Consumer satisfaction survey optimization method and system based on big data

Abstract

The invention discloses a consumer satisfaction investigation optimization method and system based on big data, and belongs to the technical field of electronic digital data processing, and the method comprises the following steps: carrying out the statistics of each current on-sale online platform of a commodity, recording the current on-sale online platform as each on-sale platform, obtaining the basic data of each on-sale platform, carrying out the preprocessing, and carrying out the analysis of the basic data; obtaining a satisfaction survey data extraction proportion; performing data extraction according to the satisfaction survey data extraction proportion of each on-sale platform, and marking the data as a first effective data set; counting each source object corresponding to the first effective data set, and judging a data available evaluation value of each source object; based on the data available evaluation value of each source object and the first valid data set, obtaining a second valid data set through screening; and performing comprehensive processing according to the second effective data set to obtain a satisfaction survey evaluation label of the commodity, and performing survey tracing optimization prompt based on the satisfaction survey evaluation label of the commodity, thereby solving the problem of resource waste caused by excessive platform data in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a consumer satisfaction survey optimization method and system based on big data. Background Art

[0002] In a consumer-driven market environment, consumer satisfaction has become a key indicator of business success. Therefore, it is necessary to deeply mine consumer data, accurately analyze consumer needs and pain points, and improve consumer satisfaction.

[0003] The existing satisfaction survey methods mainly focus on the direct evaluation of mobile phone users on products and the users' emotional and sentimental information, and quantify these emotional data to evaluate the specific level of product attribute information, while the emotional data is used to evaluate the level of consumers' subjective feelings. Based on these quantified emotional and sentimental data, the system finally calculates the product satisfaction data to evaluate consumer satisfaction.

[0004] For example, the user satisfaction evaluation method, device, system and data display platform announced in the invention patent with announcement number: CN111523914B include: obtaining at least one comment data for a product during a product transaction; obtaining sentiment data and emotion data for the product from at least one comment data, wherein the sentiment data is used to characterize the evaluation level of the attribute information of the product, and the emotion data is used to characterize the emotion level generated when evaluating the product; determining the product satisfaction data based on the sentiment data and emotion data, wherein the satisfaction data is used to characterize the changing trend of satisfaction during the product transaction process.

[0005] For example, the invention patent announcement with announcement number: CN110914815B optimizes user satisfaction when training a cognitive hierarchical storage management system, including: the cognitive hierarchical storage management system receives a description of the user's satisfaction with the service method of previous data access requests. The system uses this feedback to associate the metadata and storage layer of each data element previously requested with the user satisfaction level. As feedback is continuously received, the system uses machine learning methods to determine the degree of association between specific metadata patterns and specific user satisfaction levels and specific storage layers. The system then uses these associations when determining whether data associated with specific metadata patterns should be migrated to other layers.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, the evaluation of user satisfaction mainly relies on the review data in the product transaction process. However, there is a lot of review data on various sales platforms, and some data are not valuable for evaluation. If all of them are evaluated, it will take up a lot of resources and waste a lot of time. Therefore, there is a problem of resource waste caused by too much data on each platform. Summary of the invention

[0008] The embodiments of the present application provide a method and system for optimizing consumer satisfaction surveys based on big data, thereby solving the problem of waste of resources caused by excessive data on various platforms in the prior art and enhancing the rationality and representativeness of survey data.

[0009] The embodiment of the present application provides a method for optimizing consumer satisfaction surveys based on big data, comprising the following steps: counting the current online platforms on which the product is sold, recorded as each selling platform, and obtaining the basic data of each selling platform for preprocessing to obtain the satisfaction survey data extraction ratio of each selling platform; counting the satisfaction questionnaire data set of each selling platform, and extracting data according to the satisfaction survey data extraction ratio of each selling platform, and marking the extracted satisfaction questionnaire data set as a first valid data set; counting the source objects corresponding to the first valid data set, and counting the consumption activity data of each source object for processing to determine the data available evaluation value of each source object; based on the data available evaluation value of each source object and the first valid data set, thereby obtaining a second valid data set through screening; based on the second valid data set, and counting the historical sales performance data of the product for comprehensive processing to obtain the satisfaction survey evaluation label of the product, and performing survey tracing optimization prompts based on the satisfaction survey evaluation label of the product.

[0010] Furthermore, the basic data of each sales platform specifically includes: the number of active users, commodity transaction volume, transaction volume growth rate, commodity return rate, commodity evaluation volume and user complaint rate of each sales platform within the first preset time period.

[0011] Furthermore, the said obtaining of the satisfaction survey data extraction ratio of each selling platform specifically comprises the following steps: obtaining a preset user activity number benchmark value, commodity transaction volume benchmark value, commodity return rate benchmark value, commodity evaluation volume benchmark value and user complaint rate benchmark value of each selling platform within a first preset time period, and recording them as preset basic data; comparing the preset basic data with the basic data of each selling platform to obtain a basic characteristic value of each selling platform, the basic characteristic value is used for quantitative analysis of the basic data of each selling platform, and is used as an analysis basis for the satisfaction survey data extraction ratio; based on the verification basic characteristic value preset in the database, and compared with the basic characteristic value of each selling platform, synchronously extracting from the database The original default survey data extraction ratios of each platform on sale are compared. If the basic characteristic value of a certain platform on sale is above the preset verification basic characteristic value, the difference between the basic characteristic value of the platform on sale and the preset verification basic characteristic value is extracted, and the difference is matched with the preset difference in the database to obtain the adjustment ratio corresponding to the preset difference, and the adjustment ratio of the platform on sale is added to the original default survey data extraction ratio of the platform on sale to obtain the satisfaction survey data extraction ratio of the platform on sale. If the basic characteristic value of a certain platform on sale is less than the preset verification basic characteristic value, the original default survey data extraction ratio of the platform on sale is used as the satisfaction survey data extraction ratio of the platform on sale.

[0012] Furthermore, the data availability evaluation value of each source object is determined, and the specific steps include: obtaining consumption activity data of each source object, the consumption activity data including: return rate, repurchase number, product purchase conversion rate, negative review rate and purchase frequency within a first preset time period; obtaining a preset consumer activity data benchmark set, and comparing it with the consumption activity data of each source object to obtain the data availability evaluation value of each source object; the preset consumer activity data benchmark set includes: return rate verification value, repurchase number verification value, product purchase conversion rate verification value, negative review rate verification value and purchase frequency verification value; the data availability evaluation value of each source object is used to quantify the consumption activity data of each source object, and is used as a basis for analyzing consumer satisfaction.

