Business management method, system and equipment based on feature classification and medium

Abstract

The invention relates to the technical field of data analysis and business management, and discloses a business management method, system and device based on feature classification and a medium. According to the business management method and system based on feature classification, the equipment and the medium, accuracy and consistency of data are ensured through systematic data acquisition and cleaning, generality and differences among various features are identified through a clear classification standard and clustering analysis, and the structured analysis not only improves classification accuracy, but also improves classification efficiency. The method provides reliable data basis for subsequent business target calculation, in the generated business analysis report, an enterprise can clearly identify own advantages, disadvantages and potential opportunities, so that a more targeted business strategy is formulated, data-driven decision support is realized, and the application of the method has the advantages of high efficiency and high reliability. The flexibility and the coping ability of an enterprise in market competition are remarkably improved, and continuous optimization and growth of services are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and business management, and particularly to a business management method, system, device, and medium based on feature classification. Background Art

[0002] In today's highly competitive market environment, enterprises are facing increasingly complex customer demands and market changes. Effective business management relies on in-depth analysis of a large amount of data to identify customer behavior patterns, evaluate product performance, and optimize marketing strategies. The business management method based on feature classification can systematically process and analyze data, thereby providing more targeted decision-making basis for enterprises. Through this method, enterprises can better understand customer preferences, improve customer satisfaction, and achieve higher efficiency in resource allocation and market promotion.

[0003] However, in the process of implementing data analysis and business management, enterprises often face problems such as low data quality, unclear classification criteria, and difficulty in effectively transforming analysis results into business strategies. These challenges limit the effectiveness and operability of data analysis, resulting in enterprises being unable to fully utilize data-driven decision support. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a business management method, system, device, and medium based on feature classification. Through systematic data collection and cleaning, the accuracy and consistency of data are ensured. Then, through clear classification criteria and clustering analysis, the commonalities and differences between various features are identified. This structured analysis not only improves the accuracy of classification but also provides a reliable data basis for subsequent business goal calculation. In the generated business analysis report, enterprises can clearly identify their own advantages, disadvantages, and potential opportunities, thereby formulating more targeted business strategies to achieve data-driven decision support. The application of this method significantly enhances the flexibility and response ability of enterprises in market competition, promotes the continuous optimization and growth of business, and solves the above problems.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions: A business management method based on feature classification, comprising the following steps:

[0008] S1. Collect customer information, product information, transaction record data, and market feedback data through market research and industry reports;

[0009] S2. Filter the data collected in S1 to process missing data, duplicate data, and abnormal data;

[0010] S3. According to business requirements and analysis objectives, formulate the criteria and dimensions for data classification, classify by customer type, product category, and transaction type, and identify the commonalities and differences among various features through cluster analysis;

[0011] S4. After data classification is completed, calculate the customer purchase frequency, customer lifetime value, and product sales according to business objectives, visualize the calculation results through charts, and generate a business analysis report;

[0012] S5. Establish a business management monitoring mechanism based on the business analysis report, and identify the strengths, weaknesses, and potential opportunities in the business.

[0013] Preferably, the formula for processing missing data is as follows:

[0014]

[0015] In the formula, X filled represents the filled data, X i represents the data point to be processed, represents the mean of the variable for which missing values need to be processed.

[0016] Preferably, the formula for processing duplicate data is as follows:

[0017] D filtered = D\{D i |D i is duplicate}

[0018] In the formula, D filtered represents the dataset after deduplication, D i represents the duplicate data rows, and D represents the original dataset.

[0019] Preferably, the formula for processing abnormal data is as follows:

[0020]

[0021] In the formula, |Z i | represents the Z-score of the i-th data point, P i represents the i-th data point, μ represents the mean of the original dataset, σ represents the standard deviation of the original dataset, and threshold represents that values exceeding 3 standard deviations are regarded as abnormal data.

[0022] Preferably, the calculation formula for the customer purchase frequency is as follows:

[0023]

[0024] In the formula, Gmsc represents the customer purchase frequency, Khzg represents the total number of customer purchases, and Zgms represents the total number of customer purchase time periods.

[0025] Preferably, the calculation formula for the customer lifetime value is as follows:

[0026]

[0027] In the formula, CLV represents the customer lifetime value, N represents the expected customer life cycle, t represents the time unit, A represents the average customer purchase frequency, M represents the gross profit per transaction, and r represents the discount rate.

[0028] Preferably, the calculation formula for the product sales amount is as follows:

[0029]

[0030] In the formula, Cp represents the product sales amount, P i represents the unit price of the i-th product, Q i represents the sales quantity of the i-th product, and n represents the total number of product categories.

[0031] A business management system based on feature classification includes a data acquisition module, a data preprocessing module, a data classification module, a data visualization module, and a business monitoring module;

[0032] The data acquisition module obtains customer information, product information, transaction record data, and market feedback data from market research questionnaires and industry reports;

[0033] The data preprocessing module filters the data collected by the data acquisition module and processes missing data, duplicate data, and abnormal data;

[0034] The data classification module formulates data classification criteria and dimensions according to business requirements and analysis objectives, classifies by customer type, product category, and transaction type, and identifies the commonalities and differences between various features through cluster analysis;

[0035] After the data classification is completed, the data visualization module calculates the customer purchase frequency, customer lifetime value, and product sales amount according to business objectives, visualizes the calculation results through charts, and generates a business analysis report;

[0036] The business monitoring module establishes a business management monitoring mechanism based on the business analysis report and identifies the advantages, disadvantages, and potential opportunities in the business.

[0037] Compared with the prior art, the present invention provides a business management method, system, device, and medium based on feature classification, and has the following beneficial effects:

[0038] Through systematic data collection and cleaning, the present invention ensures the accuracy and consistency of data. Furthermore, through clear classification criteria and clustering analysis, the commonalities and differences among various features are identified. This structured analysis not only improves the accuracy of classification but also provides a reliable data foundation for subsequent business objective calculations. In the generated business analysis report, enterprises can clearly identify their own strengths, weaknesses, and potential opportunities, thereby formulating more targeted business strategies to achieve data-driven decision support. The application of this method significantly enhances the flexibility and response ability of enterprises in market competition, promoting the continuous optimization and growth of business. Brief Description of the Drawings

[0039] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0040] Figure 2 It is a schematic diagram of the system process of the present invention. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] In view of the problems that enterprises often face in the process of implementing data analysis and business management, such as low data quality, unclear classification criteria, and difficulty in effectively transforming analysis results into business strategies, a business management method based on feature classification is proposed. Please refer to Figure 1 , and this method includes the following steps:

[0043] S1. Collect customer information, product information, transaction record data, and market feedback data through market research and industry reports;

[0044] Through combining various technical means for market research and industry report analysis, systematically collect customer information, product information, transaction record data, and market feedback data, aiming to establish a comprehensive data foundation. First, use web crawler technology to collect customer reviews, product evaluations, and industry dynamics from online platforms and social media to obtain real-time market feedback. Second, adopt data mining tools to extract and analyze key information in industry reports to identify market trends and changes in consumer behavior. In this process, combine natural language processing (NLP) technology to perform sentiment analysis on text data, so as to deeply understand customer satisfaction and preferences;

[0045] In addition, through API integration, transaction record data is retrieved from links on different trading platforms, including key information such as sales volume, purchase frequency, and return rate. At the same time, the accuracy and consistency of the data are ensured. For customer information, a customer relationship management (CRM) system is adopted to integrate data from various touchpoints to form a 360-degree customer view. Such comprehensive technical means enable us to systematically capture, organize, and analyze market and customer information, providing a solid data foundation for subsequent data analysis and decision-making, thereby achieving accurate market positioning and business optimization;

[0046] S2. Filter the data collected in S1, and process missing data, duplicate data, and abnormal data;

[0047] First, for the processing of missing data, we use data completion techniques such as interpolation, mean filling, forward filling, or backward filling, and select appropriate methods according to the characteristics of the data to effectively fill in the missing values. In addition, for time series data, we can use time series prediction models such as ARIMA (Autoregressive Integrated Moving Average Model) for more scientific filling, thereby improving the quality and usability of the data;

