Layered commission discount method based on shopping mall buying and shopping playing method

Abstract

The invention relates to the technical field of data processing of e-commerce and business management, in particular to a hierarchical rebate method based on shopping mall donation and shopping playing methods, and adopts the technical scheme that a comprehensive data acquisition system is constructed; data sources covering multiple dimensions are collected from systems of shopping mall transaction, commission discount calculation, order management, user relationship, marketing activities, user evaluation feedback and the like, the data sources are classified and integrated based on a hierarchical management mode, the data sources are synchronized to a data core processing platform through a secure channel, and marks are verified; processing data by using a commission discount calculation model fusing machine learning and a rule engine technology, accurately corresponding to a commission discount rule, constructing a complete commission discount system after calculation is completed, including a plurality of key tables to ensure information integrity and traceability, and performing sampling inspection, comparison verification and audit report generation and archiving on a commission discount result. According to the method, the problems of incomplete data acquisition, low processing efficiency, imperfect calculation model and imperfect rebate system in the background technology are solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology for e-commerce and business management, and specifically to a tiered commission rebate method based on a buy-one-get-one-free shopping model in a shopping mall. Background Art

[0002] In today's booming era of online e-commerce, major shopping malls are constantly introducing innovative shopping methods and promotions to attract users, increase user stickiness, and boost consumption. Among these, buy-one-get-one-free shopping methods have gained widespread popularity, offering users additional benefits during their shopping journey. By offering free shopping credits, alcohol red envelopes, and other rewards, users can redeem items within the mall for free or enjoy other discounts. This not only increases user enjoyment but also stimulates consumption to a certain extent.

[0003] However, as this buy-one-get-one-free shopping model continues to evolve, the associated commission rebate mechanisms face numerous challenges. Existing commission rebate methods often suffer from incomplete data collection, failing to comprehensively gather data from multiple dimensions, including mall transaction systems, commission calculation systems, order management systems, customer relationship systems, marketing campaign management systems, and user review and feedback systems. This results in an inability to accurately analyze user behavior and order status, which in turn affects the accuracy of commission rebate calculations.

[0004] Furthermore, in terms of data processing and synchronization, traditional methods lack efficient and stable data transmission channels and core data processing platforms with high concurrent processing capabilities, making it difficult to process and synchronize large amounts of complex data in a timely and effective manner. Furthermore, during the data processing process, there is a lack of scientific methods for verifying and marking data, resulting in a lack of accurate data foundation for subsequent commission calculations.

[0005] In addition, the existing rebate calculation model is not perfect. It cannot integrate machine learning algorithms and rule engine technologies well, cannot fully explore the potential correlations and patterns between data, and it is difficult to accurately map the rebate rules to specific user behaviors and orders, resulting in the rebate results not being able to meet the mall's operational needs well.

[0006] Finally, existing technologies lack completeness and traceability in building a rebate system. They fail to fully encompass basic rebate rules, user behavior-linked rebate tables, order-level rebate details, rebate summary reports, abnormal order rebate processing records, and user rebate benefit status tracking tables. Furthermore, there are deficiencies in the verification and auditing of rebate results. Effective sampling checks and data comparisons are unable to ensure the accuracy and fairness of rebate calculations, making it difficult to ensure the transparency and traceability of the rebate process. Summary of the Invention

[0007] The purpose of the present invention is to provide a tiered rebate method based on the buy-one-get-one-free shopping gameplay of the mall. By constructing a comprehensive data collection system, adopting a tiered management model to process data, using a rebate calculation model with a fusion algorithm, and building a complete and traceable rebate system, the problems of incomplete data collection, low processing efficiency, imperfect calculation model and imperfect rebate system in the background technology are solved.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a tiered commission rebate method based on a buy-one-get-one-free shopping experience in a shopping mall, comprising the following steps:

[0009] S1. Build a comprehensive data collection system and collect data sources extensively:

[0010] S11. Obtain data sources from the mall transaction system, rebate calculation system, order management system, and user relationship system, and additionally collect relevant data from the marketing activity management system and user evaluation feedback system;

[0011] S12. Ensure that the data sources collected cover multi-dimensional information such as shopping transaction data, user behavior data, order-related data, marketing activity data, and user feedback data;

[0012] S2. Data processing and synchronization based on a hierarchical management model:

[0013] S21. Classify and integrate the various data sources collected, and synchronize them to the data core processing platform with high concurrent processing capabilities through a secure and stable data transmission channel;

[0014] S22. During the data synchronization process, preliminary verification and marking of the data are performed to lay the foundation for subsequent accurate processing;

[0015] S23. Utilize a proprietary rebate calculation model that integrates machine learning algorithms and rule engine technology within the data core processing platform;

[0016] On the one hand, we use machine learning algorithms to conduct in-depth analysis of historical data to uncover potential connections and patterns between data. On the other hand, we use rule engine technology to parse and execute the mall's preset rebate rules, comprehensively processing all types of data to accurately map rebate rules to specific user behaviors and orders.

