A coupon management method, a management system, a storage medium, and a program product

Abstract

The application relates to the technical field of coupon data management, in particular to a coupon management method and system, a storage medium and a program product. The method comprises the following steps: determining a use effective value of a user for a currently issued coupon in a current stage based on static behavior data and dynamic behavior data of the user for the currently issued coupon in the current stage; when the use effective value is lower than a preset effective threshold value, determining optimization parameters based on the static behavior data and the dynamic behavior data, and optimizing original coupon data to obtain target coupon data based on the optimization parameters; generating a coupon issuing detection condition based on the target coupon data, and performing standard detection on an actually issued coupon of the user in a next stage based on the coupon issuing detection condition, wherein the target coupon data comprises a target coupon use effective period value and a target coupon face value limit value; and when a detection result of the actually issued coupon is non-compliance, generating issuing adjustment information based on the target coupon data. The application facilitates improvement of coupon issuing accuracy.

Description

Technical Field

[0001] This application relates to the field of coupon data management technology, and in particular to a coupon management method, management system, storage medium, and program product. Background Technology

[0002] As online and offline consumption scenarios become increasingly integrated, coupons have become a core marketing tool for platforms and merchants to enhance user activity, drive sales conversion, and revitalize existing users. Scientific and efficient coupon management can accurately reach target users, reduce marketing resource waste, improve the user experience, and enhance user stickiness. It can also standardize the entire process of coupon issuance, approval, redemption, and expiration, avoiding problems such as uncontrolled marketing costs, user rights disputes, and low operational efficiency. In digital operation scenarios, coupons cover multiple aspects such as new user acquisition, member repurchase, holiday promotions, and store traffic generation. Without refined management, a large number of coupons may become idle and expire, marketing investment may not match conversion returns, and users may have a weak awareness of coupons. Therefore, implementing full lifecycle management of coupons is of significant practical necessity.

[0003] Conventional coupon management methods typically categorize users into rough levels based on their historical spending amounts and push similar coupons. However, key parameters such as coupon face value, validity period, and applicable rules often adopt a uniform and fixed model. They do not differentiate and dynamically adjust based on users' current actual spending behavior, dynamic spending intentions, and the actual usage effect of the coupons. Therefore, inaccurate coupon distribution may lead to low effectiveness of the coupons used by users. Summary of the Invention

[0004] To improve the accuracy of coupon distribution and thus enhance the effectiveness of users' use of issued coupons, this application provides a coupon management method, management system, storage medium, and program product.

[0005] Firstly, this application provides a card / coupon management method, which adopts the following technical solution: A method for managing coupons, comprising: Based on the user's static and dynamic behavior data regarding the coupons issued in the current stage, the effective value of the user's use of the issued coupons in the current stage is determined. The static behavior data includes the user's historical consumption behavior data and historical coupon usage data, and the dynamic behavior data includes the user's dynamic consumption behavior data and dynamic coupon interaction data in the current stage. When the effective value is lower than the preset effective threshold, optimization parameters are determined based on the static behavior data and the dynamic behavior data, and the original coupon data is optimized based on the optimization parameters to obtain the target coupon data. The optimization parameters include the validity period optimization parameters and the face value optimization parameters. Based on the target coupon data, coupon issuance detection conditions are generated, and based on the coupon issuance detection conditions, the actual coupons issued to the user in the next stage are standardized and detected. The target coupon data includes the target coupon validity period value and the target coupon face value limit. When the detection result of the actual issued coupons is non-compliant, issuance adjustment information is generated based on the target coupon data, and the issuance adjustment information is fed back.

[0006] By adopting the above technical solution and analyzing user static and dynamic behavior data, it is possible to intuitively and comprehensively quantify the effective value of users' use of coupons issued in the current stage. When the effective value of use is low, corresponding optimization parameters can be determined in a timely manner to optimize the original coupon data. Based on the target coupon data, exclusive issuance detection conditions are generated to conduct real-time standardization detection on the coupons actually issued in the next stage. When non-compliance is detected, adjustment information is generated and fed back based on the target coupon data, which facilitates timely correction of the issuance strategy and avoids the re-issuance of invalid coupons. By improving the accuracy of coupon issuance, the effectiveness of users' use of issued coupons is improved.

[0007] In one possible implementation, determining the usage validity period optimization parameters based on the static behavior data and the dynamic behavior data includes: Identify target behavior node data from the dynamic behavior data. The target behavior node data includes target behavior nodes and behavior node values ​​for each target behavior node. The target behavior nodes include idle coupon nodes, viewing frequency nodes, decision cycle nodes, and consumption behavior gap nodes. Based on the behavior node value and preset behavior weight of each target behavior node, the validity period adjustment coefficient is determined; Based on the static behavioral data, a similarity analyst is determined for the user. The similarity analyst's effective value for similar use of similar issued coupons is higher than the preset effective threshold at the current stage. Obtain similar behavior node data of the similar analysts, and determine the influence of core abnormal nodes and the influence of extreme value abnormal nodes based on the similar behavior node data and the target behavior node data; The validity period adjustment coefficient, the impact degree of the core abnormal node, and the impact degree of the extreme value abnormal node constitute the validity period optimization parameters.

[0008] By adopting the above technical solution, the target behavior node data corresponding to idle coupon nodes, viewing frequency nodes, decision cycle nodes, and consumption behavior gap nodes are accurately identified from dynamic behavioral data. This facilitates the precise capture of users' core behavioral characteristics regarding coupon validity periods at the current stage. Combining each behavior node value with preset behavior weights, an validity period adjustment coefficient is determined, which quantifies the impact of user behavior on coupon validity periods. Then, based on static behavioral data, similar analysts who meet the validity value criteria are located. By comparing the similar behavior node data of similar analysts with the user's own target behavior node data, the core and degree of abnormality in user behavior related to coupon validity periods can be clearly defined. Finally, the validity period optimization parameters are composed of the validity period adjustment coefficient, the impact of core abnormal nodes, and the impact of extreme value abnormal nodes. This provides accurate, comprehensive, and quantifiable support for subsequent targeted optimization of coupon validity periods, ensuring that the optimized coupon validity period is highly compatible with the user's current consumption decision cycle and coupon usage habits, further improving the rationality and accuracy of coupon validity period optimization.

