The Significance and Purpose of 1. Consumer Research

Consumer research refers to the process of collecting and analyzing consumer-related information through certain methods and means to understand consumer behavior and psychological characteristics, so as to provide a basis for marketing decisions. The significance and purpose of consumer research are mainly the following:

Understand consumer needs and preferences. Consumer needs and preferences are important factors that affect their purchasing behavior. Through consumer research, companies can find consumers' potential needs and unmet needs, as well as consumers' expectations and evaluations of products or services, so as to provide products or services that are more in line with consumers' needs and preferences, and increase consumer satisfaction and loyalty.

Understand consumer behavior and psychology. The behavior and psychology of consumers are the internal driving forces that affect their purchasing behavior. Through consumer research, companies can master consumers' purchasing motivation, purchase process, purchase frequency, purchase channels, purchase amount, etc., as well as consumers' cognition and attitude., Emotions, values, etc., so as to better understand consumers' behavior patterns and psychological characteristics, and provide a basis for market segmentation, target positioning, product positioning, and promotion strategies.

Understand consumer feedback and opinions. Consumers' feedback and opinions are important indicators for evaluating the quality and effect of an enterprise's products or services. Through consumer research, an enterprise can collect consumers' satisfaction, loyalty, willingness to recommend, complaint intention, etc. to the products or services, as well as consumers' evaluation of the enterprise's image, reputation, trust, etc., so as to find and solve consumers' problems and dissatisfaction in time, improve and optimize the quality and effect of the products or services, improve consumer loyalty and reputation.

Frequently Asked Questions and Impacts of 2. Consumer Research

Consumer research is a complex task, involving multiple links and multiple factors. Therefore, consumer research also has some common problems, which may lead to the reduction of the quality and effect of consumer research, and even produce errors.Conclusionand decision-making. This article will introduce the common problems and impacts of consumer research from four aspects: sample selection, data collection, data analysis, and data interpretation.

1. Sample selection

Sample selection is the first and most critical step in consumer research, as the quality of the sample directly determines the quality of the data, which in turn directly affects the quality of the analysis andConclusioneffectiveness. The common problems and effects of sample selection are as follows:

The sample is not representative. The sample is not representative means that the sample does not reflect the characteristics and distribution of the population, resulting in the results of the sample can not be extended to the population, thus affecting the accuracy and credibility of consumer research. There may be several reasons why the sample is not representative:

Insufficient sample size. Insufficient sample size refers to the number of samples can not reach a certain level, making the sample error is large, can not effectively eliminate the influence of random factors, thus affecting the stability and reliability of the sample. The determination of the sample size should be based on the overall size, distribution, degree of variation, sampling method, confidence level, allowable error and other factors, and should not be determined arbitrarily or too sparingly.

The sample design is unreasonable. The unreasonable sample design means that the sample extraction method or stratification method can not reflect the overall structure and characteristics, resulting in a large deviation of the sample, can not effectively control the influence of system factors, thus affecting the consistency and validity of the sample. The choice of sample design should be based on the overall type, characteristics, availability, operability and other factors, not blindly adopted or too complex.

There is a failure or rejection of the sample. The failure or rejection of the sample means that some or all of the objects in the sample cannot be contacted, cannot answer, refuse to answer or answer untrue during the data collection process, resulting in the absence or change of the sample, thus affecting the integrity and consistency of the sample. The reasons for the failure or rejection of the sample may be as follows:

The data collection method is not suitable. The unsuitability of the data collection method means that the method or tool of data collection cannot adapt to the characteristics, habits, preferences, etc. of the sample object, resulting in the uncooperation or dissatisfaction of the sample object, thus affecting the efficiency and quality of data collection. The choice of data collection method should be based on the number, distribution, accessibility, credibility and other factors of the sample object, and should not be used alone or too complex.

Inappropriate time for data collection. The inappropriate time of data collection means that the time point or time period of data collection can not adapt to the rhythm of life, work and study of the sample object, which leads to the inconvenience or reluctance of the sample object, thus affecting the efficiency and quality of data collection. The determination of data collection time should be based on the behavior, psychology, emotion and other characteristics of the sample object, and should not be arranged arbitrarily or too frequently.

Data collection is unreasonable. The unreasonable content of data collection means that the problems or indicators of data collection can not adapt to the level of knowledge, ability and interest of the sample objects, which leads to the incomprehension or disinterest of the sample objects, thus affecting the efficiency and quality of data collection. The design of data collection content should be based on the characteristics, purpose, background and other factors of the sample object, and should not be too simple or too complex.

The sample is biased or misleading. Sample bias or misleading means that some or all of the objects in the sample are affected by some external or internal factors in the data collection process, resulting in untrue or unobjective results of the sample, thus affecting the fairness and authenticity of the sample. There may be several reasons why the sample is biased or misleading:

Data collection personnel are not professional. The unprofessional data collection personnel means that the executor or coordinator of data collection does not have sufficient knowledge, skills, experience or quality, which leads to some errors or mistakes in the process of data collection, thus affecting the standardization and effectiveness of data collection. The selection and training of data collection personnel should be comprehensively considered according to the requirements, difficulty, importance and other factors of data collection, and should not be arranged arbitrarily or too simple.

Inappropriate data collection environment. The unsuitable data collection environment means that the place or condition of data collection cannot adapt to the comfort, security, privacy and so on of the sample object, resulting in the sample object being unnatural or not relaxed, thus affecting the authenticity and validity of data collection. The selection and arrangement of the data collection environment should be based on the characteristics, preferences, needs and other factors of the sample object, and should not be arranged arbitrarily or overcrowded.

Data collection issues are not neutral. The problem of non-neutrality of data collection refers to the existence of some subjective or tendentious words or expressions in the problems or indicators of data collection, which leads to the unfairness or unobjectivity of the sample objects, thus affecting the fairness and effectiveness of data collection. The design and presentation of data collection issues should be based on the purpose, content, scope and other factors of data collection, and should not be too subjective or too guided.

2. Data collection

Data collection is the second and most important step in consumer research, as the quality of the data directly determines the quality of the analysis andConclusioneffectiveness. Common issues and implications of data collection are as follows:

Incomplete or inaccurate data. Incomplete or inaccurate data means that the quantity or quality of the data cannot reach a certain level, resulting in a decrease in the availability or credibility of the data, which affects the analysis and interpretation of the data. There are several possible reasons for incomplete or inaccurate data:

Missing or incorrect data. Missing data or errors refer to some omissions or errors in the recording or input process of data, resulting in the loss or change of data, thus affecting the integrity and consistency of the data. Missing or incorrect data can be caused:

Data collection tools are unreliable. Unreliable data collection tools refer to some faults or defects in the data collection methods or tools, resulting in data damage or loss, thus affecting the integrity and consistency of the data. The selection and inspection of data collection tools should be based on the requirements, difficulty, importance and other factors of data collection, and should not be used arbitrarily or too old.

Data collection personnel are not standardized. Data collection personnel are not standardized means that the executor or coordinator of data collection does not follow certain rules or standards, resulting in some errors or errors in the recording or input of data, thus affecting the integrity and consistency of the data. The training and supervision of data collection personnel should be comprehensively considered according to the requirements, difficulty and importance of data collection, and should not be arranged at will or too lax.

The data collection object is not cooperating. The non-cooperation of the data collection object means that the data collection object does not provide or answer the data as required or agreed, resulting in missing or wrong data, thus affecting the integrity and consistency of the data. The selection and motivation of data collection objects should be considered comprehensively according to the purpose, content, scope and other factors of data collection, and should not be arranged arbitrarily or too mandatory.

