Coatings market observation: the competitive landscape of enterprises in the "big data era"

Nowadays, the "big data era" is gradually moving from concept to materialization and to business.
By means of mathematics, statistics and computer programming, “big data” can not only analyze the future direction of coating companies from related information, but also provide important data for companies to deal with the relationship with consumers, such as consumer’s Expected consumption goals, consumption behaviors, consumption habits, etc.
I believe that in the future, with the continuous development of the Internet era and the continuous advancement of the coating industry, on the road where opportunities and challenges coexist, the application and control of big data is an important means for coating companies to achieve the desired development effect.

Coatings big data application case

Big data analysis of the coating industry in the data age

One: the dispute between oil and water

In the past two years, the most intensely discussed issue in the coatings industry is that the era when water-based paint will replace oil-based paint has arrived.
The coating industry has developed to today as a mature industry.
Which of the traditional oil-based coatings and the modern innovative water-based coatings has strong advantages, or the existence of a special coating that is different from the two, is in line with people’s needs.
The development needs of the times.
The advantage of big data analysis is that the collected data can be used to predict the future development trend of the coatings industry.
Through these predictions, coatings companies can carry out effective reforms and innovations.

Second: Paint companies in the field of e-commerce

More and more paint companies are beginning to get involved in big data platforms, using the accurate analysis capabilities of big data and massive information databases to gain an overall grasp of the market's demand direction.
For paint companies involved in the e-commerce field, the significance of big data is that it can reflect customer "big data" information through the network platform, so that Internet companies can more accurately analyze user behavior and demand mining.
Through the analysis of big data, coating companies will further increase investment in the promotion of high-profile products.
Drawing lessons from the "explosive >

Third: Brand promotion of coating companies

As a less well-known area in the society, the coating industry has always been "crossing the river by touching foreign stones".
Among them, foreign stones refer to famous international paint brands such as Nippon, Dulux, and Valspar.
In China, because the relevant media or institutions have not paid much attention to the coatings industry, many data values ​​in the coatings industry have been ignored by people and disappeared in the historical cycle.
In the past, coating companies’ cognition of data was also limited to some macro data, such as macro information such as annual output, annual growth rate, monthly output, etc.
The cognition of consumers was generally only collected through offline questionnaires.
 

Knowledge popularization: 

Five stages of big data analysis: 

1.
Sample: Extract some representative sample data sets (usually training set, validation set and test set).
The selection criteria of the sample size is: it contains enough important information, and at the same time, it must be easy to analyze and operate.
The processing tools involved in this step are: data import, merging, pasting, filtering, and statistical sampling methods.

2.
Explore: Explore data by investigating relevance, trend, and outliers to enhance the understanding of data.
The tools involved in this step are: statistical reports, view exploration, variable selection, and variable clustering methods.

3.
Modify: Taking model selection as the goal, modify the data set by creating, selecting and transforming variables.
The tools involved in this step are: variable conversion, missing processing, recoding, and data binning.

4.
Model: In order to obtain reliable prediction results, we need to use analytical tools to train statistical models or machine learning models.
The techniques involved in this step are: linear and logistic regression, decision trees, neural networks, partial least squares, LARS and LASSO, K nearest neighbors, and model algorithms for other users (including non-SAS users).

5.
Assess: Assess the validity and reliability of data mining results.
The technologies involved are: comparing models and calculating new fitting statistics, critical analysis, decision support, report generation, scoring code management, etc.
Data miners may not use all SEMMA analysis steps.
However, it may be necessary to repeat some or all of these steps several times before obtaining satisfactory results.
(Source: Global Coatings Network) (For more information, please log in: Global Coatings Network http:// )