Multi-round delivery data evaluation method and device, electronic equipment and readable medium

Abstract

The invention relates to a multi-round delivery data evaluation method and device, electronic equipment and a readable medium, and relates to the technical field of computers. According to the method, the data cost of obtaining user data from a third party can be determined, and the number of times of putting on the user data since the data cost is determined can be obtained; on this basis, the reduced cost of the user data is determined based on the number of times of delivery and the data cost, and the quality evaluation result of the user data is determined according to the reduced cost. According to the method, the association influence of the data cost and the delivery times of the user data can be represented by the reduced cost, so that the conversion potential and the long-term value of the user data in multiple rounds of delivery can be comprehensively evaluated in a complete delivery period, the deep association of the delivery effect in the whole delivery period is effectively quantified, and the delivery efficiency is improved. And the comprehensiveness and accuracy of the evaluation result are improved.

Description

Background Art

[0002] In the promotion and placement of goods, information, or business information, user data can be obtained through a third party to discover target customer groups in the third party's user data, thereby improving the effect of promotion and placement. Therefore, evaluating the quality of the third party's user data helps to accurately and efficiently discover the target customer group in the early stage, avoid the interference of low-quality user data on the discovery of the target customer group, and affect the effect of promotion and placement.

[0003] Currently, the quality evaluation of the third party's user data is usually achieved based on the effect indicators after one round of placement. However, in a complete placement cycle, there may be multiple rounds of placement operations, making it difficult to comprehensively evaluate the conversion potential and long-term value of user data in multiple rounds of placement, unable to quantify the deep relationship of the placement effect in the entire placement cycle, and resulting in the lack of comprehensiveness and accuracy of the evaluation results. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a data evaluation method for multiple rounds of placement, a data evaluation device for multiple rounds of placement, an electronic device, and a computer-readable medium. This method can characterize the associated impact of the data cost of user data and the number of placements in terms of the converted cost, so that in a complete placement cycle, it can comprehensively evaluate the conversion potential and long-term value of user data in multiple rounds of placement, effectively quantify the deep relationship of the placement effect in the entire placement cycle, and improve the comprehensiveness and accuracy of the evaluation results.

[0005] According to the first aspect of the present disclosure, a data evaluation method for multiple rounds of placement is provided. The method may include: determining the data cost of obtaining user data from a third party; obtaining the number of placements since determining the data cost on the user data; determining the converted cost of the user data based on the number of placements and the data cost; and determining the quality evaluation result of the user data based on the converted cost.

[0006] In an exemplary embodiment, determining the data cost of obtaining user data from a third party includes: when obtaining the user data from at least one of the third parties multiple times, determining the data cost when obtaining the user data for the last time.

[0007] In an exemplary embodiment, determining the quality evaluation result of the user data based on the converted cost includes: determining the placement effect indicator of the user data based on the converted cost; and determining the quality evaluation result of the user data based on the placement effect indicator.

[0008] In an exemplary embodiment, determining the delivery effect indicator of the user data based on the amortized cost includes: obtaining the delivery cost of each delivery since the data cost was determined for the user data; obtaining the delivery revenue since the data cost was determined for the user data; and determining the delivery effect indicator based on the amortized cost, the delivery cost, and the delivery revenue.

[0009] In an exemplary embodiment, after determining the amortized cost of the user data based on the number of deliveries and the data cost, it further includes: constructing a dynamically updated cost data table, where the fields of the dynamically updated cost data table at least include user data, amortized cost, and number of deliveries.

[0010] In an exemplary embodiment, the fields of the dynamically updated cost data table at least further include at least one of delivery cost, third-party information, and conversion result.

[0011] In an exemplary embodiment, after determining the quality evaluation result of the user data based on the delivery effect indicator, it further includes: constructing a dynamically updated quality evaluation data table, where the fields of the dynamically updated quality evaluation data table at least include third-party information and the total effect indicator of the included user data.

