I believe that everyone often hears slogans such as "data-driven products" and persuasion of "don't analyze for analysis's sake". Whether it is a slogan or persuasion, it is a pit that the predecessors in this industry have stepped on.
How to do product data analysis? I will combine the work of product data analysis in the past year, talk about my understanding of this in detail, and introduce how to conduct product data analysis with actual work cases.
Seeing this title, many people should say mobile number list that both are important. Indeed, data analysis can be used as a whole noun, which can represent a profession or a behavior. But if the two are split, they can actually exist as a gerund: analysis based on data.
Based on data analysis, is the data more important or the analysis more important? In my opinion, although data is the premise of analysis, data can only describe the current state, and the value of pure data is limited.
What is more important is how to interpret deeper content through data and multi-dimensional data, and reflect the discovered content into the product. This is also a situation that many people often encounter:
The article will explain with the cases encountered in actual work, why I think analysis is more important than data.