Intelligent manufacturing and marketing integrated intelligent market subdivision optimization method
By integrating big data analysis, machine learning and artificial intelligence technology, an intelligent market segmentation optimization method is built, which solves the problem of inefficiency in the existing market segmentation method and disconnection between manufacturing and marketing when processing massive complex data, and achieves efficient and real-time market segmentation and in-depth collaboration between manufacturing and marketing.
Patent Information
- Application Number
- CN202411882173.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-19
- Publication Date
- 2025-05-16
AI Technical Summary
The existing market segmentation method is inefficient when processing massive complex data, making it difficult to update strategies in real time to adapt to market changes, and manufacturing is out of touch with marketing, making it impossible to achieve optimal allocation of resources.
By integrating big data analysis, machine learning and artificial intelligence technology, we can realize in-depth mining and intelligent analysis of market data, and build an intelligent market segmentation optimization method, including data collection and integration, data cleaning and preprocessing, feature engineering, market segmentation model construction and training, real-time monitoring and data update, manufacturing and marketing collaboration and other steps.
It has achieved efficient processing of complex data, adapted to market changes in real time, achieved in-depth coordination between manufacturing and marketing, improved the accuracy and reliability of market segmentation, and enhanced the competitiveness of enterprises in the market.
Smart Images
Figure CN120013596A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent market technology, and in particular to an intelligent market segmentation optimization method integrating intelligent manufacturing and marketing. Background Art
[0002] With the rapid development of intelligent manufacturing technology, the manufacturing industry is gradually transforming to a personalized and customized production model. In this transformation process, how to effectively segment the market to meet the needs of different consumer groups has become an important challenge facing the manufacturing industry. Existing market segmentation methods are usually based on traditional statistical analysis and consumer surveys. This method is inefficient when processing large amounts of complex data, and it is difficult to update market segmentation strategies in real time to adapt to market changes. In addition, traditional market segmentation methods often ignore the dynamic matching between product design, production and market demand, resulting in a disconnect between production and marketing, and the inability to achieve optimal resource allocation;
[0003] The intelligent market segmentation optimization method integrating intelligent manufacturing and marketing aims to solve the above problems. By integrating technologies such as big data analysis, machine learning, and artificial intelligence, it can realize deep mining and intelligent analysis of market data, so as to accurately identify and dynamically adjust market segmentation strategies. However, there is no complete and systematic intelligent market segmentation optimization method that can effectively integrate manufacturing and marketing data and achieve deep synergy between the two.
[0004] The existing market segmentation methods still have certain problems:
[0005] Insufficient data processing capabilities: Faced with massive amounts of market and production data, traditional methods are difficult to conduct in-depth analysis and cannot fully tap the value of data; Poor adaptability to market changes: Updates to market segmentation strategies often lag behind market changes, resulting in reduced effectiveness of segmentation strategies; Disconnection between manufacturing and marketing: There is a lack of effective mechanisms to directly feed back market segmentation results to product design and production, making it difficult to achieve precise matching of personalized production with market demand; Therefore, there is an urgent need for an intelligent market segmentation optimization method that can efficiently process complex data, adapt to market changes in real time, and achieve deep collaboration between manufacturing and marketing. Summary of the invention
[0006] The purpose of the present invention is to provide an intelligent market segmentation optimization method integrating intelligent manufacturing and marketing to solve the problems raised in the above-mentioned background technology.
[0007] To achieve the above objectives, in a first aspect, the present invention provides an intelligent market segmentation optimization method integrating intelligent manufacturing and marketing, comprising the following steps:
[0008] S1: Data collection and integration, extensive collection of market data, data sources include sales records of e-commerce platforms, user feedback and discussions on social media, consultation and complaint records of customer service centers, and internal sales and customer management systems of enterprises;
[0009] S2: Data cleaning and preprocessing: using data cleaning algorithms to remove noise, errors and missing values in the data, identifying and processing outliers in sales data through outlier detection algorithms, processing missing values in data using methods such as mean filling or regression prediction, detecting and deleting duplicate data, and ensuring the uniqueness and accuracy of the data;
[0010] S3: Feature engineering, using natural language processing technology to perform lexical analysis, syntactic analysis and semantic understanding on unstructured text data such as user comments and social media posts to extract key features;
[0011] S4: Market segmentation model construction, based on machine learning and data mining algorithms, to build a market segmentation model;
[0012] S5: Model training and optimization: Use historical data to train the constructed market segmentation model, divide the data set into training set, validation set and test set through random sampling to ensure the generalization ability and stability of the model, and use optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta to adjust the model parameters to improve the performance and accuracy of the model;
[0013] S6: Model evaluation and verification. Use multiple evaluation indicators such as accuracy of 80%-90%, recall of 75%-88%, F1 value of 0.78-0.89, and mean square error of 0.08-0.15 to evaluate and verify the trained model. Compare the evaluation results of different models, select the model with the best performance as the final market segmentation model, and perform visualization analysis on the selected model to intuitively display the market segmentation results and feature distribution to assist in understanding and explaining the model output.