[0012] According to a second aspect of the present disclosure, there is provided a data evaluation device for multi-round delivery, which may include: a data cost determination module for determining the data cost of obtaining user data from a third party; a delivery number determination module for obtaining the number of deliveries on the user data since the data cost was determined; an amortized cost calculation module for determining the amortized cost of the user data based on the number of deliveries and the data cost; and a data quality evaluation module for determining the quality evaluation result of the user data based on the amortized cost.

[0013] In an exemplary embodiment, the data cost determination module is specifically configured to determine the data cost when the user data was last obtained in the case of obtaining the user data from at least one of the third parties multiple times.

[0014] In an exemplary embodiment, the data quality evaluation module is specifically configured to determine the delivery effect indicator of the user data based on the amortized cost; and determine the quality evaluation result of the user data based on the delivery effect indicator.

[0015] In an exemplary embodiment, the data quality evaluation module is specifically configured to obtain the delivery cost of each delivery since the data cost was determined for the user data; obtain the delivery revenue since the data cost was determined for the user data; and determine the delivery effect indicator based on the amortized cost, the delivery cost, and the delivery revenue.

[0016] In an exemplary embodiment, the device may further include a cost data table construction module for constructing a dynamically updated cost data table on a periodic basis. The fields of the dynamic cost data table at least include user data, converted cost, and number of launches.

[0017] In an exemplary embodiment, the fields of the dynamic cost data table at least further include at least one of launch cost, third-party information, and conversion result.

[0018] In an exemplary embodiment, the device may further include a quality evaluation table construction module for constructing a dynamically updated dynamic quality evaluation data table on a periodic basis. The fields of the dynamic quality evaluation data table at least include third-party information and the total effect index of the included user data.

[0019] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0020] a processor; and

[0021] a memory for storing a computer program of the processor;

[0022] wherein the processor is configured to implement the data evaluation method for multi-round launches as described in the first aspect above by executing the computer program.

[0023] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the data evaluation method for multi-round launches as described in the first aspect.

[0024] According to a fifth aspect of the present disclosure, there is provided a computer program product, which when running on an electronic device, causes the electronic device to implement the data evaluation method for multi-round launches as described in the first aspect when executed.

[0025] The data evaluation method for multi-round launches, the data evaluation device for multi-round launches, the electronic device, and the computer-readable medium provided by the present disclosure. The method can determine the data cost of obtaining user data from a third party, and obtain the number of launches since the data cost was determined for the user data; on this basis, based on the number of launches and the data cost, determine the converted cost of the user data, and determine the quality evaluation result of the user data based on the converted cost. The method can characterize the associated impact of the data cost and the number of launches of user data in terms of the converted cost, so that in a complete launch cycle, the conversion potential and long-term value of user data in multi-round launches can be comprehensively evaluated, the deep association of the launch effect in the entire launch cycle can be effectively quantified, and the comprehensiveness and accuracy of the evaluation result can be improved.

[0026] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0027] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments in accordance with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0028] Figure 1 One of the step flowcharts of the multi-round delivery data evaluation method provided by the embodiments of the present disclosure.

[0029] Figure 2 Another step flowchart of the multi-round delivery data evaluation method provided by the embodiments of the present disclosure.

[0030] Figure 3 The structural block diagram of the multi-round delivery data evaluation device provided by the embodiments of the present disclosure.

[0031] Figure 4 The structural schematic diagram of an electronic device provided by the embodiments of the present disclosure. Detailed Description of the Embodiments

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0033] In addition, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0034] It should be noted that the data obtained in this disclosure is accessed, collected, stored, and applied to subsequent analysis and processing only after clearly informing the user or the relevant data owner of information such as the content of data collection, data usage, and processing methods, and with the consent and authorization of the user or the relevant data owner. Moreover, the ways to access, correct, and delete the data, as well as the methods to revoke consent and authorization, can be sent to the user or the relevant data owner.

[0035] Figure 1 This is one of the step flowcharts of the data evaluation method for multi-round delivery provided by the embodiments of this disclosure. As Figure 1 shown, the method may include the following steps 101 to 104.

