Objectives of 1. data collection and analysis

Data collection and analysis is the core link of brand sales certification. Its goal is to obtain the real sales of brand products or services within a certain range in a certain period of time, and compare them with similar products or services to show the brand's position and advantages in the market. The objectives of data collection and analysis should be consistent with the objectives and needs of brand sales certification, namely:

Improve brand awareness, reputation and influence;

Enhance consumer trust and loyalty to the brand;

Promote market transparency and fair competition;

Support brand marketing strategies and decisions.

Therefore, before data collection and analysis, the following questions should be clarified:

What is the target of brand sales certification? Is it a single product or multiple products? Is it online or offline? Is it domestic or international?

What is the scope of brand sales certification? Is it national or regional? Is it omni-channel or partial channel? Is it the whole year or quarter?

What is the standard of brand sales certification? Is it based on sales or sales volume? Is it based on absolute value or relative value? Is it based on market share or growth rate?

What is the reference for brand sales certification? Is it a similar product or a brand in the same industry? Is it the same level or the same price? Is it the same period or year-on-year?

After these issues are clarified, appropriate data collection and analysis programs can be developed to achieve the desired goals.

Methods of 2. data collection and analysis

There are two main methods of data collection and analysis: active and passive. Active refers to proactively sending requests or inquiries to data sources (e. g., brands, channels, consumers, etc.) to obtain relevant data; passive refers to the use of existing or publicly available data resources (e. g., official statistics, industry reports, web platforms, etc.) to extract relevant data. The two methods have their own advantages and disadvantages, and should be flexibly selected or used in combination according to the specific situation.

The advantage of the proactive approach is that it can obtain more accurate, detailed, and real-time data that better reflects the true sales of the brand; the disadvantage is that it takes more time, effort, and cost, and may encounter data source rejection, perfunctory or failure. The proactive approach works in the following scenarios:

The data source is credible, cooperative and controllable;

Data requirements are clear, specific and professional;

Data quality is important, critical, and sensitive.

The advantage of the passive approach is that it provides access to a wider, more diverse and more convenient data that better reflects overall market trends and changes; the disadvantage is that it requires more noise, error and lag to be dealt with, and there may be incomplete, inconsistent or unreliable data. The passive approach works in the following scenarios:

Data sources are public, rich and available;

Data requirements are vague, generalized, and universal;

Data quality is secondary, secondary, and referential.

3. the process of data collection and analysis

The process of data collection and analysis generally includes the following steps:

Determine the data source: according to the objectives and methods of data collection and analysis, select the appropriate data source, such as brand side, channel side, consumer, official statistics, industry reports, network platform, etc;

Design data model: according to the scope and standard of data collection and analysis, define the structure and dimensions of data, such as product category, channel type, sales quota, sales time, etc;

Develop a data plan: according to the object and reference of data collection and analysis, determine the content and form of data, such as data indicators, data format, data frequency, etc;

Implement data collection: according to the methods and tools of data collection and analysis, perform data collection tasks, such as sending questionnaires, visiting websites, downloading files, etc;

Data cleaning: according to the quality requirements of data collection and analysis, the original data collected are filtered, verified, converted and supplemented to improve the accuracy and integrity of the data;

Data analysis: according to the objectives and needs of data collection and analysis, the effective data after cleaning are summarized, calculated, compared, classified and other operations to extract the value and significance of the data;

Generate data report: According to the results and presentation of data collection and analysis, the useful data after analysis is organized, visualized, and interpreted to form a data report.

Tools 4. data collection and analysis

There are two main types of tools for data collection and analysis: software tools and hardware tools. Software tools are tools that enable data collection and analysis functions through computer programs or network platforms, suchExcel, SPSS, SQL, Pyzon, watch, Painting, Power BIhardware tools are tools that enable data collection and analysis functions through physical devices or sensors, such as code scanning guns., RFID, GPS,Camera, Face recognition and so on. The two types of tools have their own characteristics and should be selected or used in combination according to the actual situation.

Software tools are characterized by the ability to handle large amounts of complex and variable digital or text-based data, providing powerful, flexible and diverse data processing and analysis capabilities; the disadvantage is that they require certain computer skills and knowledge, and there may be risks to data security and privacy. The software tools are suitable for the following scenarios:

The amount of data is large, complex and multi-source;

Data types are numeric, textual, and structured;

Data processing is complex, variable, and self-defined.

Hardware tools are characterized by the ability to directly collect non-digital or non-text data such as real-time and accurate images or sounds, providing simple, convenient and efficient data collection and analysis capabilities; the disadvantage is that they require a certain amount of physical space and equipment costs, and there may be a risk of data corruption and loss. Hardware tools are suitable for the following scenarios:

The amount of data is small, simple, single;

The data types are image, sound, and unstructured;

Data processing is simple, fixed and standardized.