[0017] S3. After completing the rebate calculation, build a complete rebate system and conduct verification and audit:

[0018] S31. Build a complete rebate system that includes not only a basic rebate rules table, a user behavior-related rebate table, and order-level rebate details, but also rebate summary reports, abnormal order rebate processing records, and user rebate rights status tracking tables to ensure the integrity and traceability of rebate information.

[0019] S32. After the rebate system is established, the verification and audit steps of the rebate results will be carried out. Through sampling inspection and data comparison, the accuracy and fairness of the rebate calculation will be ensured. At the same time, a rebate audit report will be generated and archived for subsequent query and tracing.

[0020] Preferably, the shopping transaction data is obtained from the mall transaction system and the rebate calculation system, the user behavior data is obtained from the user relationship system, and the order-related data is obtained from the order management system; wherein, the shopping transaction data provides basic data support for subsequent rebate calculations, the user behavior data will be used for subsequent processing of rebate rules associated with user behavior, and the order-related data provides key information of the order dimension for each rebate calculation link.

[0021] Preferably, the rebate system includes the following key tables:

[0022] The basic rebate rule table is used to store basic rebate rule information and provide the basic rule basis for the entire rebate calculation;

[0023] User behavior-related rebate table, which links user behavior with rebates and reflects the impact of user behavior on rebates;

[0024] Order level rebate details, detailed record of rebate status of orders at different levels, and detailed rebate calculation results.

[0025] Preferably, step S2 includes the following steps:

[0026] S211. For the shopping transaction data obtained from the mall transaction system, combined with order-related data, data segmentation and rebate calculation are performed according to the basic buy-one-get-one-free rules to obtain a basic rebate rules table;

[0027] S2111. Preprocess the order-related data and the shopping transaction data obtained from the mall transaction system simultaneously, including data extraction, format conversion, data cleaning, and data loading operations, to obtain corresponding order details tables and transaction data tables;

[0028] S2112. After synchronizing the order details table and transaction data table to the data core processing platform, input them into the rebate calculation model in the platform to obtain the basic rebate rule table;

[0029] S212: Process the user behavior data obtained from the user relationship system in combination with order-related data according to the rebate rules associated with user behavior to obtain a user behavior-associated rebate table;

[0030] S2121. Preprocess the order-related data separately, including data extraction, format conversion, data cleaning, and data loading operations, to obtain the corresponding order details table;

[0031] S2122. After synchronizing the order details table and the user behavior data obtained from the user relationship system to the data core processing platform, the data is input into the rebate calculation model in the platform to obtain a user behavior-related rebate table;

[0032] S213. Calculate the shopping transaction data obtained from the rebate calculation system in combination with the order-related data according to the order-level rebate rules to obtain order-level rebate details.

[0033] S2131. Preprocess the order-related data and the shopping transaction data obtained from the rebate calculation system simultaneously, including data extraction, format conversion, data cleaning, and data loading operations, to obtain the corresponding order details table and rebate calculation data table;

[0034] S2132. After synchronizing the order details table and the rebate calculation data table to the data core processing platform, input them into the rebate calculation model in the platform to obtain the order-level rebate details.

[0035] Preferably, the step S211 specifically includes the following steps:

[0036] S2111. Perform the following pre-processing operations on the order-related data and the shopping transaction data obtained from the mall transaction system:

[0037] Data extraction: extracting required information from raw data;

[0038] Format conversion: convert the extracted data into a format suitable for subsequent calculation and storage;

[0039] Data cleaning: remove noise and erroneous data;

[0040] Data loading: loading the processed data into the corresponding storage structure;

[0041] After the above preprocessing operations, the order details table and transaction data table are obtained;

[0042] S2112. Synchronize the order details table and transaction data table to the data core processing platform, input the rebate calculation model, and generate the basic rebate rule table based on the internal algorithm and basic buy-one-get-one-free rules.