[0014] S7: Real-time monitoring and data update: deploy a real-time monitoring system to continuously monitor and collect market data;
[0015] S8: Collaboration between manufacturing and marketing: Feedback the results of market segmentation to the manufacturing process in real time to guide the adjustment of product design and production plans. According to the demand characteristics of different market segments, optimize the function, performance and appearance design of products. In the marketing process, formulate precise marketing strategies and promotion plans based on the results of market segmentation, and carry out personalized advertising for different market segments.
[0016] S9: Effect evaluation and feedback. Regularly evaluate the effect of market segmentation strategy implementation. Evaluation indicators include sales performance growth, customer satisfaction improvement, and market share changes. Collect feedback information through user surveys, customer feedback, sales data analysis, etc., understand the advantages and disadvantages of market segmentation strategies in actual applications, and use evaluation results and feedback information as input to adjust and optimize market segmentation models and strategies, forming a closed loop of continuous improvement.
[0017] S10: Risk management and response. Identify the risks that may be faced in the process of market segmentation optimization, such as data security risks, model overfitting risks, market uncertainty risks, etc., and formulate corresponding risk response strategies and plans. For example, strengthen data security protection, use regularization technology to prevent model overfitting, establish a market early warning mechanism to deal with uncertainty, etc. Regularly monitor and evaluate risk conditions, and adjust risk response strategies in a timely manner to ensure the smooth progress of market segmentation optimization.
[0018] Furthermore, S1 also uses web crawler technology to capture product sales data and user reviews from major e-commerce platforms, and uses API interfaces to obtain relevant data from social media platforms. It integrates and uniformly manages multi-source heterogeneous data, builds a centralized data warehouse, and lays the foundation for subsequent analysis and processing.
[0019] Furthermore, the S2 identifies and removes duplicate customer information and sales records through hash algorithms or data comparison technology, and standardizes the data to unify the data format, measurement unit and encoding method to facilitate subsequent analysis and processing, and converts product price data from different e-commerce platforms into the same currency unit and measurement standard.
[0020] Furthermore, the S3 also uses the bag-of-words model and word embedding technology to convert text into quantifiable feature vectors, and uses statistical analysis and data transformation methods to extract meaningful features for numerical data. It calculates statistical indicators such as the mean, variance, and growth rate of sales data, performs logarithmic transformation and standardization on data such as price and sales volume, and uses feature selection algorithms, specifically variance analysis and mutual information, to screen out the most influential and representative feature subsets for market segmentation from a large number of features, reduce data dimensions, and improve subsequent analysis efficiency and accuracy.
[0021] Furthermore, S4 selects K-means clustering algorithm, hierarchical clustering algorithm, support vector machine, decision tree and other algorithms, combines multiple algorithms to build a hybrid model, determines the input features and output categories of the model, customizes the model according to business needs and data characteristics, takes consumers' age, gender, region, purchasing behavior, preferences and other characteristics as input, and segments the market into different consumer group categories as output.
[0022] Furthermore, S5 also uses cross-validation, grid search and other technologies to optimize the hyperparameters of the model to find the optimal model configuration, determine the optimal number of clusters in the K-means clustering algorithm through cross-validation, and determine the optimal kernel function and parameter combination in the support vector machine through grid search.
[0023] Furthermore, the S7 also uses real-time stream processing technologies such as Apache Kafka and Apache Flink to obtain the latest updates on social media, real-time transaction data on e-commerce platforms, etc., establish a data update mechanism, regularly incorporate newly collected data into the data warehouse, and supplement and update historical data.
[0024] Furthermore, the S8 also establishes an information sharing and communication mechanism between manufacturing and marketing to ensure that both parties can obtain the latest results and changes of market segmentation in a timely and accurate manner and achieve collaborative work.