[0013] Furthermore, the method for determining the available evaluation value of the data of each source object is:

[0014]

[0015] In the formula, E i represents the data availability evaluation value of the i-th source object, i represents the number of the source object, i=1, 2, ..., n, n represents the number of source objects, r i represents the return rate of the i-th source object in the first preset time period, r1 represents the return rate verification value, e represents the base of the natural logarithm, and f irepresents the number of repurchases of the i-th source object within the first preset time period, f1 represents the repurchase number verification value, c i represents the commodity purchase conversion rate of the i-th source object in the first preset time period, c1 represents the commodity purchase conversion rate verification value, q i represents the negative review rate of the i-th source object in the first preset time period, q1 represents the negative review rate verification value, and p i represents the purchase frequency of the i-th source object in the first preset time period, p1 represents the purchase frequency verification value, α1 represents the return rate influence weight of the source object in the first preset time period, α2 represents the repurchase times influence weight of the source object in the first preset time period, α3 represents the commodity purchase conversion rate influence weight of the source object in the first preset time period, α4 represents the negative review rate influence weight of the source object in the first preset time period, and α5 represents the purchase frequency influence weight of the source object in the first preset time period.

[0016] Furthermore, the second valid data set is obtained by screening, and the specific steps include: obtaining a data availability evaluation threshold in the database, and comparing it with the data availability evaluation value of each source object; if the data availability evaluation value of a source object is above the data availability evaluation threshold, then the satisfaction questionnaire data of the source object is determined to be second valid data; if the data availability evaluation value of the source object is less than the data availability evaluation threshold, then the satisfaction questionnaire data of the source object is determined to be invalid data and deleted; all the second valid data are aggregated to obtain a second valid data set; the second valid data set includes each target source object and the satisfaction questionnaire data of each target source object.

[0017] Furthermore, the specific steps of obtaining the satisfaction survey evaluation label of the product include: based on the second valid data set, processing according to the historical sales performance data of the product to obtain the satisfaction survey evaluation value of the product; obtaining the satisfaction survey evaluation threshold of the product in the database, and comparing it with the satisfaction survey evaluation value of the product; if the satisfaction survey evaluation value of the product is above the satisfaction survey evaluation threshold of the product, then defining the satisfaction survey evaluation label of the product as a high-satisfaction product; if the satisfaction survey evaluation value of the product is less than the satisfaction survey evaluation threshold of the product, then defining the satisfaction survey evaluation label of the product as a low-satisfaction product.

[0018] Furthermore, the survey and traceability optimization prompt is performed based on the satisfaction survey and evaluation label of the product. The specific method is as follows: if the satisfaction survey and evaluation label of the product is defined as a low-satisfaction product, then based on the second valid data set obtained by screening, the satisfaction questionnaire data of each target source object is counted, and the satisfaction questionnaire data includes the evaluation text of each target source object on the product in each dimension; based on the evaluation text of each target source object on the product in each dimension, a number of evaluation keywords of each target source object on the product in each dimension are counted, and according to the positive keyword collection and negative keyword collection preset in the database, the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object on the product in each dimension are screened through mapping and matching; according to the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object on the product in each dimension, the text evaluation value of the product in each dimension is processed, and the text evaluation values ​​are arranged in ascending order to obtain the survey and traceability arrangement order of the product, and the survey and traceability arrangement order of the product is transmitted to the central controller.

[0019] Positive keyword collections include: good, great, practical, beautiful, etc., and negative keyword collections include: bad, poor, ugly, etc. The central controller refers to an electronic device used for data processing, data analysis and system control. The central controller can optimize the products according to the survey and traceability order of the products.

[0020] Furthermore, the method is based on the second effective data set and processed according to the historical sales performance data of the product to obtain a satisfaction survey evaluation value of the product. The specific steps are: based on the satisfaction questionnaire data of each target source object in the second effective data set, the satisfaction questionnaire data of all target source objects are summarized to obtain a satisfaction questionnaire survey text of the product, and a number of keywords corresponding to the product are counted therefrom, thereby matching the positive keyword collection and the negative keyword collection preset in the database, and then obtaining the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product through mapping and matching; based on the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product, and processing according to the historical sales performance data of the product to obtain the satisfaction survey evaluation value of the product; the historical sales performance data of the product include the total number of historical per capita positive evaluation keywords of the product and the total number of historical per capita negative evaluation keywords of the product; the satisfaction survey evaluation value of the product is used to quantitatively analyze the second effective data set, and is used as a basis for analyzing product satisfaction.

[0021] A consumer satisfaction survey optimization system based on big data, characterized by comprising: a data acquisition module, a data extraction module, a data evaluation module, a data screening module and a label processing module; wherein the data acquisition module is used to count the current online platforms on which the goods are sold, recorded as each selling platform, and obtain the basic data of each selling platform for preprocessing to obtain the satisfaction survey data extraction ratio of each selling platform; the data extraction module is used to count the satisfaction questionnaire data set of each selling platform, and extract data according to the satisfaction survey data extraction ratio of each selling platform, and mark the extracted satisfaction questionnaire data set as a first valid data set; the data evaluation module is used to count the source objects corresponding to the first valid data set, and count the consumption activity data of each source object for processing to determine the available evaluation value of the data of each source object; the data screening module is used to obtain a second valid data set by screening based on the available evaluation value of the data of each source object and the first valid data set; the label processing module is used to obtain the satisfaction survey evaluation label of the goods according to the second valid data set and the historical sales performance data of the goods for comprehensive processing, and to perform survey tracing optimization prompts based on the satisfaction survey evaluation label of the goods.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. The present invention provides a consumer satisfaction survey optimization method based on big data. Through multi-platform data fusion and preprocessing, the satisfaction survey data extraction ratio is accurately calculated, and the survey data of each platform is extracted according to the corresponding satisfaction survey data extraction ratio, thereby ensuring the rationality of the quantity and representativeness of the quality of the survey data, and effectively solving the problem of resource waste caused by excessive data on each platform in the prior art.