[0043] Preferably, the transaction data table includes historical transaction data and daily new transaction data, and the basic rebate rule table includes initial rebate rule data and daily updated rebate rule data;

[0044] Among them, the historical transaction data stores past transaction information and provides a historical basis for rebate calculation. The daily new transaction data records new transaction information that occurs every day to ensure the timeliness and integrity of the data. The initial rebate rule data provides the starting rule setting for the rebate calculation. The daily updated rebate rule data provides daily updated rebate rule information based on the mall operation needs, so that the rebate calculation can be carried out according to the latest rules.

[0045] Preferably, the step S212 specifically includes the following steps:

[0046] S2121. Perform the following pre-processing operations on the order-related data:

[0047] Data extraction: extract useful information from order-related data;

[0048] Format conversion: convert order-related data into a unified format;

[0049] Data cleaning: ensure data accuracy;

[0050] Data loading: store the processed data into the corresponding structure;

[0051] After the above preprocessing operations, the order details table is obtained;

[0052] S2122. Synchronize the order details table and the user behavior data obtained from the user relationship system to the data core processing platform, input the rebate calculation model, and generate a user behavior-related rebate table based on the rebate rules associated with the user behavior.

[0053] Preferably, the step S213 specifically includes the following steps:

[0054] S2131. Perform the following pre-processing operations on the order-related data and the shopping transaction data obtained from the rebate calculation system:

[0055] Data extraction: extracting required information from raw data;

[0056] Format conversion: converting data into a suitable format;

[0057] Data cleaning: remove errors and noise from the data;

[0058] Data loading: loading the processed data into the storage structure;

[0059] After the above pre-processing operations, the order details table and rebate calculation data table are obtained;

[0060] S2132. Synchronize the order details table and the rebate calculation data table to the data core processing platform, input the rebate calculation model, and use the model to generate order-level rebate details based on the order-level rebate rules.

[0061] Preferably, different weight coefficients are set in the rebate calculation model to adjust the importance of shopping transaction data, user behavior data and order-related data in the rebate calculation, and the weight coefficients can be dynamically adjusted regularly or irregularly according to the mall marketing strategy;

[0062] Among them, the setting of these weight coefficients is intended to reflect the mall's emphasis on different data at different stages or under different marketing strategies. By adjusting the weight coefficients, the flexibility and adaptability of the rebate calculation can be guaranteed, making the rebate results more in line with the mall's operational needs.

[0063] Preferably, after the commission rebate system is established, it also includes verification and auditing steps for the commission rebate results. By sampling inspection and data comparison, the accuracy and fairness of the commission rebate calculation are ensured. At the same time, a commission rebate audit report is generated and archived for subsequent query and tracing;

[0064] Among them, the sampling inspection is carried out by extracting some rebate data samples, and the data comparison compares the calculated rebate data with the expected results or historical data to ensure the accuracy of the rebate calculation. The generated rebate audit report records the verification and audit process and results, providing a basis for subsequent inquiries and tracing, and ensuring the transparency and traceability of the rebate process.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The present invention improves the comprehensiveness of data collection: a data collection system covering multi-dimensional data sources such as the mall transaction system, rebate calculation system, order management system, user relationship system, marketing activity management system, and user evaluation feedback system is constructed to ensure that the collected data includes shopping transaction data, user behavior data, order-related data, marketing activity data, and user feedback data, etc., providing a comprehensive, accurate, and in-depth data foundation for accurate analysis of user behavior and order status, overcoming the problem of incomplete data collection in existing technologies;

[0067] The present invention enhances data processing and synchronization efficiency by adopting a hierarchical management model, first classifying and integrating various data sources, then synchronizing them to a core data processing platform with high concurrent processing capabilities via a secure and stable data transmission channel, and performing preliminary verification and marking during the synchronization process. This effectively addresses the problems of traditional methods lacking efficient and stable transmission channels and high-concurrency processing platforms, as well as the lack of verification and marking during data processing. It enables the timely and effective processing and synchronization of large amounts of complex data, laying a precise data foundation for subsequent commission calculations.

[0068] The rebate calculation model in this invention is more complete: the independently developed rebate calculation model integrates machine learning algorithms and rule engine technology. Through machine learning algorithms, historical data is deeply analyzed to mine potential correlations and patterns. The rule engine technology is used to accurately analyze and execute the mall's preset rebate rules, accurately mapping the rebate rules to specific user behaviors and orders. This makes up for the imperfections of the existing rebate calculation model and makes the rebate results more in line with the mall's operational needs.

