A method for issuing an electronic coupon
By collecting user data in real time to identify decision-making thresholds, constructing a price sensitivity model, and calculating the minimum incentive amount, the problem of delayed distribution of electronic coupons was solved, achieving precise marketing and profit maximization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI YINGERSHI IND CO LTD
- Filing Date
- 2026-04-28
- Publication Date
- 2026-06-12
AI Technical Summary
Existing electronic coupons suffer from delayed and unpredictable triggering mechanisms and a lack of real-time price sensitivity metrics, leading to misallocation of marketing resources and an inability to maximize marginal benefits.
By collecting user behavior flow data and environmental context data in real time, the system identifies decision-making critical points, constructs a real-time price sensitivity model, calculates the minimum incentive amount using a dynamic game algorithm, and generates and issues electronic coupons within profit constraints. It also features overflow prevention verification and feedback iteration mechanisms.
It achieves accurate identification of users' decision-making thresholds, personalized pricing for each user, improved efficiency in marketing resource utilization, maximized profits, and the system has dynamic optimization capabilities.
Smart Images
Figure CN122199074A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of data processing and e-commerce, and in particular to a method for issuing electronic coupons. Background Technology
[0002] With the popularization of e-commerce and the maturity of mobile payment technology, online consumption scenarios are constantly expanding. Electronic coupons, with their advantages of convenience, efficiency, and ease of dissemination, have rapidly become an important digital marketing tool for merchants. They can accurately reach target users, stimulate immediate consumption, and drive order conversion. Furthermore, they can enhance user interaction and increase repurchase rates and brand loyalty through membership systems and social sharing. Today, electronic coupons have deeply integrated into daily consumption, becoming a core marketing tool connecting merchants and consumers, activating market vitality, and improving user stickiness.
[0003] Currently, existing technologies suffer from significant homogeneity in their underlying logic, severely hindering further breakthroughs in marketing effectiveness. First, the triggering mechanisms suffer from severe "spatiotemporal misalignment" and blindness. Existing technologies largely rely on static user profiles (such as historical cumulative consumption and registration duration) or rigid fixed periods (such as holidays and member days) for batch, "scattershot" distribution. This model lacks deep coupling with users' real-time consumption scenarios and fails to keenly capture key micro-moments in the user's decision-making process. Second, resource allocation lacks dynamic pricing wisdom based on "marginal benefits." Existing technologies are inadequate in quantifying users' real-time price sensitivity, making it difficult to achieve precise "personalized" strategies. On the one hand, for price-insensitive high-value users, the system mechanically issues large discounts, resulting in a serious overspending of the marketing budget (i.e., users would buy even without coupons); on the other hand, for price-sensitive low-value users, if the small coupons issued are insufficient to cross their psychological payment threshold, they cannot effectively facilitate transactions, leading to the loss of potential users. Ultimately, existing technologies lack a mechanism to calculate and balance the "minimum incentive required to facilitate a transaction" and the "profit loss" in real time, thus failing to maximize marginal benefits in dynamic games. Summary of the Invention
[0004] The purpose of this invention is to provide a method for issuing electronic coupons, which solves the problems of marketing resource misallocation and inability to maximize marginal benefits caused by the lagging and blind triggering mechanism and lack of real-time price sensitivity measurement in the existing electronic coupon issuance technology.
[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:
[0006] A method for issuing electronic coupons, characterized by including: real-time collection of behavioral flow data and environmental context data of target users in a preset consumption scenario;
[0007] Based on the behavioral flow data, it is determined whether the user is at a decision-making critical point. If it is determined to be yes, the real-time coupon value estimation process is triggered.
[0008] In the real-time coupon value estimation process, the user's historical consumption characteristics and current environmental context data are integrated to construct a real-time price sensitivity model and output the real-time price sensitivity coefficient.
[0009] Based on the real-time price sensitivity coefficient, the user's psychological payment threshold is calculated, and within the preset profit constraint range, the minimum incentive amount required to facilitate the current transaction is calculated using a dynamic game algorithm.
