What Is Buy-a-Feature? How to Run This Collaborative Prioritization Exercise
Buy-a-Feature is a collaborative prioritization technique developed by Luke Hohmann in which participants are given a fictional budget and asked to “purchase” features from a menu of options with their money. The exercise reveals which features participants value most by forcing them to make explicit trade-off decisions about where they’d spend limited resources — producing richer prioritization signal than simple ranking or voting methods.
The technique is particularly valuable because it creates genuine engagement with trade-off thinking. When participants must choose between features with a limited budget, they reason carefully about relative value in a way that “rate each feature 1–10” surveys rarely elicit.
How Buy-a-Feature Works
Setup
Create the feature menu: List the features to be prioritized, along with prices for each. Prices should be set based on the relative cost or complexity of building each feature, not on its perceived value. More expensive features will naturally be purchased less often if they cost more.
Set the budget: Each participant receives the same budget — typically a fictional currency amount calculated so that participants can buy some but not all features. A budget that allows purchasing roughly 25–40% of the total menu forces real trade-offs.
Design for groups: Buy-a-Feature works best with small groups (4–7 participants) who must collaborate and discuss as they spend. Groups typically produce richer insight than individuals working alone.
Running the Session
Participants receive their budget and the feature menu. Groups must discuss, negotiate, and agree on which features to purchase with their collective budget. The discussion itself is often as revealing as the final purchases — the arguments people make for and against features surface their underlying priorities and concerns.
Analysis
After all groups have made their purchases, results are aggregated across groups to identify which features received the most investment. Features purchased consistently across groups represent the clearest prioritization signal; features that polarized groups (purchased by some but not others) warrant further discussion.
What Buy-a-Feature Reveals
Prioritization under constraint: Unlike simple ranking, Buy-a-Feature forces participants to grapple with trade-offs. The features at the top when participants must sacrifice something to get them are the features participants truly value most.
Collaborative reasoning: The group discussion reveals the reasoning behind preferences — why participants value certain features, what use cases they’re optimizing for, and what concerns drive them toward or away from specific features.
Stakeholder alignment (or lack thereof): When different groups consistently spend on different features, it signals genuine disagreement about product direction that deserves explicit resolution.
Appetite for investment: When participants consistently decline to purchase a feature even when they have budget remaining, it’s a strong signal that the feature isn’t as valued as its advocates believe.
When to Use Buy-a-Feature
Buy-a-Feature is most valuable when:
- Gathering input from customers about which features matter most to them
- Aligning internal stakeholders on feature priorities in a structured workshop
- Generating discussion and insight rather than just a ranked list
- Complementing data-driven prioritization with direct stakeholder input
It is less useful for very large feature sets (the menu becomes unwieldy) or for precise numerical prioritization (the outputs are directional, not precise).
Running Buy-a-Feature with Customers
When conducted with actual customers, Buy-a-Feature generates valuable market validation data. Customers purchasing features with fictional money are expressing real preferences in a low-stakes environment. The aggregate across many customers provides a market-informed view of feature priority that complements internal stakeholder input.
Key Takeaways
Buy-a-Feature is a powerful technique for generating authentic prioritization signal through structured trade-off decisions and collaborative discussion. Its strength is not in producing a precise rank-ordered list but in revealing what features participants genuinely value when they must sacrifice something to get them — which is far closer to the real-world decision users make when choosing whether to adopt and use a product.