[0009] In one possible implementation, determining the face value optimization parameters based on the static behavior data and the dynamic behavior data includes: Identify target consumption node data from the dynamic behavior data. The target consumption node data includes target consumption nodes and consumption node values ​​for each target consumption node. The target consumption node includes consumption amount node, redemption abandonment rate node, and amount matching node. Based on the consumption node value and preset consumption weight of each target consumption node, the face value adjustment coefficient is determined; Based on the similar behavior node data and the target behavior node data, a step-wise optimization threshold is determined. The face value adjustment coefficient, the step optimization threshold, the influence degree of the core abnormal node, and the influence degree of the extreme value abnormal node constitute the face value optimization parameters.

[0010] By adopting the above technical solution, the target consumption node data corresponding to consumption amount nodes, redemption abandonment rate nodes, and amount matching nodes are accurately identified from dynamic behavioral data. This facilitates the effective capture of the adaptation characteristics between users' current consumption capacity and coupon face value. Combining the values ​​of each consumption node with preset consumption weights, the face value adjustment coefficient is determined, which facilitates the quantification and objectification of the basis for face value optimization. This avoids the poor adaptability problem caused by the reliance on experience in traditional face value settings. In addition, by combining effective behavioral data of similar users with users' own behavioral data to determine the tiered optimization threshold, it is easy to reasonably divide the trigger boundary of face value optimization. This ensures that face value adjustment is only initiated when the degree of abnormality is sufficiently significant, balancing optimization effect and operational cost control. Finally, the face value optimization parameters are composed of the face value adjustment coefficient, tiered optimization threshold, core abnormal node influence degree, and extreme value abnormal node influence degree. This provides a complete and reliable quantitative basis for the differentiated and tiered optimization of coupon face value, making the optimized coupon face value more in line with users' actual consumption level and usage intention, further improving the accuracy of coupon distribution and the effectiveness of user usage.

[0011] In one possible implementation, the step of optimizing the original coupon data based on the optimization parameters to obtain the target coupon data includes: Identify the validity period optimization parameter in the optimization parameters, and optimize the original validity period of the original coupon data based on the validity period optimization parameter to obtain the target validity period of the coupon; Identify the face value optimization parameter in the optimization parameters, and optimize the original face value of the original card in the original card data based on the face value optimization parameter to obtain the target face value of the card; The target coupon's validity period and face value constitute the target coupon data.

[0012] By adopting the above technical solutions, the validity period of the original coupons is adjusted based on the validity period optimization parameters, so that the optimized validity period of the target coupons is more in line with the user's consumption decision-making rhythm and behavior habits. The face value of the original coupons is optimized based on the face value optimization parameters, so that the face value of the target coupons is highly matched with the user's actual consumption ability and consumption intention.

[0013] In one possible implementation, optimizing the original coupon validity period in the original coupon data based on the validity period optimization parameters to obtain the target coupon validity period includes: The validity period adjustment coefficient, the impact degree of the core abnormal node, the impact degree of the extreme value abnormal node, and the original validity period of the coupon are imported into the preset coupon validity period calculation formula to obtain the target coupon face value. The preset coupon validity period calculation formula is as follows: ; Among them, T 目标 The validity period for the target coupon; T 原始 The original validity period of the coupon; θ is the validity period adjustment factor; I represents the impact degree of the core abnormal node; I max This represents the impact degree of the extreme value outlier node.

[0014] By adopting the above technical solution, the calculation formula can fully combine the degree of user behavior abnormality with coefficient weights for dynamic correction, making the determination of the validity period of the target coupon more objective and consistent, avoiding arbitrariness and deviation caused by human adjustment. Through the linkage calculation of influence degree and adjustment coefficient, the validity period can be adaptively shortened or adapted according to the actual degree of user abnormality, making the coupon validity period more in line with the user's real consumption cycle and usage habits, further improving the matching degree between coupons and user behavior, and effectively reducing the coupon idle rate and expiration rate.

[0015] In one possible implementation, optimizing the original card face value in the original card data based on the face value optimization parameters to obtain the target card face value includes: The face value adjustment coefficient, the step optimization threshold, the core anomaly node influence, the extreme value anomaly node influence, and the original card face value are imported into a preset card face value calculation formula to obtain the target card face value. The preset card face value calculation formula is as follows: ; Among them, M 目标 The target coupon value; M 原始 The original face value of the card / coupon; θ m This is the face value adjustment factor; λ is the step-optimization threshold.

[0016] By adopting the above technical solution, the system intelligently determines whether to adjust the face value of coupons based on whether the impact of core abnormal nodes reaches the tiered optimization threshold. This avoids ineffective optimization and waste of marketing resources under minor abnormalities. At the same time, when the degree of abnormality is significant, the face value of coupons is dynamically adjusted by combining coefficients, making the optimization logic more in line with actual operational needs. The target face value of coupons is calculated through a quantitative formula, which can make the face value setting highly matched with the user's spending power and the degree of abnormal behavior, reducing the phenomenon of redemption abandonment and unused coupons due to mismatched face values, and further improving the effectiveness of coupon usage.

