To choose a method, rely on your initial data.

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Organize hypotheses or features into a matrix with two axes: one axis reflects “customer value” (or impact on business goals), the other “ease of implementation”.
Place each hypothesis or function in the matrix depending on its score on these two parameters.
Identify four quadrants: Do Now (high value and easy to implement), Plan (high value but difficult to implement), Simplify (low value but easy to implement), Don't Do (low value and difficult to implement).
Focus on the Do Now hypotheses as they promise the most value with the least amount of resources.
Lean Prioritization helps focus on what's really important and what can be done quickly, allowing companies to be more flexible and respond to market changes at a lower cost.

If there is little data at the start and the assessment is rather list of afghanistan cell phone number subjective, use qualitative methods like the Kano model and Lean. And if at the research stage you have collected a lot of numbers that you can trust, try quantitative methods. You can collect data using web analytics or a data collection tool that is available on the site.

You don't have to choose one of the methods above, you can modify them to suit your tasks and create your own prioritization principle. Such custom methods are often used by large companies - they most often rely on internal models for assessing risks and priorities for the business. The methods can also be used to improve the task backlog, check the importance of specific mechanics.

How to test a hypothesis
After evaluating and prioritizing hypotheses, it is time to test them. In marketing, A/B testing is often used for this.

An A/B test is a comparison of two or more versions of an element (application form, advertisement, banner) with a minimum number of differences. Such tests are conducted to determine the option that better attracts users, increases conversion and generates profit.

For example, a marketer faces the task of increasing the number of clients of an online service. He wants to understand which pop-up text on the site will work better: on the entire screen or on the right side. To do this, you need to run A/B testing. How to do this:

1. Prepare several design options for the pop-up. Add a link to the messages where you can learn more about the product.

2. Define the target audience. You can select website visitors who are interested in a specific topic and have not yet communicated with managers or tested the service. Do not forget about the ideal customer profile .

3. Launch testing. Show a pop-up with the first option to some of the site visitors, and the second option to another group.

4. Analyze the results. After a set period, compare, for example, the conversion rate or CTR (the ratio of the number of clicks to the number of impressions) for each option and choose the one that showed the best result.

Example of option A in A/B networking
Option A: a regular pop-up opens over the content, but does not completely cover it - 8.9% response conversion
Option B in A/B testing
Option B: fullscreen pop-up completely covers the site content - response conversion 13% (1.5 times higher)
The result of such a hypothesis in the online service MPSTATS showed that the fullscreen pop-up attracts more attention, so the conversion in response is higher. With the help of this mechanics, the company received: