Use the product version comparison, time Comparison, peak value, data volume and other changes to check products and conduct self-dialectical cognition. The job function email list analysis and mining of big data will help the product go further and go more stably. The product still needs to be rooted in the user, and the user's feedback channel can be built, so that it job function email list can contact the user more directly, and can better find those details and special circumstances. This is another insistence, step by step inspection. In the whole process of sorting,
It will be found that a lot of time and energy are spent job function email list in sorting and realizing the requirements. In actual implementation, because the requirement pool is clear, many things before requirement screening are omitted. The most important thing is that all job function email list requirements can be continuously excavated according to in-depth thinking, but in actual situations, due to the life cycle and product stage, there will be some functions that are hidden deeply and are not used. Therefore, the product does not have to be perfect in the initial demand design.
After completing the realization of the core business job function email list logic, it is necessary to put it into the market as soon as possible, and make more timely revisions based on market feedback to ensure that the product is deeply rooted in the user group. This is how the job function email list Minimum Most Valuable Version of the product is designed, the MVP, and indeed the MVP. The evolution of demand pools and version trees can go very fast for one person, and a long way for a group of people! The current content sharing mainly communicates with you a large framework of actual work and locks down the scope of work.