What You Should Know About Getting Started with Data Analytics

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1. Clear the goal of analysis To



do data analysis, you must have a clear purpose, know why you want to do data analysis, What effect do you want to achieve. For example: in order to evaluate that the effect of the product revision has been improved compared with the previous one; or through data analysis, to find the direction of product iteration, etc.



The purpose of data analysis is clear, and the next step is to determine what data should be collected.




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2. Methods of collecting data



When it comes to collecting data, we must first do a good job of data burying.



The so-called "buried point", personal understanding is to add statistical code to the normal functional logic to count the data you need.



At present, there are two mainstream methods of data burying:



the first one: self-developed. Add statistical code during development and build your own data query system.



The second: use third-party statistical tools.



Common third-party statistical tools include:



website analysis tool



Alexa, China website ranking, Internet media ranking (iwebchoice), Google Analytics, Baidu statistics,



mobile application analysis tool



Flurry, Google Analytics, Umeng, TalkingData, Crashlytics



Different products, different purposes, Different supporting data are required. After determining the data indicators, choose a method suitable for your company to collect the corresponding data.



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3. Basic data indicators of the product



New: The number and rate of new user additions. Such as: daily increase, monthly increase and so on.



Active: How many people are using the product. Such as daily active (DAU), monthly active (MAU), etc. The more active users are, the more likely they are to bring value to the product.



Retention rate: How long users will use the product. Such as: next day retention rate, weekly retention rate, etc.



Spread: On average, each old user brings in several new users.



Churn rate: Users who lost a period of time, as a percentage of the number of active users during this period.



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4. Common data analysis methods and models



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