A Venture Capital Study
Data-Driven VCs: How 83 Venture Capital Firms Use Data, AI & Proprietary Software to Drive Alpha Returns
Overview of the most innovative approaches towards sourcing, evaluation, and supporting investments + 263 sources (articles, podcasts, videos, etc.) for further learning
For an industry which is supposed to be at the forefront of innovation, it’s shocking how outdated, or rather broken, venture capital really is.
It’s almost 2020, and it’s still (mostly) the same biased guys investing in their nearby living friends solving imaginary problems, basing their investment decisions on crystal ball called intuition, bringing on average net negative value to the entrepreneurs & losing investors’ money in the process. Add a hefty dose of misaligned incentives, massive operational inefficiency, resistance to adapt tech, and you’ve got a fairly decent picture of VC AD2019.
This status quo, however, is finally starting to get questioned by a new breed of investors. Equipped with data, algorithms, and custom-made software, those rebel venture capitalists arrogant enough to question “the way things have always been done” are quickly gaining a foothold in the venture world, creating a new paradigm of meritocracy.
During the last couple months, I have spent 600+ hours researching this changing landscape of venture, trying to gain a better understanding of the models, strategies, and tactics utilized by some of the world’s most innovative venture firms.
The below article, first in a series of many, aims to answer two questions: which firms are building their own tech and how the capabilities they are developing help them gain an edge in today’s cutthroat funding environment.
Some of the things you will learn from this article include:
- How is Social Capital able to invest in startups sight unseen?
- Who is working on a solution to identify founders BEFORE they actually become founders?
- How do YCombinator, Initialized Capital, and Daphni unlock the network effects within their portfolios?
- What custom software allows Kima Ventures to be one of the most active funds in the world with just three investment team members?
- Why does Follow[the]Seed ask entrepreneurs to install an SDK as part of their evaluation process?
- What piece of software did Georgian Partners develop to help its portfolio companies roll out AI solutions faster?
- What does Nauta Capital use Monte-Carlo simulations for?
- How do the powerful web-scrapers of EQT Ventures, Inreach Ventures, and Signalifre work?
…and much more.
The list has been split into 3 groups: 1- funds which are vocal about their use of data/software and much information about their specific approaches is available in the public domain; 2– some information available 3 — next to none. Within groups, the funds have been listed in alphabetical order.
Please keep in mind that below list & accompanying descriptions barely scratch the surface of the topic. For those of you wanting to dig deeper, I have compiled a knowledge database containing 263 items (articles, blog posts, podcasts, youtube videos, etc.), all neatly organized in Airtable, which you can find at the very end of this article.
Honorary mention — Signalfire
Signalfire, hailing from San Francisco, is a self-described “most quantitative fund in the world” & “the only VC that brings a data platform to its portfolio companies”. It is also the fund whose approach I find the most appealing.
At the heart of its operations lies an end-to-end real-time data platform called Beacon, a sort of a Bloomberg terminal for startup industry or as Chris Farmer (CEO of Signalfire) describes it, “a proprietary mini-Google”, powering the entire value chain of a venture — from deal origination through picking the right investments, and deal syndication to portfolio support.
Beacon tracks the performance of more than 6 million companies in real-time by drawing upon 10milion data sources, such as academic publications, patent registries, open-source contributions, regulatory filings, company webpages, sales data, appstore rankings, social networks, and even raw credit card data.
Companies that are outperforming or doing something notable are flagged up on a dashboard, effectively allowing Signalfire to see deals earlier than traditional venture firms.
However, it doesn’t end here. Beacon can also provide benchmarking and markets insights like: how well does a given company’s team stacks up against its main competitor’s team, what are the recurring patterns in customer spend in a given market segment, how much does a certain product cost in different geographies or how much a special or discount deal would impact revenue growth and profit margins. It basically allows users to fluidly zoom out their perspective up to the broad market level and zoom in down to an individual company level, seeing all the accompanying data on every step of the way. This information can be used both for investment evaluation as well as portfolio support purposes.
Apart from being a market intelligence platform, Beacon serves also as the communication-enabling layer between the various stakeholders in the Signalfire ecosystem.
All of the Signalifire’s 75 advisors (who are also LPs in the fund), founders of the portfolio companies, and the fund’s employees are wired into the system, unlocking a powerful network effect by making it super easy to tap into the resources (know-how, capital, contacts, etc.) of other members in the network.
To ensure maximum engagement on the platform, the firm went as far as creating differing, tailor-made versions of the software for different interest groups- there’s a Beacon version for founders, for advisors, and a central system.
The last, but equally important piece of the puzzle is Beacon Talent, an AI-based system for identifying and sourcing talent. This product directly addresses one of the most significant friction points in recruiting — research, which typically consumes half of the time it takes to conduct a search. The firm’s goal is to reduce this time-intense effort while simultaneously expanding the scope, quality, and diversity of candidates.
Beacon talent tracks and provides deep intelligence on nearly the entire talent ecosystem of the tech industry, including engineers, data scientists, product managers, designers and business leaders, ranking each person with dozens of quality dimensions, providing real-time predictions on how likely they are to switch jobs, and even proactively pushing new ones as they become available to help Signalfire’s portfolio companies with the recruitment of rising stars. Signalfire claimed that in just 6 months from launching Beacon Talent it was able to place 55 candidates, a quarter of whom on executive level, in their portfolio companies.
Signalfire claims it spends more than USD 10M a year (!!!) on the platform.
Here you can catch Chris Farmer talking about SignalFire’s platform for Bloomberg:
For an even more detailed overview, I highly recommend listening to this podcast: Chris Farmer on The Full Ratchet
645 Ventures, a NY-based early stage venture firm, has always been known to put much emphasis on data and analytics. The firm has developed 645 Voyager- a comprehensive software platform that powers the entirety of the firm’s operations — from automated deal sourcing, through deal evaluation and due diligence to syndication and portfolio support.
Some of its unique features include:
a) potential co-investor analytics — Voyager provides comprehensive data on where specific angels and VCs invest, along with a dynamic score of how those investors perform to help create top-performing investment syndicates
b) automatic tracking of diligence work — any diligence work on potential investments is automatically logged and tracked into the system. This allows the firm to easily document changes in companies performance over time — e.g., the changes in headcount, revenue growth, or even shifts in unit economics
c) benchmarking tool — shows how well does a given company perform in key metrics relative to its peer group
Backed.vc is USD 30M pan-European seed fund that is trying to bring community-driven ethos to European venture world. Part of how the firm wants to address the needs of the markets is through the use of in-house developed software. As described by Andre de Haes, the firm’s founding partner in an interview for Forbes:
“We’re building a technology platform to serve the whole community. It’s a set of systems where founders can help each other or help themselves without our firm being the bottleneck. Through the platform, entrepreneurs can share job candidates that they liked but weren’t the right fit for the industry or culture. Say you meet a great front-end developer that you didn’t hire, you can share them with the community. There are a lot of inefficiencies that happen in Europe because we aren’t a unified country. For example, the best start-ups are in London, Paris, and Berlin, but the best engineers are often from Bulgaria or Estonia. Our platform gives founders full access to talent anytime they want without Backed slowing things down as a bottleneck. We do the same with marketing — we take content from the whole portfolio and share it on the platform.”