[0069] The integrity and traceability of the commission rebate system in this invention are guaranteed: the constructed commission rebate system includes a basic commission rebate rules table, a user behavior-related commission rebate table, order-level commission details, a commission summary report, abnormal order commission processing records, and a user commission rebate rights status tracking table, ensuring the integrity and traceability of commission rebate information. At the same time, commission rebate results are verified and audited through sampling inspections and data comparisons, and audit reports are generated and archived. This effectively addresses the shortcomings of existing technologies in commission rebate system construction and result verification and auditing, ensuring the transparency and traceability of the commission rebate process. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a system flow chart of the present invention;

[0071] Figure 2 This is a diagram of the data acquisition system of the present invention;

[0072] Figure 3 is a data processing and synchronization flow chart of the present invention;

[0073] Figure 4 A diagram showing the steps for calculating the rebate of the present invention;

[0074] Figure 5 This is a verification audit diagram for the rebate system of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0076] Example 1

[0077] like Figure 1 As shown, an embodiment of the present invention provides a tiered commission rebate method based on the buy-one-get-one-free shopping gameplay in the mall, which builds a comprehensive data collection system:

[0078] For example, in the daily operations of a shopping mall, a comprehensive and in-depth data collection system is required to achieve accurate and reasonable tiered commission calculations. Data sources are distributed across multiple key systems. The mall's transaction system collects detailed information about user purchases, including the specific types, quantities, transaction amounts, and purchase times of various products, such as popular online foods, rice, flour, grains, oils, branded alcoholic beverages, and high-quality teas. This data forms the basis for commission calculations. For example, if a purchase of a 79 yuan item comes with a 48 yuan delivery credit, this transaction data will support commission calculations.

[0079] The commission calculation system provides completed commission calculation data, including the basis and results of past commission calculations. By analyzing this data, you can trace back commission history and summarize experience to provide reference for current and future commission calculations.

[0080] The order management system provides order-related data such as order creation time, payment status (e.g., paid, unpaid, or payment failed), shipping status (e.g., shipped, unshipped, in transit, or received), and order completion status. This information provides key order-specific information for commission calculations, helping to accurately map commission rules to specific orders. For example, when processing wine redemption orders, commission timing can be determined based on order status.

[0081] The user relationship system focuses on user interaction data, such as sharing and transferring red envelopes of wine to friends. For example, in the "My Wine Red Envelope" campaign, transferring a red envelope of wine can be linked to a sharing relationship. If a friend successfully redeems the red envelope, the sharer will receive a 10 yuan delivery bonus equal to the payment amount. This behavioral data will be used to process subsequent commission rebate rules closely linked to user behavior, highlighting the importance of user behavior in the commission rebate system.

[0082] The marketing campaign management system provides marketing campaign data such as "My Wine Red Packet" campaign rules, number of participants, and campaign effectiveness. For example, certain promotions may adjust rebate rules, or users with high participation may receive additional rebate rewards. This data can help us gain a deeper understanding of the impact of marketing campaigns on rebates.

[0083] The user evaluation and feedback system provides user reviews and feedback on products and services. While seemingly unrelated to commission calculations, it indirectly reflects user shopping experiences and behavioral tendencies, assisting in comprehensive analysis of user behavior and commission effectiveness, and providing a reference for optimizing commission strategies.

[0084] During the data collection process, strictly adhere to the requirements of S12 in Claim 1 to ensure that the collected data sources comprehensively cover multi-dimensional information such as shopping transaction data, user behavior data, order-related data, marketing activity data, and user feedback data. Only in this way can a comprehensive, accurate, and in-depth data foundation be provided for subsequent commission calculation and analysis. Multi-dimensional data can more accurately reflect user shopping behavior and mall operations, provide richer input parameters for the commission calculation model, and make the commission calculation results more accurate and reasonable, in line with the actual operational needs of the mall and user expectations.

[0085] Example 2

[0086] like Figure 2 As shown, another embodiment provided by the present invention is a tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall, and data processing and synchronization based on a tiered management model:

[0087] After collecting various data sources, the first task is to classify and integrate the data. Data from different systems may differ in format, structure, and content. Classification and integration can make the data more organized and facilitate subsequent processing and analysis. Through a secure and stable data transmission channel, the integrated data is synchronized to a data core processing platform with high concurrent processing capabilities. This platform is a key hub in the data processing process, with powerful computing and storage capabilities, and can efficiently process large amounts of data. A secure and stable data transmission channel is an important guarantee to ensure accurate and complete data transmission, and to avoid data loss, errors, or tampering during transmission. This step is the basis for implementing data processing in a hierarchical management model. Only by ensuring that data can be efficiently and accurately transmitted to the core processing platform can subsequent data processing work be carried out smoothly.

