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Asana·Product Manager·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
Jun 2026

Summary

Got a product design question at Asana that was framed around Amazon of all things. Single question, fairly open-ended, and the whole thing hinged on whether you could connect UI decisions back to a business hypothesis rather than just making something pretty.

Questions Asked (1)

Q1

Redesign the Amazon Wishlist UI in a way that tests whether users who share wishlists more frequently drive higher revenue.

Product Sense & IdeationA/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

I spent way too long on the UI side and not enough tying it back to the actual hypothesis.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: to test if increased sharing frequency causes higher revenue. Then propose UI changes that lower friction to sharing, define metrics for sharing and revenue, and design an A/B test with sharing frequency as the treatment and revenue as the outcome. Finally, discuss how to measure causality and potential confounders.

Pro tip: Focus on establishing causality, not just correlation. Suggest using an instrumental variable or a randomized encouragement design if direct manipulation of sharing is difficult.

1. Clarify Objective and Hypotheses

Restate the goal: determine if more frequent sharing leads to higher revenue. Formulate a clear hypothesis, e.g., 'Increasing sharing frequency by X% will increase revenue by Y% through increased social exposure and purchases.'

2. Identify Levers and Redesign UI

Brainstorm UI changes that could increase sharing frequency, such as one-click share buttons, social proof prompts, or reminders. Prioritize changes that are easy to implement and likely to impact sharing behavior.

3. Define Metrics and Experiment Design

Select primary metrics: sharing frequency (e.g., shares per user per week) and revenue (e.g., average revenue per user). Design an A/B test where the treatment group sees the redesigned UI and the control group sees the current UI. Ensure randomization and sufficient sample size.

4. Analyze Results and Establish Causality

Compare sharing frequency and revenue between groups. Use statistical tests to determine if differences are significant. To infer causality, consider instrumental variables or mediation analysis to rule out confounders.

5. Iterate and Scale

If the treatment increases both sharing and revenue, consider rolling out the redesign. If not, analyze why and iterate on the UI changes. Also, explore long-term effects and potential negative consequences.

Key Points to Mention

  • Define sharing frequency precisely (e.g., number of shares per user per time period).
  • Revenue metrics: average revenue per user, conversion rate from shared links, or total revenue.
  • A/B testing best practices: randomization, control group, sample size calculation, and avoiding peeking.
  • Potential confounders: user engagement level, seasonality, or external events.
  • Causality vs. correlation: use of instrumental variables or randomized encouragement designs.
  • Consider network effects: sharing may affect other users' behavior, complicating measurement.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.