[0010] Perform an overflow prevention check. If the check passes, generate an electronic coupon containing the minimum incentive amount and send it to the user's terminal in real time. Otherwise, execute an alternative strategy or stop sending the coupon.
[0011] As an improvement, the identification of whether a user is at a decision critical point specifically includes:
[0012] Monitor the duration of users' stay on the product details page, the area covered by the page scrolling trajectory, and their intention to exit after adding items to the shopping cart;
[0013] When the dwell time exceeds a preset threshold, the page scrolling trajectory covers the price parameter area, and the intention to close the page or remove the shopping cart is detected, the target user is determined to be at the price sensitivity decision threshold.
[0014] As an improvement, the construction of the real-time price sensitivity model and the output of the real-time price sensitivity coefficient specifically include:
[0015] Extract users' historical transaction prices, historical coupon redemption rates, and historical price elasticity coefficients as static features;
[0016] The environmental context data is used as dynamic features, which at least include the current product category, current time period, remaining battery power of the user device, and network environment type.
[0017] The static and dynamic features are weighted using a machine learning model to output a real-time price sensitivity coefficient that represents the user's current responsiveness to price changes.
[0018] As an improvement, the calculation of the user's psychological payment threshold is specifically as follows:
[0019] Get the price of the item you want to buy;
[0020] Based on the real-time price sensitivity coefficient, predict the probability curve of user transaction under different discount amounts;
[0021] The payment amount corresponding to the critical point where the probability of a transaction significantly increases is determined as the user's psychological payment threshold.
[0022] As an improvement, the calculation of the minimum incentive amount required to facilitate the current transaction using a dynamic game theory algorithm specifically includes:
[0023] Define the marketing budget constraint function and the expected profit loss function;
[0024] Construct an objective function, which is defined as: (Expected transaction profit after issuing coupons - Natural transaction probability without coupons × Product profit) - Coupon face value;
[0025] Under the condition that the user's payment amount is less than or equal to the psychological payment threshold, the minimum coupon amount that maximizes the objective function is calculated as the minimum incentive amount.
[0026] As an improvement, the implementation of overflow prevention verification specifically includes:
[0027] Before calculating the minimum incentive amount, assess the user's natural conversion probability;
[0028] If the probability of a natural transaction is higher than the preset threshold for high-value users, the user is determined to be a high-value user who is not price-sensitive. In this case, a strategy is implemented to stop issuing high-value coupons or only issue low-value trial coupons with no threshold, in order to avoid overspending of the marketing budget.
[0029] As an improvement, the method also includes a potential user retention mechanism:
[0030] If a user is identified as a price-sensitive, low-value user and their current browsing behavior shows signs of churn, but the calculated minimum incentive amount is insufficient to cross their psychological payment threshold;
[0031] Dynamically adjust profit constraints to recalculate minimum cost coupons, or trigger alternative marketing strategies when budgets are insufficient.
[0032] As an improvement, the generated electronic coupon has a dynamic expiration time:
[0033] The validity period of electronic coupons is dynamically set based on the urgency of the user's current decision and the popularity of the scenario.
[0034] For scenarios with a high sense of urgency, shorten the effective lifespan to stimulate immediate conversion; for scenarios with a low sense of urgency, extend the effective lifespan to maintain user stickiness.
[0035] As an improvement, the method also includes a feedback iteration mechanism:
[0036] Record the face value of each coupon issued, the actual redemption results by users, and transaction profit data to form a feedback dataset;
[0037] The real-time price sensitivity model is periodically trained using the feedback dataset to optimize the accuracy of subsequent real-time price sensitivity coefficient calculations.
[0038] An electronic coupon distribution system, characterized in that it includes:
[0039] The data acquisition module is used to collect real-time behavioral flow data and environmental context data of the target user;
[0040] The critical point identification module is used to identify whether a user is at a decision critical point based on the behavior flow data.
[0041] The intelligent analysis module is used to build a real-time price sensitivity model and calculate the real-time price sensitivity coefficient and the user's psychological payment threshold;
[0042] The decision calculation module is used to calculate the minimum incentive amount within the profit constraint using a dynamic game algorithm and to perform overflow prevention verification.