[0088] Accurately extract key information closely related to rebate calculations from raw data, such as transaction amount, order number, and corresponding product pickup credit. For example, in the "My Wine Red Envelope" campaign, a wine redemption fee of 20 yuan for a bottle of wine is redeemed, and a successful redemption grants an additional 10 yuan in pickup credit. This key information is core to rebate calculations, and its accuracy and completeness directly impact the results. Advanced data extraction techniques and algorithms are required to accurately extract the required information from massive amounts of raw data.

[0089] Because data from different systems may have different formats, it's necessary to convert this data into a standardized format to facilitate subsequent calculations and storage. For example, dates can be standardized, and the units of monetary amounts can be standardized. Format conversion improves data processing efficiency and avoids calculation errors caused by inconsistent data formats.

[0090] Data is rigorously cleaned through a combination of algorithms and manual review. Algorithms can quickly detect and remove duplicate data, while manual review further verifies data accuracy, identifying and correcting any errors. Data cleaning is crucial for ensuring data quality; only cleaned data can provide a reliable basis for subsequent commission calculations.

[0091] The preprocessed data is loaded into the core data processing platform. During the loading process, the integrity and accuracy of the data must be ensured, and the data storage structure must be properly arranged to facilitate subsequent data query and analysis. These preprocessing operations strictly comply with the relevant data preprocessing provisions in Claim 4, ensuring data accuracy and usability, and laying a solid foundation for subsequent data processing and rebate calculation.

[0092] During data synchronization to the core data processing platform, it is categorized and tagged based on transaction type (e.g., regular purchase, wine red envelope redemption), and user level (e.g., regular user, VIP user). For example, in the "My Wine Red Envelope" campaign, different transaction types are handled differently within the commission rebate rules. This categorization and tagging enables more accurate and efficient subsequent data processing. For example, wine red envelope redemption transaction data for VIP users can be individually tagged and processed, allowing them to receive corresponding discounts and rewards when calculating commissions. Categorization and tagging also facilitates data statistics and analysis, providing data support for mall operational decisions.

[0093] Example 3

[0094] like Figure 3 and Figure 4 As shown, another embodiment provided by the present invention is a tiered commission method based on the buy-one-get-one-free shopping gameplay in the mall, which uses a commission calculation model to calculate the commission:

[0095] Based on data acquired from the mall's transaction system and order management system, and closely integrated with basic buy-one-get-one-free rules, this data is accurately input into the rebate calculation model. This rebate calculation model integrates advanced machine learning algorithms and rule-based engine technology. The model first uses machine learning algorithms to conduct in-depth analysis of a large amount of historical transaction data, identifying patterns in rebate payments corresponding to different products and purchase amounts. For example, this analysis may reveal that purchases of a particular brand of alcoholic beverages have a relatively high rebate percentage, or that rebate amounts significantly increase when purchase amounts reach a certain threshold. Then, using rule-based engine technology, the mall's pre-set rebate rules are analyzed and executed, combining the discovered patterns with the pre-set rules to generate a table of basic rebate rules. This process is carried out strictly in accordance with step S211 of claim 4. From data acquisition and preprocessing to model calculation, every step is meticulously designed and rigorously controlled to ensure the accuracy and rationality of the calculation of basic rebate rules, providing a basic rule framework for the entire rebate calculation. For example, in the "My Wine Red Envelope" campaign, the free delivery credit associated with a purchase can be counted as part of the basic rebate, and the rebate amounts in different scenarios are calculated based on relevant rules.

[0096] Based on the user behavior data obtained from the user relationship system, combined with order-related data, and in accordance with the rebate rules associated with user behavior, these data are accurately input into the rebate calculation model to obtain a user behavior-related rebate table. For example, in the "My Wine Red Packet" activity, when a user successfully shares a wine red packet and enables a friend to redeem it, the sharer can receive a pick-up quota reward of 10 yuan for the payment amount. This calculation result is obtained through the user behavior-related rebate calculation. This process fully considers the impact of user behavior on rebates, and closely links user interaction behavior with rebates. It can not only motivate users to actively participate in mall interactive activities, but also further enhance user stickiness and activity. This process complies with the relevant requirements of S212 in claim 4. From data collection to model calculation, it revolves around user behavior, ensuring the accuracy and effectiveness of the user behavior-related rebate calculation.

