A data analysis method, device, and related components for the express delivery industry

Abstract

The application discloses a data analysis method and device based on an express industry and related components. The method comprises collecting all business data according to a preset time period and storing; classifying all the business data based on the label of the business data to obtain a dimension data set and a fact data set, and constructing a corresponding business data model based on the dimension data set and the fact data set; monitoring the business order quantity data in the corresponding business data model, and performing key tracking based on the obtained monitoring result; judging and identifying the important level of the user based on the business data in the business data model. The method monitors the business data to understand the order quantity change and the important level of the user in the first time, and further deepens the contact between the user and the service, thereby facilitating the improvement of the satisfaction of the customer in the service process.

Description

Technical Field

[0001] This invention relates to the field of data processing in the express delivery industry, and in particular to a data analysis method, apparatus and related components based on the express delivery industry. Background Technology

[0002] After decades of rapid development, leading companies in the industry have established mature, efficient, and widely covered express delivery networks, gradually widening the gap with smaller companies and building a strong competitive advantage. These leading companies cannot differentiate themselves from their peers in terms of network coverage, branch lines, or vehicle transportation. Based on the current state of the industry and reflections on the express delivery sector—logistics provides services—this is crucial.

[0003] In response, the inventors discovered that existing express delivery service platforms mainly focus on the accuracy of data related to express delivery outbound and inbound shipments, while neglecting the degree of connection between users and the express delivery industry. This results in the inability to understand users' needs in a timely manner. Therefore, the express delivery industry urgently needs a data analysis system that can understand users' situations in a timely manner. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis method, device, and related components for the express delivery industry, aiming to solve the problem that existing express delivery service platforms cannot understand users' situations in a timely manner.

[0005] To solve the above-mentioned technical problems, the objective of this invention is achieved through the following technical solution: providing a data analysis method based on the express delivery industry, comprising: All business data is collected and stored according to a preset time period, including user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. Based on the labels of the business data, all the business data are classified to obtain dimensional datasets and fact datasets, and corresponding business data models are constructed based on the dimensional datasets and fact datasets. Monitor the business order volume data in the corresponding business data model, and conduct key tracking based on the monitoring results obtained; Based on the business data in the business data model, the user's important level is determined and identified.

[0006] The above technical solution first collects business data, then classifies the collected business data and builds corresponding business data models. Finally, it monitors the business data to understand changes in user order volume in real time, and then judges the importance level of users, so that operation and maintenance personnel can provide corresponding services to users with higher levels. By using user order volume analysis and user level analysis in combination, the connection between users and services is deepened, which helps to improve customer satisfaction during the service process.

[0007] Furthermore, the construction of the corresponding business data model based on the dimensional dataset and the fact dataset includes: Based on preset information binding rules, the user's basic information data is obtained and stored to obtain an industry relationship sub-model. The user's basic information data includes user name, user address, user contact information, user network access date, user type, platform source, user level, affiliated network point, and affiliated business personnel. Based on user source data and real-time market planning data, user and industry attribution information is divided and stored to obtain industry relationship sub-models; Based on preset business indicators, business order volume data under different business dimensions is obtained and stored to obtain a single quantum model. The business dimensions include collection points, waybill points, collection provinces, signing provinces, sending provinces, ecosystem, industry, project, timeliness type, and time. Based on the analysis of the actual settlement data of users on each line according to the settlement caliber, a traffic flow sub-model is obtained; Based on the quality indicator rules, the corresponding quality data is obtained and stored to obtain the quality sub-model; Based on user status rules, data on the degree of alignment between user and company interests is obtained and stored, resulting in a lifecycle sub-model.

[0008] The above technical solution divides and stores various business data, which facilitates subsequent modules in calling the corresponding business data and reduces the waste of computing resources.

[0009] Furthermore, the monitoring includes tracking the business order volume data in the corresponding business data model, and focusing on tracking based on the obtained monitoring results, including: Acquire business order volume data and, based on different market dimensions, analyze the changes in business order volume indicators in different business time periods. Based on the aforementioned business order volume change indicators, users who meet the tracking requirements will be tracked in a focused manner.

