What are the commonly used methods for data analysis?

Understand the timely delivery of suppliers by obtaining trends in real time. Such as product type, supplier area (flow coefficient), purchase amount, and the proportion of purchase amount to suppliers.

2. Multidimensional decomposition

According to the analysis needs, the indicators are decomposed from multiple dimensions. Such as product purchase amount, supplier scale (to be quantified), product complexity and so on.

3, conversion funnel

According to the known transformation path, the transformation of the whole and each step is analyzed with the help of funnel model. Common transformation scenarios include the trend of timely delivery rate of different suppliers.

4. User grouping

In the refined analysis, it is often necessary to analyze and compare the supplier groups of a certain behavior; Data analysis needs to take multi-dimension and multi-index as clustering conditions to optimize the supply chain and improve its stability.

5. Check the path carefully

Data analysis can observe the behavior track of suppliers and explore the interaction process between suppliers and our company; Then find the problem, inspire or test the hypothesis.

6. Residue analysis

Retention analysis is to explore the relationship between user behavior and return visit. Generally speaking, what do we mean by retention rate? New supplier? For a while? Repeated behavior? The proportion of. By analyzing the retention differences of different supplier groups and suppliers who have used different functions, we can find the optimization point of supply chain.

7.A/B test

A/B testing is to test several schemes in parallel at the same time, but only one variable of each scheme is different. Then choose the best scheme according to certain rules. Data analysis needs to select reasonable grouping samples, monitor data indicators, post-event analysis and evaluation of different schemes in this process.