Spent the first minute just trying to figure out who I was designing for because 'bad booking' covers a lot of ground.
Start by clarifying the scenario and defining what a 'bad booking' means, then segment users by issue type and severity. Focus on the post-booking experience, propose improvements across the user journey, and prioritize based on impact and feasibility. Tie your recommendations to metrics like CSAT, retention, and booking recovery rate.
Pro tip: Show empathy by walking through the emotional journey of a frustrated user, but balance it with business impact—demonstrate how solving their problem drives loyalty and lifetime value.
Ask clarifying questions to understand what constitutes a 'bad booking' (e.g., host cancellation, inaccurate listing, cleanliness issues) and the scope (all users or specific segments). Define success metrics such as reduced support contacts, increased rebooking rate, and improved NPS.
Segment users based on issue type, severity, and user value (e.g., first-time vs. loyal guests). Prioritize segments where intervention can have the highest impact on satisfaction and retention.
Walk through the post-booking experience: discovery of the issue, contacting support, resolution, and follow-up. Identify friction points such as slow response, lack of transparency, or inadequate compensation.
Brainstorm improvements like proactive issue detection, instant rebooking options, automated compensation, or a dedicated resolution team. Use an impact/effort matrix to prioritize quick wins and strategic bets.
Propose A/B tests or pilot programs to validate solutions. Define metrics like time to resolution, rebooking rate, and CSAT. Outline a rollout plan and iterate based on feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.