Inconsistent or non-standard data. Data inconsistency or non-standard means that there are some differences or inconsistencies in the source or format of the data, resulting in difficulties in comparing or integrating the data, thus affecting the availability and credibility of the data. There may be several reasons for inconsistent or non-standard data:

Data sources are different. Different data sources mean that the data comes from different channels or platforms, resulting in some differences or incompatibilities in the quality or content of the data, thus affecting the comparability and integrability of the data. The selection and evaluation of data sources should be comprehensively considered according to the purpose, content, scope and other factors of the data, and should not be used arbitrarily or too diverse.

The data format is different. Different data formats refer to some differences or inconsistency in the way data is stored or displayed, resulting in difficulties in data conversion or processing, thus affecting the availability and credibility of the data. The choice and specification of data format should be considered comprehensively according to the requirements, difficulty, importance and other factors of the data, and should not be used arbitrarily or too complicated.

Insufficient or irrelevant data. Insufficient or irrelevant data means that the quantity or quality of data cannot meet a certain level, resulting in a decrease in the validity or usefulness of the data, thereby affecting the analysis and interpretation of the data. The data may be inadequate or irrelevant for several reasons:

Too little or too much data. Too little or too much data means that the amount of data cannot be adapted to the needs or capabilities of the analysis, resulting in some difficulties or errors in the analysis of the data, thereby affecting the validity or usefulness of the data. The amount of data should be determined according to the purpose, content, scope and other factors of the analysis, and should not be too economical or too wasteful.

Data is too old or too new. Data that is too old or too new means that the time span of the data cannot be adapted to the needs or capabilities of the analysis, resulting in some bias or error in the analysis of the data, thereby affecting the validity or usefulness of the data. The time span of the data should be determined according to the purpose, content, scope and other factors of the analysis, and should not be too old or too updated.

The data is over-simplified or over-repeated. Data over-simplification or over-complexity refers to the fact that the dimensions or levels of the data do not adapt to the needs or capabilities of the analysis, resulting in some deficiencies or redundancies in the analysis of the data, thus affecting the validity or usefulness of the data. The determination of the dimension or level of the data should be considered comprehensively according to the purpose, content, scope and other factors of the analysis, and should not be too simple or too complex.

3. Data analysis

Data analysis is the third and most central step in consumer research, as the quality of data analysis directly determinesConclusionquality and effectiveness. The common problems and impacts of data analysis are as follows:

Inappropriate or unreasonable data analysis methods. The inappropriate or unreasonable data analysis method means that the method or model of data analysis cannot adapt to the characteristics or needs of the data, resulting in inaccurate or unreliable results of data analysis, thus affecting the quality and effectiveness of data analysis. Data analysis methods may be inappropriate or unreasonable for the following reasons:

Data analysis method mismatch. Data analysis method mismatch refers to the inconsistency between the method or model of data analysis and the type, distribution, relationship, etc. of the data, resulting in the results of data analysis do not conform to the actual situation of the data, thus affecting the accuracy and validity of data analysis. The choice of data analysis methods should be based on the characteristics, purpose, background and other factors of the data, and should not be used arbitrarily or too simple.

The data analysis method is not perfect. Incomplete data analysis methods refer to some defects or limitations in the methods or models of data analysis, resulting in incomplete or incomplete results of data analysis, thus affecting the effectiveness and usefulness of data analysis. The improvement of data analysis methods should be considered comprehensively according to the characteristics, purpose, background and other factors of the data, and should not be used arbitrarily or too complicated.

The data analysis method is not standardized. Data analysis method is not standardized means that the method or model of data analysis does not comply with certain rules or standards, resulting in the results of data analysis is not credible or comparable, thus affecting the credibility and comparability of data analysis. The specification of data analysis methods should be considered comprehensively according to the requirements, difficulty, importance and other factors of the data, and should not be used arbitrarily or too arbitrarily.

