Promotion content data tracking method and device, equipment and storage medium

Abstract

The invention relates to the field of data tracking, and discloses a promotion content data tracking method and device, equipment and a storage medium, and the method comprises the steps: responding to the displayed promotion content, and collecting the basic attribute data of the promotion content; aiming at the promotion content, capturing a system-level response generated after the promotion content is triggered to interact; extracting target behavior feedback data corresponding to the promotion content interaction from the system-level response; and carrying out association binding on the basic attribute data of the promotion content and the target behavior feedback data to obtain association tracking data. The problem that in the prior art, promotion content data are not credible is solved.

Description

Technical Field

[0001] This invention relates to the field of data tracking, and more particularly to a method, apparatus, device, and storage medium for tracking promotional content data. Background Technology

[0002] Currently, content promotion typically integrates with third-party aggregation SDKs, such as UBIKS and Takoo. The exposure and click-through rates of the promoted content are provided by the partners, but there is a risk that the data provided by these partners may be unreliable. Summary of the Invention

[0003] This invention provides a method, apparatus, computer device, and storage medium for tracking promotional content data, in order to solve the problem of unreliable promotional content data in the prior art.

[0004] Firstly, a method for tracking promotional content data is provided, including: In response to displaying promotional content, basic attribute data of the promotional content is collected; For the aforementioned promotional content, capture the system-level response generated after the interaction is triggered; Extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response; The basic attribute data of the promoted content is associated and bound with the target behavior feedback data to obtain associated tracking data.

[0005] Optionally, after obtaining the associated tracking data, the process includes: The associated tracking data is sent to the server, and the associated tracking data is used by the server to verify the authenticity of the performance conversion data provided by the promotion content advertiser.

[0006] Optionally, in response to displaying promotional content, collecting basic attribute data of the promotional content includes: When the promotional content is detected to start rendering on the display interface, the basic attribute data collection process associated with the promotional content is started. The basic attribute data is collected in real time according to the lifecycle of the promotional content.

[0007] Optionally, capturing the system-level response generated after the promotional content is triggered by an interaction includes: During the display of the promotional content, hook points related to the interaction with the promotional content are pre-set at the system layer; When the promotional content is detected to be interacted with, the system call chain triggered by the interaction is intercepted through the Hook point, and the system-level response generated in the chain is captured.

[0008] Optionally, when the promotional content is detected to be interacted with, the system call chain triggered by the interaction is intercepted through the Hook point, and the system-level response generated in the chain is captured, including: Obtain the characteristic information of the system interface call during this interaction; Based on the feature information, the system-level response corresponding to the interaction in the system call chain is selected.

[0009] Optionally, the step of associating and binding the basic attribute data of the promoted content with the target behavior feedback data to obtain associated tracking data includes: Extract a unique identifier for the promotional content from the basic attribute data, and extract a unique identifier for the interaction behavior from the target behavior feedback data; By using preset identifier mapping rules, and by comparing the association between the unique identifier of the promotional content and the unique identifier of the interactive behavior, the association between the basic attribute data and the target behavior feedback data is established. The basic attribute data and the target behavior feedback data are associated and integrated according to a preset format to generate associated tracking data and store it.

[0010] Optionally, the step of filtering out the system-level response corresponding to the interaction in the system call chain based on the feature information further includes: If it is an Android platform, filter out the system-level responses that match the package name information in the feature information from the system-level response chain corresponding to the interaction; If it is an iOS platform, based on the predefined irrelevant class identifier in the feature information, the system-level responses containing the irrelevant class identifier in the system-level response chain are filtered out through the configuration file, and the system-level responses corresponding to the interaction with the promotional content are retained.

[0011] Secondly, a promotional content data tracking device is provided, comprising: The data collection module is used to collect basic attribute data of the promotional content in response to its display. The capture module is used to capture the system-level response generated after the promotional content is triggered and interacted with. The extraction module is used to extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response; The association module is used to associate and bind the basic attribute data of the promotional content with the target behavior feedback data to obtain association tracking data.

[0012] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described promotional content data tracking method.

[0013] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described promotional content data tracking method.