[0048] Next, in terms of removing duplicate data, we adopt primary key constraints and data deduplication algorithms. Each record is uniquely identified through a hash function, so as to quickly identify and remove duplicates. At the same time, we also use data fingerprint technology to detect potential redundant data by constructing a data fingerprint library to ensure that the data set does not contain duplicate information and maintain the uniqueness and accuracy of the data;

[0049] Finally, in the processing of abnormal data, we use statistical methods such as Z-Score and IQR (Interquartile Range) methods for outlier detection to identify data points that deviate from the normal range. In addition, combined with the Isolation Forest and outlier detection algorithms in machine learning, the accuracy and flexibility of outlier identification are further improved;

[0050] Among them, the formula for processing missing data is as follows:

[0051]

[0052] Missing data may lead to incomplete information and distorted analysis results. By appropriately processing missing values (such as filling, interpolation, etc.), the integrity of the data set can be significantly improved, thereby providing a more accurate information basis for analysis. In the formula, X filled represents the filled data, and X i represents the data point to be processed, Denote the mean of the variable for which missing values need to be processed. The handling of missing data enables all relevant user behaviors and transaction records to be taken into account during data analysis, thus providing more comprehensive business insights;

[0053] The formula for handling duplicate data is as follows:

[0054] D filtered = D\{D i |D i is duplicate}

[0055] Duplicate data not only occupies storage space but also leads to biases in analysis results. By removing duplicate data, it can be ensured that each record is unique, enhancing the credibility of the data. In the formula, D filtered represents the dataset after deduplication, D i represents the duplicate data rows, and D represents the original dataset. Analyzing duplicate data may lead to incorrect business decisions. By cleaning up duplicates, it can be ensured that decisions are based on accurate and reliable data, thereby improving the effectiveness of business decisions;

[0056] The formula for handling outlier data is as follows:

[0057]

[0058] Outliers often reflect specific problems or opportunities in business. After properly handling these data, potential business risks or innovation opportunities can be revealed, promoting the improvement and development of the enterprise. In the formula, |Z i | represents the Z-score of the i-th data point, P i represents the i-th data point, μ represents the mean of the original dataset, σ represents the standard deviation of the original dataset, and threshold represents that values exceeding 3 standard deviations are regarded as outlier data. If outlier data is not handled, it may lead to incorrect business conclusions or decisions. Through effective handling of outlier data, the occurrence of these misleading results can be reduced;

[0059] S3. According to business requirements and analysis objectives, formulate the criteria and dimensions for data classification, classify by customer type, product category, and transaction type, and identify the commonalities and differences among various features through cluster analysis;

[0060] According to business requirements and analysis objectives, we first need to develop scientific data classification criteria and dimensions to ensure that data can be accurately classified by customer type, product category, and transaction type. To this end, we have adopted a variety of statistical analysis and machine learning techniques. First, we conduct exploratory data analysis (EDA), using descriptive statistics and visualization tools (such as scatter plots, box plots, and heat maps) to deeply understand the distribution characteristics and potential relationships of the data, thereby providing a basis for formulating classification criteria.

[0061] After determining the classification dimensions, we use data preprocessing methods to clean and standardize the original data. We classify customers into different types (such as high-value customers, medium-value customers, and low-value customers), and systematically classify product categories (such as electronic products, consumer goods, and luxury goods) and transaction types (such as online transactions, offline transactions, and repeat purchases). To enhance the credibility of the classification, we also use techniques such as label encoding or one-hot encoding to convert the classification information into a format acceptable to machine learning models.

[0062] Next, we use clustering analysis methods, such as K-Means, Hierarchical Clustering, or DBSCAN, to identify the commonalities and differences between various features. These clustering algorithms calculate the distances between data points in the feature space, grouping samples with similar features into the same class and samples with significant differences into different classes, thus helping us extract customer preference tendencies and product characteristics.

[0063] In addition, in combination with dimensionality reduction techniques such as principal component analysis (PCA), when dealing with high-dimensional data, we transform complex feature sets into lower-dimensional features to simplify the clustering process, improve efficiency and accuracy. Through these comprehensive technical means, we can clearly identify and understand the relationships between different customer groups and product categories, thereby providing data support for business decisions and formulating more targeted marketing strategies and product optimization plans.

