Data-driven Fund Management Techniques
Forecasting, Scenario Analysis, Reserve Planning & More
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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My first episode of Data-driven VC had the title “Why VC is broken and where to start fixing it”. The most interesting takeaway is that around 2/3 of the value in VC is created in the sourcing and screening stages. Said differently, VC is a finding and picking the winners game.
Practical experience seems to nicely align with the literature as most Data-driven VCs started leveraging technology exactly in this sequence: top-down through the funnel. Last year, we were finally able to support this qualitative assumption through actual data from the “Data-driven VC Landscape 2023”.
On this note, we’re about to conclude the data collection for the 2024 edition of the landscape, but preliminary results from 200+ submissions clearly show that the adoption of data-driven initiatives continues to propagate through the value chain, now presenting more use cases for due diligence, portfolio value creation, but also back office functions.
Today, I’m excited to have Anubhav Srivastava, Founder and CEO of Tactyc contribute a guest post to explore what data-driven approaches beyond sourcing, screening, and due diligence can look like.
Thank you Anubhav for sharing how modern investors can leverage data-driven approaches across fund management functions below.
The Data-Driven Fund Manager
The “data-driven” venture fund manager is a term that has gained popularity in recent years. The rise of AI algorithms, data science, and access to larger datasets have increased the quantitative rigor at traditional venture funds. But what does it really mean to be “data-driven” when it comes to portfolio management and planning?
There is a school of thought, that to be data-driven, a fund manager must
Have access to proprietary data
Run complex algorithms or simulations (i.e. Monte Carlo simulations)
At Tactyc, we work with hundreds of data-driven managers globally in helping them with portfolio construction, reserve planning, and portfolio management functions. In our work, we have consistently seen the same common patterns and workflows in our best-performing managers.
Interestingly, we’ve concluded that being “data-driven” doesn’t necessarily mean the most sophisticated algorithms, custom-built software, or complicated Monte Carlo simulations. Instead, the most data-driven investors mostly work with simple quantitative methods - but follow them in a disciplined manner across all their processes, repeatedly and consistently.
In this post, we’ll summarize the most common patterns and workflows that we see successful data-driven managers employ.
Maintaining a Forecast Post-Close
Most managers build a portfolio construction model when raising capital, but very few actually maintain a forward-looking model of their fund after deploying capital. In fact, most portfolio constructions never get opened after the fund is launched. A current forecast is a live forward-looking view of fund performance that takes into account actual portfolio companies.
The fund’s current forecast is built by combining the following:
expected performance of actual investments and their reserves. This is typically done by forecasting upside, downside and base exit scenarios for each portfolio company
expected performance of undeployed capital. This is usually assumed to be the same performance as the original construction plan
Course Correction
Once the manager starts building and maintaining a current forecast for the fund, they can answer the following questions:
Pacing: Are we on track in capital deployed? On the number of portfolio companies in total and by stage?
Changing Market Conditions: Are valuations and round sizes meaningfully different than when we launched our fund and built our original construction plan? If so, how should our allocations or check sizes change in response to the market?
Investment Terms and Reserves: Are our actual investment terms meaningfully different from our original assumptions? Are we getting our target ownership in each company? If not, what can we change going forward?
These questions are typically asked every 6 months or each quarter in deep internal reviews and allow the investment team to course-correct should actual performance start to meaningfully deviate from projected returns.
The benefit of having a flexible current forecast model is that the manager can input new assumptions for strategies to apply on undeployed capital and immediately see the impact on returns. This allows the manager to understand what they need to execute to get back on track.
Risk-Weighted Scenario Analysis on Portfolio Companies
Over the life of your fund, your view of each portfolio company’s potential will continue to evolve and become more refined as they demonstrate product-market fit (or not), growth (or not), and a likelihood of exit. New competitors, changes in TAM, management team changes - these can all heavily influence exit outcomes.
Most data-driven managers we work with create the following scenarios for each deal: