MVP (Minimum Viable Product) is a hypothesis testing tool used to change user behavior and create successful products. It involves formulating and testing hypotheses, identifying barriers to behavior change, and measuring results. To understand what an MVP is, we must first recognize that business aims to change people's behavior in a way that they are willing to pay for.
To create an MVP, we need three things: a hypothesis to test, people whose behavior we want to change, and a way to measure the outcome. The hypothesis follows a simple formula: "If we do [this], we can change the behavior of [these people] [this way], thanks to [these qualities] of the product."
Let's consider an example where we want to create a product better than Zoom for webinars. We start by describing the behavior we want to change, such as encouraging participants to turn on their cameras. We avoid focusing on specific features and instead focus on behavior change.
We identify barriers, such as people not being accustomed to turning on their cameras, and aim to overcome them without negatively impacting other user groups. MVP helps break these barriers.
A good MVP tests multiple hypotheses quickly, provides deep insights, delivers reliable results, and minimizes resource and time costs. It is important to note that an MVP is not the basis for a future platform but a means to gain valuable experience.
MVPs can be built using promises, manual service provision, existing services, or custom program code. The goal is to observe changes in user behavior to determine if the MVP is effective.
In the business cycle, we test hypotheses related to payment, customer experience, and cost-effectiveness. By successfully changing behavior and reducing costs, we create innovation.
To start, we create a fake landing page or interact with potential customers directly, making promises and gathering feedback. We then determine who our paying customers are and provide value accordingly.
For instance, if we help webinar organizers avoid technical issues, we may initially hire students to handle technical work at a higher cost than what we earn. Once we confirm customer willingness to pay, we optimize production costs to increase margins.
This approach allows us to test hypotheses and gather insights without investing heavily in copywriters, designers, or programmers. While it may not align with the traditional definition of MVP, it serves as a cost-effective and insightful tool for experiments focused on delivering value. Future steps may involve online testing, service integration, and eventual programming, which is a separate process altogether.
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