[0024] 2. The present invention determines the available evaluation value of the data by comprehensively evaluating the consumer activity data, thereby realizing the screening of high-quality data and avoiding the influence of the accuracy of the analysis results on the uneven data quality.

[0025] 3. Obtain the satisfaction questionnaire data of each target source object and summarize the satisfaction questionnaire data of all target source objects to obtain the satisfaction questionnaire survey text of the product. Then, obtain the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product through mapping and matching, and then arrange the text evaluation values ​​in order from small to large to obtain the survey traceability arrangement order of the product, which is beneficial for enterprises to optimize services according to the survey traceability arrangement order. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1A flowchart of a method for optimizing a consumer satisfaction survey based on big data provided in an embodiment of the present application;

[0027] Figure 2 A module diagram of a consumer satisfaction survey optimization system based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiment of the present application solves the problem of resource waste caused by excessive data on each platform in the prior art by providing a consumer satisfaction survey optimization method and system based on big data. The method comprises: counting the current online platforms on which the product is sold, recording them as the selling platforms, obtaining the basic data of each selling platform for preprocessing, and obtaining the satisfaction survey data extraction ratio of each selling platform; counting the satisfaction questionnaire data set of each selling platform, and extracting data according to the satisfaction survey data extraction ratio of each selling platform, and marking the extracted satisfaction questionnaire data set as the first valid data set; counting the source objects corresponding to the first valid data set, and counting the consumption activity data of each source object for processing, and determining the available evaluation value of the data of each source object; based on the available evaluation value of the data of each source object and the first valid data set, obtaining the second valid data set through screening; according to the second valid data set, and counting the historical sales performance data of the product for comprehensive processing, obtaining the satisfaction survey evaluation label of the product, and performing survey tracing optimization prompts based on the satisfaction survey evaluation label of the product, thereby ensuring the quantitative rationality and quality representativeness of the survey data.

[0029] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of resource waste caused by excessive data on each platform. The overall idea is as follows: by counting the current online platforms on which the product is sold, recorded as each selling platform, and obtaining the basic data of each selling platform for preprocessing, the satisfaction survey data extraction ratio of each selling platform is obtained; the satisfaction questionnaire data set of each selling platform is counted, and data is extracted according to the satisfaction survey data extraction ratio of each selling platform, and the extracted satisfaction questionnaire data set is marked as the first valid data set; the source objects corresponding to the first valid data set are counted, and the consumption activity data of each source object is counted for processing to determine the data available evaluation value of each source object; based on the data available evaluation value of each source object and the first valid data set, a second valid data set is obtained by screening; according to the second valid data set, the historical sales performance data of the product is counted for comprehensive processing to obtain the satisfaction survey evaluation label of the product, and based on the satisfaction survey evaluation label of the product, survey tracing optimization prompts are performed, thereby enhancing the rationality and representativeness of the survey data.

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, it is a flow chart of a consumer satisfaction survey optimization method based on big data provided by an embodiment of the present application, and the method includes the following steps: counting the current online platforms where the goods are sold, recorded as each selling platform, and obtaining the basic data of each selling platform for preprocessing to obtain the satisfaction survey data extraction ratio of each selling platform; counting the satisfaction questionnaire data set of each selling platform, and extracting data according to the satisfaction survey data extraction ratio of each selling platform, and marking the extracted satisfaction questionnaire data set as the first valid data set; counting the source objects corresponding to the first valid data set, and counting the consumption activity data of each source object for processing to determine the data available evaluation value of each source object; based on the data available evaluation value of each source object and the first valid data set, thereby obtaining a second valid data set through screening; according to the second valid data set, the historical sales performance data of the goods is counted for comprehensive processing to obtain the satisfaction survey evaluation label of the goods, and based on the satisfaction survey evaluation label of the goods, survey tracing optimization prompts are given.

[0032] In this embodiment, the basic data of each platform on sale is obtained for preprocessing to obtain the extraction ratio of the satisfaction survey data of each platform on sale. Wherein data extraction is performed according to the extraction ratio of the satisfaction survey data of each platform on sale, and the sampling method is random sampling, that is, a number of satisfaction survey data are randomly extracted from the satisfaction survey data of each platform on sale at the corresponding satisfaction survey data extraction ratio, and these satisfaction survey data are constructed into a satisfaction questionnaire data set. Random extraction ensures that the sample data is representative in the population, making the analysis results more objective and accurate, and can more truly reflect the overall situation. The basic data of each platform on sale is obtained for preprocessing to obtain the extraction ratio of the satisfaction survey data of each platform on sale, which is conducive to cost saving. In a big data environment, processing all data is often time-consuming and labor-intensive. By determining a reasonable extraction ratio of the satisfaction survey data, the time and resource cost of data processing can be greatly reduced while ensuring the reliability of the analysis results. The consumption activity data of each source object is statistically processed, and the available evaluation value of the data of each source object is determined. It can identify and eliminate erroneous, incomplete or abnormal data, ensure the accuracy and reliability of the data, and identify and eliminate malicious evaluations or abnormal data to ensure the fairness and objectivity of the evaluation results. Determining the available evaluation value of the data of each source object is to ensure that the data can be effectively used in the subsequent analysis, decision-making and business optimization process. The purpose of providing survey traceability optimization prompts based on the satisfaction survey evaluation label of the product is that through the satisfaction survey evaluation label, the company can deeply understand the real feedback of customers on the product, so as to identify the deficiencies of the product, and then take targeted measures to improve it to improve customer satisfaction. At the same time, survey traceability can help companies discover defects or unreasonable aspects in product design, provide data support for product iteration and optimization, and make products more in line with market demand and consumer preferences. By tracing the supply chain information behind the evaluation label, companies can monitor the performance of suppliers and ensure the quality of raw materials and parts, thereby optimizing supply chain management and reducing risks caused by supply chain problems.

[0033] Furthermore, the basic data of each sales platform specifically includes: the number of active users, commodity transaction volume, transaction volume growth rate, commodity return rate, commodity evaluation volume and user complaint rate of each sales platform within the first preset time period.

[0034] In this embodiment, the first preset time period refers to a relatively long period of time, for example, one month.

