Data asset sharing platform and monitoring method based on marketing science

Abstract

The present invention relates to the technical field of sales management, and discloses a data asset sharing platform based on marketing science and a monitoring method thereof. A data conversion building module is used to construct a number of data format conversion standard rules with user industry characteristics; it is also used to construct a conversion rule adjustment model; a real-time data module is used to obtain source data and extract adjustment trend characteristics therefrom; a conversion rule determination module is used to determine corresponding data format conversion standard rules according to the industry characteristics of user data; it is also used to determine whether to adjust the data format conversion standard rules according to the adjustment trend characteristics; the data conversion module is used to perform corresponding data format conversion and update historical data assets on historical and current user data and their sales behavior data according to the rules dynamically determined by the conversion rule determination module under regulatory conditions; a data asset module is used to receive and process the converted data, and the processing includes forming data assets that represent sales strategies and are available for sharing according to preset strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of sales management, and in particular to a data asset sharing platform based on marketing science and a monitoring method thereof. Background Art

[0002] In today's digital age, sales teams are facing unprecedented challenges. Customer needs are constantly changing, market competition is increasingly fierce, and traditional sales methods can no longer meet the development needs of enterprises. In order to gain an advantage in the competition, companies need sales management systems to help them manage sales processes, manage customers, and improve sales efficiency, ultimately achieving business growth.

[0003] However, although the sales management system has brought many conveniences to the sales activities of enterprises, the current system still has some significant defects: many systems lack effective data archiving strategies, and a large amount of historical data is directly deleted or stored in unoptimized databases for a long time based on the simple relationship between storage time and system storage pressure. This not only wastes valuable storage resources, but may also lead to permanent loss of key information. For example, valuable historical data such as early customer purchase records and market activity feedback have not been properly preserved, missing the opportunity to mine potential patterns and trends from them. Secondly, traditional sales management systems usually use fixed field structures to store data. With the development of business and changes in demand, this rigid structure is difficult to accommodate new data types or adjusted business logic. When enterprises try to introduce new sales indicators or analysis dimensions, they find that the original system cannot respond flexibly and must be complexly reconstructed to meet the needs. This not only increases technical costs, but also delays the response speed to market changes. Summary of the invention

[0004] The present invention aims to provide a data asset sharing platform based on marketing science and a monitoring method thereof, which can dynamically adjust the data format conversion rules on the basis of small impact on the existing system architecture changes, so as to achieve the whole system's management of sales data, especially data assets with sales strategies, to quickly respond to market changes, optimize data asset quality, and enhance the system's data asset management capabilities.

[0005] The basic solution provided by the present invention is: a data asset sharing platform based on marketing science, the sharing platform includes:

[0006] The data conversion building module is used to build a number of data format conversion standard rules with user industry characteristics; it is also used to build a conversion rule adjustment model to map the logical relationship between the adjustment trend characteristics and the positive adjustment or negative adjustment of the corresponding conditions of the preset adjustment elements in the data format conversion standard rules;

[0007] A real-time data module is used to obtain source data and extract adjustment trend features from the source data, where the source data includes user data and sales behavior data;

[0008] The conversion rule determination module is used to determine the corresponding data format conversion standard rules according to the industry characteristics of the user data; it is also used to determine whether to adjust the data format conversion standard rules according to the adjustment trend characteristics. If the adjustment is determined, the adjustment trend characteristics are input into the conversion rule adjustment model, and after the logical relationship adjustment preset adjustment elements are retrieved, the data format conversion target rules are determined;

[0009] A data conversion module is used to convert the historical and current user data and their sales behavior data into corresponding data formats according to the rules dynamically determined by the conversion rule determination module under regulatory conditions to form converted data; and is also used to update historical data assets according to the rules dynamically determined by the conversion rule determination module;

[0010] Data asset module: used to receive and process converted data; the processing includes forming data assets that represent sales strategies and can be shared according to preset strategies.

[0011] The present invention is based on a data asset sharing platform based on marketing science, and also provides a monitoring method for data asset sharing based on marketing science; the method includes supervision of the data conversion process:

[0012] S100, comparing the data format conversion standard rule and the data format conversion target rule, obtaining the adjustment content of the data format conversion target rule based on the data format conversion standard rule, the adjustment content including the adjustment elements and their corresponding conditions;

[0013] S200, determining whether the adjustment element is a preset adjustment element; if so, proceeding to S300; if not, immediately stopping the adjustment of the data conversion rule;

[0014] S300, judging whether the adjustment of the corresponding condition meets the adjustment trend characteristics; if so, turning to S400; if not, immediately stopping the adjustment of the data conversion rule;

[0015] S400, marking the user data and its sales behavior data before, during and after conversion, and conducting supervision and inspection on other data except the adjustment elements at each stage.