[0036] In step 101, determine the data cost of obtaining user data from a third party.

[0037] In the embodiments of this disclosure, the third party may be a data party holding user data. The user data held by different third parties may be different or there may be an intersection. User data may include user identity information, attribute information, behavior information, etc., such as user identification, affiliation, gender, age, etc., user interest preference information, user commodity transaction behavior information, social interaction behavior information, etc. The embodiments of this disclosure do not make specific limitations on this.

[0038] On this basis, there are corresponding data costs when obtaining user data from a third party, such as transmission time cost, network resource cost, payment price cost, etc. Those skilled in the art can determine the content of the data cost according to actual needs. The embodiments of this disclosure do not make specific limitations on this.

[0039] In step 102, obtain the number of deliveries on the user data since the data cost was determined.

[0040] In the embodiments of this disclosure, when evaluating the quality of user data, the number of deliveries on the user data can be determined after determining the data cost of obtaining the user data. For example, based on the delivery of AI outbound calls, it may include the first call, supplementary call, etc., and may also include the task of retrieving high-intent users. Among them, high-intent users may be users who click on the promotion link delivery, users whose first call or supplementary call duration exceeds the predetermined duration, users whose first call or supplementary call conversation turns exceed the predetermined number of turns, high-intent users predicted by the data model analysis, etc.

[0041] For example, for user data 1 obtained from a third party, its data cost is determined to be n. After determining n, an AI outbound call is made on user data 1 and the first call is connected, and user data 1 appears in the task of retrieving clicks on the promotion link delivery, so as to determine that the number of deliveries on user data 1 is 2.

[0042] In step 103, the converted cost of user data is determined based on the number of deliveries and the data cost.

[0043] In the embodiments of the present disclosure, the converted cost may be an index characterizing the correlation between the number of deliveries and the data cost since the data cost of user data is determined. Among them, the converted cost may be the average data cost per delivery; or the converted number of times may be determined by weighting according to the delivery method each time based on the number of deliveries, so as to determine the weighted average of the converted number of times and the data cost. For example, the first call weight value of AI outbound calls may be set to 1.5, and the weight value of the promotion link retrieval task may be set to 0.8. Then, when the number of deliveries is 2, the converted number of times may be 2.3, and the weight value of the delivery method may be set according to historical experience analysis. The embodiments of the present disclosure do not specifically limit the method for determining the converted cost and the setting of the weight value for calculating the converted number of times.

[0044] In step 104, the quality evaluation result of user data is determined based on the converted cost.

[0045] In the embodiments of the present disclosure, the quality of user data may be evaluated based on the converted cost to obtain a more comprehensive and comprehensive quality evaluation result in possible multiple rounds of long-term deliveries. For example, a high converted cost may indicate a high data cost and a small number of deliveries, resulting in a low quality evaluation result; or a low converted cost may indicate a low data cost and a large number of deliveries, etc., resulting in a high quality evaluation result; a large number of deliveries may indicate a high user intention, resulting in a high quality evaluation result; a high data cost may affect the intention and efficiency of delivery implementation, resulting in a low quality evaluation result. Those skilled in the art can select a scheme for determining the quality evaluation result of user data based on the converted cost according to actual needs, and the embodiments of the present disclosure do not specifically limit this.

[0046] Furthermore, in the embodiments of the present disclosure, the converted cost may also be further processed to characterize the attribute characteristics of user data in more dimensions, so as to obtain a more sufficient and comprehensive quality evaluation result.

[0047] Figure 2 This is the second step flowchart of the data evaluation method for multiple rounds of deliveries provided by the embodiments of the present disclosure. As Figure 2 shown, the method may include the following steps 201 to 205.

[0048] In step 201, when obtaining user data from at least one third party multiple times, the data cost at the time of the last obtaining of user data is determined.

[0049] In the embodiments of the present disclosure, user data may be obtained from a third party multiple times, or may be obtained separately from different third parties multiple times, and the data cost for each acquisition may be the same or different. Based on the user data obtained multiple times, the data cost at the time of the last acquisition of user data can be determined as the basis for subsequent quality evaluation. Further, the determination of the data cost can refer to the relevant description in step 101 above. To avoid repetition, it will not be elaborated here.