[0035] It should be noted that the method for obtaining the basic characteristic values ​​of each selling platform is:

[0036]

[0037] In the formula, Z irepresents the basic characteristic value of the jth platform on sale, j represents the number of the jth platform on sale, j=1, 2, ..., J, J represents the number of each platform on sale, h j represents the number of active users of the jth selling platform in the first preset time period, △h j M represents the preset user activity benchmark value of the jth selling platform, j represents the commodity transaction volume of the jth selling platform in the first preset time period, △M i represents the preset commodity transaction volume benchmark value of the jth selling platform, b aj represents the transaction volume growth rate of the jth selling platform in the first preset time period, △b aj represents the preset transaction volume growth rate benchmark value of the jth selling platform in the first preset time period, g j represents the product return rate of the jth selling platform in the first preset time period, △g j represents the preset benchmark value of the product return rate on the jth selling platform, y j represents the number of product reviews on the jth selling platform in the first preset time period, △y j A represents the preset benchmark value of product evaluation on the jth selling platform, j represents the user complaint rate of the jth selling platform in the first preset time period, △A j It represents the preset user complaint rate benchmark value of the jth selling platform, β1 represents the influence weight of the number of active users, β2 represents the influence weight of the commodity transaction volume, β3 represents the influence weight of the commodity return rate, β4 represents the influence weight of the commodity evaluation volume, β5 represents the influence weight of the user complaint rate, and β6 represents the influence weight of the commodity transaction volume.

[0038] It should be noted that the basic data of each selling platform can be obtained by querying the background data of each selling platform, the preset basic data can be obtained by querying the database, and the influence weight can be obtained through the database, for example: the influence weight of the number of active users, which represents the numerical value of the influence degree of the number of active users on the basic characteristic values ​​of each selling platform. When used, the influence weight corresponding to the number of active users of each selling platform in the first preset time period can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the number of active users of each selling platform in the first preset time period and the influence weight of the number of active users form a mapping set, and the real-time number of active users is input into the mapping set to obtain the influence weight corresponding to the number of active users. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. The acquisition method of other influence weights such as the commodity transaction volume influence weight, the commodity return rate influence weight, the commodity evaluation volume influence weight, the user complaint rate influence weight, and the commodity transaction volume influence weight is consistent with the acquisition method of the user active number influence weight, which will not be repeated here.

[0039] It should also be noted that in this embodiment, the basic characteristic values ​​of each sales platform are obtained by processing the number of active users, commodity transaction volume, transaction volume growth rate, commodity return rate, commodity evaluation volume and user complaint rate of each sales platform within the first preset time period, taking into account the mutual influence between these parameters, for example: the number of active users is the basis of commodity transaction volume. The more active users there are, the more potential buyers there are, which may drive the growth of commodity transaction volume. Conversely, the growth of commodity transaction volume may also attract more users to participate and increase user activity. An increase in commodity transaction volume usually brings more user reviews. After purchasing and experiencing the product, users tend to post reviews on the platform. Positive reviews can attract more potential users and promote the growth of commodity transaction volume; while negative reviews may reduce users' willingness to buy and affect transaction volume.

[0040] It should be understood that the preset user active number benchmark value, commodity transaction volume benchmark value and commodity evaluation volume benchmark value of each selling platform represent the data minimum reference standard under ideal conditions, that is, the ideal minimum reference standard. The user active number, commodity transaction volume, transaction volume growth rate and commodity evaluation volume of each selling platform in the basic data of each selling platform in the first preset time period are compared with the ideal minimum reference standard. The purpose is to consider that if the user active number, commodity transaction volume, transaction volume growth rate and commodity evaluation volume of each selling platform in the first preset time period are at a higher numerical level than the ideal minimum reference standard, it indicates that the basic characteristic value of each selling platform is better. The preset return rate benchmark value and user complaint rate benchmark value of each selling platform represent the data reference standard under ideal conditions, that is, the ideal reference standard. The commodity return rate and user complaint rate of each selling platform in the first preset time period are compared with the ideal reference standard. The purpose is to consider that if the commodity return rate and user complaint rate of each selling platform in the first preset time period are at a lower numerical level than the ideal reference standard, it indicates that the basic characteristic value of each selling platform is better.

[0041] Furthermore, the satisfaction survey data extraction ratio of each selling platform is obtained, and the specific steps include: obtaining a preset user active number benchmark value, commodity transaction volume benchmark value, commodity return rate benchmark value, commodity evaluation volume benchmark value and user complaint rate benchmark value of each selling platform in a first preset time period, and recording them as preset basic data; comparing the preset basic data with the basic data of each selling platform to obtain the basic characteristic value of each selling platform, and the basic characteristic value is used for quantitative analysis of the basic data of each selling platform, and is used as an analysis basis for the satisfaction survey data extraction ratio; based on the verification basic characteristic value preset in the database, and compared with the basic characteristic value of each selling platform, the satisfaction survey data extraction ratio of each selling platform is extracted from the database synchronously. The original default survey data extraction ratio of the platforms on sale is compared. If the basic characteristic value of a certain platform on sale is above the preset verification basic characteristic value, the difference between the basic characteristic value of the platform on sale and the preset verification basic characteristic value is extracted, and the difference is matched with the preset difference in the database to obtain the adjustment ratio corresponding to the preset difference, and the adjustment ratio of the platform on sale is added to the original default survey data extraction ratio of the platform on sale to obtain the satisfaction survey data extraction ratio of the platform on sale. If the basic characteristic value of a certain platform on sale is less than the preset verification basic characteristic value, the original default survey data extraction ratio of the platform on sale is used as the satisfaction survey data extraction ratio of the platform on sale.

[0042] In this embodiment, each selling platform refers to an online platform that is authorized by each online commodity management manufacturer to sell commodities.

[0043] It should be noted that the preset verification basic characteristic value can be obtained through the database. The preset verification basic characteristic value is a judgment standard set for the basic characteristic value. When the basic characteristic value of the platform on sale is higher than the preset verification basic characteristic value, it means that the platform on sale has a high representativeness.