[0017] In one possible implementation, the method further includes: The user conversion rate is determined based on the increase in the effective value of the coupons issued in the previous stage by the user in two adjacent stages. Based on the user's static behavior data in two adjacent phases, the user's predicted consumption content is determined, and suitable coupons are determined based on the predicted consumption content. Based on the user conversion rate, the validity period of the adapted coupon is extended to optimize the validity period, and regular reminders are sent to the user based on the optimized validity period.

[0018] By adopting the above technical solution, the user conversion rate is determined based on the increase in the effective value of coupon usage between two adjacent stages. This objectively reflects the increase in users' willingness to consume and their conversion potential. By mining users' predicted consumption content based on static behavioral data from adjacent stages and matching it with corresponding suitable coupons, the precise matching of coupons and users' potential consumption needs can be achieved. This further enhances the targeting and attractiveness of coupon distribution. Combined with the user conversion rate, the validity period of suitable coupons is reasonably extended to obtain an optimized validity period that better fits the user's conversion cycle. This effectively reduces the situation where users abandon using coupons due to excessively tight expiration dates. At the same time, based on the optimized validity period, regular reminder messages are pushed to users to reduce the number of coupons that expire due to users forgetting to use them, thereby improving the effectiveness of coupon usage.

[0019] Secondly, this application provides a management system, which adopts the following technical solution: A management system comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described coupon management method.

[0020] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and execute the above-described coupon management method.

[0021] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned coupon management method.

[0022] In summary, this application includes at least one of the following beneficial technical effects: By analyzing static and dynamic user behavior data, it is possible to intuitively and comprehensively quantify the effective usage value of coupons issued in the current stage. When the effective usage value is low, corresponding optimization parameters can be determined in a timely manner to optimize the original coupon data. Based on the target coupon data, specific issuance detection conditions are generated to conduct real-time standardization detection on the coupons actually issued in the next stage. When non-compliance is detected, adjustment information is generated and fed back based on the target coupon data, which facilitates timely correction of the issuance strategy and avoids the re-issuance of invalid coupons. By improving the accuracy of coupon issuance, the effectiveness of users' use of issued coupons is improved.

[0023] By determining the user conversion rate based on the increase in the effective value of coupon usage between two adjacent stages, it is possible to objectively reflect the increase in users' willingness to consume and their conversion potential. Based on the static behavioral data of adjacent stages, predictive consumption content of users is mined and matched with corresponding suitable coupons, which can achieve precise matching between coupons and users' potential consumption needs, further improving the targeting and attractiveness of coupon distribution. Combined with the user conversion rate, the expiration period of suitable coupons is reasonably extended to obtain an optimized expiration period that is more in line with the user's conversion cycle. This can effectively reduce the situation where users abandon coupons due to the expiration date being too tight. At the same time, based on the optimized expiration period, regular reminder messages are pushed to users to reduce the idle expiration caused by users forgetting to use coupons, thereby improving the effectiveness of coupon usage. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a coupon management method according to an embodiment of this application; Figure 2 This is a schematic diagram of a process for determining periodic reminder information in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a management system according to an embodiment of this application. Detailed Implementation

[0025] The following is in conjunction with the appendix Figures 1 to 3 This application will be described in further detail.

[0026] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0029] Specifically, this application provides a coupon management method executed by a management system, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this connection.

[0030] refer to Figure 1 , Figure 1 This is a flowchart illustrating a card and coupon management method according to an embodiment of this application. The method includes steps S110-S140, wherein: Step S110: Based on the user's static and dynamic behavior data on the coupons issued in the current stage, determine the effective value of the user's use of the coupons issued in the current stage. The static behavior data includes the user's historical consumption behavior data and historical coupon usage data, and the dynamic behavior data includes the user's dynamic consumption behavior data and dynamic coupon interaction data in the current stage.

[0031] Specifically, issued coupons refer to coupons that have been approved, generated, and pushed to users or made available for collection by the operator or merchant according to preset issuance rules. These coupons are already viewable, collectable, and usable by users, including but not limited to member-specific coupons, promotional coupons, and customer service compensation coupons. The current stage can be considered the coupon issuance period, which can be 30 or 40 days. The specific duration is not specifically limited in this application embodiment. Each issued coupon has an activity validity period, i.e., the time period during which it can be collected. For example, a coupon set to be valid from August 1, 2023 to August 10, 2023 means that users can collect the coupon between August 1 and August 10. A usable validity period can also be set for the coupon, i.e., the time period during which it can be used. For example, a coupon's usable validity period is 7 days, meaning the coupon's usable validity period is 7 days from the date of collection. In addition, you can set the number of times a user can receive the card, whether it is available on the day of receipt, whether the card can be stacked within the same batch, whether it can be stacked across batches, and the maximum number of times a single user can use the card per day. The specific distribution rules can be set by the relevant operators or merchants according to their actual needs.

[0032] Static behavioral data refers to relatively stable and traceable historical behavioral data formed before the current stage. It serves as the basic benchmark for judging users' consumption / coupon usage habits. This includes, but is not limited to, users' historical consumption behavior data and historical coupon usage data. Historical consumption behavior data can refer to data such as users' consumption amount, consumption frequency, consumption category, consumption time, average order value, and preferred stores over a certain period. Historical coupon usage data can refer to users' records of past coupon redemption / expiration, including dimensions such as historical coupon redemption rate, idle rate, expiration rate, average coupon usage duration, and historical coupon face value / expiration period compatibility. Dynamic behavioral data refers to real-time changing behavioral data generated by users in the current stage, reflecting their current consumption intentions and coupon interaction status. This includes, but is not limited to, dynamic consumption behavior data and dynamic coupon interaction data. Dynamic consumption behavior data can refer to users' current consumption intentions (e.g., browsing unordered goods / services), temporary consumption preferences (e.g., recent additions to coffee purchases), fluctuations in spending amounts, and uncompleted payment / redemption behaviors. Dynamic coupon interaction data can refer to users' actions on issued coupons in the current stage, including coupon viewing frequency, idle time after redemption, time spent on the redemption page, redemption abandonment rate, and coupon sharing / transfer behavior. Static and dynamic behavioral data of users can be extracted from the platform's database.

