Begin with behavior.
A commercial model should reflect how customers evaluate, adopt, use, and expand—not simply how the company prefers to charge.
About Sarah Zou
I’m an economist who cares about what happens behind the model.
I’ve spent my career learning how evidence becomes judgment—from research and finance to transformation and infrastructure operations.
Economist · Operator · Advisor
The path
Four contexts that changed how I see commercial problems.
- 01
Economics
Learning to ask what drives the outcome.
Doctoral training at Rutgers and research with NBER and the World Bank taught me to separate signal from noise—and to make assumptions visible before trusting a conclusion.
- 02
Finance
Learning what a model must withstand.
At Citigroup, I worked with risk and forecasting models where precision mattered, but so did judgment, governance, and the ability to explain what the numbers could not guarantee.
- 03
Transformation
Learning to translate across disciplines.
At Capgemini, I led generative-AI and digital-transformation work across technical and business teams. The challenge was rarely the technology alone; it was creating shared language around the decision.
- 04
Operations
Learning where strategy meets constraint.
Operating an infrastructure data platform as COO brought the work closest to reality: pricing against cost floors, structuring paid pilots, and building an economic story that could survive diligence.
A working thesis
The strongest commercial decisions are neither purely analytical nor purely intuitive.
They connect evidence with judgment. They make uncertainty explicit without becoming paralyzed by it. And they give the people responsible for the outcome a logic they can carry forward.
Point of view
What I have come to believe.
Make complexity legible.
Technical products do not need to be made simplistic. They need an economic logic that buyers, teams, and investors can understand from their own vantage point.
Use the model to expose the decision.
The purpose of analysis is not a more elaborate spreadsheet. It is to reveal the tradeoff, define what matters, and make the next move easier to defend.
Stay close to operating reality.
A recommendation is only strong if it can survive product constraints, cost behavior, sales conversations, and the imperfect information of a growing company.
Why EconNova
A practice built for questions without an off-the-shelf answer.
I started EconNova to bring economic reasoning and operating context into the same conversation. Technical founders are often told to borrow a familiar SaaS playbook even when their products, costs, buyers, and adoption patterns behave very differently.
I am most interested in the moment before the answer looks obvious—when the right framing can prevent months of optimizing the wrong model.
From perspective to practice