Enterprise market segmentation and positioning method based on data mining

Abstract

The invention discloses an enterprise market segmentation and positioning method based on data mining, and relates to the technical field of enterprise market analysis and decision making. According to the method, through dynamic relation modeling and real-time interaction mode tracking, in combination with time sequence analysis and an iterative updating mechanism, the market dynamic change can be accurately captured, and the problem that a traditional method is slow in response is solved. Meanwhile, potential relations among data points are deeply mined by adopting information correlation analysis and a graph network algorithm, the limitation of a traditional method is broken through, and more accurate market subdivision is realized; besides, based on prediction scene generation and decision path optimization, a scene simulation technology and external verification data are combined to generate a high-confidence prediction scene, scientific, comprehensive and dynamically optimized decision support is provided for enterprises, and it is ensured that the enterprises keep competitive power in a complex and changeable market.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise market analysis and decision-making, and particularly relates to an enterprise market segmentation and positioning method based on data mining. BACKGROUND

[0002] Market segmentation and positioning is a crucial field in enterprise strategic decision-making, directly related to resource allocation efficiency and the construction of competitive advantage. In today's complex and changing economic environment, accurately identifying target groups and developing differentiated strategies have become the core competence of enterprises in the market. Research in this field not only affects the survival and development of enterprises themselves, but also plays a key role in optimizing resources and creating value for the entire industry.

[0003] However, existing methods often struggle to respond to market dynamics. Many solutions focus more on the accumulation and analysis of surface data, ignoring the deep connections between information from different sources, leading to slow responses to market changes. In addition, traditional tools often fail to effectively integrate resources when dealing with large-scale and diverse information, resulting in one-sidedness and lag in strategy development, making it difficult to adapt to rapidly changing market demands. SUMMARY

[0004] The purpose of the present application is to provide an enterprise market segmentation and positioning method based on data mining, which solves the shortcomings of traditional methods in market dynamic response, accurate segmentation and decision support, significantly improves the market response speed, resource allocation efficiency and decision-making scientificity of enterprises, and ensures the maintenance of competitive advantage in complex market environments.

[0005] The purpose of the present application can be achieved by the following technical solutions: The present application provides an enterprise market segmentation and positioning method based on data mining, comprising the following steps: Obtain market data from different channels and perform preliminary cleaning and format unification, structure the data through classification algorithms, and obtain a preliminary organized information set; Use information correlation analysis technology to mine potential connections between different data points, determine the correlation strength between data points by constructing a correlation matrix, and output a correlation feature map; According to the correlation feature map, implement dynamic relationship modeling, use graph network algorithms to calculate the weights of nodes and edges in the feature map, and generate a relationship network structure reflecting the dynamic connections between market entities.

[0006] Perform deep processing of the relationship network structure to further build the relationship network, update the connection strength between nodes through iteration, obtain the real-time interaction mode between market entities, and determine the evolution path of dynamic relationships; According to the evolution path of the dynamic relationship, the demand change capture is carried out, the time series analysis method is used to track the changed nodes in the relationship network, and the key trigger factors of demand change are obtained; According to the key trigger factors of demand change, market trend prediction is implemented, the probability distribution of trend change is generated through weighted analysis of the trigger factors, and the potential trend of the future market is determined; According to the potential trend of the future market, the prediction scenario generation is carried out, the multi-dimensional mapping of trend change is carried out by using scenario simulation technology, and the prediction scenario set fitting the real demand is output; According to the prediction scenario set, the decision path optimization is implemented, the scene parameters are adjusted by introducing external verification data, and the optimized scene result is obtained.

[0007] Further, the information set obtained after preliminary arrangement includes: Through the integration of multi-source information, market data from different channels is obtained, an initial data set is constructed, data cleaning tools are used for preliminary cleaning of the data, and the completeness of the cleaned data is judged. Through the data set after cleaning, format uniformity operation is performed, the data format of different channels is converted into the preset standard format, and the uniform data structure is determined; The support vector machine algorithm in the classification algorithm is used for structured processing, the data features are classified, the classified data subset is obtained, the core features of each category are obtained, and the adjusted category set is determined; The preliminary arranged information set is constructed, the correlation between categories is verified, the final arranged information set is obtained, the structured data storage framework is generated, the field mapping of storage requirements is obtained, and the complete data set is obtained.

[0008] Further, the correlation feature map is output, including: The correlation analysis technology is used to mine the potential relationship between data points, the correlation matrix is constructed to determine the correlation strength between data points, the correlation distribution structure is obtained, the main potential relationship between data points is extracted, the clustering analysis method is used to group the connection points with high strength, and the preliminary division result of market segmentation groups is determined; The internal relationship between market segmentation groups is analyzed, the adjusted group classification set is obtained, the feature map is constructed, the core correlation features between groups are obtained, the uniformity of feature distribution is judged, and the complete mapping of the feature map is obtained; According to the complete mapping of the feature map, the correlation strength change trend between data points is analyzed, the data smoothing tool is used to process the places where the correlation strength of the region appears fluctuation, and the smoothed correlation distribution is determined; By extracting key connection points from market segment groups, constructing a visual structure, obtaining a dynamic relationship view between groups, determining the final set of related features, integrating the inherent relationships between data points, and using storage tools to structurally save the feature set, a market segmentation data framework is obtained.