[0033] The system can quantify and score static and dynamic behavioral data based on preset scoring rules to obtain the effective value of a user's use of issued coupons in the current stage. The preset scoring rules include the dimension scoring rules corresponding to each evaluation dimension. For example, the dimension scoring rule corresponding to the coupon viewing dimension is 10 points for viewing ≥2 times per day and 0 points for viewing 0 times; the dimension scoring rule corresponding to the redemption abandonment rate dimension is 10 points for an abandonment rate of 0% and 0 points for an abandonment rate of ≥50%. The preset scoring rules can be determined by relevant staff based on experimental data and uploaded to the management system in advance. The specific content is not specifically limited in this application embodiment. The dimension score corresponding to each dimension in the static and dynamic behavioral data is obtained. The summation of all dimension scores can be used to calculate the effective value of a user's use of issued coupons in the current stage. The higher the effective value, the stronger the user's willingness to use the issued coupons in the current stage and the higher the degree of fit between the issued coupons and the user's consumption behavior and consumption needs.

[0034] Step S120: When the valid value used is lower than the preset valid threshold, the optimization parameters are determined based on static behavior data and dynamic behavior data, and the original card data is optimized based on the optimization parameters to obtain the target card data. The optimization parameters include the validity period optimization parameters and the face value optimization parameters.

[0035] Specifically, when the calculated effective value is lower than the preset effective threshold, it indicates that the coupons issued at the current stage are poorly matched with user needs and users have a low willingness to use them. At this time, it is necessary to optimize and adjust the relevant parameters of the coupons to improve the subsequent coupon issuance and usage effect. The preset effective threshold can be set by the relevant operators or merchants based on the operation goals, coupon types and historical operation data. It is the minimum score required to judge whether the effectiveness of coupon usage meets the standard.

[0036] The original coupon data can be extracted from the preset issuance rules corresponding to the coupons already issued in the current stage. This includes the original validity period optimization parameters and the original face value optimization parameters. Optimization parameters can be obtained by analyzing static and dynamic behavioral data. These optimization parameters are used to quantitatively correct and adaptively adjust the original coupon data, including the validity period optimization parameters and the face value optimization parameters. The validity period optimization parameters are mainly determined based on behavioral nodes such as the user's coupon idle status, viewing frequency, consumption decision cycle, and consumption behavior gaps in the current stage. They are used to characterize the degree of mismatch between the coupon validity period and user behavior habits. The face value optimization parameters are determined based on consumption nodes such as user consumption amount, redemption abandonment, and the degree of match between coupon face value and consumption amount. They are used to characterize the degree of mismatch between coupon face value and user spending power.

[0037] Furthermore, to improve the optimization rationality and accuracy of coupon validity period, the method provided in this application embodiment determines the optimization parameters for validity period based on static and dynamic behavioral data, which may specifically include: Identify target behavior node data from dynamic behavior data. Target behavior node data includes target behavior nodes and their respective behavior node values. Target behavior nodes include idle coupon nodes, viewing frequency nodes, decision cycle nodes, and consumption behavior gap nodes. Determine the validity period adjustment coefficient based on the behavior node value of each target behavior node and a preset behavior weight. Identify similar analysts for each user based on static behavior data. These analysts must have a similar usage validity value for similar issued coupons that exceeds a preset validity threshold at the current stage. Obtain similar behavior node data from these analysts and determine the impact of core abnormal nodes and extreme value abnormal nodes based on this data and the target behavior node data. The validity period adjustment coefficient, the impact of core abnormal nodes, and the impact of extreme value abnormal nodes constitute the validity period optimization parameters.

[0038] Specifically, target behavior node data and the node data value corresponding to each target behavior node data can be identified from dynamic behavior data based on a preset feature recognition algorithm. Then, all node data values ​​are normalized and quantized to obtain the behavior node value of each target behavior node. The specific preset feature recognition algorithm and normalization and quantization algorithm are not specifically limited in this application embodiment. Target behavioral nodes include, but are not limited to, coupon idleness nodes, viewing frequency nodes, decision-making cycle nodes, and consumption behavior gap nodes. Specifically, the coupon idleness node reflects the degree of user idleness of issued coupons at the current stage; a higher value indicates lower user attention and weaker willingness to use the coupons. The viewing frequency node represents the user's level of attention to issued coupons and their willingness to actively check them at the current stage; a higher value indicates stronger user awareness and a clearer intention to use the coupons. The decision-making cycle node represents the time it takes for a user to make a decision from generating a consumption intention to completing the consumption / redemption at the current stage; a lower value indicates a slower decision-making speed, a longer decision time, a greater mismatch between the coupon's validity period and the user's decision-making rhythm, and a higher likelihood of failure to redeem due to insufficient validity. The consumption behavior gap node represents the continuous duration of no effective consumption behavior at the current stage; a higher value indicates lower user activity and a lower probability of coupons being used.

[0039] Each target behavior node corresponds to a preset behavior weight, which can be determined by relevant staff based on historical experimental data and uploaded to the management system in advance. The total behavior score is calculated by weighted summation based on the behavior node value of each target behavior node and the corresponding preset behavior weight. Then, the corresponding validity period adjustment coefficient is determined according to the preset behavior score mapping relationship. The preset behavior score mapping relationship is the correspondence between the total behavior score and the validity period adjustment coefficient. The specific content is not specifically limited in this application embodiment.