[0014] The aforementioned method, apparatus, device, and storage medium for tracking promotional content data extracts target behavior feedback data by capturing "system-level responses." These system-level responses are automatically generated by the terminal system, avoiding the possibility of human tampering or selective reporting. Furthermore, the basic attribute data of the promotional content is "associated and bound" with the target behavior feedback data generated by the interaction, forming a complete data chain spanning from display to interaction, thus eliminating the problem of false attribution caused by data fragmentation. Therefore, this invention solves the problem of unreliable promotional content data in the prior art. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for tracking promotional content data according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a content data tracking device according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0019] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," or "in response to determination." Similarly, the phrase "if determined" or "if matched to [described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once matched to [described condition or event]," or "in response to matched to [described condition or event]."

[0021] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized.

[0023] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0024] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0025] Please see Figure 1 As shown, Figure 1 A flowchart illustrating the promotional content data tracking method provided in this embodiment of the invention includes the following steps: S11: In response to displaying promotional content, collect the basic attribute data of the promotional content.

[0026] This step serves as the starting point for data anchoring in the promotional content data tracking system. Its core logic is to link the user-perceptible scenario of promotional content display with backend data collection behavior, capturing basic attribute data through a real-time triggered collection mechanism. The promotional content typically includes advertisements or marketing information.

[0027] This step involves initiating the data collection process immediately after the content begins rendering on the user interface (such as in an app pop-up, splash screen, or news feed) and is ready to be seen by the user. This immediacy avoids "content and data mismatch" caused by delays (such as loading two ads in the same location but mistakenly associating the basic data of the latter ad with the former), ensuring accurate correspondence between data and the display scenario.

[0028] The "basic attribute data" generally needs to cover three types of core information: first, identity identification data (such as unique ID of promotional content and ID of the advertiser), which is used to distinguish different promotional content in the future; second, content feature data (such as promotion type, content theme, and source SDK), which is used to describe the attributes of the promotional content itself; and third, environmental scenario data (such as display time, device model, and App version), which is used to locate the display scenario of the promotional content.

[0029] In one example, taking the "Home Appliance Promotion Ads" (promotional content) on the homepage of a certain app as an example, the specific data collection process and data are as follows: When a user opens the shopping app, the homepage loads and displays a banner ad for a refrigerator from a certain brand with a price reduction of 1000 yuan (at this point, the ad appears at the top of the screen, and the user can clearly see the ad content; the "display" scenario is triggered, and the data collection process starts immediately), the system collects basic attribute data according to the following logic: Collect identity information: Obtain the unique number of the refrigerator advertisement from the third-party SDK that provides the advertisement. This number is unique in the entire shopping app's advertising system and can accurately distinguish the refrigerator advertisement from all other promotional content in the app. At the same time, the identifier of the advertiser is also collected for subsequent association with the data provided by the advertiser.

[0030] Collect content feature information: Record the type of this advertisement as "homepage banner ad", clarifying its difference from "in-feed ads" and "pop-up ads" in the app; record the core theme of the advertisement as "a certain brand of refrigerator is reduced by 1,000 yuan, with a promotion period of 5 days", clearly describing the marketing information conveyed by the advertisement; record the source SDK of the advertisement, which is convenient for subsequent statistics on the differences in the performance of advertisements provided by different SDKs; record the preset display duration of the advertisement as "30 seconds", that is, if the user does not take any action, this advertisement will be displayed on the homepage for 30 seconds and then automatically switch to the next one.

[0031] Collect display environment information: The time when the advertisement started displaying was obtained as "10:25:30 on May 20, 2024", accurate to the second, for subsequent matching of possible user click times; the device model and App used by the user were recorded to facilitate subsequent analysis of the advertisement display effect on different devices and different App versions; the display position of the advertisement was recorded as "the top banner area of ​​the shopping App homepage", clarifying its specific display position in the App and avoiding confusion with other promotional content in the middle and bottom of the homepage.

[0032] S12: For the promotional content, capture the system-level response generated after the interaction is triggered.

[0033] Essentially, this step involves using technical means to intercept and record the device's system-level response behavior after a user interacts with promotional content (such as ads or marketing cards) (e.g., clicking or long-pressing), thereby obtaining the true trajectory of the user's interaction. The key value of this step lies in breaking the dependence on third-party SDKs. Instead of relying solely on the interaction data provided by the advertiser, it directly obtains objective system-level responses from the system's underlying layers, providing "original evidence" for subsequent verification of data authenticity and tracing of interaction behavior.

