Flow loss stopping method and related equipment
Through near-real-time data statistics and multi-indicator evaluation, combined with dynamic adjustment strategies, the data lag and single evaluation problems of existing traffic stop-loss methods are solved, and efficient utilization of traffic resources and overall revenue improvement are achieved.
Patent Information
- Application Number
- CN202510892846.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-30
- Publication Date
- 2025-09-19
AI Technical Summary
Existing traffic loss prevention methods have data lags, insufficient single indicator evaluation, and rough adjustment strategies, resulting in waste of traffic resources and reduced business benefits.
Adopting near-real-time data statistics, comprehensive multi-indicator monitoring and dynamic adjustment of stop-loss weights, the incremental exposure value and conversion rate are calculated through the exposure, sales and number of customers of the experimental and control groups. The stop-loss signal value is determined in combination with the indicator threshold, and the product exposure priority is dynamically adjusted.
It achieves efficient utilization of traffic resources, improves overall exposure benefits, improves the accuracy and flexibility of stop-loss decisions, avoids traffic waste, and enhances the company's business competitiveness and sustainable development.
Smart Images
Figure CN120672399A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of flow control, and more specifically, to a flow loss prevention method and related equipment. Background Art
[0002] In today's digital business landscape, traffic resources play a core role in numerous sectors, including e-commerce platforms and online advertising, becoming a crucial element for achieving business growth and profitability. E-commerce platforms rely on traffic to attract potential customers and generate purchases, while online advertising relies on traffic to achieve ad display and user interaction. Therefore, effectively utilizing traffic resources and increasing the efficiency of converting traffic into actual business results have become a focus across various industries.
[0003] The need for traffic loss control is driven by several factors. First, traffic resources are scarce, and acquiring them requires significant human, material, and financial resources. For example, e-commerce platforms spend money on search engine advertising for each click. If this traffic doesn't effectively translate into sales or customer conversions, the cost isn't recouped, resulting in wasted resources. Given the rising cost of traffic acquisition, traffic loss control is essential to reduce operating costs and improve profitability. Second, traffic utilization efficiency varies across products or business units. Some products, due to insufficient competitiveness, poor market positioning, or inappropriate marketing strategies, struggle to achieve ideal performance despite receiving significant traffic exposure. Meanwhile, some promising products lack sufficient exposure due to uneven traffic distribution, hindering business growth. Traffic loss control can optimize resource allocation and improve overall business efficiency. Furthermore, the market environment is constantly changing, with consumer demand, competitor strategies, and industry trends shifting constantly, and the traffic conversion performance of products or businesses can fluctuate accordingly. When the conversion rate of certain products plummets due to shifting market focus or competition, continued traffic investment will further increase costs if loss control is not implemented promptly.
[0004] Based on this, this application provides a traffic stop-loss solution that can stop traffic losses in a timely manner, enabling enterprises to respond more flexibly to market dynamics and maintain business stability and sustainable development. Summary of the Invention
[0005] In view of this, the present application provides a traffic stop-loss method and related equipment, which effectively overcomes the shortcomings of existing stop-loss methods through near-real-time data statistics, multi-indicator comprehensive monitoring and dynamic adjustment of stop-loss weights, and realizes efficient utilization of traffic resources and improvement of overall exposure benefits.
[0006] A flow stop-loss method, comprising:
[0007] Split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and count the exposure, sales, and number of customers of the experimental group and the control group in near real time;
[0008] By substituting the statistical exposure, sales and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated;
[0009] Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determining a stop-loss signal value based on the comparison result combined with the indicator threshold, and substituting the stop-loss signal value into a stop-loss weight formula to calculate a stop-loss weight value;
[0010] The product exposure priority is adjusted based on the stop-loss weight value to achieve traffic stop-loss.
[0011] Optionally, the calculation formula for the incremental exposure value is:
[0012]
[0013] The calculation formula for the incremental exposure conversion rate is:
[0014]
[0015] in, is the incremental exposure value, is the sales volume of the experimental group, is the exposure of the experimental group, is the sales volume of the control group, is the exposure level of the control group, is the incremental exposure conversion rate, is the number of customers in the experimental group, is the number of customers in the control group.