[0040] Core feature dimensions can be extracted from static behavioral data based on a preset feature recognition algorithm. For example, static behavioral data includes multiple dimensions such as historical coupon redemption rate, idle rate, expiration rate, average coupon usage duration, and historical coupon value / expiration period compatibility. The core feature dimensions can be historical coupon redemption rate, idle rate, and expiration rate. The specific extraction method for these core feature dimensions can be set by relevant personnel according to actual needs. The user pool can be traversed based on these core feature dimensions to obtain the corresponding similarity analysts for each user. The feature matching degree between the similarity analyst's similarity feature dimensions and the user's core feature dimensions must be higher than a preset matching degree threshold. To improve the effectiveness of the similarity comparison process, it is necessary to ensure that the similarity analyst's valid similarity usage of coupons issued to them in the current stage is higher than a preset valid threshold.

[0041] After identifying similar analysts, similar behavioral node data of these analysts can be determined based on the aforementioned method for determining target behavioral node data. The two data points are then compared to determine the node difference between each target behavioral node and its corresponding similar behavioral node. The target behavioral node with the largest node difference is identified as the core anomalous node. The core anomalous node influence is then determined based on this largest node difference and a first preset influence mapping relationship. Behavioral node values ​​for all target behavioral nodes are identified, and the target behavioral node with the largest value is identified as the extreme value anomalous node. The extreme value anomalous node influence is then determined based on this largest value and a second preset influence mapping relationship. The first preset influence mapping relationship is the correspondence between node differences and core anomalous node influence, and the second preset influence mapping relationship is the correspondence between behavioral node values ​​and extreme value anomalous node influence. Specific details are not limited in this embodiment and can be pre-set by relevant personnel based on experimental data. The validity period adjustment coefficient, the core anomalous node influence, and the extreme value anomalous node influence together constitute the validity period optimization parameters.

[0042] Furthermore, to make the optimized card / coupon value more closely match users' actual spending power and usage intentions, the method provided in this application embodiment determines the value optimization parameters based on static and dynamic behavioral data, which may specifically include: Identify target consumption node data from dynamic behavioral data. Target consumption node data includes target consumption nodes and consumption node values ​​for each target consumption node. Target consumption nodes include consumption amount nodes, redemption abandonment rate nodes, and amount matching nodes. Determine the face value adjustment coefficient based on the consumption node value of each target consumption node and the preset consumption weight. Determine the tiered optimization threshold based on similar behavioral node data and target behavioral node data. The face value adjustment coefficient, tiered optimization threshold, influence of core abnormal nodes, and influence of extreme value abnormal nodes constitute the face value optimization parameters.

[0043] Specifically, target consumer node data and the node data value corresponding to each target consumer node can be identified from dynamic behavior data based on a preset feature recognition algorithm. Then, all node data values ​​are normalized and quantized to obtain the consumer node value of each target consumer node. The specific preset feature recognition algorithm and normalization and quantization algorithm are not specifically limited in this embodiment. Target consumption nodes include, but are not limited to, consumption amount nodes, redemption abandonment rate nodes, and amount matching nodes. Among them, the consumption amount node reflects the user's actual consumption level and ability at the current stage. The higher the consumption amount node value, the higher the user's current disposable consumption amount and the stronger their ability to bear the face value of the coupon. The redemption abandonment rate node represents the frequency with which users abandon using the coupon during the redemption process. The higher the consumption amount node value, the higher the probability that the user will terminate the redemption due to reasons such as face value mismatch, and the lower the match between the face value of the coupon and the user's actual needs. The amount matching node represents the degree of fit between the user's actual consumption amount and the coupon usage threshold and face value. The higher the consumption amount node value, the more suitable the face value of the coupon and the usage rules are with the user's current consumption behavior, and the greater the likelihood that the user will use the coupon to complete the redemption.

[0044] Each target consumption node has a preset consumption weight, which can be determined by relevant staff based on historical experimental data and uploaded to the management system in advance. The total consumption score is calculated by weighted summation based on the consumption node value of each target consumption node and the corresponding preset consumption weight. Then, the corresponding face value adjustment coefficient is determined according to the preset consumption score mapping relationship. The preset consumption score mapping relationship is the correspondence between the total consumption score and the face value adjustment coefficient. The specific content is not specifically limited in this application embodiment.

[0045] The target behavior node data and similar behavior node data can be sequentially mapped into discrete data points according to the node order. Corresponding behavior change data lines are then fitted to each data line. A preset curvature calculation algorithm is used to calculate the curvature of each data line at each node position, and the curvature difference between the two data lines is statistically obtained. This curvature difference is matched with a preset curvature interval. A matching threshold is selected from the threshold set corresponding to the preset interval as the step-optimization threshold. The correspondence between the preset curvature interval and the step-optimization threshold can be pre-set by relevant personnel based on experimental data and operational needs. Specific curvature calculation algorithms, data fitting methods, and interval division rules are not specifically limited in this embodiment. The step-optimization threshold is used to determine whether the degree of user behavior abnormality reaches the critical condition requiring face value optimization, thereby achieving step-wise and refined optimization of the card face value and avoiding unnecessary face value adjustments. The face value adjustment coefficient, step-optimization threshold, core abnormal node influence degree, and extreme value abnormal node influence degree together constitute the face value optimization parameters.

[0046] The optimization parameters include validity period optimization parameters and face value optimization parameters. After obtaining the optimization parameters based on the above method, the original coupon data needs to be optimized in a targeted manner based on the optimization parameters. That is, identify the validity period optimization parameter in the optimization parameters, and optimize the original coupon validity period in the original coupon data based on the validity period optimization parameter to obtain the target coupon validity period; identify the face value optimization parameter in the optimization parameters, and optimize the original coupon face value in the original coupon data based on the face value optimization parameter to obtain the target coupon face value; the target coupon validity period and the target coupon face value constitute the target coupon data.

