I spent the first minute just trying to scope it because there are a dozen directions you could go.
Start by clarifying the scope and defining the problem: which merchants, what percentage, and what impact on the marketplace. Then segment merchants by root cause (e.g., lack of inventory, operational issues, fraud) and prioritize solutions based on impact and feasibility. Finally, propose a mix of short-term fixes (e.g., automated reminders, penalties) and long-term strategies (e.g., onboarding improvements, incentives) with clear metrics to track success.
Pro tip: Emphasize the importance of balancing merchant and buyer experience: aggressive penalties may deter good merchants, so focus on enabling merchants to succeed while protecting buyers. Also, mention the need for cross-functional collaboration (e.g., with operations, risk, and engineering) to implement solutions.
Ask clarifying questions to understand the scale, impact, and context: What percentage of merchants are non-fulfilling? Is it a recent trend? What are the consequences (e.g., buyer complaints, refund costs)? Define success metrics (e.g., reduce non-fulfillment rate by X%).
Segment merchants by behavior (e.g., one-time vs. repeat offenders) and by root cause (e.g., inventory mismanagement, logistics issues, intentional fraud). Use data to identify patterns and prioritize segments based on impact and solvability.
Brainstorm interventions for each root cause: e.g., automated reminders, inventory sync tools, penalties for non-fulfillment, incentives for good performance, improved onboarding, and fraud detection. Prioritize using an impact/effort matrix.
Outline a phased rollout plan: pilot with a small group, measure results, iterate. Define key metrics (e.g., fulfillment rate, time to first fulfillment, merchant churn) and set up dashboards to monitor.
Acknowledge potential trade-offs: stricter policies may reduce merchant sign-ups; automated solutions may not address all causes. Propose mitigation strategies and emphasize continuous learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.