The basic steps of data analysis and application of 1. consumer research.

The data analysis and application of consumer research is a cyclical process that includes the following four basic steps:

Data collection: Data collection is the first step in consumer research, which involves the clarification of the purpose of the research, the selection of research methods, the determination of research samples, the design of research questionnaires, and the acquisition of research data. The quality of data collection directly affects the effect of data analysis. Therefore, data collection should follow the principles of science, system, effectiveness and objectivity to minimize data deviations and errors.

Data cleaning: Data cleaning is the second step of consumer research. It involves checking, filtering, sorting and transforming the collected data to facilitate subsequent data analysis. The purpose of data cleansing is to remove invalid, duplicate, erroneous, and abnormal values from the data to improve the quality and credibility of the data. The methods of data cleaning include data validation, data filtering, data missing value processing, data outlier processing, data standardization, etc.

Data analysis: Data analysis is the third step of consumer research. It involves describing, summarizing, inferring and predicting the cleaned data to reveal the laws and trends behind the data and the relationship between the data and the research purpose. The methods of data analysis include data description analysis, data association analysis, data classification analysis, data clustering analysis, data regression analysis, data factor analysis, data main component analysis, data decision tree analysis, data neural network analysis, etc.

Data application: Data application is the fourth step of consumer research. It involves transforming the results of data analysis into the basis and guidance of marketing strategy to achieve the marketing objectives of the enterprise. Methods for data applications include data visualization, data reporting, data stories, and data-driven marketing decisions.

Basic principles of data analysis and application of 2. consumer research

The data analysis and application of consumer research is a complex process, which needs to follow some basic principles to ensure the validity and reliability of the data, as well as the value and significance of the data. Here are some common principles:

The principle of purpose: the data analysis and application of consumer research should be guided by the purpose of the research, and the types, scope, dimensions and indicators of the data, as well as the analysis methods and application methods of the data, should be determined according to the purpose of the research. Different research purposes require different data analysis and application strategies, which cannot be generalized or blindly followed.

Systematic principle: The data analysis and application of consumer research should be data-based, taking into account the integrity, relevance and dynamics of the data, as well as the interaction of the data with the market, consumers and enterprises. Data cannot be viewed in isolation, nor can the changes and impacts of data be ignored.

The principle of practicality: The data analysis and application of consumer research should use data as a tool, focusing on the practicality, operability and feasibility of the data, as well as the effect and efficiency of the data. The theoretical, complex and accurate nature of data should not be overly pursued, nor should data be misused and indiscriminately created.

The principle of innovation: the data analysis and application of consumer research should take data as a resource, make full use of the potential and value of data, as well as the diversity and flexibility of data. We should not rigidly adhere to the surface and regular of the data, nor can we ignore the novelty and difference of the data.

Practical tools for data analysis and application of 3. consumer research

The data analysis and application of consumer research is a technical process, which requires the use of some practical tools to improve the efficiency and effectiveness of data processing and presentation. Here are some commonly used tools:

Excel:Excel is a widely used spreadsheet software, it can easily carry out data input, storage, management and calculation, as well as data charts, pivot tables and macros and other functions. Excel is suitable for handling some simple and basic data analysis and application tasks, such as data description analysis, data association analysis, data classification analysis, etc.

SPSS:SPSS is a professional statistical analysis software that can clean, transform, analyze and report data, as well as a variety of statistical methods and models of data, such as data clustering analysis, data regression analysis, data factor analysis, data principal component analysis, etc. SPSS is suitable for dealing with some complex and advanced data analysis and application tasks, such as data exploration analysis, data prediction analysis, data validation analysis, etc.

Power BI:Power BI is a powerful data visualization software that can connect, integrate, analyze, and present data, as well as multiple graphics, dashboards, and stories of data. Power BI is suitable for handling some beautiful and dynamic data analysis and application tasks, such as data visualization analysis, data report analysis, data story analysis, etc.

Specific cases of data analysis and application of 4. consumer research.

The data analysis and application of consumer research is a practical process, which needs to be combined with some specific cases to increase the practicality and reference of the data, as well as the inspiration and persuasion of the data. Here are some examples from clients served by Champ Consulting:

Case 1: A car brand wants to know the evaluation and feedback of consumers on its new models, as well as the purchase intention and behavior of consumers. Shangpu Consulting Company adopted the method of online questionnaire to investigate the target consumers and collected data on consumers' basic information, cognition, attitude, satisfaction and loyalty to the new models. Shangpu Consulting Company used SPSS software to clean, transform and analyze the data, and used data description analysis, data association analysis, data classification analysis, data clustering analysis and other methods to obtain the following analysis results:

Consumers' cognition of the new models mainly comes from the Internet, friends and advertisements, among which the Internet has the greatest influence, accounting for 60%;

Consumers' attitudes towards new models are mainly divided into three categories, namely, like, general and dislike, with 40% of them, 35% of them and 25% of them;

Consumers' satisfaction with new models is mainly affected by the appearance, performance, safety and price of the models. Among them, appearance and performance have the greatest impact, accounting for 30% and 25% of satisfaction respectively;

Consumers' loyalty to new models is mainly affected by the brand, quality, service and reputation of the models, among which brand and quality have the greatest influence, accounting for 35% and 30% of the loyalty respectively;

The purchase intention of consumers is mainly affected by the satisfaction and loyalty of the model, the higher the satisfaction and loyalty, the stronger the purchase intention, and the correlation coefficient between the two is 0.8;

Consumers' purchase behavior is mainly affected by the price of the model, promotion and competition. The lower the price, the more promotion, the smaller the competition, and the more purchase behavior.

Champ Consulting used Power BI software to visualize and display the results of data analysis, and produced a data report that included data graphics, dashboards, and stories to make it easier for marketers of car brands to more intuitively and clearly understand the meaning and value of the data.

Based on the results of data analysis and application, Champu Consulting has made the following marketing recommendations for automotive brands:

Strengthen the strength and effect of network marketing, use social media, video platform, blog and other channels to improve the popularity and reputation of new models;

According to different consumer attitudes, formulate different marketing strategies. For consumers who like it, increase loyalty and purchase intention. For general consumers, increase satisfaction and purchase intention. For consumers who don't like it, change attitude and purchase intention;

Optimize the appearance and performance of new models, highlight their differentiation and advantages with competitors, and meet the aesthetic and functional needs of consumers;

Enhance the brand and quality of new models, enhance their status and trust in the hearts of consumers, and increase consumer loyalty and reputation;

Appropriately adjust the price and promotion of new models, and formulate reasonable pricing and promotion strategies based on market supply and demand and competition to stimulate consumer buying behavior.

Case 2: A cosmetics brand wants to know consumers' evaluation and feedback on its newly launched skin care products, as well as consumers' usage habits and effects. Shangpu Consulting Company used the method of in-depth interview to investigate the target consumers and collected data on consumers' basic information, cognition, attitude, satisfaction and loyalty of new skin care products. Champu Consulting Company used Excel software to input, store, manage and calculate the data, and used the functions of data charts, pivot tables and macros to obtain the following analysis results:

Consumers' awareness of new skin care products mainly comes from brand official website, WeChat public number and beauty bloggers, of which brand official website has the greatest influence, accounting for 50%;

Consumers' attitudes towards new skin care products are mainly divided into two categories, namely satisfaction and dissatisfaction, of which 70% are satisfied and 30% are dissatisfied;

Consumers' satisfaction with new skin care products is mainly affected by the ingredients, efficacy, texture and smell of the products, among which the ingredients and efficacy have the greatest influence, accounting for 40% and 35% of the satisfaction respectively;

Consumers' loyalty to new skin care products is mainly affected by factors such as product price, packaging, service and reputation, among which price and packaging have the greatest impact, accounting for 30% and 25% of loyalty respectively;

Consumers' usage habits are mainly affected by factors such as product usage, frequency of use, usage and usage scenarios, with usage and frequency of use having the greatest impact, accounting for 35% and 30% of usage habits, respectively;

The use effect of consumers is mainly affected by the satisfaction and loyalty of the product. The higher the satisfaction and loyalty, the better the use effect.

Shangpu Consulting used Power BI software to visualize and display the results of data analysis and produced a data report, including graphics, dashboards and stories of the data, so that marketers of clothing brands can more intuitively and clearly understand the meaning and value of the data.

Based on the results of data analysis and application, Champu Consulting has put forward the following marketing suggestions for clothing brands:

Strengthen the construction and maintenance of brand Weibo, and use the content, interaction, forwarding and comment functions of Weibo to improve the visibility and reputation of the new clothing series;

According to different consumer attitudes, formulate different marketing strategies, increase loyalty and repurchase rate for consumers who like it, increase satisfaction and conversion rate for consumers who don't like it, stimulate purchase motivation and behavior for consumers who don't care, and increase awareness and attention for consumers who don't understand it;

Optimize the style and color of the new clothing series, highlight its differentiation and advantages with competitors, and meet the needs of consumers' fashion and personality;

Enhance the brand and price of the new clothing series, improve its quality and cost performance in the minds of consumers, and increase consumer loyalty and reputation;

Appropriately adjust the inventory and promotion of the new clothing series, formulate reasonable inventory and promotion strategies according to the market supply and demand and competition, and stimulate consumer buying behavior.