[0043] The execution and distribution module is used to generate electronic coupons with dynamic timeliness and push them to user terminals.
[0044] The beneficial effects of this invention are: precise interception of user churn: by monitoring subtle behavioral flow data (such as scrolling trajectory and exit intention), it can accurately identify users at the decision-making critical point of "hesitating and about to give up", and achieve millisecond-level intervention.
[0045] Personalized pricing: By combining static historical characteristics with dynamic environmental context (such as electricity consumption and network), a real-time price sensitivity model is constructed, which avoids ineffective subsidies for high-value users and insufficient incentives for low-value users.
[0046] Profit maximization: By introducing a dynamic game theory algorithm, the "minimum incentive amount" is calculated while ensuring the transaction is completed. The overflow prevention check is moved to the front end, which effectively prevents the waste of marketing budget and significantly improves ROI (Return on Investment).
[0047] Dynamic closed-loop optimization: It has a feedback iteration mechanism and dynamic timeliness settings, which enable the system to continuously evolve itself as data accumulates and adapt to the ever-changing market environment and user psychology. Attached Figure Description
[0048] Figure 1 This is an overall flowchart of a method for issuing electronic coupons according to the present invention.
[0049] Figure 2 This is a block diagram of the overall architecture of an electronic coupon distribution system according to the present invention.
[0050] Figure 3 This is a flowchart illustrating the logic of calculating the minimum incentive amount based on a dynamic game theory algorithm in an embodiment of the present invention. Detailed Implementation
[0051] To make the content of this invention easier to understand, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Identical components are represented by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.
[0052] like Figures 1 to 3 As shown, a method for issuing electronic coupons includes: real-time collection of behavioral flow data and environmental context data of target users in a preset consumption scenario; identification of whether the user is at a decision threshold based on the behavioral flow data, and if so, triggering a real-time coupon value estimation process; in the real-time coupon value estimation process, integrating the user's historical consumption characteristics and current environmental context data to construct a real-time price sensitivity model and output a real-time price sensitivity coefficient; calculating the user's psychological payment threshold based on the real-time price sensitivity coefficient, and within a preset profit constraint range, using a dynamic game algorithm to calculate the minimum incentive amount required to facilitate the current transaction; performing an anti-overflow check, and if the check passes, generating an electronic coupon containing the minimum incentive amount and issuing it to the user's terminal in real time; otherwise, executing an alternative strategy or stopping the issuance.
[0053] The process of identifying whether a user is at a decision-making threshold specifically includes: monitoring the user's dwell time on the product details page, the area covered by the page scroll trajectory, and the intention to exit after adding items to the shopping cart; when the dwell time exceeds a preset threshold, the page scroll trajectory covers the price parameter area, and the intention to close the page or remove the shopping cart is detected, the target user is determined to be at a price-sensitive decision-making threshold.
[0054] The construction of the real-time price sensitivity model and the output of the real-time price sensitivity coefficient specifically includes: extracting the user's historical transaction price, historical coupon redemption rate, and historical price elasticity coefficient as static features; using the environmental context data as dynamic features, the dynamic features including at least the current product category, current time period, remaining battery power of the user's device, and network environment type; and using a machine learning model to weight the static and dynamic features to output a real-time price sensitivity coefficient that represents the user's current responsiveness to price changes.
[0055] The calculation of the user's psychological payment threshold specifically involves: obtaining the listed price of the product to be purchased; predicting the probability curve of the user's transaction under different discount levels based on the real-time price sensitivity coefficient; and determining the payment amount corresponding to the critical point where the transaction probability significantly increases as the user's psychological payment threshold.
[0056] The process of using a dynamic game theory algorithm to calculate the minimum incentive amount required to facilitate the current transaction specifically includes: setting a marketing budget constraint function and an expected profit loss function; constructing an objective function, defined as: (expected transaction profit after issuing coupons - natural transaction probability without issuing coupons × product profit) - coupon face value; and, under the condition that the user's payment amount is lower than or equal to the psychological payment threshold, finding the minimum coupon face value that maximizes the objective function, which is then used as the minimum incentive amount.
