Cross-border e-commerce data analysis is a process of collecting, organizing, processing and analyzing data based on business purposes to extract valuable information. Its process mainly includes six links: clarifying the purpose and framework of analysis, data collection and data processing, data analysis, data presentation and report writing.
1. Clarifying the purpose and framework of analysis
Clarifying the purpose and framework of analysis is the first step in cross-border e-commerce data analysis. That is, first of all, it is necessary to clarify who the data object is, what the purpose of analyzing the data is, and what business problems to solve, and then organize the analysis framework and specific analysis ideas based on business understanding.
2. Data collection
The so-called data collection refers to a process of purposefully collecting and organizing relevant data according to the determined data analysis and framework content, which is the basis of data analysis. Data collection channels can be divided into two categories: internal channels and external channels. Internal channels mainly include internal databases of enterprises, internal personnel, customer surveys, and interviews between experts and customers; external channels mainly include the Internet, books and newspapers, statistical departments, industry associations, exhibitions, professional research institutions, etc. Common data collection methods include observation and questioning, user interviews, questionnaires, group discussions, and the use of corresponding tool software.
3. Data processing
Data processing refers to the processing and organization of collected data in order to carry out data analysis. It is an indispensable stage before data analysis and takes up the longest time in the entire data analysis process. Data processing mainly includes data cleaning and data transformation. The main objects of data cleaning and data transformation include incomplete data, erroneous data and duplicate data.
4. Data analysis
Data analysis refers to the exploration and analysis of prepared data through analytical means, methods and techniques to discover causal relationships, internal connections and business rules, and provide decision-making references for enterprises.
5. Data presentation
In general, the results of data analysis are presented in the form of charts. With the help of data presentation visualization tools, data analysts can more intuitively express the information, opinions and suggestions they want to present. Commonly used charts include pie charts, line charts, bar charts, scatter charts, radar charts, pyramid charts, matrix charts, funnel charts, Pareto charts, etc.
6. Report writing
Report writing is the final stage of data analysis, which is a presentation of the entire data analysis results. Through the analysis report, the purpose, process, results and plan of the data analysis are fully presented for the reference of the enterprise. A good data analysis report first needs to have a good analysis framework, and it should be illustrated with text and pictures, with clear levels, so that readers can understand it at a glance. A clear structure and a clear distinction between the primary and the secondary can enable readers to correctly understand the content of the report; the combination of text and pictures can make the data more vivid and intuitive, enhance the visual impact, and help readers see the problems and conclusions more vividly and intuitively, so as to generate thinking.