1. data collection

Data collection is the first and most basic step in consumer research. The purpose of data collection is to obtain sufficient, valid, reliable and representative consumer information for subsequent analysis and presentation. There are two main types of data collection methods: quantitative and qualitative. Quantitative methods refer to the collection of data on the number, frequency, proportion, and rating of consumers through numerical and quantitative methods. The advantage of quantitative methods is that they can cover a large number of samples, provide objective and accurate data, and facilitate statistical analysis and inference. The disadvantage of quantitative methods is that they do not provide insight into the motivations, attitudes, feelings and opinions of consumers, nor do they capture the diversity and complexity of consumers. Common forms of quantitative methods include questionnaires, experiments, observations, and secondary data. Qualitative methods refer to the collection of data such as consumer descriptions, interpretations, evaluations, and recommendations through verbal and non-verbal methods. The advantage of qualitative methods is that they can deeply explore the internal psychology and behavior of consumers, provide rich and detailed data, and facilitate interpretation, analysis and understanding. The disadvantage of qualitative methods is that they cannot involve a large number of samples, provide objective and accurate data, and are difficult to carry out statistical analysis and inference. Common forms of qualitative methods include interviews, focus groups, case studies, and text analysis.

The main points for attention in data collection are as follows:

Clarify the objectives and scope of data collection, select appropriate methods and tools based on research questions and assumptions, and design appropriate programs and processes.

Ensure the quality and efficiency of data collection, follow the principles of science and ethics, ensure the authenticity, accuracy, completeness and timeliness of data, and avoid data deviation, error and loss.

Appropriate combination of quantitative and qualitative methods, according to the characteristics and needs of the data, the use of multiple and complementary ways to improve the effectiveness and richness of the data.

Maintain the flexibility and innovation of data collection, adjust and optimize data collection methods and tools in a timely manner according to data changes and feedback, and use new technologies and new channels to enhance the effectiveness and value of data collection.

Example of data collection: Shangpu Consulting conducted a consumer survey for a fast-moving consumer product company to understand consumers' evaluation of the company's brand image, product satisfaction and loyalty. The following data collection methods were used by Champ Consulting:

Questionnaire survey: Through online and offline methods, a questionnaire containing questions on brand awareness, product use, purchase behavior, satisfaction and loyalty was distributed to 1000 consumers, and consumer quantitative data were collected.

Interviews: Semi-structured interviews were conducted with 100 consumers by telephone and video, collecting qualitative data on brands and products.

Focus groups: Qualitative consumer data on brands and products were collected by inviting 10 consumers to participate in a discussion led by a professional moderator.

Secondary data: Quantitative consumer data is collected by collecting and collating sales data, market share data, advertising data, social media data, etc. of the business and competitors.

2. data analysis

Data analysis is the second and most important step in consumer research. The purpose of data analysis is to extract valuable information from the collected data, discover data patterns, trends and problems, and form data insights andConclusion. There are two main types of data analysis methods: descriptive analysis and inferential analysis. Descriptive analysis refers to the description of the distribution, concentration, dispersion and variation of data by calculating the average, standard deviation, frequency, percentage, etc. The advantage of descriptive analysis is that it can visually and concisely show the basic situation of the data, and facilitate the comparison and classification of the data. The disadvantage of descriptive analysis is that it does not delve into the intrinsic relationships and influencing factors of the data, nor does it make predictions and inferences about the data. Common forms of descriptive analysis are tables, charts, indicators, etc. Inferential analysis refers to the analysis of the causality, correlation, influence and structure of data by using hypothesis testing, correlation analysis, regression analysis, factor analysis, etc. The advantage of inferential analysis is that it can dig deeper into the potential meaning and value of the data and facilitate the prediction and inference of the data. The disadvantage of inferential analysis is that it requires high statistical knowledge and skills, as well as strong logical reasoning and judgment. Common forms of inferential analysis are models, equations, coefficients, and so on.

The main points of attention in data analysis are as follows:

Clarify the objectives and scope of data analysis, select appropriate methods and tools according to the type and characteristics of the data, and design reasonable programs and processes.

Ensure the quality and efficiency of data analysis, follow the principles of science and logic, ensure the validity, reliability and consistency of data, and avoid misunderstanding, abuse and misuse of data.

