Several common ideas of data analysts

1. Simple Trends

Use real-time access trends to understand product usage and facilitate rapid product iterations. Access to user volume, access to source, access to user behavior The three major indicators are of great significance for trend analysis.

2. Multidimensional decomposition

Data analysts can decompose metrics from multiple dimensions based on the needs of the analysis. Examples include dimensions such as browser type, operating system type, access source, ad source, region, website/Phone app, device brand, app version, and so on.

3. Conversion Funnel

Use the funnel model to analyze the overall and per-step conversions according to the known conversion path. Common conversion scenarios include registered conversion analysis, purchase conversion analysis, and so on.

4. User Grouping

In the refinement analysis, it is often necessary to analyze and compare the groups of users who have a certain behavior, and the data analyst needs to use multi-dimension and multi-index as the clustering condition, to optimize the products and improve the user experience.

5. Check the path

The data analyst can observe the user's behavior trajectory, explore the user's interaction with the product, and then discover the problem, inspire the inspiration or verify the hypothesis.

6. Retention Analysis

Retention analysis is an exploration of the association between user behavior and return visits. Generally speaking, the retention rate refers to "new users" in a period of time "return visit site/app" proportion. The data analyst can find the growth point of the product by analyzing the different user groups ' retention differences and using different functional users ' retention differences.

7.a/b Test

A/B testing is the concurrent testing of multiple scenarios, but only one variable for each scenario, and then chooses the optimal scenario with some rule (for example, user experience, data metrics, etc.). Data analysts need to select a reasonable grouping sample, monitor data metrics, post-mortem data analysis, and different program evaluations during the process.

Several common ideas of data analysts