[0050] For example, when evaluating the quality of user data 1, it is determined that the data cost was n when user data 1 was previously obtained from third party 1 1 , and the data cost was n when obtained from third party 2 half a month later 2 , so the data cost n for the last acquisition of user data 1 is determined 2 .

[0051] In step 202, obtain the number of placements of the user data since the determination of the data cost.

[0052] In the embodiments of the present disclosure, step 202 can refer to the relevant description in step 102 above. To avoid repetition, it will not be elaborated here.

[0053] For example, the number of placements since obtaining user data 1 from third party 2 is m.

[0054] In step 203, determine the equivalent cost of the user data based on the number of placements and the data cost.

[0055] In the embodiments of the present disclosure, step 203 can refer to the relevant description in step 103 above. To avoid repetition, it will not be elaborated here. On this basis, taking the data cost n 2 and the number of placements m as an example, the equivalent cost Cost 1 can be calculated by the following formula (1):

[0056]

[0057] In step 204, determine the placement effect index of the user data based on the equivalent cost.

[0058] In the embodiments of the present disclosure, the placement effect index can be an index for evaluating the conversion effect of the placement and adjusted based on the equivalent cost, such as adjusting the user conversion rate, return on investment, etc. based on the equivalent cost. For example, in a user group composed of multiple users, the conversion rate is relatively high but the cumulative equivalent cost of each piece of user data is also relatively high. At this time, weights can be set based on the equivalent cost to reduce the placement effect represented by this conversion rate; or, in the process of calculating the return on investment, the equivalent cost is used as part of the cost statistics for calculation, so that the size of the equivalent cost affects the calculation of the return on investment.

[0059] In step 205, the quality evaluation result of the user data is determined based on the delivery effect index.

[0060] In the embodiments of the present disclosure, on this basis, the quality assessment of user data can be performed using delivery effect indicators. If the delivery effect indicator represents that the delivery task on the user data is achieved with low cost, high efficiency, and high recovery delivery effect, then the quality assessment result of the user data indicates that its quality is high. On this basis, the quality assessment result of the user data is adjusted according to the delivery effect it represents.

[0061] Furthermore, based on the quality assessment results of user data, the third party to which the user data belongs can be determined, and then the third party can be comprehensively assessed based on the distribution and accumulation of the quality assessment results in the user data provided by the third party to determine the quality assessment results of the third party. On this basis, the selection of third parties in subsequent delivery tasks can be guided to improve the delivery effect.

[0062] In an exemplary embodiment, the aforementioned step 204 may include the following steps A1 to A3.

[0063] In step A1, the delivery cost of each delivery since the data cost is determined on the user data is obtained.

[0064] In step A2, the delivery revenue since the data cost is determined on the user data is obtained.

[0065] In step A3, the delivery effect index is determined based on the converted cost, delivery cost and delivery income.

[0066] In the disclosed embodiments, taking the return on investment (ROI) as an example, the conventional calculation method is to use the delivery cost of a single delivery and the delivery income of a single delivery to measure the ratio of cost to income. Generally speaking, the higher the ROI, the higher the quality of user data, and vice versa. On this basis, the delivery cost of each delivery and the delivery income since the data cost can be determined. One delivery includes the delivery cost and delivery income of that delivery, and two deliveries include the delivery cost of each delivery and all delivery income during the two delivery periods. Three deliveries, four deliveries, and so on can be deduced. Then, the converted cost and delivery cost are used as the total cost in the ROI calculation, and the delivery income is used as the total income in the ROI calculation to calculate the delivery effect index, and the ROI adjusted for the converted cost is obtained.