[0097] Based on the shopping transaction data obtained from the rebate calculation system, combined with order-related data, data calculations are performed according to the rebate rules at the order level to obtain the order-level rebate details. For example, in the processing of orders related to the "My Wine Red Packet" activity, it is necessary to calculate and record the rebate situation of each order at different levels in detail, including the basic rebate of the order, the additional rebate generated by user behavior, etc. For example, an order may receive a basic rebate because the user's purchase amount reaches a certain level, and at the same time, receive an additional rebate because the user participates in the "My Wine Red Packet" activity, such as sharing wine red packets, successfully redeeming wine red packets, etc. Through the order-level rebate calculation, the rebate situation of each order at different levels can be clearly defined, and the rebate calculation results can be refined to make the rebate calculation more accurate and transparent. This process complies with the provisions of S213 in claim 4. From the data source to the calculation rules, it is strictly implemented as required to ensure the accuracy and reliability of the order-level rebate calculation.

[0098] The rebate calculation model uses different weighting factors to adjust the importance of shopping transaction data, user behavior data, and order-related data in the rebate calculation. These weighting factors are not static and can be dynamically adjusted regularly or irregularly based on the mall's marketing strategy. For example, during the "My Wine Red Packet" campaign, the weighting of user behavior data can be appropriately increased to encourage greater participation. During normal times, the weighting of shopping transaction data can be adjusted based on product sales and profit margins. By adjusting the weighting factors, the flexibility and adaptability of the rebate calculation can be ensured, making the rebate results more consistent with the mall's operational needs and in accordance with the provisions of Claim 9.

[0099] Example 4

[0100] like Figure 5 As shown, another embodiment provided by the present invention is a tiered rebate method based on the buy-one-get-one-free shopping gameplay in the mall, which builds a complete rebate system and conducts verification and auditing:

[0101] Building a complete rebate system is the key to ensuring that rebate work is carried out accurately and orderly. The rebate system includes several important components:

[0102] Detailed information should be kept on basic rebate rules, including the rebate ratio for different products and the relationship between purchase amount and rebate amount. For example, in the context of the "My Wine Red Envelope" campaign, rules such as free pick-up quotas for products within the campaign should be fully considered. For example, different rebate quotas may apply to products in different price ranges. This information forms the basis for rebate calculations. When constructing a basic rebate rules table, factors such as the mall's product variety, pricing structure, profit margins, and market competition should be fully considered to ensure the rationality and fairness of the rules.

[0103] By linking various user behaviors, such as sharing a wine red envelope, inviting friends to register, and participating in the "My Wine Red Envelope" campaign, with commission rebates, we clearly reflect the impact of user behavior on commission rebates. This table allows users to intuitively see the commission rewards associated with each behavior. For example, if a wine red envelope is successfully shared and redeemed by a friend, a 10 yuan delivery credit will be awarded. This incentivizes users to actively participate in various interactive activities, improving user activity and loyalty.

[0104] Detailed records should be kept of each order's commission rebate at different levels, including the order's base commission and additional commissions generated by user behavior. For example, for orders related to the "My Wine Red Packet" campaign, changes in commissions resulting from wine redemption, user sharing, and other behaviors should be clearly recorded. This ensures that users and mall managers clearly understand the commission structure for each order, ensuring transparency and traceability of commission calculations.

[0105] Aggregate and compile rebate data to facilitate viewing of overall rebate status. Through rebate summary reports, for example, mall managers can quickly understand the total rebate amount related to the "My Wine Red Packet" activity over a period of time, the rebate status of different products, and the rebate distribution of different user groups, providing data support for mall operational decisions.

[0106] Keep detailed records of the commission handling process and results for abnormal orders to facilitate tracing and inquiry. For example, during the "My Wine Red Envelope" campaign, abnormal orders may occur, such as errors in wine redemption order information or user disputes over commissions. Detailed records must be kept of the commission handling process for these orders to ensure a clear understanding of the handling process and results during subsequent audits and inquiries.

[0107] Real-time tracking of user rebate benefits, such as used rebate amount and remaining rebate amount. For example, in the "My Wine Red Packet" activity, the amount of goods received by users is considered as part of the rebate benefit. This table allows users to understand the status of their rebate benefits at any time, and also facilitates the management of the mall to manage and monitor the user's rebate benefits.

[0108] This rebate system encompasses the key tables mentioned in Claim 3, ensuring the integrity and traceability of rebate information. From foundational rule development to user behavior correlation, to detailed order-level records and the establishment of various reports and tracking tables, each component is closely linked to form a complete and efficient rebate system.

[0109] After the rebate system is established, in order to ensure the accuracy and fairness of the rebate calculation, verification and audit steps of the rebate results must be performed.