Bloomberg Beta, an early-stage venture firm focused on the future of work capitalized solely by Bloomberg LP, is taking a fascinating approach towards data science.
The firm has teamed up with HaaS Business School, People.co — talent data company, and Angelist to create algorithms capable of predicting “Future Founders” — people working in the tech industry, who are likely to start their own companies at some point in the future, often before they even have begun the actual process. Having identified these potential future game-changers, the firm can directly outreach and start building relationships with them months or even years before other venture firms, leading to a massive competitive advantage.
Blossom Capital, a relative newcomer to the scene, is a pan-European series A investor. Blossom views data as a necessity to deliver on its thesis, which relies on sourcing opportunities outside typical European startup hubs, such as London or Berlin.
“Half of Europe’s Series A funding rounds are raised in hub cities. The other half is raised in a “long tail” of 70 or so cities. There’s a huge opportunity to use data to cover those cities. It can be hard for investors taking a traditional sourcing approach to discover companies like software startup UIPath (founded in Bucharest) or games company Supercell (founded in Helsinki)”- said Imran Ghory, Blossom’s partner in an interview for Sifted.eu.
Ghory believes Blossom’s utilizes a different approach towards data than the competition and does a quite job describing it in his medium post:
“Traditionally, VCs who’ve used data for deal sourcing have focused on using it to produce better search engines (with few exceptions like Social Capital). Building internal tools akin to CBInsights or Pitchbook to allow them to filter companies by web traffic or employee growth to speed up research. Yet these metrics fail to capture what defines a future unicorn in early-stage investing: visionary founders, a strong team and a category-defining product in an important market. Our approach is a different one; rather than taking widely available metrics and building models around them, we start from the opposite end of the equation. We take the methodology we use to evaluate deals during research and investment committee discussions and build models to replicate the investor mindset — seeking data sources (or building them where they don’t already exist) that can feed these models. This approach has empowered us to be geographically agnostic when it comes to sourcing, allowing us to scale far beyond what’s been possible with traditional venture capital approaches. Even within startup hubs, it has enabled us to identify exceptional startups long before they hit our radar through traditional means.”
This heavily data-driven approach is not a novum for Ghory, who has been developing such capabilities at his previous employer — Index Ventures — since 2012. Blossom claims its algorithms are even more advanced then Index’s & currently track 20k companies.
Connetic, hailing from Midwest USA, is an early stage investor focused on investments outside the Silicon Valley. The firm, currently on its sophomore USD 25M fund, has developed a proprietary tool called Wendal, which fully automates the pre-screening process. Wendal reportedly takes ~8 minutes to provide all the information needed to decide to move to human due diligence or pass on an investment.
Having reduced human bias from the pre-screening process allowed a firm to reach an astounding rate of 42% portfolio companies being led by either women or minorities, sight unseen in the venture world.
Medium profile: Connetic Ventures Follow: none on medium
Unlike traditional venture firms building relatively concentrated investment portfolios and putting much emphasis on leading rounds, San Diego’s Correlation Ventures strategy revolves around making many small investments in multiple companies in a co-investor capacity.
The firm claims it has built one of the world’s most complete databases of venture capital financings, covering nearly all U.S. venture investments over the last 20 years. It tracks everything from financing details, investors, board members, and management to industry segments, business stages, and exits. Using this data, Correlation built a predictive model that enables them to cut bias out of the investment process and to radically cut the time the firm needs to reach an investment decision. While traditional VCs often take months to decide, Correlation’s leverages analytics to shorten this time to as little as two days, making the firm a perfect choice as a co-investment partner.
Daphni is a self-described VC-as-a-platform (fun fact: they sometimes also refer to themselves as “venture capital mutant”) investing in startups with European DNA and strong international ambition.
In a pursuit to find its competitive edge, the firm has turned to the community. Daphni is supported by a group called “Daphnipolis”, a community of 300+ entrepreneurs, executives, academics, artists and advisors, all connected by a proprietary software platform.
One of the use-cases of the platform is pre-investment screening and deal syndication. Thanks to a custom built dealflow management system, Daphnipolitans have access to the startup applications being studied by the team and are be able to comment & rate them, enriching the analysis process with collective intelligence. This increased visibility into the deals, combined with extra software support, also allows for smoother syndication of deals within the community.
Once a startup is selected and funded, a second use case of the platform comes into play. The platform, resembling a private Quora/LinkedIn combo enables the startups to easily connect with and tap into the resources of other members of the “Daphnipolis”. If anyone has a question, needs an introduction, or wants to poll for knowledge, they can post the request on the platform and get help from their peers.
Deep Knowledge Ventures
Deep Knowledge Ventures is an AI, precision medicine, longevity, blockchain, and investech -focused fund from Hong Kong. The firm uses software called VITAL (Validating Investment Tool for Advancing Life Sciences) to spot and asses investment opportunities and is one of the very few entities openly admitting to trying to eliminate humans from its investment decisions.
Citing one of the creators of the algorithm:
“We developed VITAL with the goal of creating software that can intuitively predict the success of a project or a company at the initial seed funding level based upon an extensive analysis of historical data. Due to lack of public disclosure, datasets on investment rounds, intellectual property, and clinical trial outcomes are not always available. In spite of this, our team of programmers — several of which have theoretical physics backgrounds — are able to use fuzzy logic to identify probable success based upon an extensive analysis of the parameters involved. Our goal through iterative releases and updates is to create a piece of software that is capable of making autonomous investment decisions.”
It seems the firm credits the algorithm with much of its success. Citing the Nikkei Asian Review article, “Dmitry Kaminskiy, managing partner of Deep Knowledge Ventures (DKV), believes that the fund would have gone under without Vital because it would have invested in “overhyped projects.” Vital helped the board to make more logical decisions”, he said.
Dorm Room Fund
Dorm Room Fund is a student-operated fund established by First Round Capital. In January 2018 the firm launched VCWiz, a tool to help founders find investors and raise money from them, which is a combination of the best functionalities of CrunchBase, Foundersuite and NFX Signal.
The platform, one part VC directory and one part CRM tool, allows founders to find the most suitable investors for their startups and supports them throughout the process of reaching out and building relationships with chosen ones. One of the platform’s core differentiating functionalities is helping founders find the best intro path to selected firms by analyzing their social graphs (ala NFX Signal). From the firm’s perspective, VcWiz is a smart way to acquire dealflow (aka software-as-a-leadgen).
Entrepreneur First is the London-headquartered “talent investor,” which recruits exceptionally talented individuals at a pre-team and pre-idea stage, helps them find a co-founder and provides them with the support needed to found new ventures.
The firm uses custom build software to spot and pre-filter talent for its program based on the characteristics such as companies they worked for, schools they attended, and people they know. The firm claims it believes it has built the world’s largest and most comprehensive dataset of what great founder look like before they become founders.