[0034] This step requires a user interaction with the promotional content as a prerequisite, and the interaction must be directly related to the promotional content (such as clicking the ad screen or clicking the "View Now" button on the ad), rather than an unrelated operation (such as clicking on a blank area outside the ad). Only when the interaction explicitly points to the promotional content will the subsequent captured system-level response have meaning in relation to that promotional content.

[0035] The system-level response refers to the underlying behavior generated by the device's operating system (such as iOS or Android) after user interaction (such as launching other apps, opening a browser, or redirecting to a page). This type of response does not rely on the statistical logic of third-party SDKs and has the characteristics of objectivity and immutability.

[0036] In one example, suppose a user is browsing the "Send Package" page of a certain express delivery app on the iOS platform and sees an in-feed ad for "a certain brand of laundry detergent with discounts" (the basic attribute data of the ad has been collected beforehand, including a unique identifier). The user then clicks the "View Details" button in the ad screen (triggering an interactive operation for the promotional content).

[0037] At this point, the system immediately activates the capture mechanism adapted for the iOS platform: intercepting the underlying method of "opening external links" in the iOS system. Since clicking the advertisement requires redirection to the official H5 store of the laundry detergent brand, the system captures the following system-level response information: First, the response type is "opening external links," clearly indicating the system behavior triggered after the interaction; second, the H5 store link, which contains parameters corresponding to the advertisement and can be directly linked to the previously collected basic attribute data of the advertisement; third, the interaction trigger time is "May 21, 2024, 15:30:22," accurate to the second, used to match the advertisement display time (the display time recorded in the basic attribute data is "May 21, 2024, 15:30:18"), confirming that the interaction occurred during the advertisement display period; fourth, the device identification information (such as the unique device code of the iOS device), which is consistent with the device information in the basic attribute data, further verifying the correlation between the interaction behavior and the advertisement.

[0038] At the same time, the system will filter out irrelevant system responses. For example, if a user clicks on an ad and a text message notification pops up in the background, the system will identify the text message notification as a "system response of the text messaging application," which is unrelated to the current ad interaction and will not be included in the capture scope, ensuring that the final system-level responses obtained only correspond to the ad interaction behavior.

[0039] If the same "Brand X laundry detergent discount promotion" ad is displayed in the Android app (the basic attribute data is the same as the iOS platform), and the user clicks the "Buy Now" button in the ad (triggering an interactive operation), the user will be redirected to the brand's official Android app.

[0040] The system will activate an Android platform-adapted capture mechanism: listening for system calls to "launch an external application" in the Android system. The captured system-level response information includes: 1) The response type is "launch an external application," clearly indicating that the system behavior is opening another app; 2) The package name of the target application (i.e., the package name of the official app of the laundry detergent brand), confirming that the redirected application matches the brand being advertised; 3) The parameters carried during the interaction trigger, corresponding to the unique identifier in the basic attribute data of the advertisement; 4) The interaction time is "May 21, 2024, 16:10:05," matching the display time "May 21, 2024, 16:09:58" in the basic attribute data, confirming that the interaction occurred during the advertisement display period; 5) The Android device model and app version, consistent with the environmental information in the basic attribute data, ensuring data consistency.

[0041] Similarly, the system will filter out irrelevant responses. For example, if the Android system triggers a "low battery reminder" pop-up when the user clicks on an ad, this pop-up is a "system power management response" and is unrelated to the ad interaction. It will be excluded from the capture results to ensure that the captured system-level responses only correspond to this ad interaction.

[0042] S13: Extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response.

[0043] The core logic of this step is to "filter, identify, and extract" the captured system-level response information, eliminating redundant data unrelated to the interaction with the promotional content, and retaining the core information that accurately reflects the user's actual interaction behavior with the promotional content. Essentially, this step transforms raw, disorganized system response data into targeted behavioral feedback data, providing effective data for subsequent correlation with the basic attribute data of the promotional content and for verifying the authenticity of the advertiser's performance data.

[0044] This step requires data filtering based on "relevance to the promoted content." Only data that explicitly contains identifiers related to the promoted content in the system-level response (such as the unique ID of the ad or the information of the advertiser) and whose response behavior is directly triggered by interaction with the promoted content (such as being redirected after clicking the ad or having details pop up after long-pressing the ad) will be included in the extraction scope. System-level responses unrelated to the interaction (such as system pop-up reminders or background application pushes) will be removed even if they overlap with the interaction time to avoid interfering with subsequent data association.

[0045] Target behavior feedback data generally needs to cover three core types of information: First, interaction identification information (such as a unique ID corresponding to the promotional content and the interaction behavior number), used to establish a connection with basic attribute data; second, interaction behavior information (such as interaction type and interaction result), used to clarify the user's specific operation on the promotional content and the resulting effect; and third, interaction scenario information (such as interaction time and interaction device status), used to supplement the scenario details when the interaction occurred and help verify the authenticity of the interaction (such as avoiding including accidental system touches that are not actively performed by the user in the statistics).

[0046] In one example, suppose a user is browsing the "Send a Package" page of a courier app on the iOS platform and sees an in-feed ad for a "discount promotion on a certain brand of laundry detergent." The system-level response has already been captured; at this point, the extraction of the target behavior feedback data is initiated. Extract interaction identifier information: Identify and extract "adId=_information flow_laundry detergent_20240521_012" from the parameters of the jump link, and define it as the "interaction-related ad ID". This ID is completely consistent with the unique identifier in the basic attribute data of the ad and is the core basis for subsequent data association. At the same time, generate a unique "interaction behavior number" (such as "iOS_interaction_20240521153022_001") to mark this interaction behavior separately.

[0047] Extract interactive behavior information: Based on the system response type "open external link", determine the "interaction type" as "click to jump to H5"; extract the "interaction result" as "jump successful" from "link loading status 'successfully opened'", clarifying the actual effect after the user clicks on the advertisement (distinguishing it from invalid interactions such as "link loading failed").

[0048] Extracting interaction scenario information: The "interaction time" was extracted from the response time as "May 21, 2024, 15:30:22". This was compared with the ad display time of "May 21, 2024, 15:30:18" in the basic attribute data to confirm that the interaction occurred during the ad display period. The "network environment at the time of interaction" was extracted from "device's current network 'WiFi'" as "WiFi" to supplement the details of the interaction scenario (this information can be used to verify if the advertiser claims that "the interaction rate is higher under 4G network").

[0049] S14: Associate and bind the basic attribute data of the promotional content with the target behavior feedback data to obtain associated tracking data.

[0050] The core of this step is to logically bind the basic attribute data of the promotional content collected in the early stage (describing "what content and in what scenario it is displayed") with the extracted target behavior feedback data (describing "what user interaction and what results are produced") through a unified association identifier, forming a closed-loop data that can completely reflect "the entire process of promotional content from display to interaction", providing a complete data basis for subsequent verification of the advertiser's performance data and analysis of the promotional content's effectiveness.

[0051] This step typically requires association based on a "unique identifier." For example, the information in the basic attribute data used to identify the promoted content (such as the unique ad ID) and the information in the target behavior feedback data used to associate the promoted content (such as the interaction-related ad ID) must be completely consistent. This uniqueness is a prerequisite for ensuring accurate matching and avoiding the problem of "incorrectly binding the basic data of ad A with the interaction data of ad B."

[0052] The aforementioned method for tracking promotional content data extracts target behavior feedback data by capturing "system-level responses." These responses are automatically generated by the terminal system, avoiding the possibility of human tampering or selective reporting. Furthermore, it "associates and binds" the basic attribute data of the promotional content with the target behavior feedback data generated by the interaction, forming a complete data chain spanning from display to interaction, thus eliminating the problem of false attribution caused by data fragmentation. Therefore, this invention solves the problem of unreliable promotional content data in existing technologies.

[0053] In one embodiment, after obtaining the correlation tracking data, the process includes: sending the correlation tracking data to a server, wherein the correlation tracking data is used by the server to verify the authenticity of the performance conversion data provided by the promotion content advertiser.

[0054] The core of this step is to transmit the previously generated, objectively reflecting the entire process of correlation tracking data to the backend server. Through the server's comparative analysis logic, it verifies whether the performance conversion data (such as click-through rate, redirect success rate, etc.) provided by the advertiser is consistent with the actual situation. Essentially, this step addresses the core issue of "data credibility verification." Since the performance data provided by the advertiser may be inflated (e.g., overreporting clicks to inflate click-through rate and obtain more advertising revenue), the correlation tracking data, captured from the system's underlying layers, possesses neutrality and authenticity. Therefore, it can serve as a "benchmark" for server-side verification, ultimately avoiding revenue losses or user experience complaints due to data misrepresentation.

[0055] Related tracking data must be sent to the server through a stable transmission channel (such as an encrypted interface), and data integrity must be ensured to prevent the loss or tampering of critical information (such as unique ad IDs, interaction times, and interaction results) during transmission, otherwise the accuracy of server-side verification will be affected. For example, verification and validation are required; if the related tracking data received by the server is inconsistent with the related tracking data sent by the client, a retransmission mechanism must be triggered.

[0056] The server-side needs to design corresponding comparison logic based on the core metrics of the advertiser's conversion data (such as click-through rate and effective bounce rate). The core idea is to compare the "performance data of a certain promotional content" provided by the advertiser (e.g., claiming 1000 ad impressions, 100 clicks, and a click-through rate of 10%) with the "related tracking data of the same promotional content" aggregated by the server (e.g., actual 980 impressions, 60 effective clicks, and a click-through rate of approximately 6.1%) after dimensional alignment, and identify data differences. After verification, the server-side needs to output clear verification results (e.g., "data consistent," "data deviation 10%-30%," "data deviation exceeding 30%"). If the deviation is small, the advertiser can be reminded to optimize the data statistics logic; if the deviation is too large, the advertising cooperation strategy can be adjusted (e.g., reducing the advertiser's ad priority).

[0057] In one example, taking a collaboration scenario involving a "Pop-up Ad for a Certain Brand of Tissue Paper" (promotional content) in a certain express delivery app, assuming this pop-up ad generates 100 sets of valid related tracking data (corresponding to 100 actual interactions) on iOS and Android platforms, the client sends this data in batches to the server via an encrypted interface. Upon receiving the data, the server performs integrity verification on the related tracking data (confirming no missing key fields) and categorizes and stores it according to the "Unique Ad ID." The performance conversion data report provided by the advertiser shows that the ad has a total of 180 clicks. The server, using the "Unique Ad ID" as the matching dimension, compares and verifies the advertiser's performance conversion data (i.e., the total number of clicks of 180) with the stored related tracking data (i.e., 100 sets), and shows that there is a significant discrepancy in the advertiser's performance conversion data.

[0058] In one embodiment, in response to displaying promotional content, collecting basic attribute data of the promotional content includes: when it is detected that the promotional content has started to render on the display interface, initiating a basic attribute data collection process associated with the promotional content; and collecting the basic attribute data in real time according to the lifecycle of the promotional content display.

[0059] The core of this step is to ensure that the data can completely and in real-time reflect the entire process of the promotional content from "start of presentation" to "end of display" by "precisely triggering the collection timing" and "dynamically covering the collection cycle," avoiding data loss and mismatch issues caused by delayed collection timing or incomplete cycles. Essentially, this step establishes a "timeline-style collection framework" for basic attribute data, clearly defining which period of basic attribute data needs to be collected, providing a precise time benchmark for subsequent time-dimensional correlation with target behavior feedback data (such as verifying whether the interaction occurred during the display period).

[0060] This step is triggered by the "promotional content begins rendering on the display interface" node. "Rendering begins" is an objective action that can be accurately captured at the system level (such as the App starting to draw the pixels of the advertisement in a specified area of ​​the screen). It is not affected by the user's subjective perception (such as the user temporarily leaving the screen) or network latency, thus avoiding the problem of the initial display data being lost when the content has been rendered but the data collection has not started.

[0061] "Collecting the basic attribute data in real time according to the lifecycle of the promotional content display" means that the collection process is not a one-time acquisition of data, but a continuous capture of basic attribute data throughout the entire cycle from "the start of rendering" to "the end of display" (such as when the ad is closed, disappears automatically, or the user swipes away). For example, when the ad is initially rendered, "start display time and initial position" are collected. If "the display is restored after being blocked by a pop-up window" during the display process, "blocking time and restoration time" are collected. When the display ends, "end display time and end reason" are collected, thus forming a complete display process data chain, rather than isolated static data.

[0062] In one example, taking the display scenario of a pop-up ad on the homepage of a certain app showing a discount of 10 yuan off for every 20 yuan spent (promotional content), the specific data collection process is as follows: Triggering of the data collection process (started when rendering begins): When a user opens an app and the homepage loads to the "pop-up ad module", the system detects that the milk tea ad has started rendering in the central area of ​​the screen (i.e., the app starts drawing the background image, text, buttons, and other elements of the ad at specified coordinates), and immediately triggers the basic attribute data collection process. The initial data collected at this time includes: the unique ad ID "WM_MilkTea_Popup_20240610_072", the ad type "pop-up ad", the start rendering time "June 10, 2024, 12:15:03", the initial display position "center of the homepage screen", the device model, and the app version.

[0063] Real-time data collection throughout the ad display lifecycle (continuous data replenishment): Once the collection process starts, data is replenished in real time as the ad display lifecycle changes. Rendering completion stage (12:15:04): The system detected that the advertisement screen was fully rendered (text and buttons were clearly visible), and supplemented the "complete display time" to "June 10, 2024, 12:15:04", and marked the "display status" as "normal display". Mid-term occlusion phase (12:15:06): The user temporarily clicked on the phone notification bar to view the text message. The milk tea advertisement was partially obscured by the text message pop-up. The system detected that "the display area is obscured by more than 50%", supplemented the "occlusion start time" to "June 10, 2024, 12:15:06", and updated the "display status" to "partially obscured". Resumption of display phase (12:15:08): The user closes the SMS pop-up, the milk tea advertisement is fully displayed again, the system re-collects the "end time of obstruction" as "June 10, 2024, 12:15:08", and updates the "display status" to "normal display" again; End of display phase (12:15:13): The preset display duration of the advertisement expires after 10 seconds, and it will automatically close and disappear. The system collects the "End of Display Time" as "June 10, 2024, 12:15:13", records the "End Reason" as "Preset Duration Expired", and stops the collection process.

[0064] After the entire collection cycle is completed, the resulting basic attribute data fully covers the entire lifecycle: unique ad ID "WM_MilkTea_Popup_20240610_072", ad type "pop-up ad", start rendering time "12:15:03", full display time "12:15:04", initial display position "center of homepage", device model, app version, obscuring start time "12:15:06", obscuring end time "12:15:08", end display time "12:15:13", end reason "preset duration expired", and changes in display status throughout the entire process.

[0065] In one embodiment, for promotional content, capturing the system-level response generated after the content is triggered by an interaction includes: during the display of the promotional content, setting up a hook point related to the interaction of the promotional content in advance at the system layer; when the promotional content is detected to be triggered by an interaction, intercepting the system call chain caused by the interaction through the hook point, and capturing the system-level response generated in the chain.

[0066] This step essentially involves precisely intercepting the underlying system behavior triggered when a user interacts with promotional content, thereby obtaining a traceable system-level response that does not rely on third-party SDKs. The hook point is set at the system layer rather than the application layer, bypassing potential statistical biases or data misreporting that may exist with third-party SDKs, and directly obtaining the device's true system-level response.

[0067] Hook points need to match the interaction scenario of the promotional content, intercepting only system-level responses related to the interaction (such as opening external apps, redirecting to web pages, launching app stores, etc.), rather than indiscriminately monitoring all system calls. For example, if the promotional content is a pop-up ad, the interaction may trigger "opening a browser to load an H5" or "launching a brand app." In this case, hook points should only be set at the system level for the core interfaces of "opening a browser" and "launching an external application" to avoid interference from irrelevant system behaviors (such as system alarm reminders or background application refreshes). In addition, hook points only take effect during the display of the promotional content: the hook listener starts when the promotional content begins to render and stops immediately when the promotional content ends to be displayed (such as being closed or disappearing automatically). This ensures that responses are accurately captured when interactions occur, while avoiding long-term listening that consumes system resources or raises privacy compliance risks.

[0068] When a user triggers an interaction (such as clicking an ad button), it triggers a series of system calls (such as calling a system interface to open a link, passing redirect parameters, and launching the target application), forming a system call chain. When intercepting this chain through a Hook point, it is necessary to completely capture all key system-level responses generated in the chain (such as the name of the interface called, the parameters passed, and the execution result). These system-level responses come directly from the system's underlying layer, have not been processed by third-party SDKs, and possess unalterable objectivity.

[0069] In one example, taking the "Splash Screen Ad for a Brand of Sports Shoes" (promotional content) of an e-commerce app on the iOS platform as an example, when the splash screen ad starts rendering in the e-commerce app on the iOS device (the hook is activated during the display), the system layer sets hook points for core behaviors in iOS that are strongly related to ad interaction: For the scenario of "clicking the ad to jump to an external H5", a hook point is set in the system's "openURL" interface (the core interface for opening links in iOS) to intercept link jump requests initiated through this interface; for the scenario of "clicking the ad to open the App Store", a hook point is set in the system's "presentViewController" interface (the core interface for pop-up pages in iOS) to intercept system calls that launch the App Store preview pop-up; at the same time, it is clearly stated that the hook points only listen to calls "from the splash screen ad module of this e-commerce app", filtering out irrelevant requests from other applications or modules to ensure targeting.

[0070] When a user clicks the "View Product Details" button in the splash screen ad (triggering an interaction), this action triggers the e-commerce app to call the iOS system's "openURL" interface, requesting to open the official H5 store link of the athletic shoe brand. At this point, the pre-set "openURL" hook point immediately takes effect, intercepting the entire system call chain. First, capture the "source of the call instruction": confirm that the "openURL" request comes from the splash screen ad module of the e-commerce app, and not from other modules (such as the product list page in the app). Next, capture the "call parameters": extract the complete H5 link passed by the interface, which contains parameters associated with the ad (such as "adId=EC_Sneaker_Splash_20240615_091", which is the unique ID of the splash screen ad). Finally, capture the "call result": record the status of the system executing the request (such as "link successfully resolved, start loading H5 page"). The intercepted information together constitutes a system-level response, fully reflecting the underlying behavior of "user clicks on ad → triggers openURL call → requests to open associated H5", and the data comes directly from the iOS system, without relying on the statistical results provided by the ad SDK.

[0071] In one embodiment, when the promotional content is detected to be interacted with, the system call chain triggered by the interaction is intercepted through a Hook point, and the system-level response generated in the chain is captured, including: obtaining the feature information when the interaction calls the system interface; and filtering out the system-level response corresponding to the interaction in the system call chain based on the feature information.

[0072] Essentially, this step involves intercepting a call chain containing multiple types of system behaviors at the Hook point, and then using "interaction-specific feature information" to eliminate redundant responses unrelated to the promotional content (such as system notifications and background application interference) to accurately locate system-level responses that reflect the user's actual interactive behavior.

[0073] In one example, using "a beauty brand promotion ad on a certain app" (promotional content, with the interaction goal of "opening the brand's official H5 store") as an example, the filtering process is illustrated: Scenario: The interaction goal is to open the brand's official H5 online store. Obtaining interaction characteristic information: The interaction target of this promotional advertisement is to open the brand's H5 store through the system browser. The characteristic information when it calls system interfaces (such as "startActivity" on Android and "openURL" on iOS) is pre-set as: "H5 store link prefix https: / / beauty.xxx.com" and "parameters containing the unique advertisement ID 'Beauty_Ad_20240701_045'". When the user clicks on the advertisement in the App, the system intercepts the system call chain through the Hook point, which contains 3 system-level responses: Response 1: Call the "openURL" API with the link "https: / / xxx.com" and the behavior "open the link through the system browser"; Response 2: Call the "Notification" interface with the content "System prompt: Weak WiFi signal" and the behavior "Pop up system network reminder"; Response 3: Call the "openURL" interface with the link "https: / / yyy.com" and the action is "Open the news recommendation page in the App".

[0074] Filter responses by feature information: The system compares the attributes of each response with preset feature information: The link prefix of response 1 is consistent with the "H5 Mall Link Prefix" and contains the unique ID of the advertisement in the parameters. It is a complete match of the feature information and is determined to be "a response corresponding to the interaction with the promotional content" and is retained. The interface type (Notification) and content (WiFi reminder) of response 2 are irrelevant to the feature information, and the link (news page) of response 3 does not match the prefix of the H5 store link. Both are judged as "irrelevant responses" and are removed. Filtering results: Only system-level response 1 was retained. Subsequently, target behavior feedback data such as "link to open H5 store, interaction time" can be extracted from it and associated with the basic attribute data of the advertisement.

