Techniques and Tools for 1. Data Acquisition

Data acquisition technologies and tools refer to the technologies and tools used to obtain data from different data sources and channels, including the following:

Network survey technology and tools: Network survey technology and tools refer to the technology and tools for questionnaire survey through the Internet, such as SurveyMonkey, Qualtrics, Wjx, etc. The advantages of network survey technology and tools are that it can collect a large amount of data quickly, conveniently and at low cost, can realize automatic and intelligent data collection and processing, and can cover a wide range of regions and people. The disadvantage of network investigation techniques and tools is that there may be data distortion and deviation, which may be affected by the security and stability of the network, and it may be difficult to obtain in-depth and detailed data.

Mobile survey technology and tools: Mobile survey technology and tools refer to the technology and tools for conducting questionnaire surveys through mobile devices, such as mobile phones, tablets, smart watches, etc. The advantages of mobile survey technology and tools are that they can collect data flexibly, in real time and efficiently, and can take advantage of the functions and features of mobile devices, such as positioning, camera, voice, etc., to increase the participation and satisfaction of survey respondents. The disadvantage of mobile investigation technology and tools is that there may be inconsistent and incomparable data, which may be affected by the compatibility and performance of mobile devices, and it may be difficult to ensure the security and confidentiality of data.

Social media survey technology and tools: Social media survey technology and tools refer to the technology and tools for conducting questionnaire surveys through social media, such as WeChat, Weibo, Facebook, Twitter, etc. The advantage of social media survey techniques and tools is that they can take advantage of the popularity and influence of social media, such as word-of-mouth, sharing, comments, etc., can obtain rich and diverse data, and can observe and analyze the social behavior and psychology of the respondents. The disadvantages of social media survey techniques and tools are that there may be inaccurate and non-representative data, which may be affected by the rules and restrictions of social media, and it may be difficult to control and manage the quality and volume of data.

2. Data Management Techniques and Tools

Data management techniques and tools refer to the techniques and tools used to clean, organize, encode, classify, summarize and count the collected data, which mainly include the following:

Database technology and tools: Database technology and tools refer to the technologies and tools used to store and manage data, such as MySQL, Oracle, SQL Server, etc. The advantage of database technology and tools is that it can realize the structure and standardization of data, ensure the integrity and consistency of data, and realize the fast and flexible query and operation of data. The disadvantage of database technology and tools is that it requires high skills and experience, may have risks to the security and confidentiality of data, and may be affected by the performance and capacity of the database.

Data cleaning techniques and tools: Data cleaning techniques and tools refer to techniques and tools used to clean and correct data, such as Trifacta, OpenRefine, Data Ladder, etc. The advantage of data cleaning technology and tools is that it can eliminate and correct data errors and anomalies, such as missing, duplication, inconsistency, unreasonable, etc., can improve the accuracy and reliability of data, and can reduce data noise and interference. The disadvantage of data cleaning techniques and tools is that they require more time and cost, there may be data loss and changes, and it may be difficult to adapt to the complexity and diversity of data.

Data integration technologies and tools: Data integration technologies and tools refer to technologies and tools used to integrate and fuse data, such as ETL, Data Warehouse, and Data Lake. The advantage of data integration technology and tools is that data from different data sources and channels can be unified and coordinated, such as format, coding, units, standards, etc., can achieve data completeness and consistency, can increase the value and meaning of data. The disadvantages of data integration techniques and tools are that they require high skills and experience, may have data conflicts and incompatibilities, and may be affected by the quality and volume of data.

3. Data Analysis Techniques and Tools

Data analysis techniques and tools refer to the techniques and tools used to analyze and interpret data, including the following:

Statistical analysis techniques and tools: Statistical analysis techniques and tools are techniques and tools used for descriptive analysis, inferential analysis, correlation analysis, etc. of data, such as SPSS, SAS, R, etc. The advantages of statistical analysis techniques and tools are that they can make objective and standard measurements and comparisons of data, verify and test hypotheses and theories, and discover and interpret patterns and trends in data. The disadvantages of statistical analysis techniques and tools are that they require high skill and experience, may have data assumptions and limitations, and may be difficult to process and analyze unstructured and non-numerical data.

Data mining techniques and tools: Data mining techniques and tools are techniques and tools used for regression analysis, factor analysis, clustering, etc. of data, such as WEKA, KNIME, RapidMiner, etc. The advantages of data mining techniques and tools are that they can perform complex and in-depth analysis and inference on data, can discover and extract patterns and structures of data, and can predict and influence changes and results of data. The disadvantage of data mining techniques and tools is that they require high skill and experience, there may be over-fitting and under-fitting of data, and it may be difficult to interpret and understand the analysis and findings of the data.

Data visualization technologies and tools: Data visualization technologies and tools are technologies and tools used to graphically and dynamically display and interact with data, such as Tableau, Power BI, D3.js, etc. The advantages of data visualization techniques and tools are that they can make data more intuitive and vivid, enhance the expression and communication of data, and improve the understanding and cognition of data. The disadvantage of data visualization techniques and tools is that they require high skills and experience, may be distorted and misleading, and may be difficult to adapt to the complexity and diversity of the data.

4. Data Presentation Techniques and Tools

Data presentation techniques and tools refer to the techniques and tools used to present and communicate the results of data analysis, including the following:

Report generation technology and tools: Report generation technology and tools refer to the technology and tools used to write a clear, complete, accurate and convincing report in the form of text, charts, tables, etc., according to logic and structure, such as Word, Excel, PowerPoint, etc. The advantage of report generation technology and tools is that it can realize the standardization and standardization of data analysis results, can ensure the integrity and consistency of data analysis results, and can realize the rapid and flexible modification and adjustment of data analysis results. The disadvantages of report generation techniques and tools are that they require more time and cost, may have redundancy and duplication of data analysis results, and may be difficult to capture and express the essence and highlights of data analysis results.

Presentation techniques and tools: Presentation techniques and tools refer to techniques and tools used to effectively and impactfully display and communicate the results of data analysis orally or in writing, such as PPT, Keynote, Prezi, etc. The advantage of demonstration techniques and tools is that they can visualize and dynamic data analysis results, enhance the expression and communication of data analysis results, and improve the understanding and cognition of data analysis results. The disadvantage of demonstrating presentation techniques and tools is that they require a high level of skill and experience, there may be simplification and distortion of data analysis results, and it may be difficult to adapt to different occasions and audiences.

Storytelling technologies and tools: Storytelling technologies and tools are technologies and tools used to tell and share the results of data analysis in the form of stories, such as Storytelling, Storybird, and Pixar. The advantage of story telling technology and tools is that it can realize the humanization and emotion of data analysis results, enhance the attraction and emotion of data analysis results, and improve the memory and influence of data analysis results. The disadvantage of storytelling techniques and tools is that they require high skills and experience, may have exaggerated and deviated data analysis results, and may be difficult to adapt to different purposes and effects.

Conclusion

Market research is the process of collecting, analyzing and interpreting information about the market, customers, competitors and the environment to help decision makers make better decisions. Market research technologies and tools are a variety of software, hardware, platforms and systems used to support and optimize the process and results of market research. Market research techniques and tools can be divided into data acquisition techniques and tools, data management techniques and tools, data analysis techniques and tools, and data presentation techniques and tools. The selection and use of market research techniques and tools should be based on different market research purposes and circumstances to ensure the quality and effectiveness of market research.