This strategy seems to be working. The firm’s portfolio is now worth over USD 1.3 B (with USD 300m already distributed back to the investors), and it has recently announced it raised USD 115M funding to back the next generation of entrepreneurs.
EQT Ventures is a Swedish multi-stage venture firm with EUR 566 under management. As a relative newcomer to the scene (launched in 2016), the firm has turned to software to help it break into tier1 VC echelon. At the heart of its efforts lies a proprietary software platform dubbed Motherbrain.
Motherbrain uses convolutional neural networks (a popular form of machine learning) to review time-series data about the several million startups it continuously tracks to help guide where the firm should invest.
One of the investments uncovered by Motherbrain is the German software virtualization company AnyDesk.
“Thanks to Motherbrain, we saw that earlier. It was a company that wasn’t even interested at the time in investment, they were really crushing the product,” said EQT’s partner Henrik Landgren. “We met the guys at the right time so we could build a relationship with them; without Motherbrain, we might have seen them much later, and so we might have gotten in way too late.”
Because it contains so much data about investors, competitors, emerging technologies, and trends in the market, Motherbrain double-serves as a market intelligence platform which the firm can use to both speed up evaluation of prospective deals and support portfolio companies, for example, with B2B companies, the firm can use Motherbrain to help them find leads for new customers.
As of March 2019, the firm had used Motherbrain to assess more than 10.000 companies.
Medium profile: EQT Ventures Follow: Hjalmar Winbladh, Henrik Landgren, Indra Sharma, Lars Jörnow, Naza Metghalchi, Ashley Lundström, Zoe Jervier, Tom Mendoza, Martin Eriksson, Iskender Dirik, Manne Larsson, Lucy Wimmer, Axel Bard Bringéus, Joao Beraldo, Konstantin Zedelius, Lyle Fong, Anton Ask Åström,
FF Venture Capital
FF, an early stage venture firm from New York, are the original creators of Totem (http://totemvc.com/). Initially developed as an internal operating system of the firm, Totem was spun out as a separate business in 2017.
Below you can find an overview of Totem’s functionalities (copied from its website):
Knowledge- Totem serves as a source of truth to make firm-wide knowledge easily accessible:
- How much have we invested in a company? What were the terms?
- How much do we own of a company? Who else invested?
- Who are the leaders at the company? Who’s on the board?
- When is the next board meeting? What were last meeting’s notes?
- What deals are we looking at as a firm?
- What are our fund returns? How much capital do we have left to deploy?
Relationships- Totem maps your network to help you understand your firm’s most important relationships:
- How are we connected to Paul Graham? Who has the strongest tie?
- Who do we know (or not know) at Y Combinator? What deals in common?
- When did we last engage with an LP?
- Who are the executives we know at Google?
- Who are our important relationships in Toronto?
- Who are the Fintech CEOs in our network?
Insights- Totem surfaces insights and trends across your portfolio and network:
- Who are our top co-investors in a specific sector?
- What’s our average pre-money valuation by round over time?
- What channels do our highest value deals come from?
- What do our returns look like by sector, partner, or geography?
- How much capital have we deployed in a sector this year?
- Which of our companies are running low on cash?
Berlin’s Fly Ventures is an early stage firm with EUR 41M under management focusing on AI-startups. Fly’s tech is currently focused on automating sourcing investments. Using a combination of structured and unstructured data source (including blogs, job boards, startup accelerators’ websites and internet databases like Crunchbase),and its proprietary algorithms, the firm is able to uncover startups that often have not even started looking for an investment, giving the firm a massive advantage in today’s cutthroat venture world.
The firm claims, its algorithms find more than 1000 companies a week. The startups found by the algorithms are presented to the investment team in a Tinder-like interface that lets them quickly decide whether the firm wants to go more in-depth on each respective opportunity.
About 60% of the startups that the firm speaks to have been approached cold.
Follow The Seed, a venture capital firm active in Silicon Valley, Tel Aviv, Sydney and Beijing, investing in the post-seed, pre-A round stage, takes a unique perspective on using data analytics to assess deals in consumer internet space.
The firm has developed an algorithm named RavingFans®, which reportedly allows it to identify applications and technologies that are likely to become viral hits well before they reach critical mass, sometimes as early as in MVP or Proof-of-concept phase with only a few thousand active users.
How is it possible?
First, the Entrepreneurs looking for funding from Follow The Seed are asked to implement a RavingFans tracking SDK into their product. During the next 2–6 weeks, the algorithm analyzes how customers interact with a given product, looking for, in the firm’s own words, “patterns of irrational, obsessive, compulsive, addictive behavior”, which the firm believes to be a good proxy for future success. If such patterns do emerge, the firm will usually make a decision to invest.
In a move to boost its worldwide presence, the firm has recently decided to share its algorithm with other VCs, angels, and family offices in exchange for dealflow.
Georgian Partners is a Canadian venture firm focused on later-stage SaaS-based business software companies within the realms of applied artificial intelligence and security.
As a mean of competitive differentiation, the firm is developing a suite of software tools which will allow its portfolio companies to accelerate the pace at which they can roll out and apply AI.
The first product Georgian has developed is called Epsilon. This is how Georgian describes Epsilon on its website:
“Georgian Partners Epsilon v1.0 is our differentially private machine learning software. Epsilon enables companies to quickly adopt differential privacy to provide your customers with privacy guarantees. Specifically, differential privacy measures how effective particular privacy techniques — such as inserting random noise into a dataset — are at protecting the privacy of individual data records within that dataset. With Epsilon, you can guarantee your customers’ privacy, earn their trust, gain access to more data, and ultimately improve your products.”
As noted on its website, Georgian is also planning to launch software in two other areas:
1) “Accelerated AI learning — while Epsilon brings privacy guarantees and enables data and ML model aggregation, benefiting from existing data or ML models requires incorporating research from the area of transfer learning, i.e., transferring information from one ML model to another. We plan to release software in this area later this year. We are also exploring data generative approaches, to reduce the dependency on real data when training ML models.”
2) “AI transparency — in most industries, customers require some form of explanation as to how ML model predictions and recommendations are generated. Regulation is also a driver. We are investigating areas such as interpretability, fairness, and bias, to help our companies increase the adoption of their AI solutions and meet regulatory requirements.”
Hatcher is a globally-focused early-stage fund based in Singapore. The firm has developed proprietary data analytics and software platform, which it licenses to other venture firm, accelerators, family offices, and other investors in exchange for access to their dealflow.
The firms and its partners enjoy the benefits of software/data-driven approach in 3 main areas:
- Pre-screening — The company uses mainly rules-based systems (“there’s a bit of AI used during this stage, but not much”) to determine the quality and completeness of the applications for funding it receives. This first level of evaluation is called Hatcher+ Quality Score
- Investment evaluation — If an application meets Hatcher’s quality standards, the AI-powered evaluation platform takes over. Feeding on data derived from 450k venture investments across 20years, Hatcher’s machine learning algorithms provide a so-called Hatcher+ Opportunity score — an evaluation of the business itself.