[0010] The above technical solution allows for focused tracking of users who meet the tracking requirements, enabling timely understanding of changes in user business order volume.

[0011] Furthermore, the analysis of user transaction volume changes across different business time periods based on different market dimensions includes: Based on the overall market, various ecosystems, and different provinces, we obtain user order volume data on a daily, weekly, and monthly basis. Based on the acquired order volume data, the corresponding penetration ratio, month-on-month ratio, and month-on-month difference index are calculated. The penetration ratio includes the large network penetration ratio and the market penetration ratio. Determine whether the value of the network penetration ratio indicator is within the preset threshold range of the network penetration ratio indicator. If the value of the network penetration ratio indicator is within the preset threshold range of the network penetration ratio indicator, it is determined that the market performance of all users in the market is normal. If the value of the network penetration ratio indicator is not within the preset threshold range of the network penetration ratio indicator, it is determined that the market performance of all users in the market is abnormal. The system determines whether the value of the market penetration ratio indicator is within the preset threshold range. If the value of the market penetration ratio indicator is within the preset threshold range, the current market performance of the industry user is determined to be normal. If the value of the market penetration ratio indicator is not within the preset threshold range, the current market performance of the industry user is determined to be abnormal.

[0012] By using the above technical solutions and judging the values ​​of the network penetration rate and market penetration rate, it is possible to promptly understand whether users are experiencing any abnormal situations.

[0013] Furthermore, after calculating the corresponding penetration rate index, month-on-month index, and month-on-month difference index based on the acquired order volume data, the method further includes: Based on daily, weekly, and monthly time dimensions, the user's month-on-month indicators are connected in chronological order to obtain and display the corresponding market trend chart. The month-on-month indicators include daily month-on-month indicators, weekly month-on-month indicators, and monthly month-on-month indicators. Determine whether the value of the month-on-month difference indicator is within the threshold range of the month-on-month difference indicator. If the value of the month-on-month difference indicator is not within the threshold range of the month-on-month difference indicator, then mark the user and determine whether the value of the month-on-month difference indicator reaches the tracking threshold. If the value of the month-on-month difference indicator reaches the tracking threshold, then determine that the user is a target abnormal user and perform data tracking on the target abnormal user.

[0014] The above technical solution uses the month-on-month difference index to determine whether a user has any abnormal situation. If an abnormal situation is found, the user's data is tracked, which helps maintenance personnel to pay more attention to the user.

[0015] Furthermore, the step of determining and identifying the user's important level based on the business data in the business data model includes: Acquire user metrics such as average daily order volume, average monthly gross profit, and brand influence. Based on the preset weight ratio, the scores of the daily average order volume, monthly average gross profit, and brand influence indicators are obtained, and the scores of all the indicators are added together to obtain the total indicator score. Users are identified based on their position in a preset hierarchical table according to their total score on the indicator.

[0016] Using the above technical solution, users are scored based on daily average order volume, monthly average gross profit, and brand influence to determine their importance level.

[0017] Furthermore, based on preset weight ratios, the scores corresponding to the daily average order volume, monthly average gross profit, and brand influence indicators are obtained, and all the indicator scores are summed to obtain the total indicator score, including: The score of the first indicator is obtained based on the position of the average daily order volume indicator in the preset average daily order volume score table. The second indicator score is obtained based on the position of the average monthly gross profit index in the preset average monthly gross profit score table. The third indicator score is obtained based on the position of the brand influence indicator in the preset brand influence score table. The scores of the first indicator, the second indicator, and the third indicator are assigned values ​​according to a weight of 4:3:1, and the assigned indicator scores are added together to obtain the total indicator score.

[0018] Using the above technical solution, users are scored based on daily average order volume, monthly average gross profit, and brand influence to determine their importance level.

[0019] Furthermore, the technical problem to be solved by the present invention is to provide a data analysis device based on the express delivery industry, which includes: The data acquisition module is used to collect business data, which includes user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. The data processing module is used to classify all the business data based on the labels of the business data to obtain a dimension dataset and a fact dataset, and to construct a corresponding business data model based on the dimension dataset and the fact dataset. The order volume analysis module is used to monitor the order volume data in the corresponding business data model and to track key items based on the monitoring results. The hierarchical analysis module is used to determine and identify the user's important hierarchical level based on the business data in the business data model.