[0057] The process of performing anti-overflow verification specifically includes: assessing the user's natural conversion probability before calculating the minimum incentive amount; if the natural conversion probability is higher than the preset high-value user threshold, then the user is determined to be a high-value user who is not price-sensitive, and a strategy of stopping the issuance of high-value coupons or only issuing low-value trial coupons with no threshold is implemented to avoid marketing budget overflow.
[0058] The method also includes a potential user retention mechanism: if a user is identified as a price-sensitive low-value user and their current browsing behavior shows signs of churn, but the calculated minimum incentive amount is insufficient to cross their psychological payment threshold, the profit constraint range is dynamically adjusted to recalculate the minimum cost coupon, or alternative marketing strategies are triggered when the budget is insufficient.
[0059] The generated electronic coupons have dynamic expiration: the validity period of the electronic coupons is dynamically set according to the user's current decision urgency and the popularity of the scenario; for scenarios with high urgency, the validity period is shortened to stimulate immediate conversion; for scenarios with low urgency, the validity period is extended to maintain user stickiness.
[0060] The method also includes a feedback iteration mechanism: recording the face value of each coupon issued, the actual redemption results of users, and transaction profit data to form a feedback dataset; using the feedback dataset to periodically iterate and train the real-time price sensitivity model to optimize the accuracy of subsequent real-time price sensitivity coefficient calculations.
[0061] An electronic coupon distribution system, characterized in that it includes:
[0062] The data acquisition module is used to collect real-time behavioral flow data and environmental context data of target users; the critical point identification module is used to identify whether the user is at a decision critical point based on the behavioral flow data; the intelligent analysis module is used to construct a real-time price sensitivity model and calculate the real-time price sensitivity coefficient and the user's psychological payment threshold; the decision calculation module is used to calculate the minimum incentive amount using a dynamic game algorithm within the profit constraint range and perform overflow prevention verification; the execution and distribution module is used to generate electronic coupons with dynamic timeliness and push them to the user terminal.
[0063] During implementation, the system first collects real-time behavioral flow data (such as dwell time, scrolling trajectory, and exit intention) and environmental context data (such as battery level and network type) of target users in consumption scenarios to identify whether users are at the decision-making threshold of "hesitating and about to give up." Once determined, the system immediately integrates the user's historical consumption characteristics with the current dynamic environment to construct a real-time price sensitivity model, outputs a sensitivity coefficient, and predicts the user's psychological payment threshold accordingly. Subsequently, within a preset profit constraint range, a dynamic game algorithm is used to calculate the minimum incentive amount required to facilitate the transaction with the goal of maximizing the expected transaction profit. Before execution, an anti-overflow check is performed to exclude high-value users who naturally make transactions or to adjust the strategy for low-value users. After the check passes, the system generates electronic coupons with dynamically set validity periods based on the urgency of the scenario and distributes them to the user's terminal in real time. At the same time, the system records the redemption and profit data to form a feedback set for periodic iterative optimization of the price sensitivity model, thereby achieving precise interception of churn, personalized pricing, and maximization of marketing marginal benefits.
[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for issuing electronic coupons, characterized in that, include: Real-time collection of target user behavior flow data and environmental context data in preset consumption scenarios; Based on the behavioral flow data, it is determined whether the user is at a decision-making critical point. If it is determined to be yes, the real-time coupon value estimation process is triggered. In the real-time coupon value estimation process, the user's historical consumption characteristics and current environmental context data are integrated to construct a real-time price sensitivity model and output the real-time price sensitivity coefficient. Based on the real-time price sensitivity coefficient, the user's psychological payment threshold is calculated, and within the preset profit constraint range, the minimum incentive amount required to facilitate the current transaction is calculated using a dynamic game algorithm. Perform an overflow prevention check. If the check passes, generate an electronic coupon containing the minimum incentive amount and send it to the user's terminal in real time. Otherwise, execute an alternative strategy or stop sending the coupon.