[0047] Specifically, this application adjusts the original validity period of coupons based on validity period optimization parameters to better align the optimized target coupon validity period with users' consumption decision-making rhythm and behavioral habits. The specific implementation method for optimizing the original coupon validity period in the original coupon data based on validity period optimization parameters to obtain the target coupon validity period is as follows: The validity period adjustment coefficient, the impact of core anomaly nodes, and the impact of extreme value anomaly nodes from the validity period optimization parameters, along with the original card validity period, are imported into the preset card validity period calculation formula to obtain the target card face value. The preset card validity period calculation formula is as follows: ; Among them, T 目标 The validity period for the target coupon; T 原始 The original validity period of the coupon; θ is the validity period adjustment factor; I represents the impact degree of the core abnormal node; Imax This represents the impact degree of the extreme value outlier node.

[0048] The calculation formula can dynamically adjust the validity period of target coupons by fully combining the degree of user behavior abnormality with coefficient weights. This makes the determination of the validity period of target coupons more objective and consistent, avoiding arbitrariness and deviation caused by human adjustment. Through the linkage calculation of influence degree and adjustment coefficient, the validity period can be adaptively shortened or adapted according to the actual degree of user abnormality. This makes the validity period of coupons more in line with the user's real consumption cycle and usage habits, further improving the matching degree between coupons and user behavior, and effectively reducing the idle rate and expiration rate of coupons.

[0049] This application provides a method for optimizing the original face value of coupons based on face value optimization parameters to ensure that the target face value closely matches the user's actual spending power and intentions. The optimization process includes: The face value adjustment coefficient, step optimization threshold, core anomaly node influence, extreme value anomaly node influence, and original card face value are imported into the preset card face value calculation formula to obtain the target card face value. The preset card face value calculation formula is as follows: ; Among them, M 目标 The target coupon value; M 原始 The original face value of the card / coupon; θ m This is the face value adjustment factor; λ is the step-optimization threshold.

[0050] Specifically, since the impact of core anomaly nodes is used to characterize the degree of actual abnormal deviation in user behavior regarding coupon usage compared to similar analysts at the current stage, its magnitude directly reflects the degree of mismatch between coupon value and user consumption behavior. By using the tiered optimization threshold as a preset threshold for triggering value optimization, it is possible to effectively distinguish whether abnormal behavior has reached the level requiring correction. When the impact of core abnormal nodes is not less than the tiered optimization threshold, it indicates that the user behavior deviation is significant enough, and there is a clear mismatch between the face value of the coupon and the user's spending power and spending habits. If no optimization is carried out, it is highly likely that users will give up redemption and the coupon will be left idle and expire. Therefore, it is necessary to optimize the original face value of the coupon in a targeted manner. When the impact of core abnormal nodes is less than the tiered optimization threshold, it indicates that there is only a slight deviation in user behavior. The existing original card and coupon values ​​can still meet the normal usage needs of users, and there is no need to adjust the values. This avoids over-optimization, saves operational resources, and ensures the stability and continuity of card and coupon rules.

[0051] Based on whether the impact of core anomalies reaches the tiered optimization threshold, the system intelligently determines whether to adjust the coupon value. This avoids ineffective optimization and waste of marketing resources under minor anomalies. At the same time, when the anomaly is significant, the coupon value is dynamically adjusted using coefficients. This makes the optimization logic more aligned with actual operational needs. The target coupon value is calculated through a quantitative formula, ensuring that the value setting is highly matched with users' spending power and the degree of anomalies. This reduces redemption abandonment and unused coupons due to mismatched values, further improving the effectiveness of coupon usage.

[0052] Step S130: Generate card issuance detection conditions based on the target card data, and conduct standardized detection on the actual issuance of cards by users in the next stage based on the card issuance detection conditions. The target card data includes the target card validity period value and the target card face value limit.

[0053] Step S140: When the detection result of the actual issued coupons is non-compliant, generate issuance adjustment information based on the target coupon data and provide feedback on the issuance adjustment information.

[0054] Specifically, the coupon distribution detection conditions are verification standards determined based on the optimized target coupon data, used to standardize the coupon distribution parameters for the next stage. These mainly include coupon validity period detection conditions and coupon face value detection conditions. The coupon validity period detection condition requires that the validity period of the coupons actually distributed to the user in the next stage should fall within a preset reasonable range centered on the target coupon validity period. The coupon face value detection condition requires that the face value of the coupons actually distributed to the user in the next stage should fall within a preset reasonable range centered on the target coupon face value limit. The next stage refers to the next coupon distribution cycle. When conducting standardized testing on the actual distribution of coupons in the next stage, the coupon information to be distributed to the user can be obtained in real time. The actual validity period and the actual face value of the coupons are extracted and compared with the coupon distribution testing conditions. If the actual validity period meets the validity period testing condition and the actual face value meets the face value testing condition, the actual distribution coupon test result is deemed compliant. If either the actual validity period or the actual face value does not meet the corresponding testing condition, the actual distribution coupon test result is deemed non-compliant.

[0055] When the detection result is non-compliant, corresponding issuance adjustment information is generated based on the target coupon's validity period and face value limit. This information includes suggestions for adjusting the coupon's validity period and face value in the next phase, as well as a notification of the reason for the abnormal issuance. This adjustment information is fed back to the coupon issuance management module or relevant operational terminals in real time to remind users to automatically correct or manually intervene in the coupon issuance parameters. This ensures that the final coupon parameters issued in the next phase match the target coupon data, avoiding the issuance of mismatched or non-compliant coupons to users. This continuously ensures the accuracy of coupon issuance and improves the effectiveness of coupon usage for users.