[0025] Furthermore, the types of sales products specifically collected in step S7 are electronic products, clothing, and household items, and the data transaction amount ranges from 6,000 to 10,000. It also includes sales records, and the consumption frequency range is 1 to 3 times.
[0026] Compared with the prior art, the present invention has the following beneficial effects:
[0027] First, in the present invention, in response to the problem of insufficient data processing capabilities of existing market segmentation methods, this technical solution widely collects multi-source heterogeneous data, including e-commerce platform sales records, social media feedback, etc., and uses advanced data cleaning and preprocessing technologies, such as removing noise, errors and missing values, standardizing data formats, etc., to effectively process massive and complex data and mine valuable information. In order to solve the problem of poor adaptability to market changes, a real-time monitoring system is deployed in the solution, which uses real-time stream processing technology to obtain the latest data, continuously updates the data warehouse, and can adjust the segmentation model in time according to market dynamics to ensure that the segmentation strategy always keeps pace with market changes. For the situation where manufacturing and marketing are out of touch, by establishing an information sharing and communication mechanism, the market segmentation results are fed back to the manufacturing and marketing links in real time, guiding product design and production plan adjustments, and formulating precise marketing strategies and promotional activities, thereby achieving deep collaboration between the two.
[0028] Secondly, in the present invention, through comprehensive data collection, fine data cleaning and preprocessing, and effective feature engineering, accurate and meaningful features can be extracted from multi-source heterogeneous data, providing a high-quality data foundation for the subsequent market segmentation model construction, thereby improving the accuracy and reliability of market segmentation. Real-time monitoring and data update mechanisms enable enterprises to quickly respond to market changes, adjust market segmentation strategies in a timely manner, better meet the dynamic needs of different consumer groups, and help enterprises maintain their advantages in the fiercely competitive market. The collaborative work of manufacturing and marketing can design and produce products according to the precise needs of the segmented market, formulate targeted marketing strategies, avoid waste of resources, improve production efficiency and marketing effects, thereby improving the company's sales performance, market share and customer satisfaction, and through risk management and response measures, identify and respond to various risks in the market segmentation optimization process, provide a more scientific and robust basis for corporate decision-making, reduce losses caused by uncertainty, help enterprises innovate products and services, meet the personalized needs of consumers, enhance brand image and market competitiveness, and promote the sustainable development of enterprises.
[0029] Third, in the present invention, by analyzing the transaction amount range data of 6000-10000 and studying the consumption frequency records of 1-3 times, it is possible to accurately divide customer groups with different consumption levels and consumption habits. For electronic products, it is possible to identify the group of technology enthusiasts who pursue high-end configurations, for clothing, it is possible to distinguish the consumer group that pays attention to fashion trends and quality; for household products, it is possible to identify the practical demand group mainly composed of professional workers. In this way, each segmented group is provided with products and services that better meet their needs, thereby improving customer satisfaction and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION
[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0032] Example 1
[0033] See also Figure 1 In an embodiment of the present invention, an intelligent market segmentation optimization method integrating intelligent manufacturing and marketing includes the following steps:
[0034] S1: Data collection and integration. Market data is collected extensively. Data sources include sales records of e-commerce platforms, user feedback and discussions on social media, consultation and complaint records of customer service centers, and sales and customer management systems within the company. Web crawler technology is used to capture product sales data and user reviews from major e-commerce platforms. API interfaces are used to obtain relevant data from social media platforms. Multi-source heterogeneous data is integrated and uniformly managed to build a centralized data warehouse, laying the foundation for subsequent analysis and processing.