Standards for 5. data collection and analysis

There are four main criteria for data collection and analysis: scientificity, objectivity, accuracy and timeliness. These four criteria are an important basis for measuring the quality and effectiveness of data collection and analysis, and should be used throughout the data collection and analysis process to ensure that data collection and analysis meet the goals and needs of brand sales certification.

Scientificity means that data collection and analysis should follow scientific methodology and logic, avoid subjective assumptions and blind inferences, and ensure that data collection and analysis are justified. Scientific embodied in the following aspects:

Data sources should be representative, reliable and stable;

The data model should be complete, reasonable and consistent;

The data scheme should be clear, feasible and operable;

Data collection should be normative, systematic and effective;

Data cleaning shall be complete, correct and consistent;

Data analysis should be reasonable, valid and interpretable;

Data reports should be concise, clear and readable.

Objectivity means that data collection and analysis should reflect the truth, avoid subjective bias and misleading, and ensure that data collection and analysis are ruthless and unbiased. Objectivity is reflected in the following aspects:

The data source should be free of conflicts of interest, concealment and exaggeration;

The data model should be free of ambiguity, no arbitrary changes, no deliberate neglect;

The data scheme shall be free of random addition, deletion, random adjustment and deliberate selection;

The data collection shall be free of missampling, tampering, forgery and selective filtering;

Data cleaning shall be free of arbitrary deletion, modification and supplement;

Data analysis shall be free of arbitrary calculation, comparison and classification;

The data report shall be free of arbitrary arrangement, visualization and interpretation.

Accuracy means that data collection and analysis should be consistent with the facts, avoid errors and deviations, and ensure that data collection and analysis are well-documented. Accuracy is reflected in the following aspects:

The data source should match the data requirements and provide the required data;

The data model should be adapted to the data structure and define the required dimensions;

The data scheme should be consistent with the data standards and identify the required indicators;

Data collection should be consistent with the data format to obtain the required content;

Data cleaning should be consistent with data quality to ensure required accuracy;

Data analysis should be consistent with data objectives and the required results should be calculated;

The data report should correspond to the data results and show the required information.

Timeliness means that data collection and analysis should follow the facts, avoid outdated and ineffective delays, and ensure that data collection and analysis are sometimes orderly. Timeliness is reflected in the following aspects:

Data sources should be updated in a timely manner, timely feedback, timely provision;

The data model should be adjusted, revised and optimized in time;

The data scheme shall be formulated, communicated and implemented in a timely manner;

Data collection shall be started, ended and delivered in time;

Data cleaning should be carried out, completed and checked in time;

Data analysis should be started, carried out and ended in time;

Data reports shall be generated, reviewed and released in a timely manner.

Risks of 6. data collection and analysis

There are four main risks of data collection and analysis: missing data, data errors, data leakage and data disputes. These four risks are important factors affecting the quality and effectiveness of data collection and analysis. They should be prevented and properly handled in advance during the entire data collection and analysis process to ensure the smooth progress of data collection and analysis.

Missing data means that during the data collection process, all or part of the required data cannot be obtained due to various reasons, resulting in incomplete or unusable data. There are several possible reasons for missing data:

The data source is uncooperative, unresponsive or unreachable;

Incomplete, unreasonable or inconsistent data models;

The data scheme is not clear, feasible or operational;

Failure, malfunction or damage of data acquisition tools;

Data collection personnel negligence, error or violation.

The impact of missing data can be as follows:

Affect the accuracy and validity of the data, resulting in distorted or meaningless data analysis results;

Affect the comparability and referencability of the data, resulting in the data analysis results can not be compared with other data or reference;

Affect the integrity and credibility of the data, resulting in data analysis results can not form a complete logic or be trusted.

To avoid or reduce the risk of missing data, the following measures can be taken:

Establish a good cooperative relationship with the data source, clarify the rights and obligations of both parties, and formulate a reasonable reward and punishment mechanism;

Design a sound and reasonable data model, consider all possible situations and changes, and maintain consistency with other models;

Develop a clear and feasible data plan, clarify the content and form of each link, and fully communicate and coordinate with relevant personnel;

Select effective and reliable data acquisition tools, regularly check maintenance and upgrade, back up important files and records;

Train, supervise and guide data collection personnel, standardize operation procedures and methods, and prevent negligence and irregularities.