[0016] Optionally, comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, and determining a stop-loss signal value based on the comparison result in combination with the indicator threshold, includes:
[0017] If the incremental exposure value is less than a preset first monitoring threshold, a stop-loss signal value is calculated based on the incremental exposure value and the indicator threshold using a first stop-loss signal calculation formula;
[0018] If the incremental exposure conversion rate is less than a preset second monitoring threshold, a stop-loss signal value is calculated using a second stop-loss signal calculation formula based on the incremental exposure conversion rate and the indicator threshold.
[0019] Optionally, the first stop-loss signal calculation formula is:
[0020]
[0021] The calculation formula for the second stop-loss signal is:
[0022]
[0023] in, 、 As a stop loss signal, is the indicator threshold, is the incremental exposure value, is the incremental exposure conversion rate.
[0024] Optionally, the stop-loss weight formula is:
[0025]
[0026] in, For stop loss weight, As a stop loss signal, The value range belongs to The super parameter of is an exponential function.
[0027] Optionally, the update frequency of the near real-time statistics is in seconds or minutes to match the control requirements of high concurrent traffic scenarios.
[0028] A flow loss prevention device, comprising:
[0029] A traffic splitting unit is used to split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and to count the exposure, sales, and number of customers of the experimental group and the control group in near real time;
[0030] An exposure calculation unit, configured to calculate an incremental exposure value and an incremental exposure conversion rate by substituting the statistical exposure, sales, and number of customers of the experimental group and the control group into a monitoring indicator formula;
[0031] a stop-loss weighting unit, configured to compare the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determine a stop-loss signal value based on the comparison result in combination with the indicator threshold, and substitute the stop-loss signal value into a stop-loss weighting formula to calculate a stop-loss weighting value;
[0032] A stop-loss control unit is used to adjust the product exposure priority based on the stop-loss weight value to achieve traffic stop-loss.
[0033] A flow loss prevention device, comprising a memory and a processor;
[0034] The memory is used to store programs;
[0035] The processor is used to execute the program to implement each step of the traffic loss stop method as described in any one of the above items.
[0036] A readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, each step of the traffic stop-loss method as described in any one of the above items is implemented.
[0037] A computer program product includes a computer program, characterized in that when the computer program is run by a processor, it executes each step of the traffic loss control method as described in any one of the above items.
[0038] It can be seen from the above technical solutions that the traffic stop-loss method and related equipment provided by the embodiment of the present application have many significant advantages and can effectively avoid the shortcomings of traditional stop-loss methods. In terms of data statistics, the existing methods have a lag, and the present application uses a near-real-time method to count the exposure, sales and number of customers of the experimental group and the control group. This advantage enables companies to obtain the latest updates on the traffic conversion effect of goods in a timely manner, greatly shortening the data feedback cycle. Once an abnormality is found in the traffic conversion of a certain product, a quick response can be made to avoid the continuous investment of a large amount of invalid traffic, effectively solving the problem of traffic waste caused by the untimely data update of the existing method.
[0039] Existing evaluation methods often rely on a single metric, making it difficult to comprehensively measure the traffic utilization efficiency of a product or business. This application introduces two monitoring metrics: incremental exposure value and incremental exposure conversion rate, and compares them against pre-set monitoring thresholds. This multi-dimensional, comprehensive evaluation method can more comprehensively and accurately reflect the actual performance of a product, avoid misjudgments caused by fluctuations in a single metric, and significantly improve the accuracy and reliability of stop-loss decisions.
[0040] In terms of adjustment strategies, existing methods are often simple and crude, directly reducing exposure or removing promotions without considering product relevance and market dynamics. This application determines the stop-loss signal value by combining the comparison results with the indicator threshold, and then substitutes it into the stop-loss weight formula to calculate the stop-loss weight value, and finally dynamically adjusts the product exposure priority based on this value. This method is more flexible and accurate, giving products a certain amount of adjustment space, and can be optimized in time according to the real-time performance of the product and market changes. Even if a product performs poorly temporarily, it will not be abandoned immediately. Instead, by reasonably adjusting the exposure strategy, it will have the opportunity to improve the conversion effect, thereby making full use of traffic resources and improving overall business benefits.
[0041] To sum up, this application effectively overcomes the shortcomings of existing stop-loss methods in data feedback, evaluation accuracy and adjustment flexibility by relying on near real-time data statistics, multi-indicator comprehensive evaluation and dynamic and flexible adjustment strategies, and achieves efficient utilization of traffic resources and significant improvement in overall exposure benefits. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.