[0044] Furthermore, the data availability evaluation value of each source object is determined, and the specific steps include: obtaining the consumption activity data of each source object, the consumption activity data including: return rate, repurchase number, product purchase conversion rate, negative review rate and purchase frequency within a first preset time period; obtaining a preset consumer activity data benchmark set, and comparing it with the consumption activity data of each source object to obtain the data availability evaluation value of each source object; the preset consumer activity data benchmark set includes: return rate verification value, repurchase number verification value, product purchase conversion rate verification value, negative review rate verification value and purchase frequency verification value; the data availability evaluation value of each source object is used to quantify the consumption activity data of each source object, and is used as a basis for analyzing consumer satisfaction.

[0045] In this embodiment, it should be noted that the consumption activity data of each source object can be obtained by big data statistics of the historical consumption of each source object. The greater the available evaluation value of the data of each source object, the more valuable the data of the source object is for analysis, and the more representative it is of the consumption of mass consumers.

[0046] Furthermore, the method for determining the available evaluation value of the data of each source object is:

[0047]

[0048] In the formula, E i represents the data availability evaluation value of the i-th source object, i represents the number of the source object, i=1, 2, ..., n, n represents the number of source objects, r i represents the return rate of the i-th source object in the first preset time period, r1 represents the return rate verification value, e represents the base of the natural logarithm, and f i represents the number of repurchases of the i-th source object within the first preset time period, f1 represents the repurchase number verification value, c i represents the commodity purchase conversion rate of the i-th source object in the first preset time period, c1 represents the commodity purchase conversion rate verification value, q i represents the negative review rate of the i-th source object in the first preset time period, q1 represents the negative review rate verification value, and p i represents the purchase frequency of the i-th source object in the first preset time period, p1 represents the purchase frequency verification value, α1 represents the return rate influence weight of the source object in the first preset time period, α2 represents the repurchase times influence weight of the source object in the first preset time period, α3 represents the commodity purchase conversion rate influence weight of the source object in the first preset time period, α4 represents the negative review rate influence weight of the source object in the first preset time period, and α5 represents the purchase frequency influence weight of the source object in the first preset time period.

[0049] In this embodiment, it should be noted that the consumption activity data of each source object can be statistically obtained through the detailed data analysis function provided by the backend system of each sales platform, and the preset consumption activity data benchmark set can be obtained through the database.

[0050] It should also be noted that the return rate influence weight of the source object within the first preset time period represents the numerical value of the influence degree of the return rate of the source object within the first preset time period on the available evaluation value of the data of each source object. When used, the influence weight corresponding to the return rate within the first preset time period can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the mapping relationship forms a mapping set between the return rate within the first preset time period and the return rate influence weight of the source object within the first preset time period, and the real-time return rate is input into the mapping set to obtain the influence weight corresponding to the return rate of the source object within the first preset time period. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. The acquisition method of other influence weights such as the influence weight of the number of repurchases, the influence weight of the product purchase conversion rate, the influence weight of the bad review rate, and the influence weight of the purchase frequency is consistent with the acquisition method of the return rate influence weight of the source object within the first preset time period, and will not be repeated here.

[0051] It should be understood that in this embodiment, the available evaluation value of the data of each source object is obtained by processing the consumption activity data of each source object, taking into account the mutual influence between these parameters, for example: return rate, number of repurchases, commodity purchase conversion rate, bad review rate and purchase frequency are interrelated indicators, which jointly affect the overall performance of consumption activities. A high return rate may reduce the commodity purchase conversion rate and the number of repurchases, while increasing the bad review rate and reducing the purchase frequency. On the contrary, a high number of repurchases and purchase frequency usually means that customers have high loyalty and satisfaction with the brand, which helps to improve the commodity purchase conversion rate and reduce the return rate and bad review rate. The level of commodity purchase conversion rate is directly related to the brand's ability to attract new customers and retain old customers, further affecting the return rate, number of repurchases and bad review rate. As an important indicator of customer feedback, the bad review rate has a negative impact on the return rate, number of repurchases, purchase frequency and commodity purchase conversion rate.

[0052] Furthermore, a second valid data set is obtained through screening, and the specific steps include: obtaining a data availability evaluation threshold in the database, and comparing it with the data availability evaluation value of each source object; if the data availability evaluation value of a source object is above the data availability evaluation threshold, then the satisfaction questionnaire data of the source object is determined to be second valid data; if the data availability evaluation value of the source object is less than the data availability evaluation threshold, then the satisfaction questionnaire data of the source object is determined to be invalid data and deleted; all second valid data are aggregated to obtain a second valid data set; the second valid data set includes each target source object and the satisfaction questionnaire data of each target source object.

[0053] In this embodiment, it should be noted that the satisfaction questionnaire data is an important basis for evaluating the service quality and customer satisfaction of the source object, and the available evaluation value of the data is of great value for the analysis of satisfaction, so it is necessary to analyze the satisfaction questionnaire data to avoid waste of resources due to excessive platform data.

[0054] Furthermore, a satisfaction survey evaluation label of the product is obtained. The specific steps include: based on the second valid data set, and processed according to the historical sales performance data of the product, to obtain the satisfaction survey evaluation value of the product; obtaining the satisfaction survey evaluation threshold of the product in the database, and comparing it with the satisfaction survey evaluation value of the product; if the satisfaction survey evaluation value of the product is above the satisfaction survey evaluation threshold of the product, then the satisfaction survey evaluation label of the product is defined as a high-satisfaction product; if the satisfaction survey evaluation value of the product is less than the satisfaction survey evaluation threshold of the product, then the satisfaction survey evaluation label of the product is defined as a low-satisfaction product.

[0055] In this embodiment, it should be noted that the method for obtaining the satisfaction survey evaluation value of the product is:

[0056]

[0057] In the formula, G represents the satisfaction survey evaluation value of the product, W P represents the total number of historical average positive evaluation keywords of the product, W N represents the total number of historical average negative evaluation keywords for the product, H represents the number of target source objects, and T P Indicates the total number of positive evaluation keywords corresponding to the product satisfaction questionnaire text, T N Indicates the total number of negative evaluation keywords corresponding to the product satisfaction questionnaire text.

[0058] It should be noted that the satisfaction survey evaluation value of a product is used to represent the consumer's satisfaction with the product. When a consumer is surveyed and evaluated, there must be a certain number of historical average positive evaluation keywords and historical average negative evaluation keywords for the product. Therefore, there is no situation where both are zero at the same time. Therefore, the historical average positive evaluation keywords and historical average negative evaluation keywords for the product are not equal to zero.