[0067] Specifically, the converted cost is Cost 1 Taking the Revenue as an example, the delivery cost can include the voice cost of AI outbound call delivery on user data 1. 2And the cost of SMS for SMS link delivery, Cost 3 , then the ROI can be calculated by the following formula (2):

[0068]

[0069] Where, Cost 1 can represent the converted cost of a single piece of user data, and the corresponding Cost 2 and Cost 3 represent the voice cost and SMS link delivery cost of this single piece of user data, and Revenue represents the delivery revenue of this single piece of user data; Cost 1 can also represent the converted cost of multiple pieces of user data, and the corresponding Cost 2 and Cost 3 represent the voice cost and SMS link delivery cost of these multiple pieces of user data, and Revenue represents the delivery revenue of these multiple pieces of user data.

[0070] In an exemplary embodiment, after the foregoing step 203, the following step B may further be included.

[0071] In step B, a dynamically updated dynamic cost data table is constructed. The fields of the dynamic cost data table at least include user data, converted cost, and delivery times.

[0072] In the embodiments of the present disclosure, the dynamic cost data table is a data table that is periodically updated based on the delivery rounds, delivery cycles, etc. The fields of the dynamic cost data table can at least include user data, converted cost, and delivery times. Among them, each piece of user data can be recorded under the field of user data, and the converted cost field can include the converted cost corresponding to each piece of user data, as well as the corresponding delivery times. The content under each field can be periodically updated according to the delivery rounds, data costs, and changes of third parties. This periodic update can be real-time or updated at time intervals such as minutes, hours, days, etc. The embodiments of the present disclosure do not make specific limitations on this.

[0073] In an exemplary embodiment, the fields of the dynamic cost data table at least further include at least one of delivery cost, third-party information, and conversion result.

[0074] In the embodiments of the present disclosure, the fields of the dynamic cost data table can be expanded according to actual business requirements. For example, it can also include placement costs, third-party information, conversion results, etc. Among them, the placement cost can be the cost consumed for each placement, such as the voice cost in AI outbound calls and the SMS cost in the placement of SMS promotion links; the third-party information can be the relevant information of the third party when the user data is obtained for the last time, such as the name and type of the third party. The type of the third party can be a trading platform, an information platform, a short video platform, etc.; the conversion effect can be the feedback of the conversion behavior of the user corresponding to the user data for the placement. According to different placement purposes, the content of the conversion effect can be different, such as whether the user clicks or places an order. On this basis, it can also include basic information such as date, location, etc.

[0075] In an exemplary embodiment, after the aforementioned step 205, the following step C can also be included.

[0076] In step C, a periodically updated dynamic quality evaluation data table is constructed. The fields of the dynamic quality evaluation data table at least include third-party information and the total effect index of the user data contained therein.

[0077] In the embodiments of the present disclosure, the dynamic quality evaluation data table is a data table that is updated periodically based on the placement rounds, placement cycles, etc. The dynamic quality evaluation data table includes the total effect index of the user data contained in the third party, so as to evaluate the quality of the user data provided by the third party as a whole. Among them, the fields of the dynamic quality evaluation data table at least include third-party information and the total effect index of the user data contained therein. The third-party information can include the name, type, etc. of the third party. The total effect index of the user data contained therein can be obtained by aggregating the effect indexes statistically obtained from all user data. By dynamically updating and monitoring the total effect index, the change in the quality of the user data provided by the third party as a whole can be reflected in a timely manner, so as to adjust the placement strategy.

[0078] In an exemplary embodiment, the fields of the dynamic quality evaluation data table can also include basic information such as date, location, etc., and can also include placement costs, placement revenues, converted costs, etc. It can also include the daily placement cost, daily placement revenue, daily converted cost, daily effect index, as well as the cumulative placement cost, cumulative placement revenue, cumulative converted cost, cumulative effect index, etc. according to different statistical durations. Among them, "daily" represents the data statistics within one day indicated by the date, and "cumulative" represents the time from the intervention of this third party to the time indicated by the date.