[0020] In addition, this embodiment of the invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the data analysis method based on the express delivery industry described in the first aspect above.

[0021] In addition, embodiments of the present invention also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the data analysis method based on the express delivery industry described in the first aspect above.

[0022] This invention discloses a data analysis method, apparatus, and related components for the express delivery industry. The method includes: collecting and storing all business data according to a preset time period, wherein the business data includes user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data; classifying all business data based on tags to obtain dimensional datasets and fact datasets, and constructing corresponding business data models based on the dimensional datasets and fact datasets; monitoring the order volume data in the corresponding business data models and focusing on key tracking based on the monitoring results; and determining and identifying the important user levels based on the business data in the business data models. This method monitors business data to understand changes in user order volume and the status of important customer levels in a timely manner, thereby deepening the connection between users and services and improving customer satisfaction during the service process. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a data analysis method for the express delivery industry provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the first sub-process of the data analysis method based on the express delivery industry provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the second sub-process of the data analysis method based on the express delivery industry provided in an embodiment of the present invention; Figure 4 A schematic block diagram of a data analysis system based on the express delivery industry provided in an embodiment of the present invention; Figure 5 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] Please see Figure 1 , Figure 1 A flowchart illustrating a data analysis method for the express delivery industry provided in an embodiment of the present invention; like Figure 1 As shown, the method includes steps S101 to S104.

[0030] S101. Collect and store all business data according to a preset time period, wherein the business data includes user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. S102. Based on the labels of the business data, classify all the business data to obtain a dimension dataset and a fact dataset, and construct a corresponding business data model based on the dimension dataset and the fact dataset. S103. Monitor the business order volume data in the corresponding business data model and conduct key tracking based on the monitoring results obtained; S104. Based on the business data in the business data model, determine and identify the important levels of users.

[0031] In this embodiment, the collected business data is divided into dimensional data and fact data. Then, all the dimensional data is used to construct a dimensional dataset, and all the fact data is used to construct a fact dataset. It should be noted that dimensional data refers to data describing the environment or entities, including but not limited to organizational dimension table data, user basic information table data, and market industry table data; while fact data refers to process data that occurs or is generated during the express delivery business, including but not limited to material collection, dispatch, delivery, and signing. Therefore, corresponding fact table data is established based on the above processes, such as order fact table data and settlement caliber fact table data. This application aims to make the above... To facilitate application layering and save computing resources, this application addresses the connection between users and the express delivery industry through steps S103 and S104. Specifically, step S103 monitors business data to understand changes in user order volume in real time, and tracks customers with significant changes in order volume (i.e., customers with large order volume fluctuations). Step S104 determines the user level, facilitating maintenance personnel to provide services to higher-level users. The combined use of steps S103 and S104 deepens the connection between users and services, thereby improving customer satisfaction during the service process.

[0032] In this embodiment, the user definition data in step S101 includes basic company information, such as name, address, quantity, industry, social reputation, industry influence, customer recognition, and company operation status. It should be noted that this user definition data requires regular monitoring and maintenance by operations personnel to ensure its accuracy and availability. User communication data includes basic information about the company's contact person responsible for communicating with users, such as contact person, phone number, email address, and WeChat ID. This data allows for immediate access to user contact information and communication when user status changes. User requirement data is used when communicating with users and signing contracts. The data processed includes user commitments such as the types of logistics services required, service quality requirements, order pricing requirements, timeliness requirements, logistics route arrangements, and user commitments to the company, such as the committed order volume and average weight, contract price, and pickup timeliness for each route. User lifecycle data is generated during daily business operations, including traffic flow data, order volume data, settlement data, and timeliness performance data. This data is an important component for analyzing actual customer behavior. User transaction behavior data, including user satisfaction and level of active participation during the cooperation process, can be maintained, recorded, and saved jointly by business development personnel and users.