[0043] Figure 1 This is a flow chart of a traffic loss prevention method disclosed in an embodiment of the present application;
[0044] Figure 2 A schematic diagram of a flow loss stop device disclosed in an embodiment of the present application;
[0045] Figure 3 This is a hardware structure block diagram of a flow stop-loss device disclosed in an embodiment of the present application. DETAILED DESCRIPTION
[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0047] The present application can be used in a variety of general or special computing device environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, and distributed computing environments including any of the above devices or devices.
[0048] Next, we will introduce the application scheme. This application proposes the following technical scheme, please see below for details.
[0049] Figure 1 This is a flow chart of a flow loss prevention method disclosed in an embodiment of the present application.
[0050] like Figure 1 As shown, the method may include:
[0051] Step S1: Split the product traffic into an experimental group using a preset stop-loss weight and a control group without any intervention, and count the exposure, sales and number of customers of the experimental group and the control group in a near real-time manner.
[0052] Specifically, in a digital business environment, traffic resource management and loss prevention strategies are crucial for improving a company's operational effectiveness and competitiveness. Step S1, the key initial step in the traffic loss prevention method, employs scientific and rational grouping methods and advanced data statistics, laying a solid foundation for subsequent accurate analysis and effective decision-making.
[0053] The splitting of product traffic into an experimental group using pre-set stop-loss weights and a control group receiving no intervention is based on rigorous experimental design principles. The application of pre-set stop-loss weights to the experimental group aims to simulate the effects of implementing a specific traffic control strategy. By dynamically adjusting traffic allocation in the experimental group, we can systematically observe changes in key business metrics such as product exposure, sales, and customer base under different traffic configurations, thereby further exploring the impact of the stop-loss strategy on product performance. The control group, on the other hand, remains in a natural state without any intervention, providing a stable reference baseline for the entire experiment. By comparing the data with the control group, we can accurately distinguish whether changes in the experimental group's business metrics are caused by the stop-loss strategy or by other external factors, thereby scientifically evaluating the actual effectiveness of the stop-loss strategy.
[0054] The use of near-real-time statistics on the exposure, sales and number of customers of the experimental group and the control group is a necessary measure to adapt to today's high-concurrency traffic scenarios. In a high-concurrency traffic environment, traffic data changes extremely rapidly and complexly. Traditional timed statistical methods have serious lags and cannot capture the real-time dynamic changes of traffic and business indicators in a timely manner. The near-real-time statistical method with an update frequency of seconds or minutes adopted in this application can ensure that enterprises obtain the latest traffic and business data in a timely manner. For example, when the exposure of a product fluctuates greatly in a short period of time, or the growth trend of sales and the number of customers does not meet expectations, near-real-time statistics can quickly feedback these abnormal situations, enabling enterprises to promptly activate the stop-loss mechanism and adjust the traffic allocation strategy to avoid cost increases caused by traffic waste, and effectively improve the utilization efficiency of traffic resources.
[0055] Step S2: Substituting the statistical exposure, sales volume, and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated.
[0056] Specifically, step S2 relies on the accurate, real-time data obtained in step S1 to calculate two key metrics: incremental exposure value and incremental exposure conversion rate. These metrics are valuable for evaluating the effectiveness of stop-loss strategies and optimizing traffic resource allocation.
[0057] Incremental impression value is an important quantitative indicator that measures the change in the contribution of each unit of impression to sales. The incremental impression conversion rate is a key indicator that measures the change in the effectiveness of each unit of impression in promoting customer conversion. By calculating and analyzing the incremental impression value and incremental impression conversion rate, companies can comprehensively and objectively evaluate the effectiveness of stop-loss strategies, gain a deeper understanding of the efficiency of traffic resource utilization and customer conversion, and provide a scientific basis for subsequent traffic allocation adjustments and business strategy optimization, thereby achieving efficient utilization of traffic resources and maximizing corporate economic benefits.
[0058] The calculation formula for the incremental exposure value is:
[0059]
[0060] The calculation formula for the incremental exposure conversion rate is:
[0061]
[0062] in, is the incremental exposure value, is the sales volume of the experimental group, is the exposure of the experimental group, is the sales volume of the control group, is the exposure level of the control group, is the incremental exposure conversion rate, is the number of customers in the experimental group, is the number of customers in the control group.
