How to sell data to hedge funds

Step 1: Know your audience

Hedge funds are not all the same. Below are the main types.
Quantitative Investing (a.k.a. Quant, Systematic, Algorithmic)
Quant funds utilize automated trading strategies based on algorithms and data. They are not looking to be right on every trade. They just want to be right more than they’re wrong and trade a lot of securities. They typically purchase data that
  • Apply to 100s or 1,000s of securities
  • Has a long history of data they can backtest
  • Is published frequently. 5 years of historical data, published quarterly provides a weaker backtest than 5 years of historical data published daily
The best part about selling to quant funds is that their business model is based on data, so they employ professionals to speak to data vendors, understand their data and make compelling proposals when valuable. The difficult part about selling to quant funds is if your dataset does not meet the above attributes, you’re probably not going to sell them anything. But you’ll learn that pretty fast. More Info: Largest Quantitative Hedge Funds
Fundamental Investing (a.k.a. Discretionary, Stock-Picking)
Platform Funds (a.k.a. Pod, Multi-Manager, Multi-Strategy)
Platform Funds consist of many individual teams of PMs & Analysts who share centralized resources such as assets under management, trading & execution, compliance, data, office space, training and more. Platform funds with discretionary strategies typically purchase data that
  • Is highly correlated with a company KPI (“Key Performance Indicator”) or key investor question of specific securities
  • Has a history of data they can backtest
  • Is unique
Since platform funds provide infrastructure shared among their investing teams (or “pods”), they often employ data sourcing professionals similar to Quant funds. These are also quick and pleasant conversations to have that require minimal sales infrastructure. More info: Multi-Manager Funds
Long/Short Equity Hedge Funds
Long/Short Equity Hedge funds pick stocks. They tend to purchase similar data to Platform Funds The Long/Short Equity Hedge Funds who spend the most on data:
  • Have a large amount of Assets Under Management (AUM)
  • Trade frequently. The more often they trade, the more data they want to look at. A rough proxy for this is “Turnover %” on a 13F database such as Whale Wisdom
  • Make concentrated bets. The larger their positions, the more they can spend. A proxy for this is “% of Portfolio” on Whale Wisdom
More Info: Institutional Investors Top 100 Hedge Funds (also includes Quant and Platform funds)
Event Driven Funds
These firms invest based on specific catalysts such as a merger, acquisition, bankruptcy, spinoff or legislation. If your dataset allows investors unique insights into key events, they may be a good match for Event Driven Funds.
Other Types of Funds
The above designations are neither mutually exclusive nor collectively exhaustive. Many funds are combinations of the above or something else entirely, including:
  • Long Only/Mutual Funds: much longer term oriented investors with a different business model
  • Macro Funds: These firms invest across broader trends that affect a lot of stocks. If your data speaks to broader trends like inflation, currencies, weather, interest rates or global events they may be a good match for Macro Funds.
  • Credit Funds: invest in debt
  • Family Offices: manage funds of an individual family or group of families
  • Fund of Funds: invests in other funds
  • Sovereign Wealth Funds / Pension Funds: manages money of countries, endowments
  • Private Equity/Venture Capital: make large investments in mainly private companies

Step 2: Understand key use cases for your data 

For background on why hedge funds value alternative data, see Matt Turck’s excellent piece on the subject. High level, it helps to divide the institutional investor market into quantitative funds and discretionary funds, as the two have very different requirements and use cases for data. To attract quant funds, your data should speak to a lot of companies and have a long time series. A good example is a panel of consumer transactions touching many public companies, that has a positive correlation with share prices. Once a quant fund has an understanding of your dataset, they can run a backtest to establish value. To attract fundamental investors, it’s easier to start with a few case studies about specific public companies. Pick a few that your data can best speak to and run a correlation to their KPIs (e.g., Revenue, GMV, Gross Profit). The best companies are:
  • Stock price is driven by a key metric or investor question your data can speak to
  • Large market cap / average trading volume
  • High volatility
  • Always nice: high hedge fund ownership (examples on page 5)
The right company will vary by dataset, but a few examples:
  • Panel of Mobile App Usage: Correlate to observed app usage with reported DAUs for SnapChat, Facebook or Twitter
  • Panel of Consumer Transactions: Correlate to same store sales of retailers or GMV/sales of ecommerce companies
  • Social Sentiment: Correlate shifts in sentiment to revenue or share price of consumer / apparel brand companies

Step 3: Start with early adopters: quant funds and platform funds

Both types of organizations employ teams of people looking to speak with data owners, can move quickly and offer you a price for your data. You can speak to these people directly and don’t need to work with a data broker or other intermediary, who can demand a revenue share of 50% or more.

