My first instinct was to lump them together which was probably the wrong move.
Start by defining each complaint type precisely and identifying the distinct stages in the delivery process where they originate. Then, for each type, outline a separate analysis plan that includes data sources, metrics, segmentation, and root cause hypotheses, emphasizing how the investigations differ.
Pro tip: Highlight that missing items often stem from merchant-side errors (e.g., forgotten items) while wrong items often result from Dasher-side errors (e.g., swapped bags), so your analysis should focus on different parts of the funnel and use different data signals.
Clearly define what constitutes a 'missing item' (item not in bag) versus a 'wrong item' (incorrect item delivered). Scope the analysis to specific time periods, regions, and customer segments to ensure comparability.
Break down the order fulfillment process into stages: order placement, merchant preparation, Dasher pickup, and delivery. Identify where missing items (likely merchant) and wrong items (likely Dasher or merchant) can occur.
For missing items, analyze merchant error rates, item-level data, and customer reports. For wrong items, examine Dasher accuracy, order swapping, and photo verification. Use separate datasets and metrics for each.
Segment data by merchant, Dasher, region, order size, and time to uncover patterns. Compare missing vs. wrong item rates to see if they correlate with different factors (e.g., missing items with high-volume merchants, wrong items with new Dashers).
Based on analysis, hypothesize root causes for each complaint type and suggest targeted interventions (e.g., merchant training for missing items, Dasher verification for wrong items). Prioritize solutions by impact and feasibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.