[0063] Step S3: compare the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determine a stop-loss signal value based on the comparison result combined with the indicator threshold, and substitute the stop-loss signal value into the stop-loss weight formula to calculate a stop-loss weight value.
[0064] Specifically, by comparing the incremental exposure value and incremental exposure conversion rate calculated in step S2 with the preset monitoring threshold, the stop-loss signal value is determined based on the comparison result, and then the stop-loss weight value is calculated to provide a quantitative basis for subsequent traffic adjustments.
[0065] Comparing the incremental impression value and incremental impression conversion rate against pre-set monitoring thresholds forms the foundation of this step. Pre-set monitoring thresholds are standard values set by the company based on its business objectives, historical data, market conditions, and other factors. They represent the company's desired traffic utilization efficiency and customer conversion levels. This comparison provides a clear assessment of whether current traffic operations are meeting expectations.
[0066] When the incremental exposure value is less than the preset first monitoring threshold, it indicates that the sales growth brought about by the unit exposure has not met the company's expectations, and the economic value of the traffic is being utilized inefficiently. At this time, it is necessary to use the first stop-loss signal calculation formula to calculate the stop-loss signal value based on the incremental exposure value and the indicator threshold. The first stop-loss signal calculation formula is a quantitative assessment tool that can accurately measure the degree of insufficient sales contribution of the current traffic operation based on the gap between the incremental exposure value and the indicator threshold, providing a specific signal strength reference for subsequent stop-loss decisions.
[0067] Similarly, if the incremental exposure conversion rate is less than the preset second monitoring threshold, it indicates that the number of customer conversions generated per unit exposure has not met expectations, and the traffic flow is not effective in attracting and converting customers. In this case, the second stop-loss signal calculation formula, based on the incremental exposure conversion rate and the indicator threshold, calculates the stop-loss signal value, accurately assessing the extent of the current traffic operation's customer conversion deficiencies.
[0068] After obtaining the stop-loss signal value, it is substituted into the stop-loss weight formula to calculate the stop-loss weight. The hyperparameter in the stop-loss weight formula is a parameter with a specific range of values that can be adjusted based on the company's actual situation and risk appetite. By using an exponential function, the stop-loss signal can be nonlinearly amplified or reduced, allowing the stop-loss weight value to more flexibly reflect the actual situation of traffic operations and the company's decision-making intentions. The stop-loss weight value is a quantitative indicator that comprehensively considers the value of incremental exposure and the conversion rate of incremental exposure. It will serve as an important basis for subsequent adjustments to product exposure priority.
[0069] That is to say, the comparison results are divided into the following two cases:
[0070] First, if the incremental exposure value is less than a preset first monitoring threshold, a stop-loss signal value is calculated based on the incremental exposure value and the indicator threshold using a first stop-loss signal calculation formula.
[0071] The calculation formula for the first stop-loss signal is:
[0072]
[0073] Second, if the incremental exposure conversion rate is less than a preset second monitoring threshold, a stop-loss signal value is calculated based on the incremental exposure conversion rate and the indicator threshold using a second stop-loss signal calculation formula.
[0074] The calculation formula for the second stop-loss signal is:
[0075]
[0076] in, 、 As a stop loss signal, is the indicator threshold, is the incremental exposure value, is the incremental exposure conversion rate.
[0077] The stop-loss weight formula is:
[0078]
[0079] in, For stop loss weight, As a stop loss signal, The value range belongs to The super parameter of is an exponential function.
[0080] Step S4: adjusting the product exposure priority based on the stop-loss weight value to achieve traffic stop-loss.
[0081] Specifically, the stop-loss weight reflects the comprehensive performance of a product in terms of traffic utilization efficiency and customer conversion. Products with high stop-loss weights indicate significant issues with their current traffic operations, perhaps insufficient sales contribution per unit of exposure, or low customer conversion rates. For these products, their exposure priority should be lowered, reducing the traffic resources allocated to them to avoid further traffic waste.
[0082] Conversely, products with lower stop-loss weights demonstrate strong traffic utilization and customer conversion, effectively converting traffic into sales and customers. These products should be prioritized and allocated more traffic resources to fully leverage their strengths and improve overall business efficiency.