[0035] S2: Data cleaning and preprocessing: Use data cleaning algorithms to remove noise, errors, and missing values from the data; use outlier detection algorithms to identify and process outliers in sales data; use mean filling or regression prediction methods to process missing values in the data; detect and delete duplicate data to ensure the uniqueness and accuracy of the data; use hash algorithms or data comparison techniques to identify and remove duplicate customer information and sales records; and standardize the data to unify the data format, measurement unit, and encoding method for subsequent analysis and processing; convert product price data from different e-commerce platforms into the same currency unit and measurement standard;
[0036] S3: Feature engineering, using natural language processing technology to perform lexical analysis, syntactic analysis and semantic understanding on unstructured text data such as user comments and social media posts, extract key features, and use the bag-of-words model and word embedding technology to convert text into quantifiable feature vectors. For numerical data, statistical analysis and data transformation methods are used to extract meaningful features. It calculates statistical indicators such as the mean, variance, and growth rate of sales data, performs logarithmic transformation and standardization on data such as price and sales volume, and uses feature selection algorithms, specifically variance analysis and mutual information, to select the most influential and representative feature subsets for market segmentation from a large number of features, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis;
[0037] S4: Market segmentation model construction, based on machine learning and data mining algorithms, build market segmentation models, select K-means clustering algorithm, hierarchical clustering algorithm, support vector machine, decision tree and other algorithms, combine multiple algorithms to build a hybrid model, determine the input features and output categories of the model, customize the model according to business needs and data characteristics, take consumer age, gender, region, purchase behavior, preferences and other characteristics as input, and segment the market into different consumer group categories as output;
[0038] S5: Model training and optimization. Use historical data to train the constructed market segmentation model. Divide the data set into training set, validation set and test set by random sampling to ensure the generalization ability and stability of the model. Use optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta to adjust the model parameters to improve the performance and accuracy of the model. Use cross-validation, grid search and other technologies to optimize the model's hyperparameters and find the optimal model configuration. Determine the optimal number of clusters in the K-means clustering algorithm through cross-validation. Determine the optimal kernel function and parameter combination in the support vector machine through grid search.
[0039] S6: Model evaluation and verification. Use multiple evaluation indicators such as accuracy of 80%, recall of 75%, F1 value of 0.78, and mean square error of 0.15 to evaluate and verify the trained model. Compare the evaluation results of different models, select the model with the best performance as the final market segmentation model, and perform visualization analysis on the selected model to intuitively display the market segmentation results and feature distribution to assist in understanding and explaining the model output.
[0040] S7: Real-time monitoring and data update: deploy a real-time monitoring system to continuously monitor and collect market data. Use real-time stream processing technologies such as Apache Kafka and Apache Flink to obtain the latest social media trends and real-time transaction data from e-commerce platforms. Establish a data update mechanism to regularly incorporate newly collected data into the data warehouse and supplement and update historical data. The types of sales products collected are electronic products, clothing, and household goods. The transaction amount range of the data is 6,000. It also includes sales records, and the consumption frequency range is 1 time.
[0041] S8: Collaboration between manufacturing and marketing: Feedback the results of market segmentation to the manufacturing process in real time to guide the adjustment of product design and production plans. According to the demand characteristics of different market segments, optimize the function, performance and appearance design of products. In the marketing process, formulate precise marketing strategies and promotion plans based on the results of market segmentation, carry out personalized advertising, promotional activities and pricing strategies for different market segments, establish information sharing and communication mechanisms between manufacturing and marketing, ensure that both parties can obtain the latest results and changes of market segmentation in a timely and accurate manner, and achieve collaborative work;
[0042] S9: Effect evaluation and feedback. Regularly evaluate the effect of market segmentation strategy implementation. Evaluation indicators include sales performance growth, customer satisfaction improvement, and market share changes. Collect feedback information through user surveys, customer feedback, sales data analysis, etc., understand the advantages and disadvantages of market segmentation strategies in actual applications, and use evaluation results and feedback information as input to adjust and optimize market segmentation models and strategies, forming a closed loop of continuous improvement.
[0043] S10: Risk management and response. Identify the risks that may be faced in the process of market segmentation optimization, such as data security risks, model overfitting risks, market uncertainty risks, etc., and formulate corresponding risk response strategies and plans. For example, strengthen data security protection, use regularization technology to prevent model overfitting, establish a market early warning mechanism to deal with uncertainty, etc. Regularly monitor and evaluate risk conditions, and adjust risk response strategies in a timely manner to ensure the smooth progress of market segmentation optimization.
[0044] Example 2
[0045] S1: Data collection and integration. Market data is collected extensively. Data sources include sales records of e-commerce platforms, user feedback and discussions on social media, consultation and complaint records of customer service centers, and sales and customer management systems within the company. Web crawler technology is used to capture product sales data and user reviews from major e-commerce platforms. API interfaces are used to obtain relevant data from social media platforms. Multi-source heterogeneous data is integrated and uniformly managed to build a centralized data warehouse, laying the foundation for subsequent analysis and processing.
