Low Fidelity
Low Fidelity
Low Fidelity
Feature Voting
Overview
Feature Voting is a customer-driven prioritisation experiment designed to test which features, services, or product enhancements customers value most—based on their willingness to “spend” on them. At Future Foundry, we run this experiment when teams are unsure which elements of a proposition drive the most value, helping to avoid feature bloat and focus on what matters most. Unlike surveys that ask customers what they like, this test makes them trade off limited resources, forcing them to choose between competing options. This provides deeper insight into what customers actually care about rather than just what they say they do.
Feature Voting is a customer-driven prioritisation experiment designed to test which features, services, or product enhancements customers value most—based on their willingness to “spend” on them. At Future Foundry, we run this experiment when teams are unsure which elements of a proposition drive the most value, helping to avoid feature bloat and focus on what matters most. Unlike surveys that ask customers what they like, this test makes them trade off limited resources, forcing them to choose between competing options. This provides deeper insight into what customers actually care about rather than just what they say they do.
Feature Voting is a customer-driven prioritisation experiment designed to test which features, services, or product enhancements customers value most—based on their willingness to “spend” on them. At Future Foundry, we run this experiment when teams are unsure which elements of a proposition drive the most value, helping to avoid feature bloat and focus on what matters most. Unlike surveys that ask customers what they like, this test makes them trade off limited resources, forcing them to choose between competing options. This provides deeper insight into what customers actually care about rather than just what they say they do.
Process
We begin by selecting a set of potential features, service options, or pricing models and assigning each one a cost, representing the effort or value required to implement it. Participants are given a fixed budget of virtual currency and asked to “vote” for the features they want most. Once the session is complete, we analyse which features received the highest number of votes, what customers ignored, and any unexpected trade-offs. If customers consistently vote for a certain feature but ignore others, it tells us exactly where to focus development efforts. If no feature gets strong buy-in, it may signal a fundamental positioning issue.
We begin by selecting a set of potential features, service options, or pricing models and assigning each one a cost, representing the effort or value required to implement it. Participants are given a fixed budget of virtual currency and asked to “vote” for the features they want most. Once the session is complete, we analyse which features received the highest number of votes, what customers ignored, and any unexpected trade-offs. If customers consistently vote for a certain feature but ignore others, it tells us exactly where to focus development efforts. If no feature gets strong buy-in, it may signal a fundamental positioning issue.
We begin by selecting a set of potential features, service options, or pricing models and assigning each one a cost, representing the effort or value required to implement it. Participants are given a fixed budget of virtual currency and asked to “vote” for the features they want most. Once the session is complete, we analyse which features received the highest number of votes, what customers ignored, and any unexpected trade-offs. If customers consistently vote for a certain feature but ignore others, it tells us exactly where to focus development efforts. If no feature gets strong buy-in, it may signal a fundamental positioning issue.
Requirements
To run this test, we need a clearly defined set of options with assigned values, a structured customer workshop, and a way to collect and analyse results. It works best when paired with live customer interactions, where teams can probe into why participants make certain decisions.
To run this test, we need a clearly defined set of options with assigned values, a structured customer workshop, and a way to collect and analyse results. It works best when paired with live customer interactions, where teams can probe into why participants make certain decisions.
To run this test, we need a clearly defined set of options with assigned values, a structured customer workshop, and a way to collect and analyse results. It works best when paired with live customer interactions, where teams can probe into why participants make certain decisions.
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