[0083] By adjusting product exposure priorities, companies can optimize the allocation of traffic resources. Limited traffic resources can be allocated more efficiently to products that utilize traffic effectively, while reducing investment in products with low traffic utilization efficiency, thereby achieving the goal of limiting traffic losses. This not only reduces operating costs and improves traffic utilization efficiency, but also enhances the company's overall business competitiveness and achieves sustainable development.
[0084] In practice, exposure priority can be adjusted by adjusting the ranking of products on search results pages and ad placement. For example, on an e-commerce platform's search results page, products with lower stop-loss weights can be ranked higher, making them more visible and clickable; while products with higher stop-loss weights can be ranked lower to reduce unnecessary exposure. This allows traffic allocation to be dynamically adjusted based on product performance, ensuring that traffic resources always flow to the most valuable products.
[0085] It can be seen from the above technical solutions that the traffic stop-loss method and related equipment provided by the embodiment of the present application have many significant advantages and can effectively avoid the shortcomings of traditional stop-loss methods. In terms of data statistics, the existing methods have a lag, and the present application uses a near-real-time method to count the exposure, sales and number of customers of the experimental group and the control group. This advantage enables companies to obtain the latest updates on the traffic conversion effect of goods in a timely manner, greatly shortening the data feedback cycle. Once an abnormality is found in the traffic conversion of a certain product, a quick response can be made to avoid the continuous investment of a large amount of invalid traffic, effectively solving the problem of traffic waste caused by the untimely data update of the existing method.
[0086] Existing evaluation methods often rely on a single metric, making it difficult to comprehensively measure the traffic utilization efficiency of a product or business. This application introduces two monitoring metrics: incremental exposure value and incremental exposure conversion rate, and compares them against pre-set monitoring thresholds. This multi-dimensional, comprehensive evaluation method can more comprehensively and accurately reflect the actual performance of a product, avoid misjudgments caused by fluctuations in a single metric, and significantly improve the accuracy and reliability of stop-loss decisions.
[0087] In terms of adjustment strategies, existing methods are often simple and crude, directly reducing exposure or removing promotions without considering product relevance and market dynamics. This application determines the stop-loss signal value by combining the comparison results with the indicator threshold, and then substitutes it into the stop-loss weight formula to calculate the stop-loss weight value, and finally dynamically adjusts the product exposure priority based on this value. This method is more flexible and accurate, giving products a certain amount of adjustment space, and can be optimized in time according to the real-time performance of the product and market changes. Even if a product performs poorly temporarily, it will not be abandoned immediately. Instead, by reasonably adjusting the exposure strategy, it will have the opportunity to improve the conversion effect, thereby making full use of traffic resources and improving overall business benefits.
[0088] To sum up, this application effectively overcomes the shortcomings of existing stop-loss methods in data feedback, evaluation accuracy and adjustment flexibility by relying on near real-time data statistics, multi-indicator comprehensive evaluation and dynamic and flexible adjustment strategies, and achieves efficient utilization of traffic resources and significant improvement in overall exposure benefits.
[0089] A flow stop-loss device provided in an embodiment of the present application is described below. The flow stop-loss device described below and the flow stop-loss method described above can be referenced to each other.
[0090] See also Figure 2 , Figure 2 This is a schematic diagram of a flow stop-loss device disclosed in an embodiment of the present application.
[0091] like Figure 2 As shown, the flow loss prevention device may include:
[0092] The traffic splitting unit 110 is used to split the product traffic into an experimental group using a preset stop-loss weight and a control group without any intervention, and to count the exposure, sales and number of customers of the experimental group and the control group in a near real-time manner;
[0093] The exposure calculation unit 120 is configured to calculate the incremental exposure value and the incremental exposure conversion rate by substituting the statistical exposure, sales, and number of customers of the experimental group and the control group into the monitoring indicator formula;
[0094] A stop-loss weighting unit 130 is configured to compare the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determine a stop-loss signal value based on the comparison result combined with the indicator threshold, and substitute the stop-loss signal value into a stop-loss weighting formula to calculate a stop-loss weighting value;
[0095] The stop-loss control unit 140 is used to adjust the product exposure priority based on the stop-loss weight value to achieve traffic stop-loss.
