Fake Door Testing
Fake Door Testing is a method used to gauge user interest in a potential new feature or product without actually building it. It involves creating the facade of the feature or product, such as a landing page or an advertisement, and measuring user interaction with the facade to assess interest.
Goal
The primary goal of Fake Door Testing is to validate demand for a new feature or product before committing significant resources to its development. It allows teams to make data-driven decisions about whether to proceed with the development based on actual user interest.
Context
Most features fail to deliver the expected value. If we can show that people will click on a button to learn more about a feature, it is a strong signal that we can use to start investing more in the development of that feature.
Types
Type | Description |
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Landing Page | A webpage that presents the illusion of a fully functional product or feature, tracking user engagement metrics. |
Email Campaign | An email sent to potential users describing the feature or product, with links to gauge interest through clicks. |
In-App Notification | A notification within an existing application that advertises the new feature or product, measuring user interactions. |
Inputs
Artifact | Description |
---|---|
Experiment Plan | A detailed document outlining the experiment objectives, methods, timeline, and success criteria. |
Usability Test Guide | A guide for the moderator to follow during a usability test, including the tasks to be performed, the questions to be asked, and the data to be collected. |
Digital Assets | A live webpage or other digital asset to be used in a Landing Page, Fake Door, A/B or multivariate experiment. |
Outputs
Artifact | Description | Benefits |
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Validated Assumptions | A list of assumptions that have been validated. |
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Invalidated Assumptions | A list of assumptions that have been invalidated. |
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Anti-patterns
- Misleading Users: Creating a fake door that deceives users into believing the product exists.
- Over-reliance on Clicks: Basing decisions solely on click-through rates without understanding user motivations.