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Splunk·Product Manager·Onsite - Product Sense / Strategy·Intermediate

Intermediate
Jun 2026

Summary

PM interview at Splunk with a product design question about building a marketplace for antiques. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

Design an app for buying and selling antiques.

Product Sense & IdeationProduct StrategyPricing & Monetization
Author's notes

I went straight into user personas and forgot to ask any clarifying questions first, which I regret.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal and constraints, then segment the market and prioritize a target user. Focus on solving a key pain point with a differentiated product, and outline a sustainable monetization strategy.

Pro tip: Anchor your design around trust and authentication, as these are the biggest barriers in high-value antique transactions. Show how your solution leverages Splunk's data capabilities to enhance trust and user experience.

1. Clarify Objectives and Constraints

Ask questions to understand the goal (e.g., revenue, user acquisition), target market, and any constraints (budget, timeline, platform).

2. Segment Users and Identify Pain Points

Define user personas (buyers, sellers, browsers) and their key pain points such as authenticity verification, price discovery, and logistics.

3. Prioritize and Define MVP

Select a primary user segment and core problem to solve, then outline the minimum viable product features that address that problem.

4. Design Key Features and User Flow

Describe the main features (e.g., authentication, search, bidding) and how users will interact with them to complete transactions.

5. Define Monetization and Metrics

Propose a revenue model (e.g., commission, subscription) and key success metrics (e.g., GMV, active users, retention).

Key Points to Mention

  • Trust and authentication mechanisms (e.g., expert verification, blockchain provenance)
  • Search and discovery features tailored to antiques (e.g., image recognition, category filters)
  • Pricing and bidding models (e.g., auctions, fixed price, best offer)
  • Logistics and escrow services for safe transactions
  • Monetization strategies (e.g., transaction fees, premium listings, ads)
  • Leveraging Splunk's data analytics for market insights and fraud detection

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