[0079] In an exemplary embodiment, when the fields of the dynamic cost data table include third-party information, the dynamic quality assessment data table can be obtained by aggregating the dynamic cost data table using a group by statement. For example, aggregating based on the fields of third-party information, determining the delivery revenue based on the fields of conversion effect, aggregating the corresponding delivery costs of the third party with the delivery costs of user data, and aggregating the corresponding conversion costs of the third party with the conversion costs of user data. When the fields of the dynamic cost data table also include dates, the results of the "same day" related fields can be obtained by aggregating with the date and the fields of third-party information, and the results of the "cumulative" related fields can be obtained by aggregating with the fields of third-party information. The above ways of obtaining the dynamic quality assessment data table are only for illustration, and those skilled in the art can create and maintain the dynamic quality assessment data table according to actual processing conditions and business requirements. The embodiments of the present disclosure do not make specific limitations on this.

[0080] On this basis, the conversion cost in the embodiments of the present disclosure can effectively evaluate the user data retrieved multiple times in the long term. When the real-time effect indicators of a single delivery do not meet the requirements, the long-term potential of multiple rounds of delivery can be evaluated through the conversion cost. On this basis, an overall quality assessment of the third party corresponding to the user data can also be performed, so as to adjust the delivery strategy, such as obtaining more user data from the third party with a higher quality assessment result, or giving a warning to the third party with a lower quality assessment result, or interrupting the acquisition of user data from this third party.

[0081] The data evaluation method for multiple rounds of delivery provided by the present disclosure can determine the data cost of obtaining user data from a third party, and the number of deliveries on the user data since the data cost is determined. On this basis, the conversion cost of the user data is determined based on the number of deliveries and the data cost, and the quality assessment result of the user data is determined based on the conversion cost. This method can characterize the associated impact of the data cost and the number of deliveries of user data with the conversion cost, so that in a complete delivery cycle, the conversion potential and long-term value of user data in multiple rounds of delivery can be comprehensively evaluated, the deep association of the delivery effect in the entire delivery cycle can be effectively quantified, and the comprehensiveness and accuracy of the evaluation result can be improved.

[0082] Figure 3 A structural block diagram of a data evaluation device 300 for multiple rounds of delivery is also provided. As Figure 3 shown, the device 300 may include: a data cost determination module 301, configured to determine the data cost of obtaining user data from a third party; a delivery number determination module 302, configured to obtain the number of deliveries on the user data since the data cost is determined; a conversion cost calculation module 303, configured to determine the conversion cost of the user data based on the number of deliveries and the data cost; and a data quality assessment module 304, configured to determine the quality assessment result of the user data based on the conversion cost.

[0083] In an exemplary embodiment, the data cost determination module 301 is specifically configured to determine the data cost when obtaining the user data for the last time in the case of obtaining the user data from at least one of the third parties multiple times.

[0084] In an exemplary embodiment, the data quality evaluation module 304 is specifically configured to determine the placement effect index of the user data based on the converted cost; and determine the quality evaluation result of the user data based on the placement effect index.

[0085] In an exemplary embodiment, the data quality evaluation module 304 is specifically configured to obtain the placement cost of each placement since the data cost is determined for the user data; obtain the placement revenue since the data cost is determined for the user data; and determine the placement effect index based on the converted cost, the placement cost, and the placement revenue.

[0086] In an exemplary embodiment, the apparatus may further include a cost data table construction module, configured to construct a dynamically updated cost data table, where the fields of the dynamically updated cost data table at least include user data, converted cost, and number of placements.

[0087] In an exemplary embodiment, the fields of the dynamically updated cost data table at least further include at least one of placement cost, third-party information, and conversion result.

[0088] In an exemplary embodiment, the apparatus may further include a quality evaluation table construction module, configured to construct a dynamically updated quality evaluation data table, where the fields of the dynamically updated quality evaluation data table at least include third-party information and the total effect index of the user data included.

[0089] The multi-round placement data evaluation apparatus provided by the present disclosure can determine the data cost of obtaining user data from a third party, and obtain the number of placements since the data cost is determined for the user data; on this basis, determine the converted cost of the user data based on the number of placements and the data cost, and determine the quality evaluation result of the user data based on the converted cost. This method can characterize the associated impact of the data cost and the number of placements of the user data with the converted cost, so that in a complete placement cycle, the conversion potential and long-term value of the user data in multiple rounds of placements can be comprehensively evaluated, the deep association of the placement effect in the entire placement cycle can be effectively quantified, and the comprehensiveness and accuracy of the evaluation result can be improved.