Data error refers to the discovery of errors or errors in the collected raw data due to various reasons during the data cleaning process, resulting in inaccurate or unreliable data. There are several possible causes of data errors:

The data provided by the data source is untrue, inaccurate or inconsistent;

The dimensions defined by the data model are unclear, unreasonable or inapplicable;

The indicators determined by the data scheme are not scientific, objective or standard;

Error, deviation or interference of data acquisition tools;

The operation of data collection personnel is not standardized, incorrect or not timely.

Data errors may have the following effects:

Affect the authenticity and credibility of the data, resulting in the results of data analysis being questioned or denied;

Affect the validity and availability of data, resulting in data analysis results can not meet the expected objectives or needs;

Affects the comparability and referencability of the data, resulting in differences or contradictions between the results of the data analysis and other data.

In order to avoid or reduce the risk of data errors, the following measures can be taken:

Verify the authenticity, accuracy and consistency of the data with the data source, and require relevant certificates or sources;

Check the structure and dimensions of the revised data model to ensure that it matches the data requirements and standards, and eliminate ambiguity and redundancy;

Evaluate and optimize the content and format of data solutions to ensure consistency with data objectives and needs, and to improve science and objectivity;

Calibrate the function and performance of the test data acquisition tool to ensure compliance with data format and quality, and reduce errors and deviations;

Review and correct data collection personnel's operations and results to ensure compliance with data schemes and processes and to avoid errors and violations.

Data leakage refers to the data analysis process, due to various reasons, resulting in the collection of sensitive or private data by unauthorized third parties to obtain or use, resulting in data security and privacy violations. There are several possible reasons for a data breach:

The data source fails to properly store or encrypt the data provided, resulting in theft or tampering;

The data model fails to distinguish or identify sensitive or private dimensions, resulting in exposure or disclosure;

The data scheme fails to limit or regulate the use scope and authority of sensitive or private indicators, resulting in abuse or disclosure;

The data collection tool fails to set or enable security protection measures, resulting in being attacked or cracked;

Data collection personnel fail to comply with or enforce security confidentiality agreements, resulting in exploitation or disclosure.

The impact of a data breach may be as follows:

Affect the interests and rights of brands, channels, consumers and other related parties, resulting in economic losses or legal liabilities;

Affect the reputation and image of the brand sales certification body, resulting in a crisis of confidence or termination of cooperation;

Affect market transparency and fair competition, resulting in market disorder or competition imbalance.

In order to avoid or reduce the risk of data leakage, the following measures can be taken:

Sign a security and confidentiality agreement with the data source, clarify the responsibilities and obligations of both parties in terms of data security and privacy, and formulate emergency plans;

Encrypt, desensitize, or anonymize sensitive or private dimensions to avoid direct exposure or disclosure in the data model;

Limit or regulate sensitive or private indicators and use them only within the necessary scope and authority, avoiding arbitrary additions, deletions or adjustments in the data scheme;

Security protection of data collection tools, setting passwords, firewalls, anti-virus software, etc., to avoid being attacked or cracked in the data collection process;

Provide security and confidentiality training for data collection personnel to comply with or implement security and confidentiality agreements to avoid being used or leaked in the data analysis process.

Data dispute means that after the release of the data report, due to various reasons, the brand, channel, consumer and other relevant parties have doubts or dissatisfaction with the content or form of the data report, resulting in the impact of data trust and recognition. There may be several reasons for data disputes:

The data source does not approve or is not satisfied with the results of the data report, and believes that it is inconsistent with its own data or feelings;

The data model does not approve or is not satisfied with the structure of the data report and considers it inconsistent with its own needs or standards;

The data scheme does not approve or is not satisfied with the contents of the data report and considers that it is inconsistent with its own objectives or needs;

The data collection tool does not approve or is not satisfied with the form of the data report, and considers that it is inconsistent with its own function or performance;

The data collection personnel do not approve or are not satisfied with the display of the data report, and think that it is inconsistent with their own operation or results.

The impact of data disputes may be as follows:

Affect the cooperation relationship and trust basis between the brand sales certification body and related parties, resulting in communication barriers or termination of cooperation;

Affect the credibility and influence between the brand sales certification body and the market, resulting in reputation loss or market share decline;

Affect the trust and loyalty between brand sales certification agencies and consumers, causing consumers to lose confidence or turn to other brands.

In order to avoid or reduce the risk of data disputes, the following measures can be taken:

Establish a good communication mechanism with relevant parties, timely feedback and explain the results and basis of the data report, and listen to and respond to the opinions and suggestions of relevant parties;

Adequate review and testing of data reports to ensure that the content and form of data reports meet the expectations and requirements of relevant parties, and to eliminate possible errors and errors;

Reasonable interpretation and presentation of data reports, highlighting the value and significance of data reports, and avoiding exaggerating or belittling the results of data reports;

The data report shall be properly protected and backed up to prevent the data report from being tampered with or lost for subsequent verification or review.