[0056] In this embodiment of the application, by analyzing user static and dynamic behavior data, it is easy to intuitively and comprehensively quantify the effective value of users' use of coupons issued in the current stage. When the effective value of use is low, the corresponding optimization parameters are determined in a timely manner to optimize the original coupon data in a targeted manner. Then, based on the target coupon data, exclusive issuance detection conditions are generated to conduct real-time standardization detection on the coupons actually issued in the next stage. When non-compliance is detected, adjustment information is generated based on the target coupon data and feedback is provided to facilitate timely correction of the issuance strategy and avoid the re-issuance of invalid coupons. By improving the accuracy of coupon issuance, the effectiveness of users' use of issued coupons is improved.

[0057] Furthermore, to improve the effectiveness of coupon usage, the method provided in this application embodiment also includes steps S210-S230, such as... Figure 2 As shown, where: Step S210: Determine the user conversion rate based on the increase in the effective value of the coupons issued in the previous stage between two adjacent stages.

[0058] Specifically, two adjacent stages include the current stage and the previous stage. The coupons issued in each stage refer to those issued to users by merchants or operators in the previous stage, and those issued to users in the current stage after optimization. Each user has a corresponding effective usage value for each stage. This effective usage value can be determined by referring to the method used to determine the effective usage value of issued coupons in the current stage, which will not be elaborated here. The growth amount is determined by comparing the effective usage value corresponding to the current stage with the effective usage value corresponding to the previous stage, i.e., growth amount = effective usage value of the current stage - effective usage value of the previous stage. This growth amount can be positive or negative; a positive growth amount indicates that the user's coupon usage effectiveness has improved, and a negative growth amount indicates that the user's coupon usage effectiveness has decreased. Then, based on a preset conversion rate mapping relationship, the user conversion rate corresponding to this growth amount is determined. The preset conversion rate mapping relationship is the correspondence between the growth amount and the user conversion rate; its specific content is not specifically limited in this embodiment.

[0059] Step S220: Based on the user's static behavior data in two adjacent stages, determine the user's predicted consumption content, and determine the appropriate coupons based on the predicted consumption content.

[0060] Specifically, the static behavioral data for two adjacent stages includes historical consumption behavior data and historical coupon usage data for the user in the previous and current stages. This includes data such as consumption category preferences, consumption time distribution, frequently used consumption amount ranges, frequently used coupon types, and coupon application scenario preferences. Based on a preset feature extraction algorithm, the user's consumption behavior change characteristics and stable preference characteristics between the two adjacent stages can be extracted from this static behavioral data. Examples include recent changes in the proportion of consumption categories, upward or downward shifts in consumption amount ranges, and the face value and applicable scenarios of frequently used coupons. Based on the extracted consumption behavior change characteristics and stable preference characteristics, a preset content prediction model analyzes the user's potential consumption tendencies in the next stage to determine the user's predicted consumption content. This predicted consumption content includes, but is not limited to, predicted consumption categories, predicted consumption price ranges, predicted consumption scenarios, and predicted consumption times. For example, a user might consistently prefer evening prime time consumption, prefer medium and large private rooms, have stable beverage consumption within a certain amount range, and frequently use discount coupons and room fee reduction coupons in both stages.

[0061] After determining the predicted consumption content, coupons matching the predicted consumption content can be sifted through the coupon library as suitable coupons. The applicable scenarios, usage thresholds, face values, and applicable time periods of the suitable coupons are consistent with or highly consistent with the predicted consumption content. For example, if the predicted consumption content is evening room rental and beverage consumption, then evening room rate discount coupons, exclusive discount coupons for room rentals, and beverage consumption vouchers will be identified as suitable coupons; if the predicted consumption content is weekday daytime consumption, then weekday daytime special offer coupons and hourly discount coupons will be identified as suitable coupons. The basic information of various coupons in the coupon library can be pre-entered by the operator, and the specific matching rules can be flexibly set according to the actual operational needs of the store. In this embodiment, no specific limitations are made.

[0062] Step S230: Based on the user conversion rate, extend the usage period of the adapted coupons to obtain an optimized usage period, and provide users with regular reminder information based on the optimized usage period.

[0063] Specifically, the user conversion rate is used to characterize the increase in the effective value of coupon usage between adjacent stages, reflecting the user's acceptance of coupons and consumption conversion potential. Based on the mapping relationship between the user conversion rate and the preset optimization duration, the original usage period of the adapted coupons is extended. The preset optimization duration mapping relationship is the correspondence between the user conversion rate and the optimization duration. The higher the user conversion rate, the stronger the user's willingness to consume and the more significant the coupon optimization effect; the corresponding optimization duration is longer, and therefore the greater the extension of the usage period. The specific content of the preset optimization duration mapping relationship is not specifically limited in this embodiment.

[0064] After determining the optimized usage period, periodic reminders can be generated based on this period and pushed to users according to preset reminder nodes. For example, reminders can be sent to users on the day the coupon is issued, 3 days before the optimized usage period expires, and 1 day before the expiration date. The reminder content can be "Your evening room rate discount coupon will expire in 3 days, remember to use it in time" or "Your beverage voucher has 1 day remaining, welcome to the store." The specific reminder content can be set in advance by relevant staff according to actual needs and uploaded to the management system.

[0065] In this embodiment of the application, the user conversion rate is determined by the increase in the effective value of coupon usage between two adjacent stages. This objectively reflects the increase in users' willingness to consume and their conversion potential. By mining users' predicted consumption content based on static behavioral data from adjacent stages and matching it with corresponding suitable coupons, the coupons can be accurately matched with users' potential consumption needs. This further enhances the targeting and attractiveness of coupon distribution. The validity period of suitable coupons is reasonably extended based on the user conversion rate, resulting in an optimized validity period that better fits the user's conversion cycle. This effectively reduces the situation where users abandon using coupons due to excessively tight expiration dates. At the same time, regular reminders are pushed to users based on the optimized validity period, which helps reduce the number of coupons that expire due to users forgetting to use them, thereby improving the effectiveness of coupon usage.