[0064] S4. After data classification is completed, calculate the customer purchase frequency, customer lifetime value, and product sales according to business objectives, visualize the calculation results through charts, and generate a business analysis report.

[0065] After data classification is completed, according to specific business objectives, we use data analysis tools to calculate key indicators such as customer purchase frequency, customer lifetime value (CLV), and product sales. First, through data processing languages such as SQL or Python, we extract relevant information from the cleaned dataset and use time series analysis to calculate the purchase frequency of each customer, analyze their consumption behavior over a period of time, and understand customer activity and loyalty.

[0066] Next, to calculate the customer lifetime value, we comprehensively consider the average customer spending, purchase frequency, and customer life cycle, and apply relevant equations and data modeling techniques such as regression analysis to evaluate the total revenue that each customer may bring to the enterprise in the future. For the calculation of product sales, we quickly obtain the overall sales performance by summarizing the sales data of each product within a specified time period and combining methods such as pivot tables and data aggregation;

[0067] Once the calculations of these key metrics are completed, we use data visualization tools (such as Tableau, PowerBI, or Matplotlib, etc.) to display the calculation results through charts. We select appropriate chart types, such as bar charts, line charts, and pie charts, to clearly present the changing trends of customer purchase frequency, the distribution of customer lifetime value, and the comparative analysis of product sales. These visual charts not only facilitate the management to quickly understand the data but also help to discover potential business opportunities and risks;

[0068] Finally, to ensure the comprehensiveness and operability of the analysis results, we prepare detailed business analysis reports. These reports will combine chart interpretations and insightful analyses to systematically present the story behind the data, providing clear recommendations for decision-makers to facilitate the formulation of precise marketing strategies and business development plans. Through such a data processing and analysis process, we can effectively support the enterprise in making scientific and timely decisions in a highly competitive market environment;

[0069] S5. Establish a business management monitoring mechanism based on the business analysis report and identify the strengths, weaknesses, and potential opportunities in the business;

[0070] Based on the results of the business analysis report, we will establish a comprehensive business management monitoring mechanism aimed at effectively tracking and evaluating various key metrics of enterprise operations. In this step, we first set a series of key performance indicators (KPIs) that cover aspects such as customer satisfaction, sales growth rate, market share, and customer retention rate to ensure that the health status of the business can be comprehensively reflected;

[0071] To achieve real-time monitoring, we adopt data monitoring tools and dashboard technologies (such as Grafana, Tableau, or PowerBI) to visualize business metrics and construct a dynamic monitoring panel for the management team to view and analyze data in real time. These dashboards will be updated in real time through data streams to ensure that the management can obtain the latest information in a timely manner and quickly identify changes in business trends;

[0072] On this basis, we also introduced machine learning algorithms to analyze historical data through training models to identify the strengths and weaknesses in the business. For example, classification algorithms (such as decision trees or random forests) are used to conduct a detailed analysis of customer behavior to help identify high-value customer groups, and then analyze the company's strengths in products or services. At the same time, through anomaly detection techniques, problems in business operations, such as declining sales and customer churn, can be discovered in a timely manner to ensure that the enterprise can respond quickly;

[0073] In addition, we also comprehensively integrate business data through SWOT analysis (Strengths, Weaknesses, Opportunities, Threats analysis) to clarify the enterprise's market positioning and core competitiveness, and identify potential market opportunities and risks. For example, by combining competitor analysis and market trend data, emerging markets or unmet customer needs can be identified to provide guidance for the enterprise's product development and market entry. Through this multi-dimensional supervision and analysis mechanism, we can effectively grasp business dynamics, thereby providing strong data support for the enterprise's strategic decision-making and ensuring that the enterprise maintains a leading position in the competition.

[0074] Please refer to Figure 2 , a business management system based on feature classification, including a data collection module, a data preprocessing module, a data classification module, a data visualization module, and a business monitoring module;

[0075] The data collection module obtains customer information, product information, transaction record data, and market feedback data from market research questionnaires and industry reports;

[0076] The data preprocessing module filters the data collected by the data collection module and processes missing data, duplicate data, and abnormal data;

[0077] The data classification module formulates data classification criteria and dimensions according to business requirements and analysis objectives, classifies by customer type, product category, and transaction type, and identifies the commonalities and differences between various features through clustering analysis;

[0078] After the data classification is completed, the data visualization module calculates the customer purchase frequency, customer lifetime value, and product sales according to business objectives, visualizes the calculation results through charts, and generates a business analysis report;

[0079] The business monitoring module establishes a business management monitoring mechanism based on the business analysis report and identifies the strengths, weaknesses, and potential opportunities in the business.