7. the practical experience of Champ Consulting

Champu Consulting is a consulting agency specializing in brand sales certification services, with many years of experience in providing sales certification services for leading brands in various industries. When conducting brand sales certification services, Shangpu Consulting adheres to the principles of science, objectivity, accuracy and timeliness, and follows the following steps:

In-depth communication with the brand, understand the brand's goals, needs and expectations, and develop appropriate brand sales certification programs;

Establish a good cooperative relationship with the channel side, obtain the support and cooperation of the channel side, and collect relevant sales data;

Effectively interact with consumers, collect consumer feedback and evaluation, and analyze consumer behavior and preferences;

Use advanced data collection and analysis tools to clean, analyze and visualize the collected data to form a data report;

Fully communicate with the brand, timely feedback and explain the results and basis of the data report, listen to and respond to the opinions and suggestions of the brand;

Publicly release with the market, show the results and influence of brand sales certification, and enhance brand awareness and reputation.

The following are specific examples of brand sales certification services provided by Champ Consulting for some of the industry's leading brands:

Case 1: For an international cosmetics brand.2022Sales volume certification services in the Chinese market in the first quarter. The brand has multiple product lines in the Chinese market, covering skin care, makeup, perfume and other fields. Through communication and cooperation with the brand, major e-commerce platforms, offline counters and other channels as well as consumers, Shangpu Consulting has collected the brand in2022Sales, sales volume, market share, growth rate and other data in the Chinese market in the first quarter of the year were compared and analyzed with similar products to form a data report. The data report shows that the brand is in.2022Sales in the Chinese market in the first quarterxxBillion yuan, year-on-year growthxx%, ranked first in its class; sales reachedxxTen thousand pieces, up year on yearxx%, ranked second in its class; market share reachedxx%Year-on-year growth.xx%, ranked third among similar products; the growth rate reachedxx%Year-on-year growth.xx%It ranks fourth among similar products. The data report also shows the sales situation and advantages of the brand in different product lines, different channels, different regions and different consumer groups. After the data report was confirmed and recognized by the brand, it was publicly released on major media platforms, which attracted widespread attention and discussion, and improved the brand's popularity and reputation in the Chinese market.

Case two: for a domestic mobile phone brand to provide2022Global market sales certification services. The brand has multiple series in the global market, covering flagship, mid-range, low-end and other grades. Through communication and cooperation with the brand, major e-commerce platforms, offline retailers and other channels, as well as consumers, Shangpu Consulting has collected the brand in2022Annual global market sales, sales volume, market share, growth rate and other data, and with the same industry brands for comparative analysis, the formation of a data report. The data report shows that the brand is in.2022Sales in the global market reachedxxBillion US dollars, up year on yearxx%, ranked second among brands in the same industry; sales reachedxxTen thousand units, up year on yearxx%, ranking third among brands in the same industry; market share reachedxx%Year-on-year growth.xx%, ranked fourth among brands in the same industry; the growth rate reachedxx%Year-on-year growth.xx%It ranks fifth in the same industry brand. The data report also shows the sales situation and advantages of the brand in different series, different regions, different price segments, different functions and other dimensions. After the data report was confirmed and recognized by the brand, it was publicly released on major media platforms, which attracted widespread attention and discussion, and increased the brand's visibility and influence in the global market.

Case three: for a domestic TV brand to provide2022Sales certification services for the Chinese market during the Spring Festival. The brand has a number of models in the Chinese market, covering intelligent, curved, ultra-thin and other features. Through communication and cooperation with the brand, major e-commerce platforms, offline home appliance stores and other channels, as well as consumers, Shangpu Consulting has collected the brand in2022During the Spring Festival (1Month20Day2Month20Japan) in the Chinese market sales, sales volume, market share, growth rate and other data, and with similar products for comparative analysis, the formation of a data report. The data report shows that the brand is in.2022Sales in the Chinese market during the Spring Festival inxxBillion yuan, year-on-year growthxx%, ranked first in its class; sales reachedxxTen thousand units, up year on yearxx%, ranked first in its class; market share reachedxx%Year-on-year growth.xx%, ranked first in its class; the growth rate reachedxx%Year-on-year growth.xx%First place in similar products. The data report also shows the sales situation and advantages of the brand in different models, different channels, different regions, different sizes and other dimensions. After the data report was confirmed and recognized by the brand, it was publicly released on major media platforms, which attracted widespread attention and discussion, and improved the brand's popularity and reputation in the Chinese market.

Conclusion

Brand sales certification is an effective way to enhance brand image and competitiveness, but it is also a job that requires professional knowledge and skills.