data analysis is what, small partners to ask their own degree. In order to reduce the pressure of reading, do not let the length is too long, I deliberately split up, the next two to share their knowledge learned in the past few days. Reference I will be attached to the end of the next article, interested partners can understand the system.
product manager, I especially do not pretend to see the love, don’t love flashy without substance. So to write myself, to understand and experience can write as detailed as possible; do not know to go to school, and then put the notes to share out the data analysis I get more content due to the lack of combat experience, will compare the basis and theory, also hope to help you.
1 clear data analysis purposes
do data analysis, we must have a clear purpose, to know why they do data analysis, to achieve what effect. For example: in order to assess the effectiveness of the product after the revision has been improved than before; or through data analysis, to find the direction of product iteration.
defines the purpose of data analysis, and then you need to determine what data should be collected.
2 data collection method
when it comes to collecting data, we need to do a good job of burying the data.
so-called buried point, personal understanding is in the normal function of the logic to add statistical code, the data they need to come out.
There are two main ways of embedding data in
first: their own research and development. When the development of statistical code, and build their own data query system.
second: the use of third party statistical tools.
third common statistical tools are:
website analysis tool
Alexa, China’s Web site rankings, network media ranking (iwebchoice), Google Analytics, Baidu statistics
mobile application analysis tool
Flurry, Google Analytics, Friends Union, TalkingData, Crashlytics
different products, different purposes, the need to support different data, to determine a good data indicators, choose the right way to collect their own data.
3 product basic data indicators
added: new users increase the number and speed. Such as: new day, new month, etc..
active: how many people are using the product. Such as day active (DAU), monthly active (MAU), etc.. The more active users, the more likely to bring value to the product.
retention rate: how long will the user use the product. Such as: the next day retention rate, weekly retention rate, etc..