- Streamlining of operations — the company has also developed a set of tools to help with everything from deal sourcing, portfolio management, performance metric monitoring, and demo day investor analysis.
The firm aspires to become one of the most active venture investors on the planet, deploying its USD 125M fund across 1300 investments in 3 years, thus keeping a pace of almost one investment a day.
Hone Capital, the VC arm of Chinese PE firm CSC Group, became Silicon Valley’s most active seed investor of 2017 (excluding accelerators such YC, 500 and SOSV), despite being founded just two years earlier.
This quick ascent to stardom was enabled by two driving forces of today’s venture world — Angelist and machine learning. Hone, in its own words, “hacked” its way into a somewhat clubby SV venture world, by establishing a strategic partnership with Angelist which gained them unfettered access to thousands of deals flowing through the platform.
To filter through this massive volume of potential investments, the firm has created a machine-learning model feeding on a database of more than 30.000 deals from the last decade. For each of the deals, Hone has looked whether the team made it to series A and explored over 400 distinct characteristics, such as founding team background, syndicate’s lead area of expertise, total money raised, etc. and distilled 20 most predictive of future success, which were later used as a filter through which new opportunities were evaluated.
How effective was this strategy? It’s too early to say for sure, but the initial results seem positive. The firm claims, 50% of its seed stage deals have led to follow-on investments (far above the industry average), and Hone estimates its unrealized returns put it among the top 20% of investors.
Initialized Capital, a USD 225M fund run by Reddit’s Founder Alexis Ohanian and YC Partner Gary Tan, treats software as a pillar of building its competitive advantage.
Drawing upon Tan’s experience building Y Combinator’s internal CRM/ Quora/ Linkedin hybrid called Bookface, often described as YC’s greatest value add, the firm has developed its internal software which by, their own words, helps it to “run the operations of the fund in a better, cheaper and faster way — just like we’d expect of any startup we invest in.”
At the heart of it lies a custom-built CRM, which also serves as a voting tool. All the investment opportunities are logged into the system and then voted upon by the firm’s nine investors digitally, and blindly, to avoid bias. Voters get a multiplier for domain expertise, and a strong yes counts four times as much as a weak one. To get funded, a startup needs to get to the equivalent of two definite yeses of the nine votes.
On the portfolio support side, the software is an upgraded version of YC’s Bookface. It provides founders Initialized backed with an easy way to connect with each other- if anyone has a question or needs an introduction they can post the request on the platform and easily get help from its peers or the firm.
Initialized has also publicly revealed plans to roll out software that will help automate some of its investment decisions. Unfortunately, there’s not much information on the progress in this endeavor.
Inreach Ventures is a London-based early stage-firm which pitches itself as the “AI-powered VC firm.”
The firm has reportedly spent around USD 3,5M developing a proprietary dealflow platform referred internally as DIG, which tracks a plethora of points to discover and evaluate the most promising startups in Europe.
According to Roberto Bonanzinga, Inreach founding partner, DIG constitutes of three layers: data, intelligence, and workflow.
“The data layer is a mix of massive data aggregation, with deep data enhancement, including the generation of a large set of original data,” he said. “The intelligence layer makes sense of these millions of data points through an ensemble of machine learning algorithms, ranging in complexity from simple rules to advanced networks. Given this data-driven approach and the significant deal-flow this generates, we invest heavily in building a workflow product that allows us to efficiently process thousands of companies each month.” — said Bonanzinga
Thanks to DIG, the firm can analyze around 2500 startups every month, an order of magnitude more than traditional venture firms.
Kima is one of the world’s most active early-stage investors, investing in 2 to 3 startups a week across all sectors and geographies, despite having only 3 investment team members.
To be able to handle this massive investment volume, the firm has invested significant resources into custom development of several custom tools, including:
1) Dealflow watcher — as the firm describes it, “Tinder for dealfow”
2) Payment & digital signature tool
3) Accounting tool — allows the firm to track and regularly update information about all the 700+ companies in the portfolio
4) Portfolio management software dubbed “Kima Forward”
5) Networking/ community tool — similar to Daphni’s tool
6) “Kima Status” — a tool allowing any startup that applies for funding to track where they are in the pipeline in real-time
Nauta is a pan-European investor in b2b companies at late-seed and series A stages. As self-reported by Guillem Sague, Nauta’s partner in his medium post from July 2017 the firm aspires to build:
“The dealflow engine: A system that will automatically gather, enrich, analyze, and prioritize a vast amount of data of potential investment opportunities from a number of sources. Using a clear set of criteria that needs to be very carefully chosen, the engine will come up with a short list of potential investment opportunities to start tracking or to reach out to — This is probably the low hanging fruit because the hurdle is low and the ROI is quasi-immediate.
The predictive engine: A system that can assess the probability of a potential investment being successful using predictive models (note that “success” needs to be quantified, which is not obvious in VC). For that, the system has to be able to identify causality (not only correlations) between successful companies and attributes or factors that can be spotted at the time of investing, the latter being a condition sine qua non for this machine to be of any use. This is a big challenge because of the long feedback cycles in VC — It takes 3 to 5 years to find out if an investment is a success.
The dynamic reserves planner: Using an evolution of the predictive engine, Monte Carlo simulations and basic VC rules, the engine should be able to calculate the optimal distribution of reserves for follow-on investments and plan capital calls accordingly. You can think of it as an inventory optimization platform as used in the agile retail but applied to VC.”
NFX, a San Francisco based seed & series A fund investing in companies with network-effects, which just recently announced its sophomore USD 275M fund has from day differentiated itself from the competition by the use of proprietary software and data analytics to power every step of venture’s value chain — from originating investments, through picking winners all the way to supporting portfolio companies in fundraising, recruitment, and knowledge transfer.
One of the biggest problems the firm set out to fix is the fundraising process itself, which it describes as “medieval.” To help take this process to the XXI century, the firm has developed a tool called “Signal” — a publicly available matchmaking platform for startups and investors. Signal allows entrepreneurs to easily create a list of investors best suited to their companies’ needs, and, what’s even more important, find the best intro path to chosen investors by analyzing respective entrepreneurs social graph.
Project A, Berlin’s self-dubbed “Operational VC” with EUR 260M employs over 100 experts who work for its portfolio companies.
Since the early days, data has been the core area of focus for the firm. Apart from providing its portfolio companies with on-demand advisorship in this space, the firm often goes as far as operationally helping the companies set up the infrastructure needed to run truly data-driven businesses.
At some point, the firm has even decided to open source it’s business intelligence infrastructure called Mara to the public. The centerpiece of the software, essentially a library for integrating a business’s data into what is known as a data warehouse, has been released via Github github.com/mara
Quake Capital is a seed-stage investor from New York investing across a wide range of industries. Mamoon Ismail Khalid, ex-associate at Quake Capital in New York City, described the firm’s deal sourcing approach as follows:
“We use a new Web Interface to collect information from our applicants in a standardized manner. The goal is to intelligently structure the information collected and combine it with additional information scraped from the web and social media. We store it in a logically indexed manner and apply Predictive Analytics on companies’ metadata. Our proprietary data science tool cumulatively looks at 100+ factors within six macroscopic categories: Team, Financial Performance, Customer and PR traction, Industry and Competition, Product and Brand value, Valuation, and Exit probability, to quantify an investment “Merit Score”. We are taking a mathematical (weighted coefficients estimation, probabilistic modeling, etc), human-system hybrid approach to early-stage investing through this tool. We believe this way, we can extrapolate insights for our particular business model (portfolio operator VC fund) previously missed out.”
