← Anthropic Interview Insights
I fumbled the opening a bit because I started listing metrics before I even defined what ROI meant in this context.
Start by clarifying the business context and defining ROI precisely (e.g., net profit / cost). Then outline a model that combines revenue forecasting, cost estimation, and uncertainty quantification, emphasizing data sources, assumptions, and validation. Conclude with how you would communicate results and iterate.
Pro tip: Show that you understand the difference between a model and reality: explicitly state key assumptions and how you would test them with small-scale experiments or historical analogs before full launch.
Ask questions to understand the product, target market, time horizon, and what 'return' means (e.g., net profit, revenue, cost savings). Define the ROI formula and key inputs.
List internal data (historical sales, costs) and external data (market trends, competitor benchmarks). State assumptions about pricing, adoption rate, and cost structure.
Choose a modeling approach (e.g., deterministic spreadsheet, probabilistic simulation, or ML-based forecast). Break down revenue and cost components, and incorporate uncertainty via ranges or distributions.
Backtest with historical launches or analogous products. Run sensitivity analysis to see how ROI changes with key variables (e.g., adoption rate, price).
Present results with confidence intervals and key drivers. Recommend next steps (e.g., pilot launch) and plan to update the model as real data arrives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.