[0033] It should be added that, prior to step S101, this application selects Alibaba Cloud's DataWorks as the platform for basic business data processing. DataWorks has full-chain data governance capabilities for big data modeling, integration, production, governance, security, and services. Through DataWorks integration function, user business data from various business databases is pulled into MaxCompute. Then, the big data computing capabilities in MaxCompute are used to clean and transform the business data. Through the preprocessing of business data, it is ensured that the format and content of the business data meet the subsequent needs.

[0034] In one specific embodiment, step S102 includes: S10. Based on preset information binding rules, obtain and store the user's basic information data to obtain an industry relationship sub-model. The user's basic information data includes user name, user address, user contact information, user network access date, user type, platform source, user level, affiliated network outlet, and affiliated business personnel. S11. Based on user source data and real-time market planning data, the user and industry affiliation information is divided and stored to obtain the industry relationship sub-model. S12. Based on preset business indicators, acquire and store business order volume data under different business dimensions to obtain a single quantum model. The business dimensions include collection points, waybill points, collection provinces, signing provinces, sending provinces, ecosystem, industry, project, timeliness type, and time. S13. Based on the settlement caliber analysis, analyze the actual settlement data of users on each line to obtain the traffic flow sub-model; S14. Based on the quality indicator rules, obtain and store the corresponding quality data to obtain the quality sub-model; S15. Based on the user's state rules, obtain and store data on the degree of alignment between user and company interests to obtain a lifecycle sub-model.

[0035] In this embodiment, the application needs to divide the business data and divide the collected data into multiple dimensions. After obtaining the above sub-models, the upstream business can build different application models according to its own business needs.

[0036] In one specific embodiment, step S103 includes the following steps: S20. Obtain business order volume data, and analyze the business order volume change indicators in different business time periods based on different market dimensions. S21. Based on the aforementioned business order volume change indicators, users who meet the tracking requirements will be tracked in a focused manner.

[0037] In this embodiment, based on the collected business order volume data, the business order volume data is analyzed under different market dimensions to obtain the business order volume change indicators of users in different business time periods, such as daily, weekly, monthly, and yearly. Then, based on the changes in the business order volume change indicators, users who meet the tracking requirements are tracked in a focused manner to ensure that user performance is detected as soon as possible. In the event of user abnormalities, corresponding measures are taken immediately for communication and handling.

[0038] In step S11, this application categorizes users into different ecosystems and industries based on user origin and real-time market planning. For example, it categorizes users into Taobao, Cainiao, and platform ecosystems based on user origin, and into a large customer ecosystem based on user size. Within each ecosystem, users are further subdivided according to business attributes. For instance, large customers are categorized into provincial large customers and headquarters large customers based on the organization to which their business development personnel belong. This facilitates user classification and allows for policy support tailored to different user types. In step S12, business volume data includes pickup volume, signed-for volume, and order volume. In step S13, the traffic flow sub-model is a model constructed by analyzing the actual settlement situation of users on each route from the perspective of settlement, including... The dimensions include: store, origin city, receipt city, settlement point, customer project, weight range, and industry. The indicators are paid shipment volume and settlement weight. In step S14, the quality sub-model is a model that helps save costs and improve customer satisfaction. The indicators involved include those related to compensation, such as loss, damage, and delay, as well as indicators that measure logistics health, such as false receipts, problem items, and returned items. In step 15, the lifecycle model is a representation of the degree of alignment between user and company interests. That is, user lifecycle data can help the company calculate different operating costs based on different lifecycles to retain old users, attract new users, and reduce user churn. The indicators include new users, retention, suspected churn, and churn status.

[0039] Combination Figure 2 In one specific embodiment, step S20 includes the following steps: S30: Based on the overall market, various ecosystems, and provinces across the entire network, obtain user order volume data on a daily, weekly, and monthly basis. S31. Based on the acquired order volume data, calculate the corresponding penetration ratio, month-on-month ratio, and month-on-month difference index. The penetration ratio includes the large network penetration ratio and the market penetration ratio. S32. Determine whether the value of the large network penetration ratio indicator is within the preset threshold range of the large network penetration ratio indicator. If the value of the large network penetration ratio indicator is within the preset threshold range of the large network penetration ratio indicator, then execute step S33. If the value of the large network penetration ratio indicator is not within the preset threshold range of the large network penetration ratio indicator, then execute step S34. S33. Determine that the market performance of all users in the market is normal; S34. Determine if the market performance of all users in the market is abnormal; S35. Determine whether the value of the market penetration ratio indicator is within the preset threshold range of the market penetration ratio indicator. If the value of the market penetration ratio indicator is within the preset threshold range of the market penetration ratio indicator, then execute step S36. If the value of the market penetration ratio indicator is not within the preset threshold range of the market penetration ratio indicator, then execute step S37. S36. The current market performance of industry users is determined to be normal; S37. Determine that the current market performance of industry users is abnormal.

