My first instinct was to jump straight into feature ideas, which was probably wrong.
Start by clarifying the metric: is 20% tipping rate a problem? Define success (e.g., increase to 30%) and segment riders to understand who tips and why. Then generate and prioritize ideas using a framework like RICE, focusing on high-impact, low-effort solutions that address root causes.
Pro tip: Don't just brainstorm features; anchor your answer in user psychology and business impact. For example, tipping is often driven by social norms and reciprocity—design interventions that leverage these, and estimate the revenue lift from a 5% increase in tipping.
Ask clarifying questions to understand the current tipping process, data, and goal. Define a clear target metric (e.g., increase tipping rate from 20% to 30% in 3 months).
Break down the 20% by rider demographics, ride type, time of day, and geography. Identify patterns: who tips, when, and how much? Also consider driver factors (rating, friendliness).
Hypothesize why 80% don't tip: friction in the app, lack of awareness, forgetfulness, or dissatisfaction. Use data (e.g., funnel analysis of post-ride flow) to validate.
Brainstorm ideas across the funnel: pre-ride (expectation setting), post-ride (prompts, defaults), and post-tip (feedback). Prioritize using impact/effort matrix or RICE.
Define A/B tests for top ideas, track tipping rate and revenue impact. Consider long-term effects like driver satisfaction and rider retention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.