[0059] It should also be noted that the historical sales performance data of the goods can be obtained through the background management of each sales platform, and the number of target source objects can be obtained through statistical software.

[0060] Furthermore, based on the satisfaction survey evaluation label of the product, a survey traceability optimization prompt is performed, and the specific method is as follows: if the satisfaction survey evaluation label of the product is defined as a low-satisfaction product, then based on the second valid data set obtained by screening, the satisfaction questionnaire data of each target source object is counted, and the satisfaction questionnaire data includes the evaluation text of each target source object on the product in each dimension; based on the evaluation text of each target source object on the product in each dimension, a number of evaluation keywords of each target source object on the product in each dimension are counted, and according to the positive keyword collection and negative keyword collection preset in the database, the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object on the product in each dimension are screened through mapping and matching; according to the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object on the product in each dimension, the text evaluation value of the product in each dimension is processed, and the text evaluation values ​​are arranged in order from small to large to obtain the survey traceability arrangement order of the product, and the survey traceability arrangement order of the product is transmitted to the central controller.

[0061] In this embodiment, statistics of several evaluation keywords of each target source object on the product in each dimension can be obtained through e-commerce data analysis software, such as SellerSprite, Feiyu Data Advisor, and Oulu.

[0062] Obtaining satisfaction questionnaire data from each target source object is conducive to obtaining customers' real feedback and feelings about products or services, so as to gain a deeper understanding of customers' expectations, needs and dissatisfaction. Each target source object includes all consumers on each sales platform. The evaluation texts of each dimension include: evaluation text of service attitude, evaluation text of logistics speed, evaluation text of product cost performance, and evaluation text of product quality. Positive keyword collections include: good, great, practical, beautiful, etc., and negative keyword collections include: bad, poor, ugly, etc.

[0063] It should be noted that the satisfaction questionnaire data includes the evaluation texts of each target source object on the product in various dimensions, which can be obtained through the statistics of the backend system of each sales platform. Usually, the satisfaction questionnaires are distributed by enterprises, and after collection, they are analyzed using data analysis software to obtain the satisfaction questionnaire data.

[0064] The method to obtain the text evaluation value of the product in each dimension is:

[0065]

[0066] Where V k Indicates the text evaluation value of the product in each dimension, k represents the number of the dimension, k = 1, 2, 3, ..., k max , k max Indicates the number of dimensions, P kIndicates the total number of positive evaluation keywords of the product in each dimension, P k0 Indicates the total number of positive evaluation keywords of the preset product in each dimension, N k Indicates the total number of negative evaluation keywords of the product in each dimension, N k0 It represents the total check value of negative evaluation keywords of the preset product in each dimension, γ1 represents the influence weight of positive evaluation keywords of the product in each dimension, and γ2 represents the influence weight of negative evaluation keywords of the product in each dimension.

[0067] It should be noted that the text evaluation value of the product in each dimension is used to characterize the satisfaction of the product in each dimension. The total number of positive evaluation keywords of the product in the kth dimension and the total number of negative evaluation keywords of the product in the kth dimension are not equal to zero. If they are zero, they have no research significance. The preset total number of positive evaluation keywords of the product in the kth dimension can be obtained through the database, and the preset total number of negative evaluation keywords of the product in the kth dimension can be obtained through the database.

[0068] In this embodiment, the dimensions include, but are not limited to: service attitude dimension, quality dimension, price dimension, logistics dimension, and cost-effectiveness dimension.

[0069] It should also be noted that the influence weight of the positive evaluation keywords of the product in each dimension represents the numerical value of the influence degree of the positive evaluation keywords of the product in each dimension on the text evaluation value of the product in each dimension. When used, the influence weight corresponding to the positive evaluation keywords of the product in each dimension can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the positive evaluation keywords of the product in each dimension and the positive evaluation keyword influence weight of the product in each dimension form a mapping set. The real-time positive evaluation keywords of the product in each dimension are input into the mapping set to obtain the positive evaluation influence weight of the product in each dimension. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. The method of obtaining the influence weight of the negative evaluation keywords of the product in each dimension is consistent with the method of obtaining the return rate influence weight of the source object in the first preset time period, which will not be repeated here.

[0070] It should also be noted that in this embodiment, the text evaluation value of the product in each dimension is obtained by processing the total number of positive evaluation keywords and the total number of negative evaluation keywords of the product in each dimension. This takes into account the mutual influence between these parameters. For example: if the proportion of the total number of positive evaluation keywords to the total number of keywords is higher, the proportion of the total number of negative evaluation keywords to the total number of keywords will be lower. At this time, the overall evaluation of consumers is more satisfied, and vice versa.

[0071] It should be understood that the preset verification value of the total number of positive evaluation keywords for the product in each dimension represents the minimum reference standard for positive evaluation keywords under ideal circumstances, that is, the ideal minimum reference standard for positive evaluation keywords. By comparing the preset verification value of the total number of positive evaluation keywords for the product in each dimension with the ideal minimum reference standard for positive evaluation keywords, the purpose is to take into account that if the preset verification value of the total number of positive evaluation keywords for the product in each dimension is at a higher numerical level than the ideal minimum reference standard for positive evaluation keywords, it indicates that the verification value data of the total number of positive evaluation keywords for the product in each dimension is better, and the text evaluation value of the product in each dimension is higher. The preset verification value of the total number of negative evaluation keywords for the product in each dimension represents the reference standard for negative evaluation keywords under ideal circumstances, that is, the ideal negative evaluation keyword reference standard. By comparing the preset verification value of the total number of negative evaluation keywords for the product in each dimension with the ideal negative evaluation keyword reference standard, the purpose is to take into account that if the preset verification value of the total number of negative evaluation keywords for the product in each dimension is at a lower numerical level than the ideal negative evaluation keyword reference standard, it indicates that the verification value data of the total number of negative evaluation keywords for the product in each dimension is better, and the text evaluation value of the product in each dimension is higher.

