When is a hypothesis validated




















After validating the hypothesis, you need to decide on the testing tools. You need to understand that the tools are different, because the product hypothesis and feature hypothesis have different contexts. In the context of a feature, we already have a finished product that customers can reach, and we want to understand the feasibility of its implementation.

Validation is key. The main idea: We reproduce the layout of the original pages and it looks identical to the original one. We drive traffic and look at the indicators depending on what you want to check. This will help you get a foundation for analysis and comparison. In order to interpret the results on small volumes of traffic correctly, it is more correct to test one thing.

For example, if you are testing creatives, the landing page is the same for all creatives. Conversely, if an effective creative is identified, then you can test different pages. This will help clarify CPI, CTR and other important metrics, and, accordingly, plan the budget and economic model of the product.

Make a minimal set of features to get into the app store. This is a rather non-trivial way of testing product hypotheses for mobile. If the hypothesis is not confirmed, withdraw it from sale. The bonus is that you have a live page for several months in the respective app store.

This gives you small organic traffic, and the ability to drive traffic to an existing page albeit without optimizing campaigns , and collect pre-orders, which will turn into installs at the time of release. Aim: Test hypothesis very quickly. Getting the first clients, adjusting the strategy. The main idea: We create a template software product in a visual interface. By , a wide range of tools is already available not only for testing product hypotheses, but also for creating MVPs that will help you understand whether it is worth spending resources on full-fledged development.

In some cases, it allows you to create and develop complete products. The advantage of this approach is that a digital product can be created with little or no programming knowledge. It is important to have information about most of the popular tools on the market, to know their features and capabilities. Hypothesis-driven development removes these uncertainties as the project progresses.

For us, the hypothesis-driven approach provides a structured way to consolidate ideas and build hypotheses based on objective criteria. Using this approach has reliably allowed us to identify what, how, and in which order should the testing be done.

Our success in building apps that are well-accepted by users is based on the Lean UX definition of hypothesis. Once the hypothesis is proven right, the feature is escalated into the development track for UI design and development. The Dot Vote method, where team members are given dots to place on the questions, helps prioritize the questions and assumptions. We started by grouping similar ideas and use dots to vote.

The questions lead to a brainstorming session where the answers become hypotheses for the product. The hypothesis is meant to create a framework that allows the questions and solutions to be defined clearly for validation.

Our team followed a specific format in forming hypotheses. We structured the statement as follow:. Based on the hypotheses, experiments in the form of interviews, surveys, usability testing, and so forth are created to determine if the assumptions are aligned with reality. Each of the methods provides some level of confidence. Even though hypotheses validation provides a degree of confidence, not all assumptions can be tested and there could be a margin of error in data obtained as the test is conducted on a sample of people.

The experiments are designed in such a way that feedback can be compared with the predicted outcome. Ie, how much closer you are to validating your hypothesis. You can download the scorecard here. Learn more about evidence-based decision-making by signing up for our masterclass on Testing Business Ideas here. Dashboard Sign up. Teams do not see a clear validated signal, because the insights gained from the evidence are on a spectrum. In fact, this spectrum reflects our two themes of experimentation - discovery and validation - from the book Testing Business Ideas.

Discovery is much more about open-ended, directional experiments where you are trying to go from no evidence to light evidence. This is evidence of what people say, based on opinions and experiments in a lab context where customers know it's a test. Validation experiments usually come much later, when you are going from some evidence to strong evidence.

This is evidence of what people actually do, based on facts and figures from the real world. The goal is to get irrefutable evidence from the market. For example, sales from a pop-up store or mock sales. Where customers believe they are actually purchasing a product or service. As a simple way to measure and track how much risk you have reduced in your business idea. Teams measure their desirability, feasibility, viability, and adaptability risk from 0 to 5.

Based on the type and amount of evidence they collect. Not all evidence is of equal strength. The ideal approach for sustainable ongoing optimizations , capturing incremental value and maximizing value. Have you ever applied any of these approaches? Which one worked for you? Share your comments with us! What are the essential tools and software for product managers in ?

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