[0046] S2: Data cleaning and preprocessing: Use data cleaning algorithms to remove noise, errors, and missing values from the data; use outlier detection algorithms to identify and process outliers in sales data; use mean filling or regression prediction methods to process missing values in the data; detect and delete duplicate data to ensure the uniqueness and accuracy of the data; use hash algorithms or data comparison techniques to identify and remove duplicate customer information and sales records; and standardize the data to unify the data format, measurement unit, and encoding method for subsequent analysis and processing; convert product price data from different e-commerce platforms into the same currency unit and measurement standard;
[0047] S3: Feature engineering, using natural language processing technology to perform lexical analysis, syntactic analysis and semantic understanding on unstructured text data such as user comments and social media posts, extract key features, and use the bag-of-words model and word embedding technology to convert text into quantifiable feature vectors. For numerical data, statistical analysis and data transformation methods are used to extract meaningful features. It calculates statistical indicators such as the mean, variance, and growth rate of sales data, performs logarithmic transformation and standardization on data such as price and sales volume, and uses feature selection algorithms, specifically variance analysis and mutual information, to select the most influential and representative feature subsets for market segmentation from a large number of features, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis;
[0048] S4: Market segmentation model construction, based on machine learning and data mining algorithms, build market segmentation models, select K-means clustering algorithm, hierarchical clustering algorithm, support vector machine, decision tree and other algorithms, combine multiple algorithms to build a hybrid model, determine the input features and output categories of the model, customize the model according to business needs and data characteristics, take consumer age, gender, region, purchase behavior, preferences and other characteristics as input, and segment the market into different consumer group categories as output;
[0049] S5: Model training and optimization. Use historical data to train the constructed market segmentation model. Divide the data set into training set, validation set and test set by random sampling to ensure the generalization ability and stability of the model. Use optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta to adjust the model parameters to improve the performance and accuracy of the model. Use cross-validation, grid search and other technologies to optimize the model's hyperparameters and find the optimal model configuration. Determine the optimal number of clusters in the K-means clustering algorithm through cross-validation. Determine the optimal kernel function and parameter combination in the support vector machine through grid search.
[0050] S6: Model evaluation and verification. The trained model is evaluated and verified using a variety of evaluation indicators, such as accuracy of 85%, recall of 80%, F1 value of 0.82, and mean square error of 0.12. The evaluation results of different models are compared, and the model with the best performance is selected as the final market segmentation model. The selected model is visualized and analyzed to intuitively display the market segmentation results and feature distribution to assist in understanding and explaining the model output.
[0051] S7: Real-time monitoring and data update: deploy a real-time monitoring system to continuously monitor and collect market data. Use real-time stream processing technologies such as Apache Kafka and Apache Flink to obtain the latest social media trends and real-time transaction data from e-commerce platforms. Establish a data update mechanism to regularly incorporate newly collected data into the data warehouse and supplement and update historical data. The types of sales products collected are electronic products, clothing, and household items. The transaction amount range of the data is 8,000. It also includes sales records, and the consumption frequency range is 2 times.
[0052] S8: Collaboration between manufacturing and marketing: Feedback the results of market segmentation to the manufacturing process in real time to guide the adjustment of product design and production plans. According to the demand characteristics of different market segments, optimize the function, performance and appearance design of products. In the marketing process, formulate precise marketing strategies and promotion plans based on the results of market segmentation, carry out personalized advertising, promotional activities and pricing strategies for different market segments, establish information sharing and communication mechanisms between manufacturing and marketing, ensure that both parties can obtain the latest results and changes of market segmentation in a timely and accurate manner, and achieve collaborative work;
[0053] S9: Effect evaluation and feedback. Regularly evaluate the effect of market segmentation strategy implementation. Evaluation indicators include sales performance growth, customer satisfaction improvement, and market share changes. Collect feedback information through user surveys, customer feedback, sales data analysis, etc., understand the advantages and disadvantages of market segmentation strategies in actual applications, and use evaluation results and feedback information as input to adjust and optimize market segmentation models and strategies, forming a closed loop of continuous improvement.
[0054] S10: Risk management and response. Identify the risks that may be faced in the process of market segmentation optimization, such as data security risks, model overfitting risks, market uncertainty risks, etc., and formulate corresponding risk response strategies and plans. For example, strengthen data security protection, use regularization technology to prevent model overfitting, establish a market early warning mechanism to deal with uncertainty, etc. Regularly monitor and evaluate risk conditions, and adjust risk response strategies in a timely manner to ensure the smooth progress of market segmentation optimization.
