What are the e commerce operation data analysis software?

Rubik's Cube is a big data model platform, which is a tool platform for data analysis and mining based on the two technical architectures of service bus and distributed cloud computing, which uses a distributed file system to store data and support the processing of massive data. A variety of data acquisition technologies are used to support the collection of structured data and unstructured data. Through the graphical model building tool, the process model configuration is supported.

Third-party plug-in technology makes it easy to integrate other tools and services into the platform. The data analysis and judgment platform is the collection of massive information, the construction of data models, the mining and analysis of data, and finally the formation of knowledge to serve the actual combat and serve the decision-making process.

Slowly buy big data.

Data Acquisition & Processing:

Distributed crawler technology, search engine technology, and natural language processing technology are used to mine and label data in a hierarchical and hierarchical manner, establish a link between data and business, and conduct multi-dimensional analysis of the data model. I focus on industry, brand, sales, SKU data monitoring, and analyze product data through reviews, activities, ** and other dimensions.

Visual data analysis: The interface is simple, visual and intuitive data query, analysis, and the best functions help enterprises understand the market environment crisis of products, and timely grasp the channel operation, historical sales and historical adjustment of competitors. Help enterprises tap potential market demand and enhance market competitiveness.

1. Rubik's Cube is a big data model platform, which is a tool platform for data analysis and mining based on the two major technical architectures of service bus and distributed cloud computing, which uses a distributed file system to store data and support the processing of massive data.

2. Baby analysis: find out the most popular babies in the industry according to the baby name keywords, and find out the most popular babies in the industry according to the keywords. Brick Exhibition Analysis: View the specific screen**, date, delivery distribution, and sales during the Brick Exhibition.

3. At present, I am using information communication tools to do e-commerce data analysis. Launched in 2008, it is one of the earliest e-commerce analysis products, providing e-commerce big data services for more than 30,000 stores and brands.

4. Duoduo Master Toolbox is a comprehensive auxiliary tool integrating operation, management and actual combat.

5. Index: http: You can study keyword search trends, gain insight into the interests and needs of netizens, monitor public opinion trends, and locate audience characteristics. JD Business Intelligence: HTTPS: Rich operational data, covering the entire domain of e-commerce, improving operational efficiency.

What is Data Analytics Thinking?

Data analysis thinking, I think, is: turning behavior into data - acting backwards through data.

I'll give you an example:

You often come to my shop to buy auntie towels.

If you come over today to buy an aunt's towel, I know you're going to come to your aunt in about a week. Based on the quantity and specifications you buy, I can infer how long your aunt has been here and how much. Pull out your purchase time for half a year, and I can infer how often you have a great aunt is not stable.

If you haven't seen you buy an aunt's towel for two months... That must have been two months ago, when your boyfriend's raincoat broke.

Pull out your boyfriend's purchase history, and I know that the raincoat in this store may not be up to standard.

In order to verify whether he is unqualified, let's go and see if his repurchase rate within half a year is much lower than that of his peers.

Well, Hail Woo Mo just because you didn't buy an aunt towel, I suspect that the raincoat in this store is not up to standard.

This is the basic thinking of data analysis.

Learning the basic thinking of data analysis, it can only be said that you barely have the possibility of data analysis.

Then do the data analysis. There are a few things that need to be understood.

1. Data sample: If the data sample is not selected reasonably, then the result is completely wrong. For example, if I go to grab an aunt towel store that is located as a 40-year-old aunt, and ask for an aunt cycle for Chinese women, it is not scientific at all.

This is the difference between adolescence and menopause (this example shows that Lin Mubai also has a knowledge of **, and welcomes the majority of unmarried women of appropriate age to write to their friends for consultation).

An example that is often made in actual combat is: a single product with a good conversion rate of flat sales does not sell well in Juhuasuan. Some items with a poor conversion rate will be sold out instead?

Why? Think about it, don't ask me, think for yourself. If you don't understand, don't try to do e-commerce data analysis.

2. Data selection: In fact, we will encounter a lot of data, but some data is not necessarily what we want. It's like we will meet a lot of good girls in our lives, but it's hard for us to understand who can better accompany us through this life.

I can't give an example of this matter, so I'll give you a test question here:

Now our store needs to make coupons**, the purpose is to increase the unit value.

Okay, you tell me to do 100 yuan off 100.

Well, very good, then you tell me now, why is it full 100 instead of full 110, why is it 10 yuan minus instead of 20. Take out your data.

Well, don't ask me how to get it. And don't wonder if I can really analyze it, I really can.

3. Dynamic change: The most commonly used thing is to analyze what problems or changes may occur through changes between data. However, when the amount of data changes, the other data will change as well.

So we need to be clear about what data is positively correlated with each other, what is negative correlation, how they relate to each other, and under what circumstances is true. For example, the proportion of normal favorites is positively correlated with conversion rate, but these days they are inversely correlated. The lower the conversion rate, the higher the collection rate is likely to be.