet usage data. Another technique is to focus on people flows the founders and high-performing employees and where they work. After finding the candidate companies, the next step is an outreach and sales effort. You will want to get more information from the company before making an investment decision.
  2. Benefits:
    1. Less Competition: Fewer VCs are pursuing this strategy right now so there is less competition.
    2. Ongoing Competitive Advantage: If you build a sourcing software engine, you have an asset which should provide an ongoing advantage compared to other VCs.
    3. Good Story: Even if the approach only works intermittently, it is a great story for LPs and can provide a sense of differentiation.
    4. Prioritization. Data can drive efficiency in who NOT to meet, so you can focus on who you SHOULD meet – i.e. efficiency through meeting prioritization.
    5. Monitoring. Data also allows for more continuous monitoring vs a specific point in time when you held the meeting.
  3. Trade-offs:
    1. Consumer Tilt: Since there are fewer data providers for business to business transactions, it may be a better fit for investing in consumer-oriented companies. (Although if you focus on data about the team at the startup, this may be offset.) (Update: I have heard more counter examples here that funds can find B2B data sources.)
    2. Harder at Early Stage: Young companies do not have the time to generate much data. If you want to invest in very young companies, it may be hard to find them using this strategy.
    3. Companies Might Not be Raising: You may find a great company but they may not be raising money right now. You will want to prepare a strategy to convince them to accept your money. It will also change the dynamic in due diligence if they are not planning a fundraiser. You may get less information as they are not motivated to compile and share information.
    4. Requires Technical Expertise: to do this well, you will want to have expertise in data science on the team. It may be hard to find or be expensive to add.
    5. Expense. Building large scale systems of unstructured data takes time, talent, capital and lots of iteration. To be successful, it’s critical that a VC firm has a long-term commitment and a substantial budget for top talent, purchasing data, and processing for an unknown return.
    6. Internal Alignment Issues. Traditional firms that are not committed to data as a sourcing strategy may not recognize and reward work done in this area. There can be personal career risk to adopt newer and unproven strategies without firm-wide support and alignment.
  4. Examples:
    1. Signal Fire
    2. 645 Ventures
    3. Good Water Capital
    4. Vestigo
    5. InReach Ventures
    6. Basis Set Ventures

 

Thanks to Chris Farmer for input.

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