[0055] Example 3
[0056] S1: Data collection and integration. Market data is collected extensively. Data sources include sales records of e-commerce platforms, user feedback and discussions on social media, consultation and complaint records of customer service centers, and sales and customer management systems within the company. Web crawler technology is used to capture product sales data and user reviews from major e-commerce platforms. API interfaces are used to obtain relevant data from social media platforms. Multi-source heterogeneous data is integrated and uniformly managed to build a centralized data warehouse, laying the foundation for subsequent analysis and processing.
[0057] S2: Data cleaning and preprocessing: Use data cleaning algorithms to remove noise, errors, and missing values from the data; use outlier detection algorithms to identify and process outliers in sales data; use mean filling or regression prediction methods to process missing values in the data; detect and delete duplicate data to ensure the uniqueness and accuracy of the data; use hash algorithms or data comparison techniques to identify and remove duplicate customer information and sales records; and standardize the data to unify the data format, measurement unit, and encoding method for subsequent analysis and processing; convert product price data from different e-commerce platforms into the same currency unit and measurement standard;
[0058] S3: Feature engineering, using natural language processing technology to perform lexical analysis, syntactic analysis and semantic understanding on unstructured text data such as user comments and social media posts, extract key features, and use the bag-of-words model and word embedding technology to convert text into quantifiable feature vectors. For numerical data, statistical analysis and data transformation methods are used to extract meaningful features. It calculates statistical indicators such as the mean, variance, and growth rate of sales data, performs logarithmic transformation and standardization on data such as price and sales volume, and uses feature selection algorithms, specifically variance analysis and mutual information, to select the most influential and representative feature subsets for market segmentation from a large number of features, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis;
[0059] S4: Market segmentation model construction, based on machine learning and data mining algorithms, build market segmentation models, select K-means clustering algorithm, hierarchical clustering algorithm, support vector machine, decision tree and other algorithms, combine multiple algorithms to build a hybrid model, determine the input features and output categories of the model, customize the model according to business needs and data characteristics, take consumer age, gender, region, purchase behavior, preferences and other characteristics as input, and segment the market into different consumer group categories as output;
[0060] S5: Model training and optimization. Use historical data to train the constructed market segmentation model. Divide the data set into training set, validation set and test set by random sampling to ensure the generalization ability and stability of the model. Use optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta to adjust the model parameters to improve the performance and accuracy of the model. Use cross-validation, grid search and other technologies to optimize the model's hyperparameters and find the optimal model configuration. Determine the optimal number of clusters in the K-means clustering algorithm through cross-validation. Determine the optimal kernel function and parameter combination in the support vector machine through grid search.
[0061] S6: Model evaluation and verification. The trained model is evaluated and verified using a variety of evaluation indicators, such as accuracy of 90%, recall of 88%, F1 value of 0.89, and mean square error of 0.08. The evaluation results of different models are compared, and the model with the best performance is selected as the final market segmentation model. The selected model is visualized and analyzed to intuitively display the market segmentation results and feature distribution to assist in understanding and explaining the model output.
[0062] S7: Real-time monitoring and data update: deploy a real-time monitoring system to continuously monitor and collect market data. Use real-time stream processing technologies such as Apache Kafka and Apache Flink to obtain the latest social media trends and real-time transaction data from e-commerce platforms. Establish a data update mechanism to regularly incorporate newly collected data into the data warehouse and supplement and update historical data. The types of sales products collected are electronic products, clothing, and household goods. The transaction amount range of the data is 10,000. It also includes sales records, and the consumption frequency range is 3 times.
[0063] S8: Collaboration between manufacturing and marketing: Feedback the results of market segmentation to the manufacturing process in real time to guide the adjustment of product design and production plans. According to the demand characteristics of different market segments, optimize the function, performance and appearance design of products. In the marketing process, formulate precise marketing strategies and promotion plans based on the results of market segmentation, carry out personalized pricing strategies for different market segments, establish information sharing and communication mechanisms between manufacturing and marketing, ensure that both parties can obtain the latest results and changes of market segmentation in a timely and accurate manner, and achieve collaborative work;
[0064] S9: Effect evaluation and feedback. Regularly evaluate the effect of market segmentation strategy implementation. Evaluation indicators include sales performance growth, customer satisfaction improvement, and market share changes. Collect feedback information through user surveys, customer feedback, sales data analysis, etc., understand the advantages and disadvantages of market segmentation strategies in actual applications, and use evaluation results and feedback information as input to adjust and optimize market segmentation models and strategies, forming a closed loop of continuous improvement.