[0016] The working principle and advantages of the present invention are:

[0017] When processing historical data, existing sales management systems usually simply delete it directly or store it in an unoptimized database for a long time, and rely on fixed field structures and other formats. This is mainly because the sales behavior data itself and its data format conversion rules have dual complexity. On the one hand, sales behavior data covers multiple dimensions from customer information, potential customer sources to interaction history. These data are not only large in volume but also diverse in type, including structured and unstructured data. On the other hand, data format conversion rules need to adapt to changes in different business needs and technical environments to ensure seamless data transmission between different systems. In addition, traditional systems lack flexibility and find it difficult to respond quickly to market and technological changes, resulting in a lag in the way historical data is processed.

[0018] In the face of the above technical difficulties, this solution relies on marketing science-related methods and tools, builds a model based on historical sales data, and cleverly uses the adjustment trend characteristics to determine the timing of rule changes, that is, by analyzing the system status, business dynamics and development trends, accurately grasp the best time point for data format conversion. At the same time, the introduction of preset adjustment elements in the rule adjustment process ensures that each change can be made based on established standards, thereby minimizing the impact on the existing system architecture. This method not only reduces the risks caused by frequent modifications, but also effectively responds to possible changes in the future. By identifying key data features and applying predefined elements, data asset storage optimization is achieved. Specifically, it can streamline redundant data, improve storage efficiency, and maintain data consistency and integrity, so that the system can still efficiently manage massive historical data without major changes, better support decision-making and business innovation, and reasonably optimize data asset quality. This method ensures that the system can flexibly adapt to industry changes and protect important data from being lost, providing a solid data foundation for the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the structure of a data asset sharing platform based on marketing science provided by an embodiment of the present invention;

[0020] Figure 2 A schematic flow chart of a monitoring method for data asset sharing based on marketing science provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following is a further detailed description through specific implementation methods:

[0022] The embodiment is basically as shown in the attached Figure 1 As shown: A data asset sharing platform based on marketing science, the sharing platform includes:

[0023] The data conversion building module is used to construct a number of data format conversion standard rules with user industry characteristics; it is also used to build a conversion rule adjustment model to map the logical relationship between the adjustment trend characteristics and the positive or negative adjustment of the corresponding conditions of the preset adjustment elements in the data format conversion standard rules.

[0024] Specifically, the data assets in this solution mainly include data that can characterize sales strategies, which are mainly derived from sales behavior data. Sales behavior data covers the entire process from potential customer contact to final transaction and after-sales support, including customer information, potential customer sources, interaction history data, sales opportunity status, product preferences, price sensitivity, decision cycle, transaction status, feedback and evaluation, after-sales service records, etc. These data provide companies with opportunities to gain comprehensive insights into customer needs, behavior patterns, and market dynamics, and are essential for forming data assets that characterize sales strategies. Through effective management and analysis of these data, companies can better adjust sales strategies, improve sales efficiency, and ultimately achieve business growth goals.

[0025] In order to realize the formation, sharing and quality optimization of data assets, it is necessary to reasonably convert the data format of sales behavior data. Specifically, the data format conversion rules include field mapping, standardization, classification coding, enumeration type, URL normalization, timestamp unification, activity type coding, state machine model, stage mapping, product ID association, preference scoring, discount rate calculation, Boolean value tagging, duration calculation, segmented statistics, competitive product list, advantages and disadvantages analysis, sentiment analysis, keyword extraction, work order number and service type classification.

[0026] Each industry has different sales behavior processes and corresponding data characteristics, and also different data asset management requirements. Therefore, we first build data conversion standard rules based on very obvious industry characteristics, so that the sharing platform can quickly start the formation of data assets. Specifically, we can determine the data conversion standard rules with the characteristics of the user's industry by analyzing the characteristics of the industry and the conventional data conversion rules, such as retail, e-commerce, financial services, healthcare, manufacturing, etc.

[0027] As external conditions such as the increase in data volume, system operation, and industry dynamics change, it is very important to dynamically update data format conversion rules to ensure that sales behavior data is dynamically converted to optimize data assets. The purpose of adjusting data conversion rules is to better manage data assets and ensure that data assets are in dynamic quality optimization. What is important is to effectively reduce system storage space during format conversion. Faced with the dual complexity of sales behavior data and data format conversion rules, this solution cleverly makes adjustments based on element restriction conditions, which does not affect the current system architecture, and can achieve the survival of the fittest of data assets through micro-adjustments, so that the quality of data assets always meets user development needs.

