Do useful data analysis, starting with a good MVP Peru Phone Number

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Many students are ambitious to make achievements in their work. The MVP method of data analysis is recommended here, which can escort everyone's work. 1. What is the MVP of data analysis? MVP (Minimum Viable Product) was originally a method applied to product design, which means that before the product is officially launched, a simple version containing core functions is launched to test user needs and feedback, so as to quickly judge whether the product meets market needs and make Adjustment. The MVP method of data analysis is to provide virtual data results according to data requirements and usage scenarios before the data is officially produced, Peru Phone Number so as to test the validity of the data and discover the real data requirements. This method is very useful in the field of data analysis! Because it can solve the core problem of data analysis: it is useless for a long time . "Statistics", "Mathematics", "Operations Research", "Game Theory", "Machine Learning" behind the data analysis... There are so many theories, so it is easy to cause self-happiness.


I am too excited to do data, and all kinds of theoretical calculations are ups and downs, and they reach the user: "I already knew it!" "What's the use of Peru Phone Number what you're doing!" "What the hell did you do!" With one click and three consecutive hits, this project is sure to fail. The purpose of the MVP method of data analysis is to sort out in advance the logic of how data is useful to the business, so as to avoid the above-mentioned tragedies; and the data analysis that seems to be awesome, but in reality, there are many... 2. Version 1.0 MVP To give a simple example, for example, the Internet Peru Phone Number platform-advertising sales team proposed: "To establish a user portrait of salespersons, and to grasp the gender, age, behavior, and conversion rate of each salesperson to improve performance." What should we do at this time? If you use the MVP idea, don't rush to run the count, and don't rush to list a lot of "user portrait standard indicators", but directly take the initial demand from the business side: "gender, age, behavior, conversion rate" .


To improve performance" directly to a dummy result , and then confirm: "If I actually provide these things, you can really improve performance?" - let him confirm. Do useful data analysis, starting with a good MVP‍‍‍‍ At least based on this sentence alone, the conclusions that data analysis can output are completely useless. If the MVP test of version 1.0 fails, either Peru Phone Number give up this requirement, or continue to think about how to better grasp the pain points of users. This pushes the data to version 2.0. 3. MVP version 2.0 Looking further, the problem with Peru Phone Number version 1.0 is that there is no clear goal. There are a lot of so-called portrait indicators, and I have no idea what to do when I see it. If you gather the goals, for example: to find a salesperson with good performance. This is a step clearer. More analysis needs to be introduced here, because "good" and "bad" itself need to be analyzed: