Data processing method, computer readable storage medium and computer program product

Abstract

The embodiment of the invention provides a data processing method, a computer readable storage medium and a computer program product, and the data processing method is applied to a data delivery platform and comprises the steps: obtaining to-be-detected operation data of a to-be-detected business resource and reference operation data of at least one reference business resource corresponding to the to-be-detected business resource; obtaining at least one service resource index according to the to-be-detected operation data and each reference operation data; determining resource state information corresponding to the to-be-detected service resource according to at least one service resource detection index; and generating a service resource adjustment strategy for the to-be-detected service resource according to the resource state information. According to the method, real-time feedback and optimization suggestions can be performed according to real-time resource state information, a service provider can be helped to timely identify a putting problem, and a putting strategy is adjusted.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of computer technology, and in particular to a data processing method. Background Art

[0002] With the development of internet technology, many businesses are placing online advertisements to promote their products. However, the complexity and diversity of online advertising make it difficult for businesses to understand the effectiveness of their advertising in real time. This is especially true during the cold start phase, traffic acquisition, and conversion process. Businesses lack timely information on advertising performance and are unable to adjust their advertising strategies.

[0003] To address this issue, advertising platforms have emerged. Merchants can place ads on these platforms and obtain diagnostic results from them. Current ad diagnostic solutions primarily include rule-based, statistical analysis-based, and machine learning-based approaches. These methods struggle to adapt to the complex and ever-changing ad delivery environment, failing to provide timely and effective recommendations. They also require extensive training data, lacking flexibility and efficiency. Therefore, a more convenient and efficient ad diagnostic method is urgently needed to help merchants identify issues and make timely adjustments. Summary of the Invention

[0004] In view of this, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product to address technical deficiencies in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a data processing method is provided, which is applied to a data delivery platform, comprising: Acquire operation data to be detected of the service resource to be detected, and reference operation data of at least one reference service resource corresponding to the service resource to be detected; Obtaining at least one service resource indicator according to the operation data to be detected and each reference operation data; Determining resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator; A service resource adjustment strategy for the service resource to be detected is generated according to the resource status information.

[0006] According to a second aspect of the embodiments of this specification, there is provided a data processing device, applied to a data delivery platform, comprising: An acquisition module is configured to acquire the operation data to be detected of the service resource to be detected and the reference operation data of at least one reference service resource corresponding to the service resource to be detected; a statistics module configured to obtain at least one service resource indicator based on the operation data to be detected and each reference operation data; A determination module is configured to determine resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator; The generating module is configured to generate a service resource adjustment policy for the service resource to be detected according to the resource status information.

[0007] According to a third aspect of an embodiment of this specification, a computing device is provided, including: memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions. When the computer programs / instructions are executed by the processor, the steps of the above method are implemented.

[0008] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores a computer program / instruction, and the computer program / instruction implements the steps of the above method when executed by a processor.

[0009] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0010] The method provided in the embodiments of this specification can perform data comparison through the operation data of the business resources to be tested and the reference operation data of the reference business resources, and can adaptively identify problems in the business data to be tested. It can perform vertical trend comparison of the business data to be tested itself through various types of operation data in the entire link of data delivery, as well as horizontal distribution comparison with other reference business resources. Through the comparison of the two dimensions, the resource status information of the business resources to be tested can be obtained in real time. According to the real-time resource status information, instant feedback and optimization suggestions can be provided to help business providers identify delivery problems in a timely manner and adjust delivery strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flow chart of a data processing method provided by one embodiment of this specification; Figure 2 This is a schematic diagram of the architecture of an advertising delivery scenario provided by an embodiment of this specification; Figure 3 This is a schematic diagram of a business resource adjustment strategy for an advertising delivery scenario provided by an embodiment of this specification; Figure 4 This is a schematic diagram of the structure of a data processing device provided by one embodiment of this specification; Figure 5This is an architectural diagram of a data processing system provided by one embodiment of this specification; Figure 6 This is a structural block diagram of a computing device provided by one embodiment of this specification. DETAILED DESCRIPTION

[0012] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0013] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "the," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0014] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0015] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this manual are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation entrances must be provided for users to choose to authorize or refuse.

[0016] First, the terms involved in one or more embodiments of this specification are explained.

[0017] External advertising: A method of advertising that allows advertisers to connect with media outlets for off-site online advertising. The goal is to attract more users with less cost and more accurate advertising methods.

[0018] Cold start: A critical phase in the advertising process, also known as the learning period, during which a campaign needs to accumulate a certain number of conversions to be considered successful. The definition and duration of a cold start vary depending on the data delivery platform.

[0019] Traffic acquisition: refers to the process of attracting users to click on ads and enter the target page through various channels and means. The quality of traffic acquisition directly affects the effectiveness and cost of advertising.

[0020] Conversion effect: refers to the user completing the expected behavior (such as purchase, registration, etc.) after clicking on the advertisement. This is the core indicator for measuring the effectiveness of advertising.

[0021] Currently, the complexity and diversity of advertising on e-commerce platforms make it difficult for merchants to understand the effectiveness of advertising in real time. Especially during the cold start phase, traffic acquisition, and effect conversion process, merchants cannot obtain timely information on advertising status and adjust their advertising strategies. Based on this, merchants use the advertising platform's ability to connect with media to place advertisements on the advertising platform and obtain diagnostic results from the advertising platform. Currently, advertising diagnosis solutions available on the market include rule-based diagnosis methods, statistical analysis-based diagnosis methods, and machine learning-based diagnosis methods. Each of these methods has its own limitations: Rule-based diagnostic method: This method relies on pre-set rule templates and identifies problems based on fixed conditions. Its disadvantage is that the rule templates are fixed and unchanging, making it difficult to adapt to complex advertising delivery environments and requiring frequent manual updates.

[0022] Diagnostic method based on statistical analysis: Statistical analysis is performed through historical data to identify abnormal situations. Its disadvantage is that it has a delayed response to new problems and cannot provide effective optimization suggestions.

[0023] Machine learning-based diagnostic method: Automatically identifies problems through training models. Its disadvantage is that it requires a large amount of training data and computing resources, and the model has poor interpretability, making it difficult to directly guide merchants for optimization.

[0024] Based on this, a data processing method is provided in this specification. This specification also involves a data processing device, a computing device, a computer-readable storage medium and a computer program product, which are described in detail one by one in the following embodiments.

[0025] See also Figure 1 , Figure 1 A flow chart of a data processing method provided according to an embodiment of the present specification is shown. The method is applied to a data delivery platform and specifically includes the following steps.

[0026] Step 102: Acquire the operation data of the service resource to be detected and the reference operation data of at least one reference service resource corresponding to the service resource to be detected.

[0027] Among them, the business resources to be tested can be understood as resources that require business testing. The business resources to be tested are delivered through the data delivery platform. The method provided in the embodiments of this specification can be applied to, but is not limited to, the process of analyzing and evaluating exposed business resources. Business resources include, but are not limited to, advertising resources, and the type of advertising resources can be multimedia resources such as at least one of text, images, videos, and audio. The specific product forms of advertising resources can include native ads, patch ads, banner ads, search ads, etc.

[0028] In actual applications, a service provider can publish business resources through a data delivery platform and monitor the delivery of these business resources through the data delivery platform. The business resources monitored by the service provider through the data delivery platform can be considered as the business resources to be monitored. For example, the service provider can be an advertiser, the data delivery platform can be an advertising delivery platform, and the business resource to be monitored can be an advertising plan launched by the advertiser through the advertising delivery platform. The advertiser monitors the delivery of this advertising plan through the advertising delivery platform.

[0029] The operational data to be tested can be understood as the operational data of the business resources to be tested on the data delivery platform, which may include resource configuration data, resource log data, resource conversion effect data, media statistics, etc. For example, in the advertising delivery scenario, resource configuration data can be understood as the attribution configuration information configured by the advertiser when delivering the ad, resource log data can be understood as the number of clicks, playbacks, app calls, etc. after the ad delivery, resource conversion effect data can be understood as the number of transactions and registrations generated by the ad, etc., and media statistics data can be understood as the statistics of other ads in the same category, etc.

[0030] The data processing method provided in the embodiment of this specification detects and analyzes the business resources to be detected on the data delivery platform. Based on obtaining the operation data to be detected of the business resources to be detected, the reference business resources corresponding to the business resources to be detected and the reference operation data corresponding to the reference business resources are also determined.

[0031] Reference business resources can be understood as business resources that provide reference during the testing and analysis of the business resources to be tested, such as business resources deployed by other business providers on the data delivery platform, or other business resources deployed by the same business provider on the data delivery platform. Reference operation data can be understood as the operation data of reference business resources on the data delivery platform. For the specific categories and content of reference operation data, please refer to the above description of the operation data to be tested.

[0032] In a specific implementation provided in this specification, obtaining reference operation data of at least one reference service resource corresponding to the service resource to be detected includes: Determining the service resource type of the service resource to be detected; At least one reference business resource is determined according to the business resource type, and reference operation data corresponding to each reference business resource is acquired.

[0033] In actual applications, data delivery platforms may target a variety of service providers, and the same service provider may also provide different service resources. When analyzing the service resources to be detected, referring to other service resources of the same type can be more targeted. Based on this, the methods provided in the embodiments of this specification can also filter reference service resources based on the type of service resource, thereby making the subsequent analysis and comparison of operational data more accurate.

[0034] Based on this, first obtain the business resource type of the business resource to be detected. The business resource type can be understood as the category to which the business resource to be detected belongs. For example, the business resource type can be food, medicine, daily necessities, automobiles, and so on. Furthermore, the business resource type can be further divided according to actual conditions. For example, for business resource types that belong to the same medicine category, they can be further divided into cold type, anti-inflammatory type, allergy type, and so on. For business resource types that belong to the same automobile category, they can be further divided into fuel type, new energy type, and so on. In the embodiment of this specification, the dimension of the business resource type is not limited. It can serve to screen out reference business resources of the same type as the business resource to be detected from a large number of business resources.

[0035] Once the business resource type corresponding to the business resource to be detected is determined, other business resources placed on the same data placement platform can be screened according to the business resource type to determine reference business resources belonging to the same business resource type, and at the same time, obtain reference operation data corresponding to the reference business resources.

[0036] Step 104: Obtain at least one service resource indicator according to the operation data to be detected and each reference operation data.

[0037] In the above steps, when the operation data to be detected of the business resource to be detected and the reference operation data corresponding to each reference business resource are obtained, the operation data to be detected and the reference operation data can be parsed and analyzed to obtain at least one business resource indicator according to the preset detection parsing dimension.

[0038] Business resource indicators can be understood as indicator data used to determine the status of the business resource to be tested. In actual applications, when testing business resources to be tested, it is necessary to test based on specific indicator data. If a business resource indicator meets the corresponding indicator threshold, it is considered qualified. If a business resource indicator does not meet the corresponding indicator threshold, it indicates that the business resource indicator is abnormal and further evaluation of the relevant data corresponding to the business resource indicator is required.

[0039] In a specific embodiment provided in this specification, obtaining at least one service resource indicator according to the to-be-detected operation data and each reference operation data includes: Calculating at least one service resource indicator to be detected based on the operation data to be detected; The reference business resource indicators corresponding to each reference operation data are calculated based on each reference operation data.

[0040] In this embodiment, the service resource indicators are specifically divided into service resource indicators to be detected related to the operation data to be detected, and reference service resource indicators corresponding to the reference operation data.

[0041] The business resource indicators to be tested can be understood as indicator data calculated from the operational data to be tested. For example, taking advertising as the business indicator to be tested, the operations of the entire advertising chain are divided into processes such as ad exposure, playback, clicks, end-user calls, visits, and conversions. Key information can be extracted from these operations, and the business resource indicators to be tested can be calculated to represent the resource status information of the business resource to be tested. During the cold start phase, the resource cold start throughput refers to the indicator of M returns for an ad within N days. A return order refers to an ad order that has generated valid transactions through the ad. For example, if an ad specifies 5 valid transactions within 15 days, the ad is considered to have passed the cold start phase. By obtaining the operational data to be tested for the ad and counting the number of returns in the 15 days before the launch plan, it can be determined whether the ad has passed the cold start phase.

[0042] For example, business resource indicators also include resource effective playback rate and resource click-through rate. The effective playback rate is used to characterize the ratio of normal advertisement playback to avoid users mistakenly clicking on advertisements or entering advertisements in other ways. Normally, a preset duration of advertisement playback can be specified to be considered an effective playback. The effective playback rate refers to the number of effective playbacks divided by the number of exposures.

[0043] The resource click-through rate is used to represent the probability of the ad being clicked. Usually, it is calculated by dividing the number of clicks on the ad by the number of impressions.

[0044] Business resource metrics also include resource conversion rate, which indicates the effectiveness of an ad in driving customer engagement. A resource conversion order can be understood as an order that leads to a landing page through an ad and subsequently results in a fully paid order. This is a key metric for evaluating advertising campaigns. The resource conversion rate is calculated by dividing the number of orders placed through an ad by the number of impressions.

[0045] The resource funnel ratio and resource exposure cost are also important business resource indicators. The resource funnel ratio includes the click-through rate from exposure to click (clicks divided by exposures, used to measure the attractiveness of ads and the accuracy of targeting), the reach rate from clicks to visits (visits divided by clicks, used to measure the stability of ad jump technology), the conversion rate from visits to conversions (conversions processing visits, used to evaluate landing page user experience and conversion design), and the total conversion rate from exposure to conversion (conversions divided by exposures, a comprehensive indicator used to characterize advertising efficiency).

[0046] Resource exposure cost is also called thousand-exhibition exposure cost, that is, the cost per thousand impressions, which refers to the fee paid by advertisers for every thousand impressions during the advertising process.

[0047] The above is an introduction to business resource indicators. In actual applications, at least one business resource indicator to be tested can be calculated based on the operation data to be tested. The business resource indicators to be tested include but are not limited to the resource cold start throughput, resource effective playback rate, resource click-through rate, resource conversion rate, resource funnel ratio, resource exposure cost, etc. introduced above.

[0048] At the same time, reference business resource indicators corresponding to each reference operation data can also be calculated based on each reference operation data. The reference business resource indicators correspond to the reference business resources.

[0049] Step 106: Determine resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator.

[0050] In the above steps, at least one business resource detection indicator is calculated. By comparing each business resource detection indicator with the preset indicator information, abnormal business resource detection indicators are screened out, and the resource status information corresponding to the business resource to be detected can be determined through the abnormal business resource detection indicators.

[0051] Resource status information can be understood as the current status information of the service resource to be detected, which is used to characterize the current problem faced by the service resource to be detected. In the method provided in the embodiments of this specification, the current problem of the service resource to be detected is detected from multiple service resource detection indicators, and the current problem is analyzed to provide a corresponding solution.

[0052] In a specific implementation provided in this specification, determining resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator includes S1062 to S1066: S1062: Determine first status information corresponding to the service resource to be detected according to at least one indicator of the service resource to be detected.

[0053] In the method provided in the embodiments of this specification, it is determined that the business resource detection indicators include the business resource indicators to be detected and the reference business resource indicators. When the business resources to be detected are detected, the status of the business resources to be detected can be determined through two dimensions. The first dimension is to detect with indicators related to the business resources to be detected themselves, and the second dimension is to sort the business resources to be detected together with the reference business resources, and perform detection according to the sorting results. In this step, detection is performed based on the business indicators to be detected of the business resources to be detected.

[0054] Specifically, the first state information can be understood as the state information of the business resource to be detected using the business resource indicator to be detected to detect its own dimension. In a specific embodiment provided in this specification, determining the first state information corresponding to the business resource to be detected based on at least one business resource indicator to be detected includes: If the resource cold start throughput is abnormal, it is determined that the business resource to be tested has a cold start problem; If there are abnormalities in the resource effective playback rate, resource click rate, and resource conversion rate, it is determined that the business resource to be tested has a traffic acquisition problem.

[0055] In the early stages of advertising, due to the lack of sufficient data on the media side to judge the effectiveness of the advertisement and the audience's preferences, the advertisement will face certain difficulties in the initial stage, which is referred to as the cold start stage in the method provided in this specification. In this method, the resource cold start throughput is used to determine whether the business resource to be tested has passed the cold start stage. If the resource cold start throughput is abnormal (that is, it does not exceed the preset threshold), it can be determined that the business resource to be tested has a cold start problem. If the resource cold start throughput is normal, it means that the business resource to be tested has passed the cold start stage.

[0056] In addition to passing the cold start phase, other indicators of the business resources to be tested can be used to further identify traffic acquisition issues within the business resources to be tested. Traffic acquisition issues focus on the traffic funnel performance in the data delivery link.

[0057] During this process, key metrics to watch include the effective playback rate and click-through rate, which reflect the quality of the business resource materials. If the effective playback rate or click-through rate is abnormal, it indicates that the material quality of the business resources being tested, delivered by the service provider, is poor and has not attracted sufficient business traffic. This indicates a traffic acquisition problem, specifically a material quality issue within the traffic acquisition problem.

[0058] If the resource's effective playback rate and click-through rate are normal, further testing of the resource conversion rate is necessary. The resource conversion rate reflects the quality of the resource landing page. If the resource landing page is of poor quality or has an unreasonable layout, traffic that clicks through to the landing page will be lost, and traffic that clicks through to the landing page will not be effectively driven to the purchase page. This corresponds to a traffic acquisition problem, specifically the landing page quality issue within the traffic acquisition problem.

[0059] In actual applications, if the traffic funnel ratios of the business resource delivery link (such as resource effective play rate, resource click rate, resource conversion rate, etc.) are all low, it is necessary to further determine the corresponding status information in combination with reference business resource indicators. If the traffic funnel ratios of the business resource delivery link are all normal, the first status information is determined to be normal.

[0060] S1064: Determine second status information corresponding to the service resource to be detected according to at least one service resource detection indicator and each reference service resource indicator.

[0061] In the above steps, the first state information is determined based on the dimensional information of the business resource to be tested, and the comparison between the business resource detection index and the preset threshold is used to determine the first state information. In this step, the second state information corresponding to the business resource to be tested is determined based on the dimensional information between the business resource to be tested and the reference business resource. The second state information can be understood as the state information determined by comparing the business resource to be tested with the reference business resource.

[0062] In this step, the service resource detection index of the service resource to be detected is compared horizontally with the reference service resource index of the reference service resource, so as to determine the level of the service resource to be detected in the reference service resource.

[0063] In the above steps, if the ratio of the traffic funnel of the business resource delivery link of the business resource to be tested (such as the resource effective playback rate, resource click rate, resource conversion rate, etc.) is low, it will be compared with the reference business resource. If the ratio of the traffic funnel of the business resource delivery link of the business to be tested is lower than the ratio of the traffic funnel of the business resource delivery link of the reference business resource, and the resource exposure cost (Qianzhan exposure cost) is also low, the user portrait of the viewing user of the business resource to be tested will be further obtained, so as to determine that the second status information is a user quality problem in the traffic acquisition problem.

[0064] In a specific implementation provided in this specification, determining the second state information corresponding to the service resource to be detected according to at least one service resource detection indicator and each reference service resource indicator includes: In the event of anomalies in resource conversion rates, obtain reference resource funnel ratio, reference resource exposure cost, and reference business resource price; When there are anomalies in the reference resource funnel ratio, reference resource exposure cost, and reference business resource price, it is determined that the business resource to be tested has an effect conversion problem.

[0065] When comparing business resource detection indicators with those of various reference business resources, resource conversion rate is a key indicator. If the resource conversion rate of the business resource under test is lower than that of the reference business resource, it is considered abnormal. In this case, information such as the reference resource funnel ratio, reference resource exposure cost, and reference business resource price is obtained from the reference business resource to further determine the second status of the business resource under test.

[0066] Specifically, if the resource conversion rate of the business resource to be tested is lower than the resource conversion rate of the reference business resource, the attribution configuration information of the business resource to be tested will be determined. If the attribution configuration information is abnormal, the second state information will be determined as an effect conversion problem, specifically the attribution configuration problem of the effect conversion problem.

[0067] If the resource conversion rate of the business resource to be tested is lower than the resource conversion rate of the reference business resource, the price of the business resource to be tested will also be determined. If the price of the business resource to be tested is higher than the reference business resource price corresponding to the reference business resource, the second status information will be determined to be an effect conversion problem, specifically a pricing and profit-sharing problem of the effect conversion problem.

[0068] If the resource conversion rate of the business resource under test is lower than that of the reference business resource, the reference resource exposure cost will be used for further analysis. If the difference between the resource exposure cost of the business resource under test and the reference resource exposure cost is significant, the second status information will be determined as an effect conversion issue. Specifically, if the resource exposure cost of the business resource under test is lower than the reference resource exposure cost, it is a case of insufficient bidding for effect conversion; if the resource exposure cost of the business resource under test is higher than the reference resource exposure cost, it is a case of excessive bidding for effect conversion.

[0069] If the comparisons of various dimensions between the service resource to be detected and the reference service resource are normal, the second status information is determined to be normal.

[0070] S1066: Determine resource status information corresponding to the service resource to be detected according to the first status information and the second status information.

[0071] After the above steps, the service resource to be detected is detected in its own dimension to obtain first state information, and the service resource to be detected is detected in the inter-resource dimension with the reference service resource to obtain second state information. The first state information and the second state information are merged to obtain resource state information corresponding to the service resource to be detected.

[0072] In a specific implementation provided in this specification, determining the resource status information corresponding to the service resource to be detected according to the first status information and the second status information includes: In a case where the first state information is a cold start problem or a traffic acquisition problem, determining that the resource state information is the first state information; When the first state information is normal and the second state information is an effect conversion problem, determining the resource state information to be the first state information; When the first status information is normal and the second status information is normal, it is determined that the resource status information is a product abnormality, where the product is a product corresponding to the service resource to be detected.

[0073] If the first status information indicates a cold start problem or a traffic acquisition problem, it can be determined that the resource status information is the corresponding cold start problem or traffic acquisition problem.

[0074] If the first status information is normal, but the second status information indicates an effect conversion problem, it is determined that the resource status information is a resource conversion problem.

[0075] If the first status information is abnormal and the second status information is also abnormal, the resource status information will refer to both the first status information and the second status information.

[0076] The ultimate evaluation of the business resources under inspection depends on their effectiveness, specifically their return on investment (ROI). This ROI measures the profitability of an investment, specifically how much revenue the investment generates. If both the first and second status information are normal, but the ROI is still unsatisfactory, it can be determined that the product corresponding to the business resource under inspection has an anomaly. For example, the product category is inappropriate or outdated.

[0077] Step 108: Generate a service resource adjustment strategy for the service resource to be detected according to the resource status information.

[0078] After the above steps, the resource status information corresponding to the service resource to be detected can be determined, and the resource status information indicates the current problem faced by the service resource to be detected. After the resource status information is determined, the corresponding service resource adjustment strategy can be further determined based on the resource status information.

[0079] The business resource adjustment strategy can be understood as the adjustment strategy provided by the data delivery platform for the business resources to be tested, which is used to help the business resources to be tested expand their influence and improve the resource delivery effect of the business provider.

[0080] In a specific implementation provided in this specification, generating a service resource adjustment strategy for the service resource to be detected according to the resource status information includes: Determine the service resource adjustment policy corresponding to the resource status information according to the adjustment policy configuration table; or The resource status information and the operation data to be detected are input into a policy analysis model to obtain a business resource adjustment policy output by the policy analysis model.

[0081] In practical applications, the business resource adjustment policy for the business resource to be detected can be generated based on the resource status information according to a pre-set adjustment policy configuration table or a policy analysis model based on a large language model. In the method provided in this specification, the adjustment policy configuration table is used as an example for further explanation.

[0082] In the method provided in this specification, the data delivery platform pre-stores an adjustment strategy configuration table corresponding to different resource status information. The adjustment strategy configuration table can be used to obtain adjustment strategies corresponding to different types of resource status information.

[0083] Specifically, when the resource status information is a cold start problem, it means that the business resource to be tested is facing difficulties in the initial stage of data delivery. In order to overcome this difficulty, the business resource adjustment strategy provided to the business provider may be to adjust the scope of advertising attribution configuration, provide feedback compensation, relax user group targeting, increase bids, etc.

[0084] If the resource status information is a material quality issue in the traffic acquisition problem, it means that the material quality of the business resource to be tested is poor. To solve this problem, the data delivery platform can obtain high-quality materials of the same type based on the type of business resource to be tested, and provide the high-quality materials to the business provider to help the business provider improve and enrich the material quality of the business resource to be tested.

[0085] If the resource status information indicates a landing page quality issue in the traffic acquisition problem, this means that although users are accessing the landing page of the business resource under test through high-quality creative materials, the poor quality of the landing page prevents the traffic entering the landing page from being effectively converted into orders. Based on this, the business resource adjustment strategy provided to the business provider can help the business provider customize the landing page, provide high-quality landing page templates and creative materials, analyze the keywords of the business resource under test, and assist the business resource under test in creating high-quality landing pages to improve order conversion.

[0086] If the resource status information indicates a user quality issue related to traffic acquisition, this indicates that the service resource under investigation is not reaching enough customers or is not matching the target audience. Based on this, the service provider can be provided with a resource adjustment strategy to appropriately increase the bid for the service resource under investigation, or provide smart bidding to expand the number of customers reached and enrich the types of interested users.

[0087] If the resource status information indicates an attribution configuration issue related to effect conversion, it means that the attribution configuration for statistical resource conversion rate is too narrow. In this case, the service provider can be helped to expand the attribution configuration parameters to provide a better statistical cycle and thus calculate the resource conversion rate.

[0088] If the resource status information is about pricing concessions related to effect conversion, it means that the average order value of the product corresponding to the business resource to be tested is higher than that of the reference business resource. The business resource adjustment strategy that can be provided to the business provider is to adjust product prices, launch promotional activities, etc., to increase the competitiveness of the product corresponding to the business resource to be tested.

[0089] If the resource status information indicates insufficient bidding for effect conversion, it means that the business provider has a low resource exposure cost for the business resources to be tested and a small range of reachable customers. The business resource adjustment strategy provided to the business provider can be to increase the resource exposure cost and expand the reachable customer range.

[0090] If the resource status information indicates an overbidding problem due to effect conversion, the business resource adjustment strategy provided to the business provider may be to reduce the resource exposure cost.

[0091] If the resource status information indicates a product anomaly, it can be used to remind you that the product category corresponding to the business resource to be detected is inappropriate.

[0092] In another specific embodiment provided in this specification, the method further comprises: The service resource adjustment policy is sent to the service provider corresponding to the service resource to be detected.

[0093] After determining the service resource adjustment strategy corresponding to the service resource to be detected, the service resource adjustment strategy can be sent to the service provider corresponding to the service resource to be detected, helping the service provider to adjust the delivery strategy of the service resource to be detected and expand the influence of the service resource to be detected.

[0094] Through the method provided in the embodiments of this specification, data comparison can be performed through the operation data of the business resources to be tested and the reference operation data of the reference business resources, and problems in the business data to be tested can be adaptively identified. The vertical trend comparison of the business data to be tested itself can be performed through various types of operation data in the entire link of data delivery, as well as the horizontal distribution comparison with other reference business resources. Through the comparison of the two dimensions, the resource status information of the business resources to be tested can be obtained in real time. Instant feedback and optimization suggestions based on the real-time resource status information can help business providers to promptly identify delivery problems and adjust delivery strategies.

[0095] This method can adaptively identify data delivery issues and flexibly respond to diverse data delivery environments, reducing template constraints. Compared to machine learning solutions, this method reduces the reliance on large amounts of training data and computing resources, improving data processing speed and response efficiency. Furthermore, by analyzing specific data, it improves the interpretability of identified issues, allowing edge service providers to understand and implement corresponding service resource adjustment strategies.

[0096] See also Figure 2 , Figure 2 The schematic diagram of the architecture of the advertising delivery scenario provided by an embodiment of this specification is shown as follows: Figure 2 As shown, advertisers place advertisements on the advertising delivery platform, which can obtain advertising configuration data, delivery log data, conversion effect data and media statistics in real time, extract data operation records of the advertisements to be tested and reference advertisements from various types of data, and input them into the diagnosis engine.

[0097] In the diagnostic engine, various data operation records are collated and analyzed, and through vertical trend fitting and horizontal distribution scoring, the problem characteristics currently faced by the advertisement to be tested are identified and the current problem characteristics are input into the problem matching module.

[0098] In the problem matching module, the current problem features are matched with the advertising problem knowledge base, the current problem of the advertisement to be detected is analyzed, and the current problem is input into the suggestion module.

[0099] The suggestion module combines the optimization suggestion knowledge base to provide solutions to current issues and generate a diagnosis and suggestion report for the current ad. This report includes: 1. Disclosure of key issues; 2. Demonstration of reasoning evidence; and 3. Recommended optimization suggestions. This report is then pushed to merchants, helping them to promptly adjust their advertising strategies for the ads under test.

[0100] See also Figure 3 , Figure 3 FIG. 1 shows a schematic diagram of a business resource adjustment strategy for an advertisement delivery scenario provided by an embodiment of this specification. Figure 3 As shown, in this embodiment, a diagnostic threshold is set for the advertisements to be tested, which corresponds to advertisements with a daily consumption of greater than or equal to 100 yuan and a daily exposure of greater than or equal to 3,000 times.

[0101] Advertising delivery problems are divided into cold start problems, traffic acquisition problems and effect conversion problems.

[0102] For cold start issues, the key metric is the cold start pass rate. If the cold start pass rate is lower than the average for the same category, the merchant will be advised to enable smart callbacks. If the cold start rate is lower than the average for the same channel and the attribution behavior configuration is narrow, the merchant will be advised to enable smart callbacks and expand the attribution behavior configuration. If the cold start pass rate is lower than the average for the same category, the merchant's number of advertising plans is less than the threshold, and the attribution behavior configuration is narrow, the merchant will be advised to enable smart callbacks, expand the advertising plans, and expand the attribution behavior configuration.

[0103] For traffic acquisition issues, the key indicators are effective play rate, click-through rate, conversion rate, and cost per thousand-exhibition exposure. If the effective play rate and click-through rate are lower than the average for ads in the same category, it is considered a material quality issue, and high-quality materials are provided to merchants. If the effective play rate and click-through rate are normal, but the conversion rate is lower than the average for ads in the same category, it is considered a landing page quality issue, and a landing page configuration plan is provided to merchants. If the effective play rate, click-through rate, conversion rate, and cost per thousand-exhibition exposure are lower than the average for ads in the same category, it is considered a user mismatch, and merchants are advised to increase their bids, and bid hosting services are provided to merchants.

[0104] For the problem of effect conversion, the premise is that the ROI is lower than the average of the same category of advertisements. The key indicators are conversion rate, average order value and thousand-display exposure cost. If the conversion rate is lower than the average of the same category of advertisements and the attribution behavior configuration is narrow, it is considered an attribution configuration problem, and the attribution behavior configuration is broadened for the merchant; if the conversion rate is lower than the average of the same category of advertisements and the average order value is higher than the average of the same category of advertisements, it is considered a product price problem, and suggestions for price reduction and pre-discount are provided to the merchant; if the conversion rate is lower than the average of the same category of advertisements and the thousand-display exposure cost is lower than the average of the same category of advertisements, it is considered a bidding problem, and suggestions for increasing bids are provided to the merchant, and bid hosting services are provided to the merchant; if the thousand-display exposure cost is higher than the average of the same category of advertisements and exceeds the preset threshold, it is also considered a bidding problem, and suggestions for lowering bids are provided to the merchant, and bid hosting services are provided to the merchant. If the conversion rate, average order value and thousand-display exposure cost are all on par with the average of the same category of advertisements, but its ROI is still low, it can be considered a product problem, and product category analysis suggestions are provided to the merchant to re-evaluate whether the advertising track is reasonable.

[0105] Corresponding to the above method embodiment, this specification also provides a data processing device embodiment, Figure 4 FIG1 shows a schematic diagram of the structure of a data processing device provided by an embodiment of this specification. Figure 4 As shown, the device includes: The acquisition module 402 is configured to acquire the operation data to be detected of the service resource to be detected and the reference operation data of at least one reference service resource corresponding to the service resource to be detected; The statistics module 404 is configured to obtain at least one service resource indicator based on the operation data to be detected and each reference operation data; The determination module 406 is configured to determine resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator; The generating module 408 is configured to generate a service resource adjustment policy for the service resource to be detected according to the resource status information.

[0106] Optionally, the acquisition module 402 is further configured to: Determining the service resource type of the service resource to be detected; At least one reference business resource is determined according to the business resource type, and reference operation data corresponding to each reference business resource is acquired.

[0107] Optionally, the statistics module 404 is further configured to: Calculating at least one service resource indicator to be detected based on the operation data to be detected; The reference business resource indicators corresponding to each reference operation data are calculated based on each reference operation data.

[0108] Optionally, the determining module 406 is further configured to: Determine first status information corresponding to the service resource to be detected according to at least one service resource indicator to be detected; Determining second status information corresponding to the service resource to be detected according to at least one service resource detection indicator and each reference service resource indicator; The resource status information corresponding to the service resource to be detected is determined according to the first status information and the second status information.

[0109] Optionally, the determining module 406 is further configured to: If the resource cold start throughput is abnormal, it is determined that the business resource to be tested has a cold start problem; If there are abnormalities in the resource effective playback rate, resource click rate, and resource conversion rate, it is determined that the business resource to be tested has a traffic acquisition problem.

[0110] Optionally, the determining module 406 is further configured to: In the event of anomalies in resource conversion rates, obtain reference resource funnel ratio, reference resource exposure cost, and reference business resource price; When there are anomalies in the reference resource funnel ratio, reference resource exposure cost, and reference business resource price, it is determined that the business resource to be tested has an effect conversion problem.

[0111] Optionally, the determining module 406 is further configured to: In a case where the first state information is a cold start problem or a traffic acquisition problem, determining that the resource state information is the first state information; When the first state information is normal and the second state information is an effect conversion problem, determining the resource state information to be the first state information; When the first status information is normal and the second status information is normal, it is determined that the resource status information is a product abnormality, where the product is a product corresponding to the service resource to be detected.

[0112] Optionally, the generating module 408 is further configured to: Determine the service resource adjustment policy corresponding to the resource status information according to the adjustment policy configuration table; or The resource status information and the operation data to be detected are input into a policy analysis model to obtain a business resource adjustment policy output by the policy analysis model.

[0113] Optionally, the device further includes a sending module configured to: The service resource adjustment policy is sent to the service provider corresponding to the service resource to be detected.

[0114] The device provided in the embodiments of this specification can perform data comparison through the operation data of the business resources to be detected and the reference operation data of the reference business resources, and can adaptively identify problems in the business data to be detected. It can perform vertical trend comparison of the business data to be detected itself through various types of operation data in the entire link of data delivery, as well as horizontal distribution comparison with other reference business resources. Through the comparison of the two dimensions, the resource status information of the business resources to be detected can be obtained in real time. Instant feedback and optimization suggestions based on the real-time resource status information can help business providers to promptly identify delivery problems and adjust delivery strategies.

[0115] This device can adaptively identify data delivery issues and flexibly respond to diverse data delivery environments, reducing template constraints. Compared to machine learning solutions, this solution reduces the reliance on large amounts of training data and computing resources, improving data processing speed and response efficiency. Furthermore, by analyzing specific data, the interpretability of identified issues is improved, allowing edge service providers to understand and implement corresponding service resource adjustment strategies.

[0116] The above is a schematic diagram of a data processing device according to this embodiment. It should be noted that the technical solution of the data processing device and the technical solution of the above-mentioned data processing method are based on the same concept. For details not described in detail in the data processing technical solution, please refer to the description of the technical solution of the above-mentioned data processing method.

[0117] See also Figure 5 , Figure 5 1 shows an architecture diagram of a data processing system provided by an embodiment of the present specification. The data processing system may include a client 100 and a server 200. The client 100 is used to send a service resource detection request to the server 200; The server 200 is configured to obtain operation data of a service resource to be detected and reference operation data of at least one reference service resource corresponding to the service resource to be detected; obtain at least one service resource indicator based on the operation data to be detected and the reference operation data; determine resource status information corresponding to the service resource to be detected based on the at least one service resource detection indicator; generate a service resource adjustment policy for the service resource to be detected based on the resource status information; and send the service resource adjustment policy to the client 100. The client 100 is further configured to receive the service resource adjustment policy sent by the server 200 .

[0118] The data processing system may include multiple clients 100 and a server 200. The clients 100 may be referred to as client-side devices, and the server 200 may be referred to as cloud-side devices. The multiple clients 100 may establish a communication connection through the server 200. In an advertising delivery scenario, the server 200 is used to provide advertising delivery strategy recommendation services between the multiple clients 100. The multiple clients 100 may act as either senders or receivers, communicating through the server 200.

[0119] Users can interact with the server 200 through the client 100 to receive data sent by other clients 100, or send data to other clients 100, etc. In the advertising delivery scenario, users can publish data streams to the server 200 through the client 100. The server 200 generates an advertising adjustment strategy based on the data stream and pushes the advertising adjustment strategy to other clients with which communication has been established.

[0120] The client 100 and the server 200 are connected via a network. The network provides a medium for the communication link between the client 100 and the server 200. The network can include various connection types, such as wired or wireless communication links or fiber optic cables. The data transmitted by the client 100 may need to be encoded, transcoded, compressed, or other processing before being released to the server 200.

[0121] The client 100 can be a browser, an application (APP), a web application such as an H5 (HyperText Markup Language 5) application, a lightweight application (also known as a mini-program, a type of lightweight application), or a cloud application. The client 100 can be developed based on the software development kit (SDK) of the corresponding service provided by the server 200, such as a real-time communication (RTC) SDK. The client 100 can be deployed on a computing device and rely on the device or certain applications on the device to run. For example, the computing device can have a display screen and support information browsing, such as a personal mobile terminal such as a mobile phone, tablet computer, or personal computer. Various other types of applications can also be configured on the computing device, such as human-computer interaction applications, model training applications, text processing applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0122] The server 200 may include servers that provide various services, such as servers that provide communication services to multiple clients, servers that support backend training for models used on clients, and servers that process data sent by clients. It should be noted that the server 200 can be implemented as a distributed server cluster consisting of multiple servers or as a single server. The server can also be a server in a distributed system or a server integrated with blockchain. The server can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), big data and artificial intelligence platforms, or intelligent cloud computing servers or intelligent cloud hosts equipped with artificial intelligence technology.

