My first instinct was to jump straight to solutions, which was probably the wrong move.
Start by clarifying the context and defining what 'sales dropped' means (e.g., which metric, time period, segment). Then walk through a structured root-cause analysis, from high-level trends to granular drivers, and finally propose data-backed actions with clear success metrics.
Pro tip: Emphasize that you would first validate the data and rule out tracking or reporting errors before jumping to business conclusions—this shows analytical rigor and prevents false alarms.
Ask clarifying questions to understand the metric, time frame, and segments. Verify data accuracy and rule out tracking issues or seasonality.
Break down sales by dimensions like product, channel, geography, and user cohort. Compare against previous periods, forecasts, and benchmarks to isolate the drop.
Use techniques like 5 Whys, funnel analysis, and correlation to pinpoint internal (e.g., pricing change, bug) and external (e.g., competitor launch, market shift) drivers.
Estimate the impact of each driver on the sales drop and prioritize based on size and controllability.
Propose actionable solutions with expected outcomes, and define metrics to track the effectiveness of the response.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.