[0072] Furthermore, based on the second effective data set and processed according to the historical sales performance data of the product, a satisfaction survey evaluation value of the product is obtained. The specific steps are: based on the satisfaction questionnaire data of each target source object in the second effective data set, the satisfaction questionnaire data of all target source objects are summarized to obtain a satisfaction questionnaire survey text of the product, and a number of keywords corresponding to the product are counted therefrom, thereby matching the positive keyword collection and negative keyword collection preset in the database, and then obtaining the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product through mapping and matching; based on the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product, and processed according to the historical sales performance data of the product to obtain the satisfaction survey evaluation value of the product; the historical sales performance data of the product includes the historical per capita positive evaluation keywords of the product and the historical per capita negative evaluation keywords of the product; the satisfaction survey evaluation value of the product is used to quantitatively analyze the second effective data set, and is used as a basis for analyzing product satisfaction.

[0073] In this embodiment, in the above processing flow, by integrating the satisfaction questionnaire data of each target source object in the second valid data set, not only a wealth of consumer feedback is obtained, but also the positive and negative images of the product in the minds of consumers are quantified through keyword statistics and mapping matching. This process not only improves the efficiency and accuracy of data analysis, but also lays a solid foundation for the calculation of subsequent product satisfaction survey evaluation values. By counting the total number of positive and negative evaluation keywords, it is possible to fully and meticulously understand the performance of the product in different dimensions, including but not limited to service attitude dimension, quality dimension, price dimension, logistics dimension, and cost-effectiveness dimension. This multi-dimensional evaluation helps companies to more accurately identify the advantages and disadvantages of products and provide strong support for subsequent product improvements.

[0074] A consumer satisfaction survey optimization system based on big data, characterized by comprising: a data acquisition module, a data extraction module, a data evaluation module, a data screening module and a label processing module, wherein the data acquisition module is used to count the current online platforms on which the goods are sold, recorded as each selling platform, and obtain the basic data of each selling platform for preprocessing to obtain the satisfaction survey data extraction ratio of each selling platform; the data extraction module is used to count the satisfaction questionnaire data set of each selling platform, and extract data according to the satisfaction survey data extraction ratio of each selling platform, and mark the extracted satisfaction questionnaire data set as a first valid data set; the data evaluation module is used to count the source objects corresponding to the first valid data set, and count the consumption activity data of each source object for processing, and determine the available evaluation value of the data of each source object; the data screening module is used to obtain the second valid data set by screening based on the available evaluation value of the data of each source object and the first valid data set; the label processing module is used to obtain the satisfaction survey evaluation label of the product according to the second valid data set and the historical sales performance data of the product for comprehensive processing, and perform survey tracing optimization prompts based on the satisfaction survey evaluation label of the product.

[0075] In this embodiment, through the collaborative work of multiple modules, basic data is first collected and preprocessed from each sales platform to determine the extraction ratio of satisfaction survey data. Subsequently, satisfaction questionnaire data sets are extracted from each platform according to these ratios to form a first valid data set. Next, the consumption activity data in the first valid data set is processed by the evaluation module to determine the data availability and generate an evaluation value. The data screening module uses these evaluation values ​​to eliminate low-quality data and obtain a second valid data set. Finally, the label processing module combines the second valid data set and historical sales data to generate a satisfaction survey evaluation label for the product, while providing survey tracing and optimization tips to support the company's accurate decision-making and continuous improvement.

[0076] To summarize, this embodiment uses multi-platform data fusion and preprocessing to accurately calculate the satisfaction survey data extraction ratio, and extracts survey data from each platform at the corresponding satisfaction survey data extraction ratio, thereby ensuring the rationality of the quantity and representativeness of the quality of the survey data, and effectively solving the problem of resource waste caused by excessive data on each platform in the prior art.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0082] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing consumer satisfaction survey based on big data, characterized in that: The following steps are involved: Count the current online platforms where the product is sold, record them as the selling platforms, obtain the basic data of each selling platform for preprocessing, and obtain the satisfaction survey data extraction ratio of each selling platform; The satisfaction questionnaire data sets of each selling platform are counted, and data are extracted according to the satisfaction survey data extraction ratio of each selling platform, and the extracted satisfaction questionnaire data sets are marked as the first valid data set; Counting each source object corresponding to the first valid data set, and counting the consumption activity data of each source object for processing, and determining the available evaluation value of the data of each source object; Based on the data availability evaluation value of each source object and the first valid data set, a second valid data set is obtained by screening; According to the second valid data set, historical sales performance data of the product is statistically processed to obtain a satisfaction survey evaluation label of the product, and a survey traceability optimization prompt is performed based on the satisfaction survey evaluation label of the product.

2. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 1, characterized in that: The basic data of each sales platform specifically includes: The number of active users, commodity transaction volume, transaction volume growth rate, commodity return rate, commodity evaluation volume and user complaint rate of each sales platform in the first preset time period.

3. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 1, characterized in that: The specific steps of obtaining the satisfaction survey data extraction ratio of each selling platform include: Obtain the preset user active number benchmark value, commodity transaction volume benchmark value, commodity return rate benchmark value, commodity evaluation volume benchmark value, and user complaint rate benchmark value of each sales platform in the first preset time period, and record them as preset basic data; Compare the preset basic data with the basic data of each selling platform to obtain the basic characteristic value of each selling platform, which is used to quantitatively analyze the basic data of each selling platform and is used as an analysis basis for the extraction ratio of satisfaction survey data; Based on the verification basic characteristic value preset in the database and compared with the basic characteristic value of each platform on sale, the original default survey data extraction ratio of each platform on sale is simultaneously extracted from the database. After comparison, if the basic characteristic value of a certain platform on sale is above the preset verification basic characteristic value, the difference between the basic characteristic value of the platform on sale and the preset verification basic characteristic value is extracted, and the difference is matched with the difference preset in the database to obtain the adjustment ratio corresponding to the preset difference, and the adjustment ratio of the platform on sale is added to the original default survey data extraction ratio of the platform on sale to obtain the satisfaction survey data extraction ratio of the platform on sale. If the basic characteristic value of a certain platform on sale is less than the preset verification basic characteristic value, the original default survey data extraction ratio of the platform on sale is used as the satisfaction survey data extraction ratio of the platform on sale.

4. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 1, characterized in that: The specific steps of determining the available evaluation value of the data of each source object include: Obtaining consumption activity data of each source object, the consumption activity data including: return rate, number of repurchases, product purchase conversion rate, negative review rate, and purchase frequency within a first preset time period; Obtain a preset consumer activity data benchmark set, and compare it with the consumer activity data of each source object to obtain the data availability evaluation value of each source object; The preset consumer activity data benchmark set includes: return rate verification value, repurchase number verification value, product purchase conversion rate verification value, negative review rate verification value and purchase frequency verification value; The data of each source object may be evaluated to quantify the consumption activity data of each source object and serve as a basis for analyzing consumer satisfaction.

5. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 4, characterized in that: The method for determining the available evaluation value of the data of each source object is: In the formula, E i represents the data availability evaluation value of the i-th source object, i represents the number of the source object, i=1,2,...,n, n represents the number of source objects, r i represents the return rate of the i-th source object in the first preset time period, r1 represents the return rate verification value, e represents the base of the natural logarithm, and f i represents the number of repurchases of the i-th source object within the first preset time period, f1 represents the repurchase number verification value, c i represents the commodity purchase conversion rate of the i-th source object in the first preset time period, c1 represents the commodity purchase conversion rate verification value, q i represents the negative review rate of the i-th source object in the first preset time period, q1 represents the negative review rate verification value, and p i represents the purchase frequency of the i-th source object in the first preset time period, p1 represents the purchase frequency verification value, α1 represents the return rate influence weight of the source object in the first preset time period, α2 represents the repurchase times influence weight of the source object in the first preset time period, α3 represents the commodity purchase conversion rate influence weight of the source object in the first preset time period, α4 represents the negative review rate influence weight of the source object in the first preset time period, and α5 represents the purchase frequency influence weight of the source object in the first preset time period.

6. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 1, characterized in that: The step of obtaining the second valid data set by screening comprises: Obtain a data availability evaluation threshold in the database, and compare it with the data availability evaluation value of each source object. If the data availability evaluation value of a source object is above the data availability evaluation threshold, the satisfaction questionnaire data of the source object is determined to be second valid data. If the data availability evaluation value of the source object is less than the data availability evaluation threshold, the satisfaction questionnaire data of the source object is determined to be invalid data and deleted. Aggregating all second valid data to obtain a second valid data set; The second valid data set includes each target source object and satisfaction questionnaire data of each target source object.

7. A method for optimizing consumer satisfaction survey based on big data as claimed in claim 6, characterized in that: The specific steps of obtaining the satisfaction survey evaluation label of the product include: Based on the second valid data set and according to the historical sales performance data of the product, a satisfaction survey evaluation value of the product is obtained; Obtain the satisfaction survey evaluation threshold of the product in the database and compare it with the satisfaction survey evaluation value of the product. If the satisfaction survey evaluation value of the product is above the satisfaction survey evaluation threshold, the satisfaction survey evaluation label of the product is defined as a high-satisfaction product. If the satisfaction survey evaluation value of the product is less than the satisfaction survey evaluation threshold, the satisfaction survey evaluation label of the product is defined as a low-satisfaction product.

8. The method for optimizing consumer satisfaction survey based on big data as claimed in claim 7, characterized in that: The specific method of conducting survey retrospective optimization prompts based on the product satisfaction survey evaluation label is as follows: If the satisfaction survey evaluation label of the product is defined as a low-satisfaction product, then based on the second valid data set obtained by screening, the satisfaction questionnaire data of each target source object is counted, and the satisfaction questionnaire data includes the evaluation text of each target source object on the product in each dimension; Based on the evaluation texts of each target source object on the product in each dimension, several evaluation keywords of each target source object on the product in each dimension are counted, and according to the positive keyword collection and negative keyword collection preset in the database, the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object on the product in each dimension are screened through mapping and matching; According to the total number of positive evaluation keywords and the total number of negative evaluation keywords of each target source object for the product in each dimension, the text evaluation value of the product in each dimension is processed, and the text evaluation values ​​are arranged in order from small to large to obtain the investigation and tracing arrangement order of the products, and the investigation and tracing arrangement order of the products is transmitted to the central controller.

9. A method for optimizing consumer satisfaction survey based on big data as claimed in claim 8, characterized in that: The method of obtaining a satisfaction survey evaluation value of a product based on the second valid data set and processing the product's historical sales performance data is as follows: Based on the satisfaction questionnaire data of each target source object in the second valid data set, the satisfaction questionnaire data of all target source objects are summarized to obtain a satisfaction questionnaire survey text of the product, and a number of keywords corresponding to the product are counted therefrom, thereby matching the positive keyword collection and the negative keyword collection preset in the database, and then obtaining the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product through mapping and matching; Based on the total number of positive evaluation keywords and the total number of negative evaluation keywords corresponding to the satisfaction questionnaire survey text of the product, and processed according to the historical sales performance data of the product, the satisfaction survey evaluation value of the product is obtained; The historical sales performance data of the product includes the total number of historical per capita positive evaluation keywords of the product and the total number of historical per capita negative evaluation keywords of the product; The satisfaction survey evaluation value of the product is used to quantitatively analyze the second valid data set and is used as a basis for analyzing the satisfaction of the product.

10. A consumer satisfaction survey optimization system based on big data, characterized in that: include: Data acquisition module, data extraction module, data evaluation module, data screening module and label processing module; The data acquisition module is used to count the current online platforms where the product is sold, recorded as each selling platform, and obtain the basic data of each selling platform for preprocessing to obtain the satisfaction survey data extraction ratio of each selling platform; The data extraction module is used to collect statistics on the satisfaction questionnaire data sets of each selling platform, extract data according to the satisfaction survey data extraction ratio of each selling platform, and mark the extracted satisfaction questionnaire data sets as the first valid data sets; The data evaluation module is used to count the source objects corresponding to the first valid data set, and count the consumption activity data of each source object for processing, and determine the available evaluation value of the data of each source object; The data screening module is used to obtain a second valid data set by screening based on the available evaluation value of the data of each source object and the first valid data set; The label processing module is used to perform comprehensive processing based on the second valid data set and the historical sales performance data of the product to obtain a satisfaction survey evaluation label of the product, and to provide a survey traceability optimization prompt based on the satisfaction survey evaluation label of the product.

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