[0009] Furthermore, a relationship network structure reflecting the dynamic connections between market entities is generated, specifically including: Based on the correlation characteristics, the graph analysis method is used to extract preliminary interaction data between market entities, obtain the distribution of correlation patterns among entities, and obtain an initial relationship network framework. By combining dynamic modeling technology, the interaction between market entities is tracked over time to determine the evolution path of entity patterns in different time periods. Through network construction, the node weights and edge weights are quantified to obtain the relative position distribution of market entities in the relationship network and to determine the differences in the strength of connections between entities. If the connection strength of a certain market entity is lower than the preset threshold, it is merged into the adjacent entity pattern through data integration tools to obtain the adjusted relationship network structure. Then, the structural analysis method is used to extract the core connection points in the interaction and determine the key influencing factors in market positioning. By performing secondary optimization and adjustment of node weights through graph network algorithms, a more accurate distribution view of market entities is obtained, the dynamic role of each entity in market positioning is determined, and then, based on the dynamic role distribution view, visualization tools are used to present the entity patterns in the relationship network in multiple dimensions, thus obtaining the final market interaction and connection mapping.

[0010] Furthermore, the evolutionary path of the dynamic relationship is determined, specifically including: By collecting and organizing data from the relationship network, interaction information between market entities is obtained from multiple data sources, an initial connection strength matrix is ​​constructed, and the connection strength is calculated and adjusted multiple times using an iterative update method to obtain the latest association weights between nodes and determine the real-time changing trend of dynamic relationships. The random forest algorithm is used to classify and predict interaction patterns, extract key interaction characteristics of market participants, identify major influencing factors, obtain an adjusted network structure, and obtain interaction patterns that are more in line with the current market environment. Based on the adjusted interaction model, the evolution path of dynamic relationships is tracked, significant change nodes and path characteristics are extracted, the development direction of relationships between market entities is determined, and targeted adjustment support solutions are generated in combination with the needs of corporate strategies, resulting in strategy optimization directions applicable to the current market environment. Based on the strategy optimization direction, we continuously monitor the changes in interaction and fluctuations in connection strength among market participants, extract new dynamic relationship characteristics from real-time data, and judge the stability of the relationship network.

[0011] Furthermore, the key triggers for changes in demand were identified, specifically including: By continuously monitoring the evolution path of dynamic relationships, feature data of changing nodes are extracted from the relationship network to obtain preliminary signs of demand changes. Time series analysis is used to track the changing nodes in multiple dimensions to determine the triggering factors of demand changes. By analyzing the potential fluctuations in customer demand from the perspective of market positioning, we can identify the key variables that affect market positioning and determine the short-term adjustment direction of customer demand. By adjusting the characteristic data of the direction and combining it with the historical records of the evolution path, a dynamic relationship update scheme consistent with the fluctuations in customer demand is generated, the final market positioning optimization path is determined, and the characteristics of demand changes in the relationship network are continuously monitored to obtain the key triggering factors of new demand changes.

[0012] Furthermore, generating a probability distribution of trend changes helps determine the potential future direction of the market, specifically including: Based on the key triggering factors of demand changes, obtain the corresponding triggering factor details, determine the priority ranking of the triggering factors by stratifying the characteristics of the triggering factors, and use a weighted analysis method to assign different weights to each factor; By using a logistic regression model to process weighted data, we can determine the preliminary distribution of trend changes, obtain relevant market trend indicators, and obtain the fluctuation characteristics of market trends through multi-dimensional comparison of the indicators, and determine the probability distribution of trend changes. We acquire potential directional data on market trends, determine the priority sequence of market trends through combined analysis of this potential directional data, obtain dynamically updated data on changing characteristics, and obtain the final mapping of the analysis results through tracking and processing of this dynamically updated data.

[0013] Furthermore, it outputs a set of predicted scenarios that align with real-world needs, specifically including: A preliminary analysis of the potential future trends of the market is conducted. Time series analysis is used to process historical data and current dynamic information to obtain trend judgments of market changes. Scenario simulation technology is used to construct multiple prediction scenarios. Combined with multidimensional mapping methods, trend changes are decomposed into influencing factors of different dimensions to determine multiple market development paths. Acquire data metrics related to actual needs, match predicted scenarios with demand characteristics through comparative analysis, identify the set of scenarios that fit the needs, and obtain the classification results of the segmented market through data clustering methods; Based on the classification results of the segmented market and combined with relevant information on enterprise positioning, the applicability of positioning solutions in different scenarios is analyzed. If the matching degree of a certain solution with the scenario is higher than the preset threshold, then the solution is determined to be the preferred positioning direction. By simulating the preferred positioning directions and dynamically calibrating the trend changes and future potential directions, a final strategy combination scheme is obtained. Then, by integrating market trends, predicted scenarios, and multi-dimensional mapping results, a reference basis for auxiliary strategy formulation is determined.