2. The method for issuing electronic coupons according to claim 1, characterized in that, The process of identifying whether a user is at a decision-making critical point specifically includes: Monitor the duration of users' stay on the product details page, the area covered by the page scrolling trajectory, and their intention to exit after adding items to the shopping cart; When the dwell time exceeds a preset threshold, the page scrolling trajectory covers the price parameter area, and the intention to close the page or remove the shopping cart is detected, the target user is determined to be at the price sensitivity decision threshold.
3. The method for issuing electronic coupons according to claim 2, characterized in that, The construction of the real-time price sensitivity model and the output of the real-time price sensitivity coefficient specifically include: Extract users' historical transaction prices, historical coupon redemption rates, and historical price elasticity coefficients as static features; The environmental context data is used as dynamic features, which at least include the current product category, current time period, remaining battery power of the user device, and network environment type. The static and dynamic features are weighted using a machine learning model to output a real-time price sensitivity coefficient that represents the user's current responsiveness to price changes.
4. The method for issuing electronic coupons according to claim 3, characterized in that, The calculation of the user's psychological payment threshold specifically involves: Get the price of the item you want to buy; Based on the real-time price sensitivity coefficient, predict the probability curve of user transaction under different discount amounts; The payment amount corresponding to the critical point where the probability of a transaction significantly increases is determined as the user's psychological payment threshold.
5. The method for issuing electronic coupons according to claim 4, characterized in that, The calculation of the minimum incentive amount required to facilitate the current transaction using a dynamic game theory algorithm specifically includes: Define the marketing budget constraint function and the expected profit loss function; Construct an objective function, which is defined as: (Expected transaction profit after issuing coupons - Natural transaction probability without coupons × Product profit) - Coupon face value; Under the condition that the user's payment amount is less than or equal to the psychological payment threshold, the minimum coupon amount that maximizes the objective function is calculated as the minimum incentive amount.
6. The method for issuing electronic coupons according to claim 5, characterized in that, The process of performing overflow prevention verification specifically includes: Before calculating the minimum incentive amount, assess the user's natural conversion probability; If the probability of a natural transaction is higher than the preset threshold for high-value users, the user is determined to be a high-value user who is not price-sensitive. In this case, a strategy is implemented to stop issuing high-value coupons or only issue low-value trial coupons with no threshold, in order to avoid overspending of the marketing budget.
7. The method for issuing electronic coupons according to claim 6, characterized in that, This method also includes a potential user retention mechanism: If a user is identified as a price-sensitive, low-value user and their current browsing behavior shows signs of churn, but the calculated minimum incentive amount is insufficient to cross their psychological payment threshold; Dynamically adjust profit constraints to recalculate minimum cost coupons, or trigger alternative marketing strategies when budgets are insufficient.
8. The method for issuing electronic coupons according to claim 7, characterized in that, The generated electronic coupons have dynamic expiration timeliness: The validity period of electronic coupons is dynamically set based on the urgency of the user's current decision and the popularity of the scenario. For scenarios with a high sense of urgency, shorten the effective lifespan to stimulate immediate conversion; for scenarios with a low sense of urgency, extend the effective lifespan to maintain user stickiness.
9. The method for issuing electronic coupons according to claim 8, characterized in that, This method also includes a feedback iteration mechanism: Record the face value of each coupon issued, the actual redemption results by users, and transaction profit data to form a feedback dataset; The real-time price sensitivity model is periodically trained using the feedback dataset to optimize the accuracy of subsequent real-time price sensitivity coefficient calculations.
10. An electronic coupon distribution system, characterized in that, include: The data acquisition module is used to collect real-time behavioral flow data and environmental context data of the target user; The critical point identification module is used to identify whether a user is at a decision critical point based on the behavior flow data. The intelligent analysis module is used to build a real-time price sensitivity model and calculate the real-time price sensitivity coefficient and the user's psychological payment threshold; The decision calculation module is used to calculate the minimum incentive amount within the profit constraint using a dynamic game algorithm and to perform overflow prevention verification. The execution and distribution module is used to generate electronic coupons with dynamic timeliness and push them to user terminals.