I think I went too narrow too fast, jumped straight to demand-side stuff like a big local event and didn't structure it well before talking.
Start by validating the data and confirming the spike is real, not an artifact. Then systematically rule out internal and external factors, using a structured root cause analysis framework. Finally, quantify the impact and propose next steps.
Pro tip: Always check for data pipeline issues and metric definition changes first—they are the most common cause of sudden metric shifts. Also, consider seasonality and one-off events like holidays or promotions.
Check data quality, pipeline health, and metric definitions to ensure the spike is not due to logging errors, missing data, or a change in how the metric is calculated.
Break down the metric by dimensions such as city, rider segment, time of day, and device type to identify where the spike is concentrated.
Review recent product changes, pricing experiments, marketing campaigns, or algorithm updates that could have influenced Prime Time pricing.
Investigate external events like weather, holidays, concerts, or competitor actions that might have increased demand or reduced supply.
Estimate the impact of each potential cause, determine the most likely root cause, and suggest monitoring or follow-up actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.