[0028] Construct a conversion rule adjustment model to map the logical relationship between the adjustment trend characteristics and the positive or negative adjustment of the corresponding conditions of the preset adjustment elements in the data format conversion standard rules.

[0029] In this embodiment, the preset adjustment elements include data compression, field optimization, archiving of historical data, multimedia optimization and document format conversion. The adjustment elements are reasonably selected with little impact on the existing system architecture. By adjusting fewer parameters, the data format conversion form related to the data assets is changed to achieve reasonable optimization of data assets.

[0030] Adjustment trend features are used to determine the timing of dynamic adjustment of data conversion rules, and the adjustment of data conversion rules is to better manage data assets. Therefore, adjustment trend features need to be able to characterize the expected state of data asset management. Adjustment trend features determined based on the time span involved in data asset management, business status and system operation requirements include time features, data asset quality features and user business trend features.

[0031] The specific logical relationship will be described in detail later. Model training is not limited to existing artificial intelligence models, machine learning, deep learning and other models, as long as this solution can be implemented.

[0032] The real-time data module is used to obtain source data and extract adjustment trend features from the source data, wherein the source data includes target user data and target sales behavior data thereof.

[0033] Specifically, the source data also includes system basic data and current data asset information. Target user data includes the user's name, sales products and industry.

[0034] The adjustment trend characteristics extracted from the source data are data asset quality characteristics. The current data asset storage space and the proportion of the total system storage space are extracted from the system basic data for judgment. For example, if the proportion is below 30%, it means that the quality is excellent, if the proportion is between 30% and 60%, it means that the quality is medium, and if the proportion is above 60%, it means that the quality is low. The frequency of current data asset calls can also be extracted from the current data asset information to determine the proportion of the number of calls of the data asset to the total number of calls of all data assets, or the proportion of the number of calls of all data assets in a time period to the total number of calls of all data assets to determine the quality of the data asset or the data asset in the time period, and determine the positive or negative characteristics. User business trend characteristics are calculated from the transaction volume data in the sales behavior data. The algorithm process is as follows: determine the standard transaction volume x of a time step based on the historical business situation of the user, and extract the current and historical L time steps of transaction volume data feature matrix x t-L ,…,x t-1 ,x t ; Calculate x and x t-L ,…,x t-1 ,x t The deviation sequence is used to calculate the deviation change trend index, that is, the least squares method is used to fit the deviation sequence to obtain the slope k representing the deviation change trend. t , with k t Direction and size determine positive or negative characteristics. Time characteristics are used to characterize the expected update frequency of data assets. They can be pre-set time period characteristics such as seconds, minutes, hours, days, months, and years. For example, 3 months, 6 months, and 1 year can be set to extract from the basic data of the system, and the adjustment timing can be triggered after the time period is reached. It can also be the moment when the sales data supervision information is captured to adapt to external market changes.

[0035] The conversion rule determination module is used to determine the corresponding data format conversion standard rules according to the industry characteristics of the user data; it is also used to determine whether to adjust the data format conversion standard rules according to the adjustment trend characteristics. If the adjustment is determined, the adjustment trend characteristics are input into the conversion rule adjustment model, and after the logical relationship adjustment preset adjustment elements are called up, the data format conversion target rules are determined.

[0036] Specifically, when users initially use the shared platform of this solution, they directly use standard data format conversion rules that meet the characteristics of the user's industry to convert the data format, so as to quickly put the platform into use and generate data assets.

[0037] As the sharing platform runs, make reasonable adjustments to the data format conversion rules. Determine whether to adjust the data format conversion standard rules based on the characteristics of the adjustment trend. The goal is to optimize the system space and data asset quality. The adjustment is determined based on the results of the data asset quality status judgment. If the data asset quality is low, it is a negative adjustment. If the quality is good, it is a positive adjustment. If the quality is medium, no adjustment is made temporarily. The adjustment is determined based on the user business trend judgment results. If the trend is rising, it means that the data volume is large, and it is a negative adjustment. If the trend is falling, it means that the data volume is small, and it is a positive adjustment. The adjustment is determined based on the time period. After the set time period is reached, a negative adjustment is made to ensure a continuous good space size.