[0096] It can be seen from the above technical solutions that the traffic stop-loss method and related equipment provided by the embodiment of the present application have many significant advantages and can effectively avoid the shortcomings of traditional stop-loss methods. In terms of data statistics, the existing methods have a lag, and the present application uses a near-real-time method to count the exposure, sales and number of customers of the experimental group and the control group. This advantage enables companies to obtain the latest updates on the traffic conversion effect of goods in a timely manner, greatly shortening the data feedback cycle. Once an abnormality is found in the traffic conversion of a certain product, a quick response can be made to avoid the continuous investment of a large amount of invalid traffic, effectively solving the problem of traffic waste caused by the untimely data update of the existing method.
[0097] Existing evaluation methods often rely on a single metric, making it difficult to comprehensively measure the traffic utilization efficiency of a product or business. This application introduces two monitoring metrics: incremental exposure value and incremental exposure conversion rate, and compares them against pre-set monitoring thresholds. This multi-dimensional, comprehensive evaluation method can more comprehensively and accurately reflect the actual performance of a product, avoid misjudgments caused by fluctuations in a single metric, and significantly improve the accuracy and reliability of stop-loss decisions.
[0098] In terms of adjustment strategies, existing methods are often simple and crude, directly reducing exposure or removing promotions without considering product relevance and market dynamics. This application determines the stop-loss signal value by combining the comparison results with the indicator threshold, and then substitutes it into the stop-loss weight formula to calculate the stop-loss weight value, and finally dynamically adjusts the product exposure priority based on this value. This method is more flexible and accurate, giving products a certain amount of adjustment space, and can be optimized in time according to the real-time performance of the product and market changes. Even if a product performs poorly temporarily, it will not be abandoned immediately. Instead, by reasonably adjusting the exposure strategy, it will have the opportunity to improve the conversion effect, thereby making full use of traffic resources and improving overall business benefits.
[0099] To sum up, this application effectively overcomes the shortcomings of existing stop-loss methods in data feedback, evaluation accuracy and adjustment flexibility by relying on near real-time data statistics, multi-indicator comprehensive evaluation and dynamic and flexible adjustment strategies, and achieves efficient utilization of traffic resources and significant improvement in overall exposure benefits.
[0100] Optionally, the calculation formula for the incremental exposure value is:
[0101]
[0102] The calculation formula for the incremental exposure conversion rate is:
[0103]
[0104] in, is the incremental exposure value, is the sales volume of the experimental group, is the exposure of the experimental group, is the sales volume of the control group, is the exposure level of the control group, is the incremental exposure conversion rate, is the number of customers in the experimental group, is the number of customers in the control group.
[0105] Optionally, comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, and determining a stop-loss signal value based on the comparison result in combination with the indicator threshold, includes:
[0106] If the incremental exposure value is less than a preset first monitoring threshold, a stop-loss signal value is calculated based on the incremental exposure value and the indicator threshold using a first stop-loss signal calculation formula;
[0107] If the incremental exposure conversion rate is less than a preset second monitoring threshold, a stop-loss signal value is calculated using a second stop-loss signal calculation formula based on the incremental exposure conversion rate and the indicator threshold.
[0108] Optionally, the first stop-loss signal calculation formula is:
[0109]
[0110] The calculation formula for the second stop-loss signal is:
[0111]
[0112] in, 、 As a stop loss signal, is the indicator threshold, is the incremental exposure value, is the incremental exposure conversion rate.
[0113] Optionally, the stop-loss weight formula is:
[0114]
[0115] in, For stop loss weight, As a stop loss signal, The value range belongs to The super parameter of is an exponential function.
[0116] Optionally, the update frequency of the near real-time statistics is in seconds or minutes to match the control requirements of high concurrent traffic scenarios.
[0117] The flow stop loss device provided in the embodiment of the present application can be applied to flow stop loss equipment. Figure 3 The hardware structure diagram of the flow stop loss device is shown. Figure 3 ,The hardware structure of the flow stop loss device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;
[0118] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;
[0119] The processor 1 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention;
[0120] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory;
[0121] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to:
[0122] Split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and count the exposure, sales, and number of customers of the experimental group and the control group in near real time;
[0123] By substituting the statistical exposure, sales and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated;
[0124] Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determining a stop-loss signal value based on the comparison result combined with the indicator threshold, and substituting the stop-loss signal value into a stop-loss weight formula to calculate a stop-loss weight value;
[0125] The product exposure priority is adjusted based on the stop-loss weight value to achieve traffic stop-loss.
[0126] Optionally, the refined functions and extended functions of the program may refer to the above description.