Right Side Capital Management
San Francisco-based Right Side Capital Management has been one of the first venture firms to fully adopt an investment strategy of heavy portfolio diversification. Instead of trying to focus on the right industry vertical, pick winners and beat the odds, RSCM makes hundreds of small pre-seed investments of between $100,000 and $500,000 to offset the high assumed failure rate.
Since its founding in 2012, the firm has invested in more than 800 startups following this strategy, mostly without meeting founders face to face prior to the investment.
To be able to manage this kind of investment pace and volume, Right Side relies on its custom-built software built to streamline operations and semi-automate investment decisions.
Medium profile: Follow: Kevin Dick
Scale Venture Partners
Scale Venture Partners are the masterminds behind Scale Studio, a benchmarking platform for cloud-based startups.
Scale studio allows startups to compare their performance against a collection of comparable cloud companies to get a sense of how well they are performing relative to the market across metrics like growth efficiency, customer retention, and operations. For example, it allows entrepreneurs to see how well do they stack up against the Mendoza Line (acceptable growth rate for a SaaS company looking for venture funding) or far off are they from hitting the desired Magic Number (a common metric showing how efficiently Software-as-a-Service firm is growing its recurring revenue compared with sales and marketing spendings). For the firm, Scale Studio is a dealflow generation tool.
The data powering scale studio is a proprietary mix of cloud company quarterly reports and other data sources collected by the firm over the years.
Social Capital, once the hottest venture firm in the Valley which recently made headlines by a wave of high-profile departures, has always been known to be pushing limits of what is possible to achieve with data.
The firm’s most daring endeavor into quantitative investing is dubbed “capital-as-a-service” — an initiative which allows Social Capital to invest in startups from all over the world in a fully automated manner without ever meeting face to face.
As described by Techcrunch:
“Entrepreneurs from anywhere in the world can fill out a questionnaire, then submit to Social Capital revenue figures and either raw engagement or transaction logs (or both), including sometimes by granting the firm direct access to the cloud services they use. It’s entirely self-serve. If Social Capital likes what it sees, it will write a check of up to $250,000. If it doesn’t, it will at least deliver feedback to the startup regarding tweaks it might make to its business model.”
The evaluation is conducted by what the firm calls “the magic 8-ball” — a set of proprietary tools and algorithms built to quantitatively assess the if a given company has reached product-market fit, how well does it stack up on core metrics against industry benchmarks, what are its biggest drivers of growth, which areas are lagging behind, and a bunch of others.
The firm has also built a dedicated internal software infrastructure to help its portfolio companies better handle performance marketing & data analytics.
According to the latest unconfirmed reports, Social Capital plans to roll out CaaS as a standalone product to help other VCs with quantitative diligence.
Given Tribes’ roots, it should not come as a surprise, the firm follows an extremely data-driven regime across the whole value chain of operations, from automated sourcing and prescreening, through custom syndication tools to real-time portfolio monitoring — it’s all here.
One of the most distinctive tools Tribe uses is “the 8-ball” — an automated diligence tool Tribe uses both to assess potential new investments as well as to help the portfolio companies. Analyzing raw transactional data provided by the startups through the lens of growth accounting and cohort behavior, 8-ball allows to quantitatively (!) measure product-market fit and growth trajectory.
“The income statement and balance sheet are the lingua franca for an established company to communicate the financial health of its business,” Hsu writes. “These accounting concepts are often unhelpful when inspecting an unprofitable early-stage company. For a startup, what’s needed is a common quantitative language for what matters, namely, a quantitative framework for assessing product-market fit.” — said Hsu.
This article is an absolute must-read on the subject.
So far, Magic 8-Ball has poured through data provided by some 200 companies, with plans to hit 1,000 per year. Tribe’s 8-Ball tool is said to be much more complex than the Social Capital’s version, according to a source with knowledge of the platform.
Ulu Ventures is an early stage investor in enterprise cloud and smart data startups using Decision Analysis (wiki) to inform both their individual investment decisions and overall investment strategy.
Their decision analysis framework consists of 6 steps:
- Qualitative sorting
- Creation of market maps
- Risk assessment
- Quantification of uncertainties
- Risk/return calculation
What does it look like in practice? Clint Korver, Ulu’s partner, describes:
“First, we do a market-mapping session with entrepreneurs. We sit down and create a graphical picture of their target market, competition, how it will change over time, etc. — essentially, we quantify everything. We also have a decision model we use internally that accounts for things such as the company’s market share, revenue potential, team risk, and more. We end up with a weighted multiple on the investment capital, which we use to help determine whether and how much to invest.”
Venture Science, led by Matt Oguz, has been one of the very first quant-oriented firms in the world. Investing across stages, the firm deploys quantitative methods in both its selection and capital deployment processes. To define its investments, the firm has built a scoring system, which uses AI and decision theory to assess multiple attributes of every opportunity and try to determine risk levels associated with those. Some of those attributes are: Team Completeness, Stage of Product, Vision, Stage of Company, Proximity to Tech Centers, Autonomy, Global Applicability, Market Size, Traction & Growth, Mobility, Sales Funnels, Margins, Legal and Regulatory Risk, Intellectual Property.
Medium profile: n/a Follow: Matt Oguz
WR Hambrecht Ventures
WR Hambrecht Ventures is the VC arm of the IPO specialist WR Hambrecht and Company. The firm employs an investment strategy that combines predictive modeling and Clayton Christensen’s disruption theory.
The firm employs a hybrid strategy combining human and mechanical processes towards choosing investments, rather than a 100% algorithmic approach.
If the data science says “no,” there’s no deal. If the algorithms say “yes” there’s a second layer of human screening.
That yes or no depends on many factors, that fall into two categories: those inside the startup, and those external to the startup. “We’ve found only around 20% of the predictive value to come from details specific to the startup itself (e.g., the team),” said Thomas Thurston- CTO and Partner at WR Hambrecht Ventures, “whereas 80% comes from things outside of the startup,” which he listed as the market, customers, competitors, technology trends, and timing. The model is also designed to be dynamic rather than static: “we care more about how things are likely to change, rather than how things are today,” he says.
The model utilized by WR Hambrecht has reportedly been accurate on 67% of their predictions, and the funds are estimated to achieve returns over 500% based on subsequent offers over their portfolio companies.
Medium profile: n/a Follow: none on medium
Y Combinator, the world’s premier startup accelerator, has been using data and custom built software since its early days.
Part of the YC software team’s efforts involves using AI to automate some of its processes. Using a data set of over 100,000 applications that YC has tracked over the years, the YC software team has built an AI it calls HAL to help screen applications. “HAL reads applications, and he votes on them, just like humans do,” says Friedman. “And he has saved a great deal of human hours.”