[0040] In this embodiment, user-generated order volume is a crucial component of the express delivery industry. Therefore, monitoring daily order volume changes among market users is indispensable. This application acquires daily, weekly, and monthly order volume data based on all user groups, ecosystem dimensions, and provincial / regional dimensions. Then, it calculates corresponding penetration month-on-month indicators, month-on-month indicators, and month-on-month difference indicators using the order volume data across each dimension. These calculated indicators are used to understand user order volume changes. Specifically, the network penetration indicator represents the percentage of all market users' orders in the company's total orders, and the market penetration percentage indicator represents the percentage of each industry segment within the market. The market performance is analyzed using the network penetration rate metric. For example, under normal circumstances, the market order volume typically falls within the 8%-12% range for the network penetration rate metric. If the value is within this range, it indicates normal overall market performance. If the value is outside this range, it indicates abnormal overall market performance. For instance, a network penetration rate exceeding 12% could indicate the following: 1. Promotional activities have been implemented, leading to more customers placing orders; 2. Customers are offering their own promotions; 3. New large customers (customers with high order volumes) have been acquired; 4. Shentong has implemented more favorable policies for customers. Conversely, a network penetration rate below 8% could indicate the following: 1. Unfavorable policies for customers; 2. Unforeseen circumstances, such as the pandemic; 3. Customer churn. In this embodiment, the market penetration percentage metric is used to monitor the overall performance of customers in various industry sectors within the market. Users are categorized into Key Accounts (KA), Taobao Special Offers (Taobao), Cainiao, and the platform based on user characteristics and industry nature. The performance of users in each industry sector is assessed based on their percentage within each sector, facilitating the provision of different policy support to different industries. For example, the order volume share of KA industry sectors is generally around 35%, so the threshold range for the market penetration percentage metric can be set between 10% and 15%. If an industry sector's share is below this range, it indicates that the order volume share of its users is low and its brand influence is weak. Therefore, cooperation with these users can be terminated to reduce costs. Industries exceeding the market penetration percentage metric threshold range, such as KA industry sectors which include many users with strong brand influence, will receive more attention and preferential treatment from the company's annual policies.

[0041] Combination Figure 3 In one specific embodiment, after step S31, the following step is further included: S40. Based on the time dimensions of daily, weekly, and monthly, connect the user's month-on-month indicators in chronological order to obtain and display the corresponding market trend chart. The month-on-month indicators include daily month-on-month indicators, weekly month-on-month indicators, and monthly month-on-month indicators. S41. Determine whether the value of the month-on-month difference index is within the threshold range of the month-on-month difference index. If the value of the month-on-month difference index is not within the threshold range of the month-on-month difference index, then proceed to step S42. S42. Label the user and determine whether the value of the month-on-month difference index has reached the tracking threshold. If the value of the month-on-month difference index has reached the tracking threshold, then execute step S43. S43. Determine that the user is a target abnormal user, and perform data tracking on the target abnormal user.

[0042] In this embodiment, the daily, weekly, monthly, and month-on-month comparison indicators are mainly used to monitor the daily, weekly, and monthly changes in customer order volume. By connecting the various comparison indicators, a trend chart can be obtained, which makes it easier for maintenance personnel to see the trend. The month-on-month difference indicator can better show the true performance of different users. For example, this application highlights users whose month-on-month difference indicator values ​​reach 10%-20%, and then determines whether the current month-on-month difference has reached the tracking threshold. If it has, these users are tracked in a focused manner to promptly identify the reasons for abnormal user changes.