Appropriate combination of descriptive and inferential methods, according to the needs and purpose of the data, adopt a comprehensive and systematic way to improve the insight and persuasion of the data.

Maintain the flexibility and innovation of data analysis, adjust and optimize the methods and tools of data analysis in time according to the changes and feedback of data, and use new methods and technologies to improve the effect and value of data analysis.

Examples of data analysis: Champ Consulting conducted the following data analysis methods on the collected consumer data:

Descriptive analysis: describes the overall evaluation and distribution of consumers by calculating the average, standard deviation, frequency and percentage of consumer satisfaction and loyalty to brands and products. By making bar charts, pie charts, radar charts, etc., the evaluation results and differences of consumers are displayed.

Intrapolative analysis: By using hypothesis testing, it analyzes whether there are significant differences in consumer satisfaction and loyalty, and whether it is affected by brand awareness, product use, purchase behavior and other factors. By using correlation analysis, the degree of correlation between consumer satisfaction and loyalty, and the degree of correlation with other variables are analyzed. By using regression analysis, the influence factors and influence of consumer satisfaction and loyalty are analyzed, and the change trend of consumer satisfaction and loyalty is predicted.

3. Data Presentation

Data presentation is the third and final step in consumer research. The purpose of the data presentation is to combine the information obtained from the analysis andConclusion, in a clear, concise and persuasive manner, presented to decision makers and stakeholders to support their decisions and actions. There are two main types of data presentation methods: text presentation and visual presentation. Text presentation refers to expressing the meaning and value of data in writing or orally through language. The advantage of text presentation is that it can convey the information of the data in detail and accurately.Conclusionto facilitate the interpretation and understanding of the data. The disadvantage of text presentation is that it can not display the characteristics and effects of data intuitively and vividly, and it is difficult to attract and influence the attention and emotion of the audience. Common forms of text presentation are reports, articles, speeches, discussions, etc. Visual presentation refers to the way through graphics, images or animation to express the meaning and value of data. The advantage of visual presentation is that it can visually and vividly display the characteristics and effects of the data, and facilitate the comparison and induction of the data. The disadvantage of visual presentation is the inability to convey the information of the data in detail and accurately.ConclusionIt is also easy to misread and misuse data. Common forms of visual presentation are charts, diagrams, dashboards, storyboards, etc.

The main points of attention for data presentation are as follows:

Clarify the goal and scope of data presentation, select appropriate methods and tools according to the characteristics and needs of the audience, and design reasonable schemes and processes.

Ensure the quality and efficiency of data presentation, follow the principles of simplicity and clarity, ensure data readability, visibility and operability, and avoid data redundancy, confusion and distortion.

Appropriate combination of text and visual methods, according to the characteristics and purpose of the data, the use of balance and coordination, improve the expression and appeal of the data.

Maintain the flexibility and innovation of data presentation, adjust and optimize the methods and tools of data presentation in time according to data changes and feedback, and use new media and new platforms to enhance the effect and value of data presentation.

Examples of data presentation: Champu Consulting produced a consumer research report for this FMOC company, including the following data presentation methods:

Text presentation: by writing a reportAbstract, background, purpose, method, results,ConclusionThe process and results of consumer research, as well as strategic and action suggestions for the enterprise, are expressed in words.

Visual presentation: through the production of the report cover, catalog, charts, diagrams, dashboards and other parts, visually express the process and results of consumer research, as well as strategic recommendations and action recommendations for the enterprise.

Conclusion

Consumer research is an important means of marketing, which can help companies understand consumer needs, preferences, behavior and satisfaction, so as to develop effective marketing strategies. The process of consumer research usually includes three key links: data collection, analysis and presentation. This paper introduces the purpose, methods, precautions and examples of these three links, in order to provide reference and guidance for the practice of consumer research. As a professional market consulting organization, Shangpu Consulting Company has rich experience and ability in consumer research. It provides high-quality consumer research services for many FMC enterprises, helping them to improve their brand image, product satisfaction and loyalty, and increase their market share and profit margin. Shangpu Consulting looks forward to cooperating with more FMCC companies to jointly create a better market future.