[0065] S10: Risk management and response. Identify the risks that may be faced in the process of market segmentation optimization, such as data security risks, model overfitting risks, market uncertainty risks, etc., and formulate corresponding risk response strategies and plans. For example, strengthen data security protection, use regularization technology to prevent model overfitting, establish a market early warning mechanism to deal with uncertainty, etc. Regularly monitor and evaluate risk conditions, and adjust risk response strategies in a timely manner to ensure the smooth progress of market segmentation optimization.
[0066] The following is a comparison table of sales data for the product types surveyed:
[0067]
[0068]
[0069] It can be seen from the above table that embodiment 3 of the present method is the best; the following is a data comparison table for feature extraction:
[0070]
[0071] As can be seen from the above table, embodiment 3 of the present method is the best;
[0072] The following is a table of data for various effect evaluation indicators:
[0073]
[0074] It can be seen from the above table that embodiment 3 is the best:
[0075] The calculation formula of the decision tree in the present invention is:
[0076] Information entropy: H(X) = -∑(i = 1)^np(x_i)log_2p(x_i);
[0077] The logistic regression calculation formula is:
[0078] Assume function: h_θ(x)=1 / (1+e^(-θ^Tx));
[0079] Loss function: J(θ)=-1 / m∑(i=1)^m[y^(i)log(h_θ(x^(i)))+(1-y^(i))log(1-h_θ(x^(i)))];
[0080] The linear regression calculation formula is:
[0081] Assume function: h_θ(x)=θ_0+θ_1x_1+θ_2x_2+…+θ_nx_n;
[0082] Loss function: J(θ)=1 / (2m)∑(i=1)^m(h_θ(x^(i))-y^(i))^2:
[0083] The K-Means clustering calculation formula is:
[0084] Center of mass update: c_i = 1 / |S_i|∑(x∈S_i)x;
[0085] The calculation formula of the present invention also includes but is not limited to the following formula:
[0086] Naive Bayes:
[0087] P(C|X)=P(X|C)P(C) / P(X);
[0088] P(X)=∑(C)P(X|C)P(C);
[0089] Support Vector Machine (SVM):
[0090] For the linearly separable case, the decision function is: f(x) = sign(w^Tx+b);
[0091] Optimization target (soft margin): min(w,b,ξ)1 / 2||w||^2+C∑(i=1)^mξ_i;
[0092] Constraints: y_i(w^Tx_i+b)≥1-ξ_i,ξ_i≥0;
[0093] Random Forest:
[0094] OOB_error=1 / N∑(i=1)^NI(y_i≠y_hat_(i,OOB));
[0095] Where N is the number of samples, y_i is the true value, and y_hat_(i,OOB) is the predicted value of the out-of-bag data (OOB).
[0096] Adaboost:
[0097] F_m(x)=F_(m-1)(x)+α_mG_m(x);
[0098] err_m=∑(i=1)^Nw_(mi)I(y_i≠G_m(x_i));
[0099] α_m=1 / 2ln((1-err_m) / err_m);
[0100] w_(mi)=w_((m-1),i)exp(-α_my_iG_m(x_i)) / Z_m;
[0101] Where m represents the number of iterations and G_m(x) is the base classifier.