[0043] In one specific embodiment, step S104 includes the following steps: S50, daily average order volume, monthly average gross profit, and brand influence metrics for acquiring users; S51. Based on the preset weight ratio, obtain the index scores corresponding to the daily average order volume index, monthly average gross profit index, and brand influence index, and add up all the index scores to obtain the total index score. S52. Identify the user based on the position of the total score of the indicator in the preset hierarchical table.

[0044] In this embodiment, user segmentation is used to label users in the market, dividing them into important levels according to preset rules. This facilitates the identification of important users and makes it easier for business development and maintenance personnel to provide services to users, thus determining the importance of users. This application mainly evaluates users in three dimensions: average daily order volume, average monthly gross profit, and brand influence. The final score is calculated by weighting the three indicators, and the user's importance level is determined by the preset level table.

[0045] In one specific embodiment, step S51 includes the following steps: S60. Obtain the first indicator score based on the position of the daily average order volume indicator in the preset daily average order volume score table. S61. Obtain the second indicator score based on the position of the average monthly gross profit index in the preset average monthly gross profit score table; S62. Obtain the score of the third indicator based on the position of the brand influence indicator in the preset brand influence score table. S63. Assign values ​​to the first indicator score, the second indicator score, and the third indicator score according to a weight of 4:3:1, and add the assigned indicator scores to obtain the total indicator score.

[0046] In this embodiment, for ease of explanation, for example, if the average daily order volume exceeds 10,000 orders, the average daily order volume dimension is 40 points, 5,000-10,000 orders is 32 points, 3,000-5,000 orders is 24 points, 1,000-3,000 orders is 16 points, and 0-1,000 orders is 8 points; the average monthly gross profit exceeds 50,000 yuan, which is 30 points, 30,000-50,000 yuan is 24 points, 20,000-30,000 yuan is 18 points, 10,000-20,000 yuan is 12 points, 0-10,000 yuan is 6 points, and less than 0 yuan is 0 points; among them, the brand influence index is mainly scored by the user's popularity in the industry and among the public, as well as social influence. This is mainly judged and adjusted by humans periodically.

[0047] Finally, the scores of the three dimensions are added together to get the final total score. A score of 80-100 is Level A, 60-80 is Level B, 40-60 is Level C, and 0-40 is Level D.

[0048] This invention also provides a data analysis device based on the express delivery industry, which is used to execute any of the aforementioned data analysis methods based on the express delivery industry. Specifically, please refer to... Figure 4 , Figure 4 This is a schematic block diagram of a data analysis device for the express delivery industry provided in an embodiment of the present invention.

[0049] like Figure 4 As shown, the data analysis device 500 based on the express delivery industry includes: The data acquisition module 501 is used to collect business data, including user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. The data processing module 502 is used to connect to the data acquisition module to acquire the acquired business data, classify the acquired business data into labels to obtain a dimension dataset and a fact dataset, and construct a corresponding business data model based on the dimension dataset and the fact dataset. The order volume analysis module 503 is used to monitor the order volume data in the corresponding business data model and to track key items based on the monitoring results. The hierarchical analysis module 504 is used to determine and identify the important levels of users based on the business data in the business data model.

[0050] This device monitors business data through a volume analysis module to understand changes in user order volume in real time. It tracks customers with significant changes in order volume (i.e., customers with large changes in order volume). Meanwhile, the hierarchical analysis module determines the user's hierarchical level, making it easier for maintenance personnel to provide services to higher-level users. By using the volume analysis module and the hierarchical analysis module in combination, the connection between users and services is deepened, which helps to improve customer satisfaction during the service process.