[0110] Daily, we randomly select a certain percentage of commission data for inspection. For example, when sampling commission data related to the "My Wine Red Packet" campaign, we must ensure that the sample is both random and representative to ensure reliable inspection results. By sampling a portion of the commission data for detailed inspection, we can promptly identify potential issues in commission calculations, such as the accuracy of commission calculations for wine redemption redemptions, without affecting overall work efficiency.

[0111] Calculated commission data is carefully compared with the expected results calculated according to pre-set rules. For example, in the "My Wine Red Envelope" campaign scenario, data comparison can directly identify discrepancies between the calculated and expected commission results, such as whether the purchase quota reward received by users after sharing the wine red envelope complies with the rules. If a discrepancy is found, the cause is immediately identified and adjustments are made through data analysis and algorithm bug detection. Data comparison is a key step in verifying the accuracy of commission calculations, requiring the use of professional data analysis tools and methods to ensure accurate and reliable comparison results.

[0112] After completing the verification and audit, a rebate audit report is generated. This report details the verification and audit process and results, including sample data, comparison results, identified issues, and solutions. For example, in the rebate audit involving the "My Wine Red Envelope" campaign, this report not only provides a basis for subsequent inquiries and tracing, but also helps mall managers summarize lessons learned, continuously optimize the rebate system and calculation methods, and ensure the transparency and traceability of the rebate process, thus complying with the requirements for rebate result verification and auditing as set out in Claim 10.

Claims

1. A tiered commission rebate method based on the buy-one-get-one-free shopping method in a shopping mall, characterized in that: The following steps are involved: S1. Build a comprehensive data collection system and collect data sources extensively: S11. Obtain data sources from the mall transaction system, rebate calculation system, order management system, and user relationship system, and additionally collect relevant data from the marketing activity management system and user evaluation feedback system; S12. Ensure that the data sources collected cover multi-dimensional information such as shopping transaction data, user behavior data, order-related data, marketing activity data, and user feedback data; S2. Data processing and synchronization based on a hierarchical management model: S21. Classify and integrate the various data sources collected, and synchronize them to the data core processing platform with high concurrent processing capabilities through a secure and stable data transmission channel; S22. During the data synchronization process, preliminary verification and marking of the data are performed to lay the foundation for subsequent accurate processing; S23. Utilize a proprietary rebate calculation model that integrates machine learning algorithms and rule engine technology within the data core processing platform; On the one hand, we use machine learning algorithms to conduct in-depth analysis of historical data to uncover potential connections and patterns between data. On the other hand, we use rule engine technology to parse and execute the mall's preset rebate rules, comprehensively processing all types of data to accurately map rebate rules to specific user behaviors and orders. S3. After completing the rebate calculation, build a complete rebate system and conduct verification and audit: S31. Build a complete rebate system that includes not only a basic rebate rules table, a user behavior-related rebate table, and order-level rebate details, but also rebate summary reports, abnormal order rebate processing records, and user rebate rights status tracking tables to ensure the integrity and traceability of rebate information. S32. After the rebate system is established, the verification and audit steps of the rebate results will be carried out. Through sampling inspection and data comparison, the accuracy and fairness of the rebate calculation will be ensured. At the same time, a rebate audit report will be generated and archived for subsequent query and tracing.

2. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: The shopping transaction data is obtained from the mall transaction system and the rebate calculation system, the user behavior data is obtained from the user relationship system, and the order-related data is obtained from the order management system; wherein, the shopping transaction data provides basic data support for subsequent rebate calculations, the user behavior data will be used for subsequent processing of rebate rules associated with user behavior, and the order-related data provides key information of the order dimension for each rebate calculation link.

3. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: The rebate system includes the following key tables: The basic rebate rule table is used to store basic rebate rule information and provide the basic rule basis for the entire rebate calculation; User behavior-related rebate table, which links user behavior with rebates and reflects the impact of user behavior on rebates; Order level rebate details, detailed record of rebate status of orders at different levels, and detailed rebate calculation results.

4. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: The step S2 comprises the following steps: S211. For the shopping transaction data obtained from the mall transaction system, combined with order-related data, data segmentation and rebate calculation are performed according to the basic buy-one-get-one-free rules to obtain a basic rebate rules table; S2111. Preprocess the order-related data and the shopping transaction data obtained from the mall transaction system simultaneously, including data extraction, format conversion, data cleaning, and data loading operations, to obtain corresponding order details tables and transaction data tables; S2112. After synchronizing the order details table and transaction data table to the data core processing platform, input them into the rebate calculation model in the platform to obtain the basic rebate rule table; S212: Process the user behavior data obtained from the user relationship system in combination with order-related data according to the rebate rules associated with user behavior to obtain a user behavior-associated rebate table; S2121. Preprocess the order-related data separately, including data extraction, format conversion, data cleaning, and data loading operations, to obtain the corresponding order details table; S2122. After synchronizing the order details table and the user behavior data obtained from the user relationship system to the data core processing platform, the data is input into the rebate calculation model in the platform to obtain a user behavior-related rebate table; S213. Calculate the shopping transaction data obtained from the rebate calculation system in combination with the order-related data according to the order-level rebate rules to obtain order-level rebate details. S2131. Preprocess the order-related data and the shopping transaction data obtained from the rebate calculation system simultaneously, including data extraction, format conversion, data cleaning, and data loading operations, to obtain the corresponding order details table and rebate calculation data table; S2132. After synchronizing the order details table and the rebate calculation data table to the data core processing platform, input them into the rebate calculation model in the platform to obtain the order-level rebate details.

5. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 4, characterized in that: The step S211 specifically includes the following steps: S2111. Perform the following pre-processing operations on the order-related data and the shopping transaction data obtained from the mall transaction system: Data extraction: extracting required information from raw data; Format conversion: convert the extracted data into a format suitable for subsequent calculation and storage; Data cleaning: remove noise and erroneous data; Data loading: loading the processed data into the corresponding storage structure; After the above preprocessing operations, the order details table and transaction data table are obtained; S2112. Synchronize the order details table and transaction data table to the data core processing platform, input the rebate calculation model, and generate the basic rebate rule table based on the internal algorithm and basic buy-one-get-one-free rules.

6. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: The transaction data table includes historical transaction data and daily new transaction data, and the basic rebate rule table includes initial rebate rule data and daily updated rebate rule data; Among them, the historical transaction data stores past transaction information and provides a historical basis for rebate calculation. The daily new transaction data records new transaction information that occurs every day to ensure the timeliness and integrity of the data. The initial rebate rule data provides the starting rule setting for the rebate calculation. The daily updated rebate rule data provides daily updated rebate rule information based on the mall operation needs, so that the rebate calculation can be carried out according to the latest rules.

7. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 4, characterized in that: The step S212 specifically includes the following steps: S2121. Perform the following pre-processing operations on the order-related data: Data extraction: extract useful information from order-related data; Format conversion: convert order-related data into a unified format; Data cleaning: ensure data accuracy; Data loading: store the processed data into the corresponding structure; After the above preprocessing operations, the order details table is obtained; S2122. Synchronize the order details table and the user behavior data obtained from the user relationship system to the data core processing platform, input the rebate calculation model, and generate a user behavior-related rebate table based on the rebate rules associated with the user behavior.

8. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 4, characterized in that: The step S213 specifically includes the following steps: S2131. Perform the following pre-processing operations on the order-related data and the shopping transaction data obtained from the rebate calculation system: Data extraction: extracting required information from raw data; Format conversion: converting data into a suitable format; Data cleaning: remove errors and noise from the data; Data loading: loading the processed data into the storage structure; After the above pre-processing operations, the order details table and rebate calculation data table are obtained; S2132. Synchronize the order details table and the rebate calculation data table to the data core processing platform, input the rebate calculation model, and use the model to generate order-level rebate details based on the order-level rebate rules.

9. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: In the rebate calculation model, different weight coefficients are set to adjust the importance of shopping transaction data, user behavior data and order-related data in the rebate calculation, and the weight coefficients can be dynamically adjusted regularly or irregularly according to the mall marketing strategy; Among them, the setting of these weight coefficients is intended to reflect the mall's emphasis on different data at different stages or under different marketing strategies. By adjusting the weight coefficients, the flexibility and adaptability of the rebate calculation can be guaranteed, making the rebate results more in line with the mall's operational needs.

10. A tiered commission rebate method based on a buy-one-get-one-free shopping game in a shopping mall according to claim 1, characterized in that: After the commission rebate system is built, it also includes verification and auditing steps for the commission rebate results. Through sampling inspection and data comparison, the accuracy and fairness of the commission rebate calculation are ensured. At the same time, a commission rebate audit report is generated and archived for subsequent query and tracing; Among them, the sampling inspection is carried out by extracting some rebate data samples, and the data comparison compares the calculated rebate data with the expected results or historical data to ensure the accuracy of the rebate calculation. The generated rebate audit report records the verification and audit process and results, providing a basis for subsequent inquiries and tracing, and ensuring the transparency and traceability of the rebate process.

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