[0014] Furthermore, the optimized scene results are obtained, specifically including: By analyzing the set of predicted scenarios, we obtain the initial confidence value, determine whether it meets the requirements of the preset threshold, obtain evaluation data, obtain relevant verification data from external verification data sources, and determine the basic information required for calibration. Based on the obtained verification data, the scene parameters are initially compared, and the direction of parameter adjustment is determined by using pre-established matching rules. Combined with the original data in the prediction scene set, the parameter update operation is implemented to obtain the updated scene configuration information. If the updated scene configuration information still has some confidence values ​​lower than the preset threshold, then a support vector machine model is introduced to classify the abnormal scenes, determine the final optimization range, and implement local parameter adjustments for the abnormal scenes. By obtaining the final optimization result data and integrating it with the original scenario set, a complete decision path output is formed, resulting in the adjusted prediction scenario set.

[0015] Furthermore, after obtaining the optimized scenario results, the process also includes: generating the final market dynamic response plan, comparing the scenario results with historical data to determine the applicability of the response plan, and outputting executable strategy guidance.

[0016] The beneficial effects of this invention are as follows: This invention accurately captures dynamic market changes by combining dynamic relationship modeling and real-time interactive pattern tracking with time series analysis and iterative update mechanisms. It effectively solves the problem of slow response to market changes in traditional methods. It can perceive the evolution of interactive relationships between market players in real time, quickly identify key triggers for changes in demand, and enable enterprises to adjust their strategies in a timely manner to adapt to the rapidly changing market environment, thereby significantly improving market response speed and flexibility. By employing information association analysis and graph network algorithms, we construct a correlation feature map and a dynamic relationship network to deeply explore the potential connections between data points and the complex interactions between market entities. This breaks through the limitations of traditional methods that only focus on the accumulation of surface data. It can accurately identify the core correlation characteristics and dynamic roles of market segment groups, achieving more accurate and detailed market segmentation. This allows companies to formulate more targeted differentiation strategies, improve resource allocation efficiency, and enhance their competitive advantage. Based on predictive scenario generation and decision path optimization, combined with scenario simulation technology and external validation data, high-confidence predictive scenarios are generated. Through strategy simulation and dynamic calibration, actionable strategy guidance is output. This approach overcomes the limitations and lag of traditional tools when dealing with large-scale and diverse information, providing enterprises with scientific, comprehensive, and dynamically optimized decision support. By accurately predicting market trends and optimizing decision paths, enterprises can plan ahead, formulate more effective market positioning strategies, improve the scientific nature and foresight of decision-making, and ensure competitiveness in a complex and ever-changing market. Attached Figure Description

[0017] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0018] Fig. 1 A flowchart illustrating a data mining-based enterprise market segmentation and positioning method provided for this application; Fig. 2 A flowchart illustrating the process of generating a relationship network structure reflecting the dynamic connections between market entities using a data mining-based enterprise market segmentation and positioning method provided in this application; Fig. 3 This application provides a flowchart illustrating the process of obtaining key triggers for demand changes through a data mining-based enterprise market segmentation and positioning method. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0020] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0021] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0022] Please see Figs. 1-3 This embodiment provides a data mining-based method for enterprise market segmentation and positioning, including the following steps: S1. Starting with the integration of multi-source information, market data from different channels is obtained and initially cleaned and formatted. The data is then structured using a classification algorithm to obtain a pre-organized information set.

[0023] Furthermore, a preliminary set of information was obtained, specifically including: By integrating information from multiple sources, market data from different channels is obtained to build an initial dataset. Data cleaning tools are used to perform preliminary cleaning of the data. For missing or outlier data, if a missing field is detected, a default value is filled in, and the integrity of the cleaned data is judged. After cleaning the dataset, a format unification operation is performed. If inconsistent field types are found for data formats from different channels, they are converted to a preset standard format to determine the unified data structure. The Support Vector Machine (SVM) algorithm in classification is used to structure the data after it has been formatted in a unified manner. The data features are divided into categories to obtain a subset of classified data. The core features of each category are obtained. If the amount of data in a certain category is lower than a preset threshold, it is merged into an adjacent category to determine the adjusted category set. A preliminary information set is constructed, and the correlation between categories is verified. If category overlap is found, the boundaries are redefined to obtain the final information set. A structured data storage framework is generated, and field mapping is performed for storage requirements to obtain a complete dataset for subsequent analysis.

