Driving Carta’s Private Equity Expansion with Financial Modeling
Designed a financial modeling tool to help private equity companies understand complex equity distributions.
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Role
Product design lead responsible for design strategy and execution. Partnered with cross-functional team to define scope and a phased release approach.
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Team
1 PM
2 staff engineer / architects
1 front-end engineer
Business development team
Business Opportunity
Carta dominated the startup and venture capital market for equity management, but needed to diversify its revenue streams. The Private Equity (PE) market represented an untapped opportunity worth millions in potential revenue.
Private equity firms rely on scenario modeling — a financial analysis technique that projects different potential outcomes — to make investment decisions worth hundreds of millions of dollars. It’s a highly manual and time-consuming process, relying heavily on Excel spreadsheets.
Automating scenario modeling could be Carta's strategic entry point into private equity.
Research
Customer interviews revealed how PE firms performed scenario modeling today. Associates from the deal team — the team that supports financial planning — were responsible for creating and managing models and would be tasked with scheduled and ad hoc tasks that required equity calculation.
Exploration and Vision Setting
My explorations were designed to help our private equity customers and Carta leadership envision a future beyond just simplifying financial modeling. I wanted us to think about how we could build a more connected ecosystem and facilitate collaboration.
Tailoring Model Creation to End Goals
By asking users about their modeling goal upfront, I created workflows that only show relevant inputs and assumptions. This reduces cognitive load in an already complex process
Smart Scenarios
Instead of manually testing different equity values to find when performance conditions trigger, I created "Smart Scenarios" that automatically calculate these values.
Calculation Transparency
Deal team associates need to be able to check and verify why proceeds look the way they do. My idea was that just like in Excel, users could look up the “formula” or the calculation. The formula in this case give users information about which conditions were triggered based on the inputs provided.
Sensitivity analysis
I explored interactive data visualizations that allow deal associates to instantly see how different scenarios affect payouts.
Challenges and Strategic Pivot
It became evident during research that with every other financial tool living in Excel spreadsheets, adoption would be a big challenge. PE firms wanted hands-on experience with our product and prototypes disconnected from the real data did not suffice.
A Lightweight MVP
We stripped away all the "extras" and focused on creating the simplest version of the modeling tool that still delivered value — a true MVP. Deal Associates could run a single model for an investment company to see how the proceeds are distributed.
This approach provided PE firms with immediate value, giving deal associates a working product to test out their models on their own time.
Explain the Math
Distribution rules outlined in a company’s operation agreement dictate how profits are distributed amongst shareholders. Carta created a proprietary software language that could encode these rules.
PE associates wanted to be able to reference the rule-based logic we were using to calculate the distributions. Showing the code wasn’t an option — it requires technical understanding. Showing the legal text wasn’t an option either — it tends to be very complex and long.
I created a writing guide that mapped this proprietary code to easier to understand language. This guide ensured that we conveyed how the waterfall calculations were being performed without abstracting or distorting the original company rules.
Organized Payout Breakdown
I designed a structured data visualization system to help deal associates make sense of two key aspects of proceeds distribution: shareholder payouts and how the payout thresholds worked. With so much data to absorb, it was crucial to present it in a way that felt intuitive—starting with a high-level view and making it easy to drill down into details when needed.
MVP Launch Results
40% attach rate
$2M ARR
We also redirected our strategy towards integrating scenario modeling within existing features. And continued research in a broader PE-focused product strategy.