The firm has also developed proprietary software to support its portfolio companies dubbed Bookface, Bookface is the platform founders use to connect to one another resembling a combination of Facebook, Quora, and LinkedIn. Each founder has a profile and can tag themselves as an expert in any topic. If anyone has a question, needs an introduction, or wants to poll for knowledge, they can post the request to the forum on Bookface and get help from its peers.
500Startups, one of the best-known accelerators/early stage funds in the world ,has dropped hints about its use of data in sourcing and evaluation of deals since 2012. Unfortunately, there’s not much information in the public domain to understand the inner workings of the firm in this aspect.
The firm has, however, on multiple occasions, mentioned the software it has developed to streamline its internal operations — from fund accounting, through portfolio management, fundraising tracker, valuation database to CRM.
Accelerated Digital Ventures is another twist on the traditional venture model. The firm, branding itself as a “patient capital engine” employs an evergreen fund structure, which allows them not to be constrained by typical 10year investment horizon.
ADV is the mastermind behind https://www.venturemarket.org/ a matchmaking platform for startups and investors, designed to increase transparency and reduce the time taken needed to raise funding, and, of course, generate dealflow for the firm. The platform, currently in beta, is being developed with the help of some of the leading UK-investors such as Notion, Seedcamp, or Octopus.
Medium profile: n/a Follow: David Fogel
Akkadian is an SF-based investment firm focused on acquiring secondary stakes in growth stage companies.
As reported by OutsideInsight “Akkadian Ventures’ data-driven diligence software tracks 14,000 companies that fit their investment criteria — more than $20 million in revenue and growing between 75% and 100% per year — to understand the development of the hottest Silicon Valley startups and determine which companies to pre-approve for investment.”
Medium profile: n/a Follow: none on Medium
Amaranthine / Websummit
Amaranthine is a USD 50M fund launched by the creators of Websummit — one of the largest and most well-known European tech conferences.
According to sources familiar with the matter, the fund the new fund will use the massive amount of information it gathers on the thousands of startups and attendees who come through the event to support its investment decisions, such as which startups have attracted more attention at Web Summit and what trends are worth consideration.
AstoryaVC is a European early-stage fund investing in insurance technologies. The firm is known to have developed an in-house deal sourcing solution, which spotted more than 2500 potential investments within 12 months of operations.
Clear Ventures is a Palo-Alto based venture focused on companies which, in its own words, are aligned with Industry 4.0 or the fourth industrial revolution (a combination of AI, IoT, Cloud, Big Data, 5G, AR/VR, robotics, and autonomous vehicles)
According to the press release announcing the firm’s sophomore USD 180M, the firm has developed a software tool called “Clear Ecosystem Advantage”, which uses data science and automation to aggregate data from many sources and allows the entrepreneurs to take advantage of the relationships the firm has with customers, talent and investors.
E.ventures, formerly BV Capital, is a global venture firm with more than 30 years experience in the game. The firm’s efforts with data-driven investing, spearheaded by San Francisco-based partner Tom Gieselmann, head back to 2010.
One of the firm’s projects in this space is a publicly-available dashboard tracking traffic of venture-backed startups, which can be found here: https://dailygieselmann.com .
Another effort, this time on the community building side, comes from the German wing of E.ventures, which has founded an initiative called BuildersNetwork (www.buildersnetwork.co) — an uber-exclusive online community focused on network and knowledge sharing for Founders, VCs, and Angels.
First Round Capital
First Round, perhaps one of the best-known seed stage firms worldwide, has been one of the driving forces of the community/platform movement in the venture world. As part of its efforts to help connect various stakeholders (founders they invested in, portfolio companies employees, advisors, experts, etc) in its ecosystem, the firm has built multiple pieces of custom software, including : an online platform dubbed by some “a private Quora” where its portfolio entrepreneurs can share learning lessons, intelligence (compensation, service providers, option pool size, hiring techniques) directly with each other or a now-defunct platform to boost press coverage called HackPR.
Flight Ventures, founded by Gil Penchina, was one of the first funds built by leveraging Angelist syndicates. Currently operating no less than 25 syndicates, which already had five USD 1+ B exits, Flight is a poster boy of what can be achieved through Angelist. To support its growing volume of investments in a more systematic Flight has developed a (now defunct) tool called Angelmob which main purpose was to allow startups to easily request help from the ever-growing network of Flight syndicate backers.
Medium profile: n/a Follow: Gil Penchina
Force Over Mass
Force Over Mass is a London-based early-stage investment firm, often described as a missing link between crowdfunding and venture capital.
To foster co-investments, the Force Over Mass has created an online portal which allows the fund’s LP’s to easily review deals currently in the pipeline and easily participate in them. The portal can also provide LPs with real-time insights into the fund’s performance.
Hof Capital, a New York originated venture firm, has built a proprietary tool to help them with both sourcing and pre-screening deals. Some of the characteristics used by the firm to filter out deals include whether the founders of a company previously worked at a successful tech company; whether a company has a certain number of PhDs on the team; and if team members studied at a tier 1 university.
Kleiner Perkins, another legendary firm in this listing, has built the first iteration of its internal system called Dragnet back in 2010. The application was initially developed to ingest tweets and the mentions of the top 1,000 Klout influencers in startups and technology. Dragnet was basically charting which startups and companies were getting mentioned by influential people. The firm later added App Store data, Google Play Store data, Facebook platform information, AngelList startups, and much more, allowing the firm to proactively source deals — explained in 2013 now ex-partner Chi-Hua Chien. Since then, the company has kept an extremely low profile about its efforts in this space.
Labx Ventures, a San Diego-based investing firm, is championing a strategy driven by pure science, algorithms, and rules engines called “ Venture Science Capital.”
The firm describes Venture Science as “a new, multi-disciplinary field that uses valid scientific methodologies to make objective and unbiased analysis of business practices and outcomes. This field uses advanced tools of venture capital analytics, pattern-recognition, predictive algorithms, information science, artificial intelligence and machine learning to uncover evidence and develop theories about business, venture, enterprise, capitalism and how they affect start-up and emerging growth companies. Venture Science examines business core strategies and how and why deals are selected, made, qualified, and executed.”
The firm claims it was among the very first firms to have been subjected to double-blind testing, including a published major university study that showed their assessor tool was over 90% accurate in predicting successful exits.
Medium profile: n/a Follow: none on Medium
Menlo Ventures is a multistage US venture firm focused on consumer, enterprise, and life sciences technology companies. The firm has developed its proprietary software called Menlo Signals, which is used both as a sourcing engine as well as a benchmarking tool. Its model tracks a range of metrics from daily and monthly users to chart position, page views, and appearances across the web.
Lightspeed Venture Partners
Lightspeed Venture Partners is a multi-billion dollar global multi-stage venture firm. The firm has recently become increasingly vocal about its use of data to drive investment decisions, and even poached Signalifire’s ex-Head of Data Jerry Ve to further boost its capabilities in this area.