Claims
1. Intelligent market segmentation optimization method integrating intelligent manufacturing and marketing, characterized by: The steps include: S1: Data collection and integration, extensive collection of market data, data sources include sales records of e-commerce platforms, user feedback and discussions on social media, consultation and complaint records of customer service centers, and internal sales and customer management systems of enterprises; S2: Data cleaning and preprocessing: using data cleaning algorithms to remove noise, errors and missing values in the data, identifying and processing outliers in sales data through outlier detection algorithms, processing missing values in data using methods such as mean filling or regression prediction, detecting and deleting duplicate data, and ensuring the uniqueness and accuracy of the data; S3: Feature engineering, using natural language processing technology to perform lexical analysis, syntactic analysis and semantic understanding on unstructured text data such as user comments and social media posts to extract key features; S4: Market segmentation model construction, based on machine learning and data mining algorithms, to build a market segmentation model; S5: Model training and optimization: Use historical data to train the constructed market segmentation model, divide the data set into training set, validation set and test set through random sampling to ensure the generalization ability and stability of the model, and use optimization algorithms such as stochastic gradient descent, Adagrad, and Adadelta to adjust the model parameters to improve the performance and accuracy of the model; S6: Model evaluation and verification. Use multiple evaluation indicators such as accuracy of 80%-90%, recall of 75%-88%, F1 value of 0.78-0.89, and mean square error of 0.08-0.15 to evaluate and verify the trained model. Compare the evaluation results of different models, select the model with the best performance as the final market segmentation model, and perform visualization analysis on the selected model to intuitively display the market segmentation results and feature distribution to assist in understanding and explaining the model output. S7: Real-time monitoring and data update: deploy a real-time monitoring system to continuously monitor and collect market data; S8: Collaboration between manufacturing and marketing: Feedback the results of market segmentation to the manufacturing process in real time to guide the adjustment of product design and production plans. According to the demand characteristics of different market segments, optimize the function, performance and appearance design of products. In the marketing process, formulate precise marketing strategies and promotion plans based on the results of market segmentation, and carry out personalized promotion activities for different market segments. S9: Effect evaluation and feedback. Regularly evaluate the effect of market segmentation strategy implementation. Evaluation indicators include sales performance growth, customer satisfaction improvement, and market share changes. Collect feedback information through user surveys, customer feedback, sales data analysis, etc., understand the advantages and disadvantages of market segmentation strategies in actual applications, and use evaluation results and feedback information as input to adjust and optimize market segmentation models and strategies, forming a closed loop of continuous improvement. S10: Risk management and response. Identify the risks that may be faced in the process of market segmentation optimization, such as data security risks, model overfitting risks, market uncertainty risks, etc., and formulate corresponding risk response strategies and plans. For example, strengthen data security protection, use regularization technology to prevent model overfitting, establish a market early warning mechanism to deal with uncertainty, etc. Regularly monitor and evaluate risk conditions, and adjust risk response strategies in a timely manner to ensure the smooth progress of market segmentation optimization.
2. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S1 also uses web crawler technology to capture product sales data and user reviews from major e-commerce platforms, and uses API interfaces to obtain relevant data from social media platforms. It integrates and uniformly manages multi-source heterogeneous data, builds a centralized data warehouse, and lays the foundation for subsequent analysis and processing.
3. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S2 identifies and removes duplicate customer information and sales records through hash algorithms or data comparison technology, and standardizes the data to unify the data format, measurement unit and encoding method to facilitate subsequent analysis and processing, and converts the product price data of different e-commerce platforms into the same currency unit and measurement standard.
4. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S3 also uses the bag-of-words model and word embedding technology to convert text into quantifiable feature vectors. For numerical data, it uses statistical analysis and data transformation methods to extract meaningful features. It calculates statistical indicators such as the mean, variance, and growth rate of sales data, performs logarithmic transformation and standardization on data such as price and sales volume, and uses feature selection algorithms, specifically variance analysis and mutual information, to screen out the most influential and representative feature subsets for market segmentation from a large number of features, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis.
5. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: In S4, K-means clustering algorithm, hierarchical clustering algorithm, support vector machine, decision tree and other algorithms are selected, and a hybrid model is constructed by combining multiple algorithms to determine the input features and output categories of the model. The model is customized according to business needs and data characteristics, and the characteristics of consumers such as age, gender, region, purchasing behavior, and preference are used as input, and the market is segmented into different consumer groups as output.
6. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S5 also uses cross-validation, grid search and other technologies to optimize the hyperparameters of the model to find the optimal model configuration, determine the optimal number of clusters in the K-means clustering algorithm through cross-validation, and determine the optimal kernel function and parameter combination in the support vector machine through grid search.
7. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S7 also uses real-time stream processing technologies such as Apache Kafka and Apache Flink to obtain the latest updates on social media, real-time transaction data on e-commerce platforms, etc., establish a data update mechanism, regularly incorporate newly collected data into the data warehouse, and supplement and update historical data.
8. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The S8 also establishes an information sharing and communication mechanism between manufacturing and marketing to ensure that both parties can obtain the latest results and changes in market segmentation in a timely and accurate manner and achieve collaborative work.
9. The intelligent market segmentation optimization method for integrating intelligent manufacturing and marketing according to claim 1 is characterized in that: The types of sales products specifically collected in step S7 are electronic products, clothing, and household items, and the data transaction amount ranges from 6,000 to 10,000. It also includes sales records, and the consumption frequency range is 1 to 3 times.