[0038] The logical relationship between the positive or negative adjustment of the corresponding conditions of the preset adjustment elements in the data format conversion standard rules is that for data compression, by changing the compression algorithm, lossy compression is a negative adjustment, and lossless compression and improving the compression speed and reducing the size of the compressed file are positive adjustments; multimedia optimization, using tools (such as ImageOptim, TinyPNG) to automatically adjust the resolution, color depth and quality settings, reducing the resolution is a negative adjustment, and reducing the file size while maintaining good visual performance is a positive adjustment; simplifying the document format, maintaining complex document formats (such as Microsoft Word.docx, Adobe PDF) is a positive adjustment, and converting to a lighter and easier to parse format (such as plain text, Markdown) is a negative adjustment. When adjusting, data compression is adjusted first, then multimedia optimization, and then the simplified document format is adjusted again. The adjustment items can be reasonably set according to the actual system space situation. Of course, the above negative and positive adjustments can be reasonably determined based on the actual system architecture.

[0039] The data conversion module is used to perform corresponding data format conversion on historical and current user data and their sales behavior data according to the rules dynamically determined by the conversion rule determination module under regulatory conditions to form converted data; it is also used to update historical data assets according to the rules dynamically determined by the conversion rule determination module.

[0040] Specifically, if the corresponding condition of the preset adjustment element is a positive adjustment, it means that the current data asset space, system space, and user data flow are relatively balanced. In this case, no processing is required for the historical data, and a better processing method is provided for the current data, that is, the historical user data and its sales behavior data do not need to be re-converted in data format; the current user data and its sales behavior data are converted in data format through data format conversion target rules; and historical data assets are not processed.

[0041] If the corresponding condition of the preset adjustment element is a negative adjustment, it means that there is an imbalance in the current data asset space, system space, and user data flow. It is necessary to reprocess the historical data except for the data assets, and provide a better processing method for the current data, that is, re-convert the data format of the non-data asset corresponding data in the historical user data and its sales behavior data through the data format conversion target rules to further reduce the space they occupy; convert the data format of the current target user data and its target sales behavior data through the corresponding data format conversion standard rules; after the above adjustments, input the model again. If the corresponding condition of the preset adjustment element is still a negative adjustment, further screen the data assets, and re-convert the data format of data assets with low call frequency through the data format conversion target rules to further reduce the space they occupy.

[0042] Data asset module: used to receive and process converted data; the processing includes forming data assets that represent sales strategies and can be shared according to preset strategies.

[0043] Specifically, the preset strategies include case integration strategy and element classification strategy. The case integration strategy is to integrate and save the sales behavior data of all sales process stages corresponding to the user data, such as integrating the user data and the sales behavior data of one of the orders traded by the user, or the sales behavior data of all orders to obtain a complete sales strategy case; the element segmentation strategy is to classify and save according to the characteristics of the sales strategy elements, such as the sales words, and can extract data based on a certain characteristic of the sales process. The formation of sales strategies can be obtained based on user data and sales behavior data using machine learning algorithms, artificial intelligence and other technologies.

[0044] The system further includes: a sharing module, which is used to receive sharing instructions, and call out and display corresponding data assets according to the sharing instructions; wherein the sharing instructions are any characteristic elements or combinations thereof that characterize user data and sales behavior data.

[0045] The system also includes: a customer-sales matching module, which is used to establish a matching relationship between customer data and the sales module; and is also used to receive customer data, call out the matching relationship, match the corresponding sales end in the sales module, and send the customer data to the corresponding sales end.

[0046] The sales module is used to build several sales terminals; it is also used to interact based on customer data to form sales behavior data and send it to the data conversion module.

[0047] like Figure 2 As shown, the data asset sharing platform based on marketing science also provides a monitoring method for data assets based on marketing science, which includes the supervision of the data conversion process:

[0048] S100, comparing the data format conversion standard rule and the data format conversion target rule, obtaining the adjustment content of the data format conversion target rule based on the data format conversion standard rule, the adjustment content including the adjustment elements and their corresponding conditions;

[0049] S200, determining whether the adjustment element is a preset adjustment element; if so, proceeding to S300; if not, immediately stopping the adjustment of the data conversion rule;

[0050] S300, judging whether the adjustment of the corresponding condition meets the adjustment trend characteristics; if so, turning to S400; if not, immediately stopping the adjustment of the data conversion rule;

[0051] S400, marking the user data and its sales behavior data before, during and after conversion, and conducting supervision and inspection on other data except the adjustment elements at each stage. The supervision and inspection includes data quality verification, compliance review, change inspection and performance monitoring.

[0052] It is understandable that the above method can be applied to the above platform to complete the supervision of the entire data conversion process and will not be elaborated on.

