Buy a Feature

• Feature prioritization phase - When you have multiple feature ideas but limited development resources • MVP definition - Determining which core features to include in your minimum viable product • Product roadmap planning - Deciding the order of feature development for future releases • Market research stage - Understanding customer preferences before significant development investment • Pivot considerations - When evaluating which features to keep, modify, or eliminate • Competitive analysis - Testing whether users prefer your proposed features over competitor offerings • Budget allocation decisions - When development resources are constrained and you need clear priority rankings

1. Define feature set - Create a list of 5-10 potential features with clear, concise descriptions. Include rough development cost estimates to set realistic 'prices' for each feature in mock currency.

2. Set mock budget and pricing - Give each participant the same limited budget (e.g., 100 coins). Price features based on development complexity - simple features cost 10-20 coins, complex ones 30-50 coins.

3. Create the experiment interface - Build a simple webpage, use tools like Typeform, or create a physical card-sorting exercise. Display features with descriptions, prices, and a running budget counter.

4. Recruit target participants - Gather 30-100 participants from your target market. Use existing customer lists, social media, or user research platforms to find qualified participants.

5. Run the experiment - Have participants 'shop' for features within their budget constraints. Allow them to see their remaining budget and change selections before final submission.

6. Collect additional data - Include follow-up questions about their decision-making process, feature importance reasoning, and demographic information for segmentation analysis.

7. Analyze results - Calculate purchase frequency, revenue per feature, and participant segments. Look for patterns in spending behavior and feature combinations.

8. Validate findings - Cross-reference results with other validation methods like user interviews or A/B tests to confirm feature priorities before development.

Pros

• Forces real trade-offs - Eliminates the problem of users rating everything as high priority • Quantitative insights - Provides measurable data on feature demand and relative value • Budget-friendly - Can be executed with minimal resources using simple tools • Quick to implement - Results available within days rather than weeks • Mimics real decisions - Simulates actual purchasing behavior better than surveys

Cons

• Artificial constraints - Mock currency doesn't perfectly replicate real buying decisions • Limited context - Participants may not fully understand technical complexity or implementation challenges • Sample bias - Results depend heavily on participant selection and may not represent entire market • Feature interdependence - Doesn't account for features that work better together or have dependencies