The data analysis process is not clear or transparent. The unclear or opaque data analysis process means that the process or steps of data analysis cannot be clearly displayed or explained, resulting in the results of data analysis being difficult to understand or question, thus affecting the comprehensibility and acceptability of data analysis. There are several reasons why the data analysis process is unclear or opaque:

The data analysis process is not clear. The ambiguity of the data analysis process means that the process or steps of data analysis are not clearly defined or described, resulting in unclear or inconsistent results of data analysis, thus affecting the comprehensibility and acceptability of data analysis. The clarity of the data analysis process should be considered comprehensively according to the purpose, content, scope and other factors of the data analysis, and should not be arranged arbitrarily or too simple.

The data analysis process is not visible. The invisibility of the data analysis process means that the process or steps of the data analysis are not effectively displayed or presented, resulting in the results of the data analysis being unintuitive or unattractive, thereby affecting the comprehensibility and acceptability of the data analysis. The visualization of the data analysis process should be considered comprehensively according to the requirements, difficulty, importance and other factors of data analysis, and should not be used arbitrarily or too complicated.

The data analysis process is not traceable. The non-traceability of the data analysis process means that the process or steps of the data analysis are not effectively recorded or stored, resulting in the results of the data analysis that cannot be reviewed or modified, thus affecting the credibility and comparability of the data analysis. The traceability of the data analysis process should be considered comprehensively according to the requirements, difficulty, importance and other factors of data analysis, and should not be used arbitrarily or too simple.

4. Data interpretation

Data interpretation is the fourth and final step of consumer research, because the quality of data interpretation directly determinesConclusionquality and effect. Common problems and implications of data interpretation are as follows:

Data interpretation is inaccurate or unreasonable. Inaccurate or unreasonable data interpretation means that the method or logic of data interpretation cannot adapt to the characteristics or needs of the data, resulting in incorrect or inappropriate results of data interpretation, thus affecting the quality and effectiveness of data interpretation. There may be several reasons for inaccurate or unreasonable data interpretation:

Data interpretation does not fit the data. Data interpretation does not conform to the data means that the method or logic of data interpretation is inconsistent with the type, distribution, relationship, etc. of the data, resulting in the results of data interpretation do not reflect the actual situation of the data, thus affecting the accuracy and validity of data interpretation. The choice of data interpretation should be based on the characteristics, purpose, background and other factors of the data, and should not be used arbitrarily or too simple.

Data interpretation is not fit for purpose. Data interpretation does not meet the purpose refers to the method or logic of data interpretation and the purpose, content, scope, etc. are inconsistent, resulting in the results of data interpretation does not meet the needs or expectations of the data, thus affecting the effectiveness and usefulness of data interpretation. The choice of data interpretation should be considered comprehensively according to the characteristics, purpose, background and other factors of the data, and should not be used arbitrarily or too complicated.

Data interpretation is not logical. Data interpretation is not logical means that there are some errors or loopholes in the method or logic of data interpretation, resulting in unreasonable or unreliable results of data interpretation, thus affecting the credibility and comparability of data interpretation. The choice of data interpretation should be based on the requirements, difficulty, importance and other factors of the data, and should not be used arbitrarily or too arbitrarily.

Data interpretation is unclear or unclear. Unclear or unclear data interpretation means that the expression or explanation of the data interpretation cannot be clearly displayed or explained, resulting in the results of the data interpretation being difficult to understand or question, thus affecting the comprehensibility and acceptability of the data interpretation. The reasons for the unclear or unclear interpretation of the data may be the following:

Data interpretation is not clear. Unclear data interpretation means that the expression or description of data interpretation is not clearly defined or described, resulting in unclear or inconsistent results of data interpretation, thus affecting the comprehensibility and acceptability of data interpretation. The clarity of data interpretation should be considered comprehensively according to the purpose, content, scope and other factors of data interpretation, and should not be arranged arbitrarily or too simple.