Rocketship.vc is an early stage venture capital firm from San Francisco, which, in its own words, invests in companies using models built through data science. At the core of its efforts is a proprietary algorithm named “Escape Velocity,” which identifies companies with “Sustainable Growth Engine” at their core.
Rocketship’s software tracks over ten million companies all over the world by updating more than a million data points every day. To date, the firm has reportedly accumulated more than 35 terabytes of data on the startup ecosystem.
The firm claims its data-driven approach allowed it to build a portfolio of over 35 companies all over the world (inc. the US, Europe, India, Brazil), which performance compares favorably with that of the top 20 VC firms in Silicon Valley.
Since 2015, the firm has been developing its proprietary software which allows them to ingest a variety of data feeds, filter and apply themes against these data sets, and use various calculations to determine potential opportunities quality. The firm says it has decided to develop their proprietary software instead of using a data provider such as Mattermark to be able to develop a unique scoring system that isn’t limited to a single dataset.
The software has, reportedly, enabled them to approach several deals that they may have missed using traditional networking approaches.
Redstone is a Berlin-based pan-European venture firm working with multiple corporate venture funds in a VC-as-a-service model, which aims to adopt a data-driven approach towards investment sourcing and analysis.
The company is developing technologies that help them invest based on evidence and on quantitative understanding of innovation, including tools to help them create reports and monitor signals from both structured and unstructured data sources.
The Family is a hybrid accelerator / fund / advisory shop/ fellowship/ company builder/ you-name-it on a mission to support European entrepreneurs. With community building as core of their activity, it shouldn’t come as a surprise, the firm is investing heavily in software infrastructure making connections between various stakeholders of its ecosystem easier. To support its goals, the firm is using a combination of in-house developed matchmaking tools and Slack.
Unusual, a USD 160M seed-stage enterprise-focused firm, a brainchild of AppDynamics’ Founder Jyoti Bansal and first investor John Vrionis has reportedly been working on a proprietary platform to help its entrepreneurs pool information and access firm resources called Get Ahead.
Venturerock is an Amsterdam-based investment fund and company builder taking a radical approach towards redefining venture capital model through the use of software, tokenization, and a set of distinctive investment strategies.
The firm claims that thanks to its proprietary software platform, it was able to digitize and standardize the entire investment process. The firm has reportedly also built a tool to allow its portfolio companies to easily connect to its extended network of experts and advisors.
Medium profile: n/a Follow: Marc Wesselink
A16Z, the firm that has forever changed the venture game, is not very vocal about its use of data or the software it builds to support its entrepreneurs. The firm has been known to hire multiple engineers and data scientists over the years, and mentioned “proprietary data sources” in some of the publicly available materials, but details on the subject are impossible to come by without insider knowledge.
Atomico, founded by the co-founder of Skype Niklas Zennström, is a venture firm behind Europe’s largest standalone fund — USD 765M.
Similarly to other large, established funds like Sequoia, NEA in the US or Accel in Europe, Atomico keeps details about how it operates under wraps. The only public notion about Atomico’s efforts in the data/software space can be found in the bio of its Engineering Lead Chris Savvopoulos:
“Chris is Engineering Lead at Atomico and the energy behind our effort to bring technology to venture capital. Using modern engineering and data science, Chris’s team is building a platform that allows Atomico to be efficient and productive in discovering and being responsive to great companies & founders.”
Balderton Capital, a leading series-A investor in Europe, has dropped hints about its data-centric approach since early 2014.
In an article for VentureBeat from this period, Balderton described its quite novel (at the time) approach towards data — the firm used it to assist its partners to give better feedback to their portfolio companies based on the collective experiences of other startups in the firm’s portfolio.
Judging by the job offer for “research associate” the firm has recently posted, Balderton is also building a data-driven deal sourcing engine — one of the responsibilities of the prospective hire was stated as: “Compiling insights from public and private datasets, developing benchmarks from the performance of companies and using data signals to identify new opportunities”
Bessemer Venture Partners
Bessemer, a Bay Area venture firm with more than USD 4 Billion under management, has been one of the very first firms worldwide to implement the element of data in its sourcing operation.
As noted in this Forbes article by Alex Konrad the firm had built its first in-house tracking systems back when Chris Farmer (currently CEO of Signalfire) had been working there, which is between September 2005 and May 2009. Although the firm has publicly acknowledged employing data scientists and software engineers in the following years, information about the specific techniques or approaches the firm uses in its data-related remains hidden from the public domain.
Draper Fisher Jurvetson
DFJ, a legendary American venture firm, as most of the most esteemed players, keeps a relatively low-profile about the specifics of its operations. One of the very few notions about its data efforts comes from a Techcrunch article from 2013, saying:
“The firm analyzes its financial and investment data to determine what has brought the most return, helping partners figure out which companies perform best. DFJ has also mined data to support specific sectors, rounds of funding, investment size, and a team headcount that has performed best and then targets startups based on this profile.”
First Mark Capital
First Mark Capital, NYC investor in breakout successes such as Airbnb, Shopify and Pinterest leverages proprietary software to track the impact they have on the companies they backed, and to measure the performance of their investment portfolio.
Floodgate, previously known as Maples, is a Palo Alto-based fund investing across sectors and stages. Although the firm has publicly acknowledged using data mining techniques to uncover the common characteristics of the most successful companies back in 2013, the status and scope of its current efforts in the data space remain unknown.
Fyrfly is a Menlo Park, CA-based micro-VC investing in companies driving competitive advantage through data and analytics, which claims being data-driven is their number one principle.
General Catalyst, one of best known US venture firms, which recently made headlines by announcing a strategic move into seed stage investing, has reportedly been working on data-assisted investing since the beginning of the decade. One of the people responsible for establishing this data-driven approach across the firm was Chris Farmer, who left the firm in 2013 to build out one of today’s most quantitative venture firms — Signalfire.
Greycroft, an LA and NY based firm focused on investment in the Internet, and mobile markets are one of the most vocal critics of fully-automated venture strategies. The firm, however, publicly admits it sees the potential in using data to allow human investors to make better decisions and has been known to invest significant resources into its data team to help build out this capacity.
Greylock, one of the iconic SV firms, is one of the pioneers of the data-driven movement. The first publicly available notions of the firm’s usage of data towards origination and evaluation of investments head back to 2013, when Tom Frangione, Greylock’s ex-COO told Techcrunch that the firm was starting to build its internal data analysis tools. Since then, Greycroft has been known to hire data scientists and engineers, but never went into any detail about what they actually did.
Gradient Ventures is Google’s new AI-focused venture fund — investing in and connecting early-stage startups with Google’s resources, innovation, and technical leadership in artificial intelligence. Although no official confirmation is unavailable, it’s reasonable to suspect the firm uses follows the same data-driven regime as its larger brother GV.
GV (formerly Google Ventures)
Since its founding in 2009, GV has been at the very forefront of the data-driven investment movement, which really shouldn’t come as a surprise given its mother organization analytical firepower: “We have access to the world’s largest datasets you can imagine, our cloud computing infrastructure is the biggest ever. It would be foolish just to go out and make gut investments” — said famously, Bill Maris, GV founder & CEO.