[0024] Specifically, by integrating multi-source information, cleaning data, standardizing formats, and processing classification algorithms, the problems of scattered data sources, inconsistent formats, and uneven data quality in traditional methods are effectively solved, significantly improving data availability and analysis efficiency. This process ensures data integrity, eliminates differences in data formats from different channels, optimizes the degree of data structuring, and constructs a high-quality information collection and data storage framework, laying a solid foundation for subsequent market segmentation and positioning analysis, and greatly improving the accuracy and efficiency of market analysis.

[0025] S2. Based on the preliminary information set, information association analysis technology is used to explore the potential connections between different data points. By constructing an association matrix, the correlation strength between each data point is determined, and the correlation feature map is output.

[0026] Furthermore, the correlation feature map is output, specifically including: By employing association analysis techniques, we can uncover potential connections between data points. By constructing an association matrix, we can determine the correlation strength between each data point and obtain the correlation distribution structure. Then, we can extract the main potential connections between data points and use cluster analysis to group the connection points with higher strength to determine the preliminary division results of market segmentation groups. Based on the preliminary segmentation results, the intrinsic connections between market segment groups are analyzed. If the connection strength between a certain group is lower than a preset threshold, it is merged into the adjacent group to obtain the adjusted group classification set. A feature map is constructed to obtain the core correlation features between each group, and the uniformity of feature distribution is judged to obtain the complete mapping of the feature map. Based on the complete mapping of the feature map, the correlation strength change trend between data points is analyzed. If the correlation strength in a certain area fluctuates significantly, the data smoothing tool is used to process it and determine the smoothed correlation distribution. By extracting key connection points among market segments, constructing a visual structure, obtaining a dynamic relationship view between groups, determining the final set of related features, integrating the inherent relationships between data points, and using storage tools to structurally save the feature set, a market segmentation data framework can be obtained for subsequent processing.

[0027] Specifically, by employing information correlation analysis technology to uncover potential connections between data points and construct a correlation matrix, the correlation strength between each data point can be accurately determined, and a correlation feature map can be output. This process effectively solves the problem of difficulty in identifying deep correlations between data in traditional methods, significantly improving the accuracy and efficiency of market segmentation. Through cluster analysis and feature extraction, the division of market segment groups is further optimized, ensuring the uniformity of feature distribution. Data smoothing processing solves the problem of correlation strength fluctuations. Finally, through visualization and structured storage, a market segmentation data framework is generated for subsequent processing, providing strong support for enterprises' precision marketing and resource optimization.

[0028] S3. Based on the correlation feature graph, implement dynamic relationship modeling, and use graph network algorithm to calculate the weights of nodes and edges in the feature graph to generate a relationship network structure that reflects the dynamic connections between market entities.

[0029] Furthermore, a relationship network structure reflecting the dynamic connections between market entities is generated, specifically including: S31. Based on the correlation characteristics, the graph analysis method is used to extract the preliminary interaction data between market entities from the pre-established feature graph, obtain the distribution of correlation patterns between entities, and obtain the initial relationship network framework. S32. By combining dynamic modeling technology, the interaction between market entities is tracked over time to determine the evolution path of entity patterns in different time periods. Through network construction, the node weights and edge weights are quantified to obtain the relative position distribution of market entities in the relationship network and to determine the differences in the strength of connections between entities. This approach combines dynamic modeling technology to continuously monitor and analyze the interactions between market participants over time, tracking their evolution across different time periods. Specifically, it periodically collects interaction data between market participants, stores and analyzes it in time series, calculates the changes in connection strength between market participants within each time period, and uses graph network algorithms to dynamically adjust node and edge weights to generate a temporal evolution graph of market participant interactions. This graph clearly shows the changing trends of interaction patterns between market participants at different points in time, providing strong support for analyzing the dynamic evolution of market participant behavior and helping enterprises to promptly understand market dynamics and formulate corresponding strategies.

[0030] S33. If the connection strength of a certain market entity is lower than the preset threshold, it is merged into the adjacent entity pattern through data integration tools to obtain the adjusted relationship network structure. Then, the structural analysis method is used to extract the core connection points in the interaction and determine the key influencing factors in market positioning. S34. The node weights are optimized and adjusted a second time through the graph network algorithm to obtain a more accurate view of the distribution of market entities, determine the dynamic role of each entity in the market positioning, and then use visualization tools to present the entity patterns in the relationship network in multiple dimensions based on the dynamic role distribution view, so as to obtain the final market interaction connection mapping.

[0031] Specifically, through dynamic relationship modeling and graph network algorithms, this invention generates a relationship network structure that reflects the dynamic connections between market entities, effectively solving the problem that traditional methods struggle to capture complex dynamic relationships. This process, through time-dimensional change tracking and weight quantification, accurately reflects the relative positions and connection strength differences of market entities, optimizes the relationship network structure, and provides a precise view of market interactions through secondary optimization and visualization. This offers dynamic and scientific decision support for enterprises' market positioning and strategy formulation, significantly improving the scientific nature of decision-making and market adaptability.