Data interpretation is not clear. Unclear data interpretation means that the expression or explanation of data interpretation is not effectively displayed or presented, resulting in unintuitive or unattractive results of data interpretation, thus affecting the comprehensibility and acceptability of data interpretation. The clarity of data interpretation should be comprehensively considered according to the requirements, difficulty, importance and other factors of data interpretation, and should not be used arbitrarily or too complicated.

Data interpretation is not clear. Unclear data interpretation means that the expression or explanation of data interpretation is not effectively recorded or saved, resulting in the results of data interpretation can not be reviewed or modified, thus affecting the credibility and comparability of data interpretation. The clarity of data interpretation should be comprehensively considered according to the requirements, difficulty, importance and other factors of data interpretation, and should not be used arbitrarily or too arbitrarily.

3. Shangpu Consulting's Consumer Research Solution

Shangpu Consulting is a professional market research and consulting company with many years of experience and ability in consumer research, providing customers with comprehensive consumer research services, including the design, implementation, analysis and interpretation of consumer research. In the process of consumer research, Shangpu Consulting has adopted some effective solutions to avoid or reduce the common problems of consumer research and improve the quality and effectiveness of consumer research. Here are some examples of consumer research solutions from Champ Consulting:

1. Sample design

When conducting consumer research for customers, Shangpu Consulting will formulate a reasonable sample design plan according to the customer's target market, target consumers, target products or services, including the determination of sample size, sample extraction method, sample stratification, etc., to ensure the representativeness, consistency, integrity and authenticity of the sample. Here are some examples of the sample design of Champ Consulting:

Conduct consumer satisfaction research for an automotive brand. Shangpu Consulting conducts consumer satisfaction surveys for target consumers (consumers who have purchased or intend to purchase cars of the brand) in the target market (China) of the car brand to understand consumers' satisfaction with the brand's cars. Loyalty, willingness to recommend, etc. Champ Consulting adopted the following sample design:

Determination of sample size. According to the market share, sales volume, competitiveness and other factors of the brand's car, as well as the confidence level (95%) and allowable error (5%) of the consumer satisfaction survey, the sample size is calculated by using the sample error formula.

Method of sampling. According to the accessibility and credibility of the target consumers of the brand car, Shangpu Consulting adopts a stratified random sampling method to divide the target consumers into three levels according to the time (short-term, medium-term and long-term) of purchasing or intending to purchase the brand car, and then randomly select the corresponding proportion of samples in each level to ensure the stability and reliability of the samples.

The way the sample is stratified. According to the characteristics, preferences, needs and other factors of the target consumers of the brand car, Shangpu Consulting adopts a hierarchical weighting method to stratify the sample according to gender, age, income, education, region and other factors, and then according to each The proportion of factors in the population, the corresponding weight is assigned to each level to ensure the consistency and effectiveness of the sample.

Conduct consumer demand research for a cosmetics brand. Shangpu Consulting conducts consumer demand research for the target consumers (consumers who use or intend to use the brand's cosmetics) in the target market (the United States) of the cosmetics brand to understand consumers' needs, preferences, and expectations for the brand's cosmetics. Wait. Champ Consulting adopted the following sample design:

Determination of sample size. According to the market share, sales volume, competitiveness and other factors of the brand's cosmetics, as well as the confidence level (95%) and allowable error (5%) of consumer demand research, Shangpu Consulting adopted the sample error formula to calculate the sample size.

Method of sampling. According to the accessibility and credibility of the target consumers of the brand of cosmetics, Shangpu Consulting adopts a stratified random sampling method to divide the target consumers into three levels according to the frequency (high frequency, medium frequency and low frequency) of using or intending to use the brand of cosmetics, and then randomly select the corresponding proportion of samples in each level to ensure the stability and reliability of the samples.

The way the sample is stratified. According to the characteristics, preferences, needs and other factors of the target consumers of the brand's cosmetics, Shangpu Consulting adopts a hierarchical weighting method to stratify the samples according to gender, age, income, education, region and other factors, and then according to each The proportion of factors in the population, the corresponding weight is assigned to each level to ensure the consistency and effectiveness of the sample.