[0066] This application provides a management system, such as... Figure 3 As shown, Figure 3 The management system 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the management system 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this management system 300 does not constitute a limitation on the embodiments of this application.

[0067] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0068] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0069] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0070] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0071] The management system includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Tablet PCs), PMPs (Portable Multimedia Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. It can also include servers. Figure 3 The management system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0072] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0073] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0074] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0075] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for managing coupons, characterized in that, include: Based on the user's static and dynamic behavior data regarding the coupons issued in the current stage, the effective value of the user's use of the issued coupons in the current stage is determined. The static behavior data includes the user's historical consumption behavior data and historical coupon usage data, and the dynamic behavior data includes the user's dynamic consumption behavior data and dynamic coupon interaction data in the current stage. When the effective value is lower than the preset effective threshold, optimization parameters are determined based on the static behavior data and the dynamic behavior data, and the original coupon data is optimized based on the optimization parameters to obtain the target coupon data. The optimization parameters include the validity period optimization parameters and the face value optimization parameters. Based on the target coupon data, coupon issuance detection conditions are generated, and based on the coupon issuance detection conditions, the actual coupons issued to the user in the next stage are standardized and detected. The target coupon data includes the target coupon validity period value and the target coupon face value limit. When the detection result of the actual issued coupons is non-compliant, issuance adjustment information is generated based on the target coupon data, and the issuance adjustment information is fed back.

2. The card and coupon management method according to claim 1, characterized in that, The usage validity period optimization parameters are determined based on the static behavior data and the dynamic behavior data, including: Identify target behavior node data from the dynamic behavior data. The target behavior node data includes target behavior nodes and behavior node values ​​for each target behavior node. The target behavior nodes include idle coupon nodes, viewing frequency nodes, decision cycle nodes, and consumption behavior gap nodes. Based on the behavior node value and preset behavior weight of each target behavior node, the validity period adjustment coefficient is determined; Based on the static behavioral data, a similarity analyst is determined for the user. The similarity analyst's effective value for similar use of similar issued coupons is higher than the preset effective threshold at the current stage. Obtain similar behavior node data of the similar analysts, and determine the influence of core abnormal nodes and the influence of extreme value abnormal nodes based on the similar behavior node data and the target behavior node data; The validity period adjustment coefficient, the impact degree of the core abnormal node, and the impact degree of the extreme value abnormal node constitute the validity period optimization parameters.

3. The card and coupon management method according to claim 2, characterized in that, Determining the face value optimization parameters based on the static behavior data and the dynamic behavior data includes: Identify target consumption node data from the dynamic behavior data. The target consumption node data includes target consumption nodes and consumption node values ​​for each target consumption node. The target consumption node includes consumption amount node, redemption abandonment rate node, and amount matching node. Based on the consumption node value and preset consumption weight of each target consumption node, the face value adjustment coefficient is determined; Based on the similar behavior node data and the target behavior node data, a step-wise optimization threshold is determined. The face value adjustment coefficient, the step optimization threshold, the influence degree of the core abnormal node, and the influence degree of the extreme value abnormal node constitute the face value optimization parameters.

4. The card and coupon management method according to claim 3, characterized in that, The process of optimizing the original coupon data based on the optimization parameters to obtain the target coupon data includes: Identify the validity period optimization parameter in the optimization parameters, and optimize the original validity period of the original coupon data based on the validity period optimization parameter to obtain the target validity period of the coupon; Identify the face value optimization parameter in the optimization parameters, and optimize the original face value of the original card in the original card data based on the face value optimization parameter to obtain the target face value of the card; The target coupon's validity period and face value constitute the target coupon data.

5. The card and coupon management method according to claim 4, characterized in that, The optimization of the original coupon validity period in the original coupon data based on the validity period optimization parameters to obtain the target coupon validity period includes: The validity period adjustment coefficient, the impact degree of the core abnormal node, the impact degree of the extreme value abnormal node, and the original validity period of the coupon are imported into the preset coupon validity period calculation formula to obtain the target coupon face value. The preset coupon validity period calculation formula is as follows: ； Among them, T 目标 The validity period for the target coupon; T 原始 The original validity period of the coupon; θ is the validity period adjustment factor; I represents the impact degree of the core abnormal node; I max This represents the impact degree of the extreme value outlier node.

6. The card and coupon management method according to claim 4, characterized in that, The optimization of the original card face value in the original card data based on the face value optimization parameters to obtain the target card face value includes: The face value adjustment coefficient, the step optimization threshold, the core anomaly node influence, the extreme value anomaly node influence, and the original card face value are imported into a preset card face value calculation formula to obtain the target card face value. The preset card face value calculation formula is as follows: ； Among them, M 目标 The target coupon value; M 原始 The original face value of the card / coupon; θ m This is the face value adjustment factor; λ is the step-optimization threshold.

7. The card and coupon management method according to claim 1, characterized in that, Also includes: The user conversion rate is determined based on the increase in the effective value of the coupons issued in the previous stage by the user in two adjacent stages. Based on the user's static behavior data in two adjacent phases, the user's predicted consumption content is determined, and suitable coupons are determined based on the predicted consumption content. Based on the user conversion rate, the validity period of the adapted coupon is extended to optimize the validity period, and regular reminders are sent to the user based on the optimized validity period.

8. A management system, characterized in that, The management system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a coupon management method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-7.

10. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of a card management method according to any one of claims 1-7.

Images