[0032] S4. Deepen the relationship network construction process by iteratively updating the connection strength between nodes to obtain the real-time interaction patterns between market entities and determine the evolution path of dynamic relationships.

[0033] Furthermore, the evolutionary path of the dynamic relationship is determined, specifically including: By collecting and organizing data from the relationship network, interactive information between market entities is obtained from multiple data sources, an initial connection strength matrix is ​​constructed, a preliminary relationship network structure is obtained, and the connection strength is calculated and adjusted multiple times using an iterative update method to obtain the latest association weights between nodes and determine the real-time changing trend of dynamic relationships. The random forest algorithm is used to classify and predict interaction patterns, extract key interaction features of market entities, and determine the main influencing factors. If the main influencing factors exceed the preset threshold, the connection strength in the relationship network is recalibrated to obtain the adjusted network structure and obtain an interaction pattern that is more in line with the current market environment. Based on the adjusted interaction model, we track the evolution path of dynamic relationships, extract significant change nodes and path characteristics, determine the development direction of relationships between market entities, and generate targeted adjustment support solutions in conjunction with the needs of corporate strategies to obtain strategy optimization directions applicable to the current market environment. Based on the strategy optimization direction, we continuously monitor the changes in interaction and fluctuations in connection strength among market participants, extract new dynamic relationship characteristics from real-time data, and judge the stability of the relationship network.

[0034] Specifically, by deepening the construction of the relationship network and adopting an iterative update mechanism, this invention can obtain the evolution path of the interaction patterns and dynamic relationships between market entities in real time, effectively solving the problem that traditional methods cannot track market dynamic changes in real time. This process accurately captures the latest association weights and real-time trends between market entities by iteratively updating the connection strength. It uses the random forest algorithm to classify and predict the interaction patterns, extract key features, and calibrate the network structure, thereby generating interaction patterns that are more in line with the current market environment. By tracking the evolution path and monitoring interaction changes, strategies can be adjusted in a timely manner to ensure the stability and adaptability of the relationship network, providing enterprises with dynamic and accurate market decision support, and significantly improving the enterprise's responsiveness and scientific decision-making in a complex and ever-changing market.

[0035] S5. Based on the evolution path of dynamic relationships, demand changes are captured, and time series analysis is used to track the changing nodes in the relationship network to obtain the key triggering factors for demand changes.

[0036] Furthermore, the key triggers for changes in demand were identified, specifically including: S51. By continuously monitoring the evolution path of dynamic relationships, feature data of changing nodes are extracted from the relationship network to obtain preliminary signs of demand changes. Time series analysis is used to track the changing nodes in multiple dimensions to determine the triggering factors of demand changes. S52. Analyze the potential fluctuations in customer demand from the perspective of market positioning to obtain the key variables that affect market positioning. If the key variables exceed the preset threshold, recalibrate the node tracking data in the relationship network to determine the short-term adjustment direction of customer demand. S53. By adjusting the characteristic data of the direction and combining it with the historical records of the evolution path, obtain the basis for strategic adjustment that matches the market positioning, generate a dynamic relationship update plan that is consistent with the fluctuation of customer demand, determine the final market positioning optimization path, continuously monitor the demand change characteristics in the relationship network, obtain the key triggering factors of new demand changes, and cyclically update the evolution path of the dynamic relationship.

[0037] Specifically, by employing time series analysis to track changing nodes in a relationship network, this invention can accurately capture key triggers for demand changes, effectively solving the problem of traditional methods struggling to detect and respond to demand changes in a timely manner. This process not only monitors the evolution path of dynamic relationships in real time and identifies initial signs of demand changes, but also analyzes potential fluctuations in customer demand from a market positioning perspective, allowing for timely adjustments to market strategies to adapt to these changes. Through continuous monitoring and iterative updates, enterprises can dynamically optimize their market positioning path, ensuring competitiveness in a rapidly changing market environment and significantly improving market responsiveness and the scientific nature of decision-making.

[0038] S6, based on the key triggers of demand changes, implements market trend forecasting. Through weighted analysis of the triggers, it generates a probability distribution of trend changes and determines the potential future direction of the market.

[0039] Furthermore, generating a probability distribution of trend changes helps determine the potential future direction of the market, specifically including: Based on the key triggering factors of demand changes, obtain the corresponding triggering factor details, determine the priority ranking of the triggering factors by stratifying the characteristics of the triggering factors, and use a weighted analysis method to assign different weights to each factor; The logistic regression model is used to process the weighted data, determine the preliminary distribution of trend changes, obtain relevant market trend indicators, and obtain the fluctuation characteristics of market trends through multi-dimensional comparison of indicators. If the fluctuation characteristics exceed the preset threshold, the weighted data of the triggering factors are recalibrated to determine the probability distribution of trend changes. We acquire potential directional data on market trends, determine the priority sequence of market trends through combined analysis of this potential directional data, obtain dynamically updated data on changing characteristics, and obtain the final mapping of the analysis results through tracking and processing of this dynamically updated data.