Step 4: Sign up early adopters quickly with a limited distribution.

Selling data is a multi-stage game. Price discovery and productization can take years. Don’t overthink your first contracts or hold out for the last dollar. Speak to a handful of early adopters, negotiate fair, 1 year contracts with a limited distribution, say 5-10 funds. You can decide the specific number based on conversations with the funds. Do it quickly so you can then focus your your time and resources on understanding exactly how investors are using your data and determine your strategy when renewals come around.

Step 5: Productize your data

Quant, platform and other large hedge funds employ data teams who can extract value from data in nearly any format. Selling into a broader audience of funds requires additional QA and analysis investments into your data product. For more information on developing this team see our post on How to Integrate Investment Analysts, Data Analysts and Engineers. Productizing your data means providing additional QA and analysis to enable you to understand it’s value and enable a fund without a large data team to extract value from it. Productizing your data, regardless of your customer distribution, will make it easier to use by more individuals at your customers, increasing its value.

Step 6: Determine the size of your eventual market

Once your first year contract is up, you will need to decide whether you want to expand the size of your distribution. The more funds you sell to, the less valuable your data will be to your customers. Reasons you may be able to increase your distribution and sell to more types of funds:
  • It is the #1 dataset of its kind
  • Your dataset applies to lots and lots of companies
  • The data is granular and multi-dimensional. It’s more than a single datapoint and different types of investors use it to answer different types of questions
  • You can sell other services on top of the data
  • Compliance departments generally prefer datasets with a broader distribution
  • You can develop different data products for different customer types at different price points, so not everyone is getting the same experience
  • Diversify your revenue stream across more customers
Reasons you may want to maintain a limited distribution:
  • Reduce complexity
  • Reduce need for marketing, sales and customer service expenses
  • Enjoy higher ASP, higher margins
  • The primary use case of your data is a KPI estimate, which is more quickly commoditized than a granular dataset. Are you providing a revenue estimate or a way to understand an entire industry?

Frequently Asked Questions

How much money will I make?

Reasons you may have unrealistic expectations of the value of your dataset:
  • The value of your dataset will depend heavily on details such as accuracy, time series, compliance, release schedule
  • It will also depend on factors specific to the target company such as the existence of competitor datasets, the precision of sellside consensus, key investor questions, or the existence of legal/macro/regulatory overhangs
  • Investors will pay a large premium for the #1 dataset in a category. Are you #1?
  • Rumors of datasets commanding enormous premiums are more viral than ones about datasets nobody wants

What about contracts and compliance?

Hedge fund customers will demand certain representations about your dataset. The YipitData Master Services Agreement can give you a sense of what to expect. You should consult a lawyer to help you with contracts relating to your specific dataset and to help you through your customer compliance reviews.

What should I never do?

Provide material, non-public information in violation of securities laws or personally identifiable information. Provide misleading, doctored or “data-mined” historical correlations. Conceal significant data outages or other issues that may affect your data's accuracy.


We think a reasonable go-to-market strategy for hedge funds is:
  • Start with platform funds and quant funds
  • Set up one year contracts with a limited number (5-10) of buyers as soon as possible
  • Spend your next year productizing your data and learning about its use cases
  • Determine whether or not you want to expand the size of your distribution
If you believe you have data that may be of interest to hedge funds, we would be happy to speak with you, and if you are interested, refer you to hedge funds who have expressed interest in alternative data. Email me at: ___ James Moran is President/Co-Founder of YipitData, which analyzes web data to provide KPI estimates and answer key questions for investors.