I started with revenue and immediately went into ticket size and customer volume, which felt right, but I forgot to anchor the funnel properly before jumping to hypotheses.
Start by clarifying the business context and defining the problem, then outline a structured root cause analysis: decompose profit into revenue and costs, identify key metrics, form hypotheses, and prioritize tests. Emphasize a data-driven, iterative approach that balances quick wins with deeper investigation.
Pro tip: Show that you understand the difference between correlation and causation by proposing controlled experiments (e.g., A/B tests) where possible, and always tie your analysis back to actionable business recommendations.
Ask clarifying questions to understand the timeframe, magnitude of profit loss, and any recent changes (e.g., new competitors, menu changes). Define what 'losing profit' means (e.g., declining net margin) and set a clear goal for the analysis.
Break down profit into revenue and costs. Revenue = transactions × average order value; costs = fixed + variable. Identify metrics like daily sales, customer count, average spend, COGS, labor, rent, etc. Pull historical data to spot trends.
Brainstorm potential causes: declining foot traffic, lower average spend, increased costs, operational inefficiencies, or external factors. Prioritize based on impact and ease of testing.
Use data to validate or invalidate each hypothesis. For example, compare sales by daypart, product mix, customer segments; analyze cost trends; conduct cohort analysis. If possible, run experiments (e.g., promotions) to test causal relationships.
Summarize root causes, quantify their impact, and propose data-backed solutions. Prioritize actions by expected ROI and feasibility, and suggest monitoring metrics to track improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.