I went straight to upsell mechanics, like bundling drinks or suggesting add-ons at checkout, which felt obvious in retrospect.
Start by clarifying the metric and segmenting users to identify high-leverage opportunities. Then propose a feature that directly encourages larger orders, such as bundling or personalized recommendations, and validate it with a quick experiment plan.
Pro tip: Anchor your answer in Uber's unique strengths—logistics, data, and cross-platform integration—to show you understand the ecosystem, not just generic e-commerce tactics.
Define average order value (AOV) and ask clarifying questions about target users, markets, and constraints. Confirm whether the focus is on food, groceries, or alcohol.
Break down users by behavior (e.g., frequent vs. occasional, high vs. low spenders) to find segments where AOV can be increased. Look for pain points or unmet needs that lead to larger baskets.
Generate ideas like smart bundling, group ordering, loyalty rewards for higher spend, or personalized upsells. Prioritize based on impact, effort, and alignment with Uber's strengths.
Outline how you'd measure success (e.g., AOV lift, conversion, retention) and propose an A/B test or pilot to validate the feature. Consider potential cannibalization or user experience trade-offs.
Conclude with a clear recommendation, acknowledging risks and next steps. Tie back to broader business goals like profitability and user satisfaction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.