2. Data collection

When conducting consumer research for customers, Shangpu Consulting will formulate a reasonable data collection plan according to the customer's target market, target consumers, target products or services, including the method, time and content of data collection, so as to ensure the integrity, accuracy, adequacy and relevance of the data. Here are some examples of data collection by Champ Consulting:

Conduct consumer satisfaction research for a restaurant brand. Shangpu Consulting conducts consumer satisfaction surveys for target consumers (consumers who have consumed or intend to consume the brand's catering) in the target market (China) of the catering brand to understand consumers' satisfaction with the brand's catering. Loyalty, willingness to recommend, etc. Champ Consulting has adopted the following data collection schemes:

The way the data is collected. According to the number, distribution, accessibility, credibility and other factors of the target consumers of the brand's catering, Shangpu Consulting has adopted a variety of data collection methods, including online questionnaires, offline interviews, telephone return visits, etc., to ensure the integrity and accuracy of the data.

Time of data collection. According to the behavior, psychology, emotion and other characteristics of the target consumers of the brand's catering, Shangpu Consulting adopts appropriate data collection time, including after meals, weekends, holidays, etc., to ensure the adequacy and relevance of the data.

Content of data collection. According to the characteristics, purpose, background and other factors of the target consumers of the brand's catering, Shangpu Consulting adopts reasonable data collection content, including indicators such as satisfaction, loyalty, willingness to recommend, as well as the quality, service, environment, Price and other factors.

Ensure the reasonableness and validity of the data.

Conduct consumer demand research for a clothing brand. Shangpu Consulting conducts consumer demand research for the target consumers (consumers who use or intend to use the brand's clothing) in the target market (the United States) of the clothing brand to understand consumers' needs, preferences, and expectations for the brand's clothing. Wait. Champ Consulting has adopted the following data collection schemes:

The way the data is collected. According to the number, distribution, accessibility, credibility and other factors of the brand's clothing target consumers, Shangpu Consulting has adopted a variety of data collection methods, including online questionnaires, offline interviews, social media, etc., to ensure the integrity and accuracy of the data.

Time of data collection. According to the behavior, psychology, emotion and other characteristics of the target consumers of the brand's clothing, Shangpu Consulting adopts appropriate data collection time, including seasons, festivals, promotions, etc., to ensure the adequacy and relevance of the data.

Content of data collection. According to the characteristics, purpose, background and other factors of the target consumers of the brand's clothing, Shangpu Consulting adopts reasonable data collection content, including indicators such as demand, preference and expectation, as well as the style, color, material, price and other factors of the clothing, so as to ensure the rationality and validity of the data.

3. Data analysis

When conducting consumer research for customers, Shangpu Consulting will formulate reasonable data analysis solutions, including data analysis methods, models, tools, etc., according to the customer's target market, target consumers, target products or services, to ensure the quality and effectiveness of data analysis. Here are some examples of data analysis by Champ Consulting:

Conduct consumer satisfaction research for a movie brand. Shangpu Consulting conducts consumer satisfaction surveys for target consumers (consumers who have watched or intend to watch the brand's movies) in the target market (China) of the film brand to understand consumers' satisfaction with the brand's movies. Loyalty, willingness to recommend, etc. Champ Consulting has adopted the following data analysis scheme:

Methods of data analysis. According to the type, distribution and relationship of the target consumers of the brand's films, Shangpu Consulting adopts a variety of data analysis methods, including descriptive analysis, correlation analysis, factor analysis, cluster analysis, etc., to ensure the accuracy and effectiveness of data analysis.

Models for data analysis. According to the characteristics, purpose, background and other factors of the target consumers of the brand's films, Shangpu Consulting adopts appropriate data analysis models, including satisfaction model, loyalty model, recommend intention model, etc., to ensure the effectiveness and usefulness of data analysis.

