A single-feature MVP is a functioning [[minimum viable product]] with the single feature needed to test your assumption. It is ideal for learning if the core promise of your solution resonates with customers. To prepare for this experiment, you need to design the smallest version of your feature that solves a high-impact customer job, test it out internally first to make sure it works, and acquire customers for your single-feature MVP.   To execute the experiment, you should conduct the single-feature MVP experiment with customers and gather satisfaction feedback from the customers. Then, you should analyze your customer satisfaction feedback, determine how many customers converted, and calculate what it costs you to operate this solution.   The cost of single-feature MVPs is a bit more expensive than low-fidelity experiments because you're creating a higher-fidelity version that delivers value to the customer. Setting up a single-feature MVP can take 2-3 weeks, as you'll need to design, create, and test it out internally before involving customers. Running a single-feature MVP experiment can take several weeks or months. You'll want to run it long enough to analyze qualitative and quantitative feedback before prematurely optimizing or trying to scale.