[0080] Through the application of this method and system, the flexibility and response ability of the enterprise in market competition have been significantly improved, promoting the continuous optimization and growth of the business.

[0081] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A business management method based on feature classification, characterized in that: The following steps are involved: S1. Collect customer information, product information, transaction record data and market feedback data through market research and industry reports; S2, filter the data collected in S1, and process missing data, duplicate data and abnormal data; S3. According to business needs and analysis objectives, formulate standards and dimensions for data classification, classify by customer type, product category, and transaction type, and identify the commonalities and differences between various characteristics through cluster analysis; S4. After data classification is completed, calculate customer purchase frequency, customer lifetime value, and product sales according to business goals, visualize the calculation results through charts, and generate a business analysis report; S5. Establish a business management monitoring mechanism based on the business analysis report and identify strengths, weaknesses and potential opportunities in the business.

2. A service management method based on feature classification according to claim 1, characterized in that: The formula for handling missing data is as follows: In the formula, X filled Indicates the data after filling, X i represents the data points to be processed, Indicates the mean of the variable with missing values.

3. A service management method based on feature classification according to claim 2, characterized in that: The formula for processing repeated data is as follows: D filtered =D\{D i |D i is duplicate} In the formula, D filtered represents the dataset after deduplication, D i represents repeated data rows, and D represents the original data set.

4. A service management method based on feature classification according to claim 3, characterized in that: The formula for processing abnormal data is as follows: In the formula, |Z i | represents the Z-score of the i-th data point, P i represents the i-th data point, μ represents the mean of the original data set, σ represents the standard deviation of the original data set, and threshold indicates that values ​​exceeding 3 standard deviations are considered abnormal data.

5. A service management method based on feature classification according to claim 4, characterized in that: The calculation formula for the customer purchase frequency is as follows: In the formula, Gmsc represents the customer's purchase frequency, Khzg represents the customer's total purchase times, and Zgms represents the customer's total purchase time periods.

6. A service management method based on feature classification according to claim 5, characterized in that: The calculation formula for the customer lifetime value is as follows: In the formula, CLV represents customer lifetime value, N represents the expected customer life cycle, t represents the time unit, A represents the average purchase frequency of the customer, M represents the gross profit of each transaction, and r represents the discount rate.

7. A service management method based on feature classification according to claim 6, characterized in that: The sales volume of the product is calculated as follows: In the formula, Cp represents product sales, P i represents the unit price of the i-th product, Q i represents the sales quantity of the i-th product, and n represents the total number of product types.

8. A service management device based on feature classification, characterized in that: The device comprises one or more processors and one or more machine-readable media having instructions stored thereon, which, when executed by the one or more processors, cause the device to perform the method as described in one or more of claims 1-7.

9. A service management medium based on feature classification, characterized in that: Instructions are stored thereon, which, when executed by one or more processors, cause the device to perform the method as described in one or more of claims 1-7.

10. A business management system based on feature classification, characterized in that: It includes data collection module, data preprocessing module, data classification module, data visualization module and business monitoring module; The data collection module obtains customer information, product information, transaction record data and market feedback data from market research questionnaires and industry reports; The data preprocessing module filters the data collected in the data collection module and processes missing data, duplicate data and abnormal data; The data classification module formulates the standards and dimensions for data classification according to business needs and analysis objectives, classifies data by customer type, product category, and transaction type, and identifies the commonalities and differences between various features through cluster analysis; After the data classification is completed, the data visualization module calculates the customer purchase frequency, customer lifetime value and product sales according to the business objectives, visualizes the calculation results through charts, and generates a business analysis report; The business monitoring module establishes a business management monitoring mechanism based on the business analysis report and identifies strengths, weaknesses and potential opportunities in the business.

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