Tools for data analysis. According to the number, quality, format and other factors of the target consumers of the brand's films, Shangpu Consulting has adopted effective data analysis tools, including SPSS, Excel, R, etc., to ensure the availability and credibility of data analysis.

Conduct consumer demand research for a travel brand. Shangpu Consulting conducts consumer demand research for the target consumers (consumers who use or intend to use the brand travel) in the target market (the United States) of the travel brand to understand consumers' needs, preferences, and expectations for the brand travel. Wait. Champ Consulting has adopted the following data analysis scheme:

Methods of data analysis. According to the type, distribution and relationship of the target consumers of the brand tourism, Shangpu Consulting adopts a variety of data analysis methods, including descriptive analysis, correlation analysis, regression analysis, discrimination analysis, etc., to ensure the accuracy and effectiveness of data analysis.

Models for data analysis. According to the characteristics, purpose, background and other factors of the target consumers of the brand tourism, Shangpu Consulting adopts the appropriate data analysis model, including demand model, preference model, expectation model, etc., to ensure the effectiveness and usefulness of data analysis.

Tools for data analysis. According to the number, quality, format and other factors of the target consumers of the brand, Shangpu Consulting adopts effective data analysis tools, including SPSS, Excel, R, etc., to ensure the availability and credibility of data analysis.

4. Data interpretation

When conducting consumer research for customers, Shangpu Consulting will formulate a reasonable data interpretation plan according to the customer's target market, target consumers, target products or services, including data interpretation methods, logic, expression, etc., to ensure the quality and effect of data interpretation. Here are some examples of the interpretation of the data from Champ Consulting:

Conduct consumer satisfaction research for an education brand. Shangpu Consulting conducts consumer satisfaction surveys for target consumers (consumers who have used or intend to use the brand education) in the target market (China) of the education brand to understand consumers' satisfaction with the brand education. Loyalty, willingness to recommend, etc. Champ Consulting has adopted the following data interpretation scheme:

Methods of data interpretation. According to the type, distribution, relationship and other factors of the target consumers of the brand education, Shangpu Consulting adopts a variety of data interpretation methods, including average, standard deviation, frequency, percentage, etc., to ensure the accuracy and effectiveness of data interpretation.

The logic of data interpretation. According to the characteristics, purpose, background and other factors of the target consumers of the brand education, Shangpu Consulting adopts the appropriate logic of data interpretation, including causal relationship, comparative relationship, inductive relationship, deductive relationship, etc., to ensure the effectiveness and usefulness of data interpretation.

Expression of data interpretation. According to the number, quality, format and other factors of the target consumers of the brand education, Shangpu Consulting adopts effective data interpretation expressions, including text, charts, reports, etc., to ensure the comprehensibility and acceptability of data interpretation.

Conduct consumer demand research for a fitness brand. Shangpu Consulting conducts consumer demand research for target consumers (consumers who use or intend to use the brand's fitness) in the target market (the United States) of the fitness brand to understand consumers' needs, preferences, and expectations for the brand's fitness. Wait. Champ Consulting has adopted the following data interpretation scheme:-Data interpretation method. According to the type, distribution, relationship and other factors of the brand's fitness target consumers, Shangpu Consulting adopts a variety of data interpretation methods, including average, standard deviation, frequency, percentage, etc., to ensure the accuracy and effectiveness of data interpretation. -Logic of data interpretation. According to the characteristics, purpose, background and other factors of the target consumers of the brand's fitness, Shangpu Consulting adopts the appropriate logic of data interpretation, including causal relationship, comparative relationship, inductive relationship, deductive relationship, etc., to ensure the effectiveness and usefulness of data interpretation. -Expression of data interpretation. According to the number, quality, format and other factors of the target consumers of the brand's fitness, Shangpu Consulting adopts effective data interpretation expressions, including text, charts, reports, etc., to ensure the comprehensibility and acceptability of data interpretation.