[0040] Specifically, by implementing market trend forecasting, a probability distribution of trend changes can be generated based on key triggers of demand shifts, thereby determining the potential future direction of the market. This process addresses the lack of accuracy in market trend forecasting using traditional methods. By applying weighted analysis and logistic regression models to the triggering factors, the initial distribution and fluctuation characteristics of market trends can be accurately determined. Weights are dynamically adjusted when fluctuations exceed thresholds to ensure the reliability of the forecast results. Finally, by combining and analyzing potential directional data and dynamically updating change characteristics, enterprises can obtain scientific market trend forecasts, proactively plan and optimize strategies, and significantly improve the foresight of decision-making and market adaptability.

[0041] S7. Based on the potential future trends of the market, generate predictive scenarios, use scenario simulation technology to map trend changes in multiple dimensions, and output a set of predictive scenarios that fit real needs.

[0042] Furthermore, it outputs a set of predicted scenarios that align with real-world needs, specifically including: A preliminary analysis of the potential future trends of the market is conducted. Time series analysis is used to process historical data and current dynamic information to obtain trend judgments of market changes. Scenario simulation technology is used to construct multiple prediction scenarios. Combined with multidimensional mapping methods, trend changes are decomposed into influencing factors of different dimensions to determine multiple possible market development paths. Acquire data indicators related to actual needs, match predicted scenarios with demand characteristics through comparative analysis, and determine the set of scenarios that fit the needs. If at least one scenario in the set matches the market segmentation characteristics, further extract the core variables of the segmented market and obtain the classification results of the segmented market through data clustering methods. Based on the classification results of the segmented market and combined with relevant information on enterprise positioning, the applicability of positioning solutions in different scenarios is analyzed. If the matching degree of a certain solution with the scenario is higher than the preset threshold, then the solution is determined to be the preferred positioning direction. By simulating the preferred positioning directions, we obtain the adjustment parameters for flexible strategies, dynamically calibrate them in response to trend changes and potential future directions, and obtain the final strategy combination scheme. Then, we integrate market trends, predicted scenarios, and multi-dimensional mapping results, and generate a multi-dimensional strategy view through data visualization tools to determine the reference basis for assisting in the formulation of strategies.

[0043] Specifically, by using scenario simulation technology and multi-dimensional mapping to generate a set of predictive scenarios that fit real needs, it solves the shortcomings of traditional prediction methods. It uses time series analysis and demand feature matching to select scenarios that fit market segmentation, and combines enterprise positioning to conduct strategy simulation and dynamic calibration. Finally, it outputs a multi-dimensional strategy view through visualization tools, providing enterprises with accurate decision support and significantly improving the scientific nature and adaptability of decision-making.

[0044] S8. Based on the set of predicted scenarios, optimize the decision path. If the confidence level of the predicted scenario is lower than the preset threshold, perform a second calibration on the scenario by introducing external validation data to adjust the scenario parameters and obtain the optimized scenario result.

[0045] Furthermore, the optimized scene results are obtained, specifically including: By analyzing the set of predicted scenarios, an initial confidence value is obtained to determine whether it meets the requirements of the preset threshold and obtain evaluation data. If the confidence value in the evaluation data is lower than the preset threshold, a secondary calibration process is triggered to obtain relevant verification data from an external verification data source and determine the basic information required for calibration. Based on the obtained verification data, the scene parameters are initially compared, and the direction of parameter adjustment is determined by the pre-established matching rules. The adjusted parameter set is obtained, and the parameter update operation is performed by combining the original data in the predicted scene set to obtain the updated scene configuration information. If the updated scene configuration information still has some confidence values ​​lower than the preset threshold, then a support vector machine model is introduced to classify the abnormal scenes, determine the final optimization range, and implement local parameter adjustments for the abnormal scenes. By obtaining the final optimization result data and integrating it with the original scenario set, a complete decision path output is formed, resulting in the adjusted prediction scenario set.

[0046] Specifically, by optimizing the decision path and performing secondary calibration, the problem of insufficient confidence in the predicted scenarios was solved. External validation data was introduced to adjust the parameters, and a support vector machine model was used to optimize abnormal scenarios. Finally, a set of predicted scenarios with high confidence was generated, which significantly improved the scientificity and reliability of the decision path and provided strong support for enterprises to make accurate decisions.

[0047] Furthermore, after obtaining the optimized scenario results, the process also includes: generating the final market dynamic response plan, comparing the scenario results with historical data to determine the applicability of the response plan, and outputting executable strategy guidance.

