I went straight to feature ideas without grounding it in why users drop off before that first trade, which was probably the wrong move.
Start by clarifying the goal and defining the key metric (first trade completion rate). Then map the new user journey to identify friction points, prioritize the biggest drop-offs, and propose two features that directly address them, explaining how each feature impacts the metric and how you would measure success.
Pro tip: Anchor your answer in the user's emotional state—new investors are often anxious about losing money—so features that build confidence and reduce perceived risk will have the highest impact on first trade completion.
Confirm the objective is to increase the percentage of new users who complete their first trade after installing the app. Define the metric precisely: first trade completion rate = (number of new users who execute at least one trade within X days of install) / (total new users).
Outline the typical steps from install to first trade: download, sign-up/KYC, account funding, exploring the app, selecting a stock, and placing an order. Identify where users drop off and why (e.g., complex KYC, fear of loss, information overload).
Use data or assumptions to rank the biggest barriers. For example, if most users abandon during KYC, focus there; if they fund but don't trade, focus on confidence and education.
For each feature, describe what it is, which friction point it addresses, and how it directly increases first trade completion. Ensure the features are distinct and high-impact.
Explain how you would measure the impact of each feature (e.g., A/B test on first trade rate, time to first trade). Acknowledge potential trade-offs like increased complexity or regulatory constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.