Unfortunately, the firm does not disclose any information about the specific techniques or approaches it uses.
Hummingbird Ventures is an early stage investor from Belgium. The firm has been experimenting with analyzing signal data (data collected via third-party sources. For example; web traffic, funding, app downloads, reviews, employee data, or revenue) to inform its investment sourcing efforts.
IA Ventures is a fintech-focused early stage venture firm from New York. The firm has been widely rumored to amplify its sourcing and evaluation capabilities using data-science, and it has publicly acknowledged hiring data people in the past. Unfortunately, there’s hardly any information available on the specific approaches they follow.
Index, a London-based powerhouse needing no introduction, has always been quite stealthy about the way it operates. The only notion of the firm’s data-driven deal sourcing practices comes from an ex-employee Imhram Ghory (founder of the aforementioned Blossom Capital) who claims he built such capability at Index back in 2012. As mentioned by Ghory “It was a case of identifying “unicorn indicators” — great team, best-in-class product, strength in hiring — and then building a data-science model that incorporated those.[Working with data] helps identify the stars who aren’t on the radar.”
Insight Partners is a venture capital and private equity firm investing in growth stage companies based in New York, USA. The only notion about the firm’s usage of data comes from a recent article for Televisor.co.uk, in which one of the firm’s partners — Deven Parekh, said that data informs a large part of how his firm invests.
Lux Capital is a venture firm based in NYC and Menlo Park investing in counter-conventional, early-stage science and tech ventures, with $1.4B AUM across five funds.
According to sources familiar with the matter, the firm has invested significant resources in its software stack and has publicly acknowledged hiring software engineers and data scientists, yet unfortunately, specifics about its undertakings are nowhere to be found in the public domain.
Montante Ventures is a Bangalore-based fund that only recently began experimenting with implementing more data-driven approaches towards investing, mostly focusing on the origination front.
Next View Ventures
Next View, a Boston-based seed stage firm focused on “Everyday Economy” has publicly acknowledged using data mining software and quantitative analysis to evaluate investments already back in 2013. From that point, onwards — absolute silence.
Omers Ventures is a venture capital arm of Canadian pension fund Omers. The firm has recently expanded to Europe by launching a dedicate USD 300m fund focused on European series A & B startups.
According to the interview Harry Briggs (Omer’s head of European investments) gave to Techcrunch on the European fund’s launch, the firm is already working on developing technology utilizing data to source new investment opportunities, and views being data-driven as a major competitive advantage. No further details are available at this point.
OpenView Venture Partners
Open View is a global late-stage investor focused on software. The firm first mentioned using data analytics to build competitive advantage back in 2012 but has been suspiciously quiet about the subject afterward.
Sequoia is a firm which needs little introduction. One of the very few public acknowledgments of its data-related efforts can be traced back to a NY Times article from 2013 titled Google Ventures Stresses Science of Deal, Not Art of the Deal”. Notions of “programmatic sourcing systems to improve dealflow” and “tools to confer a competitive advantage in deal sourcing” can also be found on the website of web-agency responsible for the design of Sequoia’s now-defunct community site “Grove” (www.lablablab.com/sequoia)
Sequoia has also sporadically disclosed to employ data scientists, but never explained in what their efforts translate.
SOSV is one of the most active seed investors in the world, funding more than 150 startups every year, through their vertical accelerator programs: HAX, IndieBio, RebelBio, Chinaccelerator, FOOD-X, and more. The firm has developed a plethora of internal tools aimed at matchmaking, knowledge sharing, and streamlining operations.
Sunstone is a growth-stage fund focused on cloud, enterprise, healthcare, marketing tech, and security, based in San Mateo, California. According to several different sources, the firm has been using machine learning and data analytics to inform its investment process. Unfortunately, no further details could be obtained at the time of writing this article.
The San Francisco -based firm has hinted to using predictive and analytical approach towards both origination and evaluation of investments in the past. Unfortunately, there’s hardly any publicly available information on the details.
Two Sigma Ventures
Two Sigma Ventures is a venture arm of an esteemed quant hedge fund Two Sigma, which invests across industries in data-driven businesses. The firm publicly acknowledged building automated systems to source and evaluate new investment opportunities, but unfortunately, there is no further info about the specific approaches it uses.
Union Square Ventures
Union Square Ventures is one of the best performing funds in the world, with its 2004 vintage fund returning an astounding 13,91x cash-on-cash return. According to David Teten of Hof Capital, the firm has invested heavily into its technology stack. Unfortunately, there’s hardly any data publicly available to verify and expand on this claim.
Massive thanks to all the authors who made this research possible. I am forever grateful for your work.
Zoe Bernard, Jaewon Kang, Auren Hoffman, Leena K. Rao, Lizette Chapman, Connie Loizos, michael byrne, danprimack, Stefano Bernardi, Ravijot Singh Narang, Matt Turck, Anthony Mirhaydari, Kate Clark, Heather Hartnett, Francesco Corea, Arturo Moreno, Alfonso Palomero, Fabien Durand, Marcin Szelag, Louis Coppey, Evelyn Rusli, rob go, Polina Marinova, Christina Farr, Dominik Vacikar, Chance Barnett, Maija Palmer, Trevor Clawson, Ajay Saini, Jacob Kostecki, George Anders, Rob Wile, Steve O’Hear, Murray Newlands, Tyler Hayes, David Siegel, Joshua Brustein, Ashley Carroll, Fitz Tepper, Harry Briggs, Sanjiv Soni, Owen Thomas, Andrew Li, Nabeel Hyatt, Erin Griffith, Arlo Gilbert, Konstantin Schmeisser, Tiernan Ray, Ciara Byrne, Mo Aldalou, Nick Moran, Tomio Geron, Kelvin Yu, Harry Stebbings, Hadley Harris, nihal mehta, Vic Singh, Tim Young, Paul Sawers, Chris O’Brien, Andrii Degeler, Jim Edwards, Miruna Girtu, Jonathan Shieber, Reza Chowdhury, David Swan, Benjamin Romano, Sam Shead, Danielle Morrill, Mashael Makhadmi, William McQuillan, Edmund Ingham, Robin Wauters, Kim-Mai Cutler, Thomas Ohr, Yoav Vilner, Jack Ellis, Vincent Jacobs, Steven Bertoni, Taylor Soper, Hana Yang, David Spinks, Elizabeth“Beezer”Clarkson, Ryan Caldbeck, Amr Shady, Josh Constine, Jeff Chavez, Jessica Leber, Michael J. Coren, Erik Torenberg, Yuliya Chernova, Melia Robinson, Jason Heltzer, Luke Thomson, Steven Levy, Eze Vidra and many more.
As promised earlier, here’s the knowledge base:
I hope that this list will inspire current and future managers to bring much-needed innovation to the VC landscape.
This article is the first piece of a longer-term project I am pursuing to better understand how to fix venture capital. If you’re also exploring this space, I’d love to chat. You can find me here on Medium, on Linkedin, and Twiter.