Basic concepts of data description and statistical analysis for 1. industrial market research

Data description refers to the summary and presentation of the collected data by means of charts, numbers or words, so as to facilitate the observation and understanding of the characteristics and laws of the data. Data description can be divided into two categories: first, qualitative description, that is, using text or graphics to classify, sort or compare data, such as pie charts, bar charts, bar charts, etc.; second, quantitative description, that is, using numbers or formulas to measure, calculate or infer data, such as averages, standard deviations, correlation coefficients, etc.

Statistical analysis refers to the use of mathematical statistics to process and infer the collected data in order to test hypotheses, estimate parameters or predict results. Statistical analysis can be divided into two categories: descriptive statistical analysis, which summarizes and displays data by means of statistics or statistical charts, such as frequency tables, histograms, box plots, etc.; and inferential statistical analysis, which infers and judges data by means of hypothesis tests or confidence intervals, suchttest, analysis of variance, regression analysis, etc.

Data description and statistical analysis of industrial market research refers to the use of data description and statistical analysis methods to process and interpret the data collected in industrial market research in order to reveal the characteristics and laws of the industrial market and provide valuable information and advice to enterprises.

Basic Steps of Data Description and Statistical Analysis for 2. Industrial Market Research

The data description and statistical analysis of industrial market research usually includes the following four steps:

Data cleansing. That is, the raw data collected are checked and modified to eliminate or reduce factors such as errors, missing or outliers that affect data quality and reliability.

Data collation. That is, the cleaned data is classified, encoded, or transformed to facilitate data description and statistical analysis.

Data description. That is, the collated data is summarized and presented in a suitable way to reflect the distribution, concentration, dispersion, correlation and other characteristics of the data.

Data analysis. That is, the described data is processed and inferred in an appropriate way to test hypotheses, estimate parameters or predict results, etc.

Basic skills of data description and statistical analysis for 3. industrial market research

The data description and statistical analysis of industrial market research need to choose the appropriate methods and methods according to different purposes, objects and conditions. The following are some basic skills:

Clear research objectives. That is, before performing data description and statistical analysis, it is important to identify the questions to be answered or the problems to be solved in order to determine the type, scope and source of the data, as well as to select the appropriate description and analysis methods.

Identify study variables. That is, before the data description and statistical analysis, to determine the variables or indicators to be studied, as well as their relationships and roles, in order to facilitate the design of data collection and processing programs.

Select the appropriate chart. That is, when describing the data, according to the nature and characteristics of the data, select charts that can clearly, accurately and effectively display the data information, such as pie charts, bar charts, bar charts, line charts, scatter charts, etc.

Select the appropriate statistics. That is, when describing the data, select statistics that reflect the location of the data center, the degree of variation, and morphological characteristics, such as mean, median, plural, standard deviation, quartiles, bias, and kurtosis, based on the distribution and dispersion of the data.

Select the appropriate statistical method. That is, when performing data analysis, select statistical methods that effectively test hypotheses, estimate parameters, or predict results based on the type and amount of data, as well as the research objectives and assumptions, suchttest, variance analysis, regression analysis, cluster analysis, etc.

Interpret and report results. That is, after completing the data analysis, the results should be reasonably interpreted and evaluated according to the statistical results and practical significance, and the results should be reported to the relevant personnel or institutions in a concise and clear language and format.

Specific cases of data description and statistical analysis 4. industrial market research

In order to better illustrate the practical application and value of data description and statistical analysis for industrial market research, the following is a specific example of a Shangpu consulting service client:

An auto parts manufacturer wants to understand the demand scale, competitive landscape and development trend of its target market (China), and evaluate its market share and competitiveness. Champ Consulting provides the following services to the business:

Data collection. Shangpu Consulting collected historical and forecast data (such as output, sales volume, import and export volume, price, etc.) related to China's auto parts market through various channels (such as official statistics, industry associations, professional media, third-party databases, etc.), And collected relevant information (such as product type, quality, price, channel, etc.) of the company and its main competitors in the Chinese market through questionnaires and interviews.

Data cleansing. Champ Consulting reviewed and revised the raw data collected to eliminate factors such as errors, missing or outliers that affect data quality and reliability.

3. Data collation. Champ Consulting classifies, encodes, or converts the cleaned data to facilitate data description and statistical analysis. For example, the auto parts market is divided according to product type, customer type, regional type and other dimensions, the information of enterprises and their competitors is coded according to product quality, price, channel and other indicators, and the historical and forecast data is unified and time serialized.

Data description. Champ Consulting summarizes and presents the collated data in an appropriate way to reflect the distribution, concentration, dispersion, and correlation of the data. For example, pie charts are used to show the product structure and customer structure of China's auto parts market, bar charts are used to show the regional distribution and import and export of China's auto parts market, line charts are used to show the history and forecast trends of China's auto parts market, and scatter charts are used to show the relationship between product quality and price of the enterprise and its competitors in the Chinese market.

Data analysis. Champ Consulting uses appropriate methods to process and infer the described data to test hypotheses, estimate parameters, or predict results. For example,tExamine and compare whether there are significant differences in product quality and price between the company and its competitors in the Chinese market, compare whether there are significant differences in the impact of different product types, customer types and regional types on the demand for Chinese auto parts market by variance analysis, and estimate the relationship between China's auto parts market demand and auto production and sales, economic growth rate and other factors by regression analysis, and predict the market size in the next few years.

Results interpretation and reporting. According to the statistical results and practical significance, the results are reasonably interpreted and evaluated, and the results are reported to the enterprise in a concise and clear language and format. For example, based on the results of the data description and statistical analysis, Champ Consulting reached the following conclusions and recommendations:

China's auto parts market is large and growing rapidly, but it also faces fierce competition and uncertain risks.

The company has a certain market share and competitiveness in the Chinese auto parts market, but there are also deficiencies and room for improvement in product quality, price, and channels.

The company should formulate reasonable market strategies and marketing strategies based on the characteristics and trends of China's auto parts market, such as optimizing product structure and customer structure, improving product quality and price competitiveness, and expanding regional distribution and import and export channels.