[0127] The present application also provides a readable storage medium, which may store a program suitable for execution by a processor, wherein the program is used to:
[0128] Split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and count the exposure, sales, and number of customers of the experimental group and the control group in near real time;
[0129] By substituting the statistical exposure, sales and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated;
[0130] Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determining a stop-loss signal value based on the comparison result combined with the indicator threshold, and substituting the stop-loss signal value into a stop-loss weight formula to calculate a stop-loss weight value;
[0131] The product exposure priority is adjusted based on the stop-loss weight value to achieve traffic stop-loss.
[0132] Optionally, the refined functions and extended functions of the program may refer to the above description.
[0133] The present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the execution method is as follows:
[0134] Split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and count the exposure, sales, and number of customers of the experimental group and the control group in near real time;
[0135] By substituting the statistical exposure, sales and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated;
[0136] Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determining a stop-loss signal value based on the comparison result combined with the indicator threshold, and substituting the stop-loss signal value into a stop-loss weight formula to calculate a stop-loss weight value;
[0137] The product exposure priority is adjusted based on the stop-loss weight value to achieve traffic stop-loss.
[0138] Optionally, the refined functions and extended functions of the program may refer to the above description.
[0139] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.
[0140] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.
[0141] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A flow stop loss method, characterized in that: include: Split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and count the exposure, sales, and number of customers of the experimental group and the control group in near real time; By substituting the statistical exposure, sales and number of customers of the experimental group and the control group into the monitoring indicator formula, the incremental exposure value and the incremental exposure conversion rate are calculated; Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determining a stop-loss signal value based on the comparison result combined with the indicator threshold, and substituting the stop-loss signal value into a stop-loss weight formula to calculate a stop-loss weight value; The product exposure priority is adjusted based on the stop-loss weight value to achieve traffic stop-loss.
2. The method according to claim 1, characterized in that The calculation formula for the incremental exposure value is: The calculation formula for the incremental exposure conversion rate is: in, is the incremental exposure value, is the sales volume of the experimental group, is the exposure of the experimental group, is the sales volume of the control group, is the exposure level of the control group, is the incremental exposure conversion rate, is the number of customers in the experimental group, is the number of customers in the control group.
3. The method according to claim 1, characterized in that Comparing the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, and determining a stop-loss signal value based on the comparison result combined with the indicator threshold, including: If the incremental exposure value is less than a preset first monitoring threshold, a stop-loss signal value is calculated based on the incremental exposure value and the indicator threshold using a first stop-loss signal calculation formula; If the incremental exposure conversion rate is less than a preset second monitoring threshold, a stop-loss signal value is calculated using a second stop-loss signal calculation formula based on the incremental exposure conversion rate and the indicator threshold.
4. The method according to claim 3, characterized in that The calculation formula for the first stop-loss signal is: The calculation formula for the second stop-loss signal is: in, 、 As a stop loss signal, is the indicator threshold, is the incremental exposure value, is the incremental exposure conversion rate.
5. The method according to claim 1, wherein The stop-loss weight formula is: in, For stop loss weight, As a stop loss signal, The value range belongs to The super parameter of is an exponential function.
6. The method according to claim 1, characterized in that The update frequency of the near real-time statistics is in seconds or minutes to match the regulation requirements of high concurrent traffic scenarios.
7. A flow loss stop device, characterized in that: include: A traffic splitting unit is used to split product traffic into an experimental group using preset stop-loss weights and a control group without any intervention, and to count the exposure, sales, and number of customers of the experimental group and the control group in near real time; An exposure calculation unit, configured to calculate an incremental exposure value and an incremental exposure conversion rate by substituting the statistical exposure, sales, and number of customers of the experimental group and the control group into a monitoring indicator formula; a stop-loss weighting unit, configured to compare the incremental exposure value and the incremental exposure conversion rate with a preset monitoring threshold, determine a stop-loss signal value based on the comparison result in combination with the indicator threshold, and substitute the stop-loss signal value into a stop-loss weighting formula to calculate a stop-loss weighting value; A stop-loss control unit is used to adjust the product exposure priority based on the stop-loss weight value to achieve traffic stop-loss.
8. A flow stop loss device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the traffic loss stop method as described in any one of claims 1-6.
9. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the flow loss prevention method as described in any one of claims 1 to 6 is implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program executes the steps of the flow loss control method according to any one of claims 1 to 6.