[0123] It is worth noting that the data processing methods provided in the embodiments of this specification are generally executed by the server. However, in other embodiments of this specification, the client may also have similar functions to the server and thus execute the data processing methods provided in the embodiments of this specification. In other embodiments, the data processing methods provided in the embodiments of this specification may also be executed jointly by the client and the server.

[0124] Figure 6 6 shows a block diagram of a computing device 600 according to an embodiment of the present application. Components of the computing device 600 include, but are not limited to, a memory 610 and a processor 620. The processor 620 is connected to the memory 610 via a bus 630, and a database 650 is used to store data.

[0125] The computing device 600 also includes an access device 640 that enables the computing device 600 to communicate via one or more networks 660. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 640 may include one or more of any type of network interface (e.g., a network interface controller (NIC)) whether wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0126] In one embodiment of the present application, the above components of the computing device 600 and Figure 6 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 6 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as needed.

[0127] Computing device 600 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or personal computer (PC). Computing device 600 can also be a mobile or stationary server.

[0128] The processor 620 is configured to execute the following computer program / instruction, which implements the steps of the above-mentioned data processing method when executed by the processor.

[0129] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned data processing method are of the same concept. For details not described in detail in the technical scheme of the computing device, please refer to the description of the technical scheme of the above-mentioned data processing method.

[0130] An embodiment of the present specification further provides a computer-readable storage medium storing a computer program / instruction, which implements the steps of the above-mentioned data processing method when executed by a processor.

[0131] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the computer-readable storage medium embodiment is generally similar to the data processing method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the data processing method embodiment.

[0132] An embodiment of the present specification further provides a computer program product, comprising a computer program / instruction, which implements the steps of the above-mentioned data processing method when executed by a processor.

[0133] The above is a schematic solution of a computer program product of this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above-mentioned data processing method are based on the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the above-mentioned data processing method.

[0134] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0135] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased based on the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0136] It should be noted that the above description is of specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0137] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made based on the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, applied to a data delivery platform, comprising: Acquire operation data to be detected of the service resource to be detected, and reference operation data of at least one reference service resource corresponding to the service resource to be detected; Obtaining at least one service resource indicator according to the operation data to be detected and each reference operation data; Determining resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator; A service resource adjustment strategy for the service resource to be detected is generated according to the resource status information.

2. The method according to claim 1, wherein obtaining reference operation data of at least one reference service resource corresponding to the service resource to be detected comprises: Determining the service resource type of the service resource to be detected; At least one reference business resource is determined according to the business resource type, and reference operation data corresponding to each reference business resource is acquired.

3. The method according to claim 1, wherein obtaining at least one service resource indicator based on the to-be-detected operation data and each reference operation data comprises: Calculating at least one service resource indicator to be detected based on the operation data to be detected; The reference business resource indicators corresponding to each reference operation data are calculated based on each reference operation data.

4. The method according to claim 3, wherein determining resource status information corresponding to the service resource to be detected according to at least one service resource detection indicator comprises: Determine first status information corresponding to the service resource to be detected according to at least one service resource indicator to be detected; Determining second status information corresponding to the service resource to be detected according to at least one service resource detection indicator and each reference service resource indicator; The resource status information corresponding to the service resource to be detected is determined according to the first status information and the second status information.

5. The method according to claim 4, wherein determining the first status information corresponding to the service resource to be detected according to at least one indicator of the service resource to be detected comprises: If the resource cold start throughput is abnormal, it is determined that the business resource to be tested has a cold start problem; If there are abnormalities in the resource effective playback rate, resource click rate, and resource conversion rate, it is determined that the business resource to be tested has a traffic acquisition problem.

6. The method according to claim 4, wherein determining the second status information corresponding to the service resource to be detected according to at least one service resource detection indicator and each reference service resource indicator comprises: In the event of anomalies in resource conversion rates, obtain reference resource funnel ratio, reference resource exposure cost, and reference business resource price; When there are anomalies in the reference resource funnel ratio, reference resource exposure cost, and reference business resource price, it is determined that the business resource to be tested has an effect conversion problem.

7. The method according to claim 4, wherein determining the resource status information corresponding to the service resource to be detected according to the first status information and the second status information comprises: In a case where the first state information is a cold start problem or a traffic acquisition problem, determining that the resource state information is the first state information; When the first state information is normal and the second state information is an effect conversion problem, determining the resource state information to be the first state information; When the first status information is normal and the second status information is normal, it is determined that the resource status information is a product abnormality, where the product is a product corresponding to the service resource to be detected.

8. The method according to claim 1, generating a service resource adjustment policy for the service resource to be detected according to the resource status information, comprising: Determine, according to the adjustment policy configuration table, the business resource adjustment policy corresponding to the resource status information; or, The resource status information and the operation data to be detected are input into a policy analysis model to obtain a business resource adjustment policy output by the policy analysis model.

9. A computer-readable storage medium storing a computer program / instruction, wherein the computer program / instruction, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

10. A computer program product comprising a computer program / instruction, which implements the steps of the method according to any one of claims 1 to 8 when executed by a processor.

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