[0090] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0091] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0092] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of the present disclosure.

[0093] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0094] Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a device, method, or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to herein as "circuit", "module", or "system".

[0095] Next, refer to Figure 4 to describe the electronic device 400 according to this embodiment of the present disclosure. Figure 4 The shown electronic device 400 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0096] As Figure 4As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one of the above-mentioned processing units 410, at least one of the above-mentioned storage units 420, and a bus 430 that connects different system components (including the storage unit 420 and the processing unit 410).

[0097] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of this specification.

[0098] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202, and may further include a read-only storage unit (ROM) 4203.

[0099] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205. Such program modules 4205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0100] The bus 430 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0101] The electronic device 400 can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through a display unit 440 and an input / output (I / O) interface 450 connected to the display unit 440. And, the electronic device 400 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 460. As shown in the figure, the network adapter 460 communicates with other modules of the electronic device 400 through the bus 430. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0102] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0103] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above method of this specification. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code that, when the program product runs on a terminal device, causes the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0104] In an embodiment of the present disclosure, there is also provided a program product for implementing the above method, which can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0105] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0106] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0107] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0108] The program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0109] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed, for example, synchronously or asynchronously in multiple modules.

[0110] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and embodiments are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. A data evaluation method for multiple rounds of delivery, characterized in that: The method comprises: Determine the data cost of acquiring user data from third parties; Obtaining the number of times the data has been delivered to the user data since the data cost was determined; Determining the converted cost of the user data based on the number of delivery times and the data cost; A quality assessment result of the user data is determined based on the reduced cost.

2. The method according to claim 1, characterized in that The determining of the data cost of obtaining user data from a third party includes: In case that the user data is obtained from at least one of the third parties for multiple times, a data cost when the user data is obtained for the last time is determined.

3. The method according to claim 1, characterized in that The determining the quality assessment result of the user data based on the converted cost includes: Determining a delivery effect indicator of the user data based on the converted cost; The quality evaluation result of the user data is determined based on the delivery effect indicator.

4. The method according to claim 3, characterized in that The determining of the delivery effect index of the user data based on the converted cost includes: Obtaining the delivery cost of each delivery since the data cost was determined on the user data; Obtaining the delivery revenue since the data cost is determined on the user data; The delivery effect index is determined based on the converted cost, the delivery cost and the delivery income.

5. The method according to claim 1, characterized in that: After determining the converted cost of the user data based on the number of delivery times and the data cost, the method further includes: A dynamic cost data table that is updated periodically is constructed, wherein the fields of the dynamic cost data table include at least user data, converted cost, and number of delivery times.

6. The method according to claim 5, characterized in that The fields of the dynamic cost data table include at least one of delivery cost, third-party information, and conversion result.

7. The method according to claim 3, characterized in that After determining the quality evaluation result of the user data based on the delivery effect indicator, the method further includes: A periodically updated dynamic quality assessment data table is constructed, wherein the fields of the dynamic quality assessment data table at least include third-party information and a total effect index of the contained user data.

8. A data evaluation device for multiple rounds of delivery, characterized in that: The device comprises: A data cost determination module, used to determine the data cost of obtaining user data from a third party; A delivery number determination module, used to obtain the delivery number on the user data since the data cost is determined; A converted cost calculation module, used to determine the converted cost of the user data based on the number of delivery times and the data cost; A data quality assessment module is used to determine a quality assessment result of the user data based on the reduced cost.

9. An electronic device, characterized in that: include: processor; and a memory for storing a computer program for the processor; Wherein, the processor is configured to execute the multi-round delivery data evaluation method described in any one of claims 1 to 7 by executing a computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-round delivery data evaluation method as described in any one of claims 1 to 7 is implemented.

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