[0053] The data asset sharing platform based on marketing science and the monitoring method thereof provided in this embodiment rely on the relevant methods and tools of marketing science, build a model based on historical sales data, and cleverly use the adjustment trend characteristics to determine the timing of rule changes, that is, by analyzing the system status, business dynamics and development trends, accurately grasp the best time point for data format conversion. At the same time, the preset adjustment elements are introduced in the rule adjustment process to ensure that each change can be made based on the established standards, thereby minimizing the impact on the existing system architecture. This method not only reduces the risks caused by frequent modifications, but also effectively responds to possible changes in the future. By identifying key data features and applying predefined elements, the storage optimization of data assets is achieved. Specifically, it can streamline redundant data, improve storage efficiency, and maintain data consistency and integrity, so that the system can still efficiently manage massive historical data without major changes, better support decision-making and business innovation, and reasonably optimize data asset quality. This method ensures that the system can flexibly adapt to industry changes and protect important data from being lost, providing a solid data foundation for the sustainable development of the enterprise.

[0054] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement the scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A data asset sharing platform based on marketing science, characterized by: The sharing platform includes: The data conversion building module is used to build a number of data format conversion standard rules with user industry characteristics; it is also used to build a conversion rule adjustment model to map the logical relationship between the adjustment trend characteristics and the positive adjustment or negative adjustment of the corresponding conditions of the preset adjustment elements in the data format conversion standard rules; A real-time data module is used to obtain source data and extract adjustment trend features from the source data, where the source data includes user data and sales behavior data; The conversion rule determination module is used to determine the corresponding data format conversion standard rules according to the industry characteristics of the user data; it is also used to determine whether to adjust the data format conversion standard rules according to the adjustment trend characteristics. If the adjustment is determined, the adjustment trend characteristics are input into the conversion rule adjustment model, and after the logical relationship adjustment preset adjustment elements are retrieved, the data format conversion target rules are determined; A data conversion module is used to convert the historical and current user data and their sales behavior data into corresponding data formats according to the rules dynamically determined by the conversion rule determination module under regulatory conditions to form converted data; and is also used to update historical data assets according to the rules dynamically determined by the conversion rule determination module; The data asset module is used to receive and process the converted data; the processing includes forming data assets that represent the sales strategy and can be shared according to the preset strategy.

2. The data asset sharing platform based on marketing science according to claim 1, characterized in that: The adjustment trend characteristics include time characteristics, data asset quality characteristics and user business trend characteristics.

3. The data asset sharing platform based on marketing science according to claim 2, characterized in that: The source data also includes system basic data and current data asset information.

4. The data asset sharing platform based on marketing science according to claim 1, characterized in that: The preset adjustment elements include data compression, field optimization, archiving historical data, multimedia optimization and document format conversion.

5. The data asset sharing platform based on marketing science according to claim 1, characterized in that: If the corresponding condition of the preset adjustment element is a positive adjustment, the historical user data and its sales behavior data will not be re-converted in data format; the current user data and its sales behavior data will be converted in data format using the data format conversion target rule; and the historical data assets will not be processed.

6. The data asset sharing platform based on marketing science according to claim 1, characterized in that: If the corresponding condition of the preset adjustment element is a negative adjustment, the corresponding data of non-data assets in the historical user data and its sales behavior data shall be re-converted into a new data format through the data format conversion target rules; the current target user data and its target sales behavior data shall be converted into a new data format through the corresponding data format conversion standard rules; and the historical data assets shall be screened and processed.

7. The data asset sharing platform based on marketing science according to claim 1, characterized in that: The preset strategies include case integration strategy and element classification strategy. The case integration strategy is to integrate and save the sales behavior data of all sales process stages corresponding to the user data, and the element segmentation strategy is to classify and save according to the sales strategy element characteristics.

8. The data asset sharing platform based on marketing science according to claim 1, characterized in that: It also includes a sharing module for receiving sharing instructions, and calling out and displaying corresponding data assets according to the sharing instructions; wherein the sharing instructions are any characteristic elements or combinations thereof that characterize user data and sales behavior data.

9. A monitoring method for data asset sharing based on marketing science, characterized in that: The method includes supervision of the data conversion process: S100, comparing the data format conversion standard rule and the data format conversion target rule, obtaining the adjustment content of the data format conversion target rule based on the data format conversion standard rule, the adjustment content including the adjustment elements and their corresponding conditions; S200, determining whether the adjustment element is a preset adjustment element; if so, proceeding to S300; if not, immediately stopping the adjustment of the data conversion rule; S300, judging whether the adjustment of the corresponding condition meets the adjustment trend characteristics; if so, turning to S400; if not, immediately stopping the adjustment of the data conversion rule; S400, marking the user data and its sales behavior data before, during and after conversion, and conducting supervision and inspection on other data except the adjustment elements at each stage.

10. The method for monitoring data assets based on marketing science according to claim 9, characterized in that: Said regulatory checks include data quality verification, compliance review, change checks and performance monitoring.

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