[0048] The output executable strategy guidance specifically includes: By analyzing and optimizing the results of scenario analysis, we can obtain the initial trend of market dynamics. Using data comparison tools, we can extract reference patterns from historical records that match the current trend and determine whether the matching degree reaches the preset threshold. If the matching degree reaches the preset threshold, a preliminary response plan is generated for the reference pattern; if it does not reach the threshold, additional data is supplemented through market trend analysis to obtain an adjusted response plan. Combining market trends and dynamic adjustment mechanisms, specific strategy guidance content is determined. After obtaining the strategy guidance, the implementation of the plan is simulated and verified to identify potential deviations during the execution process. The deviations in the simulation are then corrected through the result verification module to obtain the final applicable evaluation conclusion.

[0049] Specifically, by comparing the optimized scenario results with historical data, a response plan that accurately matches market dynamics is generated, and simulation verification and deviation correction are performed. Finally, an executable strategy guide is output, which solves the problem of the lack of accuracy and adaptability in the decision-making and execution stages of traditional methods. It effectively improves the accuracy of corporate decision-making and the reliability of execution, and ensures a rapid and effective response to market changes.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for enterprise market segmentation and positioning based on data mining, characterized in that: Includes the following steps: Market data from different channels is acquired, preliminarily cleaned and formatted, and then structured using classification algorithms to obtain a preliminarily organized information set. By employing information association analysis technology, we can uncover the potential connections between different data points, determine the correlation strength between each data point by constructing an association matrix, and output an association feature map. Based on the correlation feature graph, dynamic relationship modeling is implemented, and graph network algorithms are used to calculate the weights of nodes and edges in the feature graph to generate a relationship network structure that reflects the dynamic connections between market entities. The relationship network structure is further processed by relationship network construction. By iteratively updating the connection strength between nodes, the real-time interaction patterns between market entities are obtained, and the evolution path of dynamic relationships is determined. Based on the evolution path of dynamic relationships, demand changes are captured, and time series analysis is used to track the changing nodes in the relationship network to obtain the key triggering factors for demand changes. Based on the key triggers of demand changes, market trend forecasting is implemented. By weighted analysis of the triggers, a probability distribution of trend changes is generated to determine the potential future direction of the market. Based on the potential future trends of the market, predictive scenarios are generated, and scenario simulation technology is used to map trend changes in multiple dimensions, outputting a set of predictive scenarios that fit real needs. Based on the set of predicted scenarios, decision path optimization is implemented by introducing external validation data to adjust scenario parameters and obtain optimized scenario results.

2. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: The preliminarily organized information set includes: By integrating information from multiple sources, market data from different channels is obtained to construct an initial dataset. Data cleaning tools are used to perform preliminary cleaning of the data, and the integrity of the cleaned data is assessed for missing or outlier data. After cleaning the dataset, a format unification operation is performed to convert the data formats from different channels to a preset standard format, thus determining the unified data structure. The support vector machine algorithm in classification is used for structured processing to divide the data features into categories, obtain the classified data subsets, obtain the core features of each category, and determine the adjusted category set. A preliminary information set is constructed, the relationships between categories are verified, a final information set is obtained, a structured data storage framework is generated, and field mapping is performed to meet storage requirements, resulting in a complete dataset.

3. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Output the correlation feature map, specifically including: By employing association analysis techniques, we can uncover potential connections between data points. By constructing an association matrix, we can determine the correlation strength between each data point and obtain the correlation distribution structure. Then, we can extract the main potential connections between data points and use cluster analysis to group the high-strength connection points to determine the preliminary segmentation results of the market segmentation groups. Analyze the intrinsic relationships between market segment groups to obtain an adjusted set of group classifications, construct a feature map, obtain the core correlation features between each group, determine the uniformity of feature distribution, and obtain a complete mapping of the feature map; Based on the complete mapping of the feature map, the correlation intensity change trend between data points is analyzed, and the fluctuation of correlation intensity in the region is processed by the data smoothing tool to determine the smoothed correlation distribution. By extracting key connection points from market segment groups, constructing a visual structure, obtaining a dynamic relationship view between groups, determining the final set of related features, integrating the inherent relationships between data points, and using storage tools to structurally save the feature set, a market segmentation data framework is obtained.

4. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Generate a relationship network structure that reflects the dynamic connections between market entities, specifically including: Based on the correlation characteristics, the graph analysis method is used to extract preliminary interaction data between market entities, obtain the distribution of correlation patterns among entities, and obtain an initial relationship network framework. By combining dynamic modeling technology, the interaction between market entities is tracked over time to determine the evolution path of entity patterns in different time periods. Through network construction, the node weights and edge weights are quantified to obtain the relative position distribution of market entities in the relationship network and to determine the differences in the strength of connections between entities. If the connection strength of a certain market entity is lower than the preset threshold, it is merged into the adjacent entity pattern through data integration tools to obtain the adjusted relationship network structure. Then, the structural analysis method is used to extract the core connection points in the interaction and determine the key influencing factors in market positioning. By performing secondary optimization and adjustment of node weights through graph network algorithms, a more accurate distribution view of market entities is obtained, the dynamic role of each entity in market positioning is determined, and then, based on the dynamic role distribution view, visualization tools are used to present the entity patterns in the relationship network in multiple dimensions, thus obtaining the final market interaction and connection mapping.

5. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Determining the evolutionary path of dynamic relationships specifically includes: By collecting and organizing data from the relationship network, interaction information between market entities is obtained from multiple data sources, an initial connection strength matrix is ​​constructed, and the connection strength is calculated and adjusted multiple times using an iterative update method to obtain the latest association weights between nodes and determine the real-time changing trend of dynamic relationships. The random forest algorithm is used to classify and predict interaction patterns, extract key interaction characteristics of market participants, identify major influencing factors, obtain an adjusted network structure, and obtain interaction patterns that are more in line with the current market environment. Based on the adjusted interaction model, the evolution path of dynamic relationships is tracked, significant change nodes and path characteristics are extracted, the development direction of relationships between market entities is determined, and targeted adjustment support solutions are generated in combination with the needs of corporate strategies, resulting in strategy optimization directions applicable to the current market environment. Based on the strategy optimization direction, we continuously monitor the changes in interaction and fluctuations in connection strength among market participants, extract new dynamic relationship characteristics from real-time data, and judge the stability of the relationship network.

6. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Identify the key triggers for changes in demand, specifically including: By continuously monitoring the evolution path of dynamic relationships, feature data of changing nodes are extracted from the relationship network to obtain preliminary signs of demand changes. Time series analysis is used to track the changing nodes in multiple dimensions to determine the triggering factors of demand changes. By analyzing the potential fluctuations in customer demand from the perspective of market positioning, we can identify the key variables that affect market positioning and determine the short-term adjustment direction of customer demand. By adjusting the characteristic data of the direction and combining it with the historical records of the evolution path, a dynamic relationship update scheme consistent with the fluctuations in customer demand is generated, the final market positioning optimization path is determined, and the characteristics of demand changes in the relationship network are continuously monitored to obtain the key triggering factors of new demand changes.

7. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Generate a probability distribution of trend changes to determine the potential future direction of the market, specifically including: Based on the key triggering factors of demand changes, obtain the corresponding triggering factor details, determine the priority ranking of the triggering factors by stratifying the characteristics of the triggering factors, and use a weighted analysis method to assign different weights to each factor; By using a logistic regression model to process weighted data, we can determine the preliminary distribution of trend changes, obtain relevant market trend indicators, and obtain the fluctuation characteristics of market trends through multi-dimensional comparison of the indicators, and determine the probability distribution of trend changes. We acquire potential directional data on market trends, determine the priority sequence of market trends through combined analysis of this potential directional data, obtain dynamically updated data on changing characteristics, and obtain the final mapping of the analysis results through tracking and processing of this dynamically updated data.

8. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: Output a set of predicted scenarios that closely match real-world needs, specifically including: A preliminary analysis of the potential future trends of the market is conducted. Time series analysis is used to process historical data and current dynamic information to obtain trend judgments of market changes. Scenario simulation technology is used to construct multiple prediction scenarios. Combined with multidimensional mapping methods, trend changes are decomposed into influencing factors of different dimensions to determine multiple market development paths. Acquire data metrics related to actual needs, match predicted scenarios with demand characteristics through comparative analysis, identify the set of scenarios that fit the needs, and obtain the classification results of the segmented market through data clustering methods; Based on the classification results of the segmented market and combined with relevant information on enterprise positioning, the applicability of positioning solutions in different scenarios is analyzed. If the matching degree of a certain solution with the scenario is higher than the preset threshold, then the solution is determined to be the preferred positioning direction. By simulating the preferred positioning directions and dynamically calibrating the trend changes and future potential directions, a final strategy combination scheme is obtained. Then, by integrating market trends, predicted scenarios, and multi-dimensional mapping results, a reference basis for auxiliary strategy formulation is determined.

9. The enterprise market segmentation and positioning method based on data mining according to claim 1, characterized in that: The optimized scene results include: By analyzing the set of predicted scenarios, we obtain the initial confidence value, determine whether it meets the requirements of the preset threshold, obtain evaluation data, obtain relevant verification data from external verification data sources, and determine the basic information required for calibration. Based on the obtained verification data, the scene parameters are initially compared, and the direction of parameter adjustment is determined by using pre-established matching rules. Combined with the original data in the prediction scene set, the parameter update operation is implemented to obtain the updated scene configuration information. If the updated scene configuration information still has some confidence values ​​lower than the preset threshold, then a support vector machine model is introduced to classify the abnormal scenes, determine the final optimization range, and implement local parameter adjustments for the abnormal scenes. By obtaining the final optimization result data and integrating it with the original scenario set, a complete decision path output is formed, resulting in the adjusted prediction scenario set.

10. The enterprise market segmentation and positioning method based on data mining according to claim 9, characterized in that: After obtaining the optimized scenario results, the process also includes: generating the final market dynamic response plan, comparing the scenario results with historical data to determine the applicability of the response plan, and outputting executable strategy guidance.

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