[0051] In this embodiment, Kimball's dimensional modeling approach is used to model the data acquisition module. The business data is logically divided from bottom to top into the ODS layer, DWD layer, and ADS layer. The ODS layer is the data acquisition module, used to store complete business data without performing any data processing to ensure accuracy. The DWD layer is the data processing module, used to partition the collected business data. Specifically, the collected business data is divided into dimensional data and fact data. Then, all dimensional data is used to construct a dimensional dataset, and all fact data is used to construct a fact dataset. It should be noted that dimensional data refers to data describing the environment or entities, including but not limited to organizational dimension table data, user basic information table data, and market industry table data. Fact data refers to process data that occurs or is generated during the express delivery business, including but not limited to material collection, dispatch, delivery, and signing. Therefore, the data is built based on the above processes. The corresponding fact table data includes, for example, order fact table data and settlement caliber fact table data. To facilitate easy referencing by upper-layer applications and to conserve computing resources, this application establishes a large, wide table starting with "dws" on top of the dimensional and fact data. The ADS layer primarily performs specific logical processing based on market business and application-specific needs. In this application, the ADS layer consists of a volume analysis module and a hierarchy analysis module. These two modules are highly targeted, specifically addressing the connection between users and the express delivery industry. Specifically, the volume analysis module monitors business data to understand changes in user volume in real time, tracking customers with significant volume changes (i.e., customers with large volume fluctuations). The hierarchy analysis module determines user hierarchy, facilitating service provision to higher-level users by operations personnel. The combined use of the volume analysis and hierarchy analysis modules deepens the connection between users and services, thereby improving customer satisfaction during the service process.

[0052] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0053] The aforementioned data analysis device for the express delivery industry can be implemented as a computer program, which can perform tasks such as... Figure 5 It runs on the computer device shown.

[0054] Please see Figure 5 , Figure 5 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device 1100 is a server, which can be a standalone server or a server cluster composed of multiple servers.

[0055] See Figure 5 The computer device 1100 includes a processor 1102, a memory, and a network interface 1105 connected via a system bus 1101. The memory may include a non-volatile storage medium 1103 and internal memory 1104.

[0056] The non-volatile storage medium 1103 can store an operating system 11031 and a computer program 11032. When the computer program 11032 is executed, it enables the processor 1102 to execute a data analysis method based on the express delivery industry.

[0057] The processor 1102 provides computing and control capabilities to support the operation of the entire computer device 1100.

[0058] The internal memory 1104 provides an environment for the execution of the computer program 11032 in the non-volatile storage medium 1103. When the computer program 11032 is executed by the processor 1102, the processor 1102 can execute data analysis methods based on the express delivery industry.

[0059] The network interface 1105 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device 1100 to which the present invention is applied. The specific computer device 1100 may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0060] Those skilled in the art will understand that Figure 5The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 5 The embodiments shown are consistent and will not be repeated here.

[0061] It should be understood that, in this embodiment of the invention, the processor 1102 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0062] In another embodiment of the invention, a computer-readable storage medium is provided. This computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the data analysis method for the express delivery industry based on embodiments of the present invention.

[0063] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code.

[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0065] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data analysis method based on the express delivery industry, characterized in that, include: All business data is collected and stored according to a preset time period, including user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. Based on the labels of the business data, all the business data are classified to obtain dimensional datasets and fact datasets, and corresponding business data models are constructed based on the dimensional datasets and fact datasets. Monitor the business order volume data in the corresponding business data model, and conduct key tracking based on the monitoring results obtained; Based on the business data in the business data model, the important levels of users are determined and identified; The construction of the corresponding business data model based on the dimensional dataset and the fact dataset includes: Based on preset information binding rules, the user's basic information data is obtained and stored to obtain an industry relationship sub-model. The user's basic information data includes user name, user address, user contact information, user network access date, user type, platform source, user level, affiliated network point, and affiliated business personnel. Based on user source data and real-time market planning data, user and industry attribution information is divided and stored to obtain industry relationship sub-models; Based on preset business indicators, business order volume data under different business dimensions is obtained and stored to obtain a single quantum model. The business dimensions include collection points, waybill points, collection provinces, signing provinces, sending provinces, ecosystem, industry, project, timeliness type, and time. Based on the analysis of the actual settlement data of users on each line according to the settlement caliber, a traffic flow sub-model is obtained; Based on the quality indicator rules, the corresponding quality data is obtained and stored to obtain the quality sub-model; Based on user status rules, data on the degree of alignment between user and company interests is obtained and stored to obtain a lifecycle sub-model; The monitoring includes business order volume data in the corresponding business data model, and key tracking is performed based on the obtained monitoring results, including: Acquire business order volume data and, based on different market dimensions, analyze the changes in business order volume indicators in different business time periods. Based on the aforementioned business order volume change indicators, users who meet the tracking requirements will be tracked in a focused manner.

2. The data analysis method based on the express delivery industry according to claim 1, characterized in that, The analysis of user transaction volume changes across different business time periods, based on different market dimensions, includes: Based on the overall market, various ecosystems, and different provinces, we obtain user order volume data on a daily, weekly, and monthly basis. Based on the acquired order volume data, the corresponding penetration ratio, month-on-month ratio, and month-on-month difference index are calculated. The penetration ratio includes the large network penetration ratio and the market penetration ratio. Determine whether the value of the network penetration ratio indicator is within the preset threshold range of the network penetration ratio indicator. If the value of the network penetration ratio indicator is within the preset threshold range of the network penetration ratio indicator, it is determined that the market performance of all users in the market is normal. If the value of the network penetration ratio indicator is not within the preset threshold range of the network penetration ratio indicator, it is determined that the market performance of all users in the market is abnormal. The system determines whether the value of the market penetration ratio indicator is within the preset threshold range. If the value of the market penetration ratio indicator is within the preset threshold range, the current market performance of the industry user is determined to be normal. If the value of the market penetration ratio indicator is not within the preset threshold range, the current market performance of the industry user is determined to be abnormal.

3. The data analysis method based on the express delivery industry according to claim 2, characterized in that, After calculating the corresponding penetration rate index, month-on-month index, and month-on-month difference index based on the acquired order volume data, the process also includes: Based on daily, weekly, and monthly time dimensions, the user's month-on-month indicators are connected in chronological order to obtain and display the corresponding market trend chart. The month-on-month indicators include daily month-on-month indicators, weekly month-on-month indicators, and monthly month-on-month indicators. Determine whether the value of the month-on-month difference indicator is within the threshold range of the month-on-month difference indicator. If the value of the month-on-month difference indicator is not within the threshold range of the month-on-month difference indicator, then mark the user and determine whether the value of the month-on-month difference indicator reaches the tracking threshold. If the value of the month-on-month difference indicator reaches the tracking threshold, then determine that the user is a target abnormal user and perform data tracking on the target abnormal user.

4. The data analysis method based on the express delivery industry according to claim 3, characterized in that, The process of determining and identifying the user's important level based on the business data in the business data model includes: Acquire user metrics such as average daily order volume, average monthly gross profit, and brand influence. Based on the preset weight ratio, the scores of the daily average order volume, monthly average gross profit, and brand influence indicators are obtained, and the scores of all the indicators are added together to obtain the total indicator score. Users are identified based on their position in a preset hierarchical table according to their total score on the indicator.

5. The data analysis method based on the express delivery industry according to claim 4, characterized in that, The method involves obtaining the scores for the daily average order volume, monthly average gross profit, and brand influence indicators based on preset weight ratios, and then summing all the indicator scores to obtain the total indicator score, including: The score of the first indicator is obtained based on the position of the average daily order volume indicator in the preset average daily order volume score table. The second indicator score is obtained based on the position of the average monthly gross profit index in the preset average monthly gross profit score table. The third indicator score is obtained based on the position of the brand influence indicator in the preset brand influence score table. The scores of the first indicator, the second indicator, and the third indicator are assigned values ​​according to a weight of 4:3:1, and the assigned indicator scores are added together to obtain the total indicator score.

6. A data analysis device for the express delivery industry, based on the data analysis method for the express delivery industry according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect business data, which includes user definition data, user communication data, user demand data, user lifecycle data, and user transaction behavior data. The data processing module is used to classify all the business data based on the labels of the business data to obtain a dimension dataset and a fact dataset, and to construct a corresponding business data model based on the dimension dataset and the fact dataset. The order volume analysis module is used to monitor the order volume data in the corresponding business data model and to track key items based on the monitoring results. The hierarchical analysis module is used to determine and identify the user's important hierarchical level based on the business data in the business data model.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data analysis method based on the express delivery industry as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the data analysis method based on the express delivery industry as described in any one of claims 1 to 5.

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