[0075] In one embodiment, the step of associating and binding the basic attribute data of the promotional content with the target behavior feedback data to obtain associated tracking data includes: extracting a unique identifier for the promotional content from the basic attribute data and extracting a unique identifier for the interaction behavior from the target behavior feedback data; establishing an association between the basic attribute data and the target behavior feedback data by comparing the association relationship between the unique identifier for the promotional content and the unique identifier for the interaction behavior through a preset identifier mapping rule; and integrating the basic attribute data and the target behavior feedback data according to a preset format after association to generate and store associated tracking data.

[0076] This step essentially uses a "unique identifier" as a "data bridge" to deeply integrate basic attribute data describing "what the promotional content is and where it is displayed" with target behavior feedback data describing "what user interactions occurred and what results were generated," ultimately generating structured, correlated tracking data that reflects the "entire lifecycle of the promotional content."

[0077] The preset mapping rules must have a clear "one-to-one" or "many-to-one" relationship (such as "the first 20 characters of the unique identifier of the interaction behavior are completely consistent with the unique identifier of the promotion content" and "one unique identifier of the promotion content can correspond to multiple unique identifiers of the interaction behavior, but the reverse is not true"), to avoid the chaotic situation of "one interaction behavior corresponding to multiple promotion contents". This is the core premise for ensuring the accuracy of the association.

[0078] After association, the integration needs to sort out the core information of basic attribute data and target behavior feedback data according to the preset format (such as unified field order and data classification logic), remove duplicate fields (such as "device model" which is included in both types of data, only one copy is kept during integration), and add association status indicators (such as "association successful / failed"). The final generated association tracking data should have the characteristics of "easy to query and easy to analyze" so that it can be quickly retrieved after subsequent storage (such as the server retrieving all corresponding interaction data by "unique identifier of promotion content").

[0079] In one example, using a "brand shampoo information feed ad" (promotional content) from a certain app as an example, the complete process of association and binding is presented: Assuming two types of core data have been collected previously: The basic attribute data of the promotional content includes: "Unique Identifier of Promotional Content (Ad ID): VS_Shampoo_Feed_20240620_123", "Ad Type: Feed Ad", "Display Time: June 20, 2024, 19:05:20", "Device Model: X100", and "Display Position: 2nd item in the feed on the right side of the video playback page". The core correlation basis is extracted from this data, namely the unique identifier of the promotional content, "VS_Shampoo_Feed_20240620_123". 3”; The target behavior feedback data includes: “Unique identifier of interaction behavior: VS_Shampoo_Feed_20240620_123_Click_001”, “Interaction type: Click to jump to the product live room”, “Interaction time: 19:05:28 on June 20, 2024”, “Interaction result: The live room was loaded successfully”, “Network during interaction: 5G”, etc. The core correlation basis is extracted from it, namely the unique identifier of interaction behavior “VS_Shampoo_Feed_20240620_123_Click_001”.

[0080] Based on the video app's preset "identifier mapping rule," which states that "the first N digits of the unique identifier of the interactive behavior (N being the length of the unique identifier of the promotional content) must completely match the unique identifier of the promotional content, and one unique identifier of the promotional content can correspond to multiple unique identifiers of interactive behavior (such as multiple clicks)," the two extracted identifiers were compared: the unique identifier of the promotional content, "VS_Shampoo_Feed_20240620_123," is 28 digits long, and the first 28 digits of the unique identifier of the interactive behavior are also "VS_Shampoo_Feed_20240620_123," which completely conforms to the preset identifier mapping rule; further verification of the time correlation was conducted: the "display time (19:05:20)" of the basic attribute data is earlier than the "interaction time (19:05:28)" of the target behavior feedback data, and both are within the "preset display duration (30 seconds)" of the advertisement, confirming that the interaction belongs to the effective display period of the promotional content; finally, it was determined that the two types of data are effectively correlated.

[0081] According to the App's preset "Association Tracking Data Format" (categorization logic: Promotion Content Identity Information → Display Environment Information → Interaction Behavior Information → Association Status), the two types of data are integrated: core non-duplicated information is retained: promotion content identity information (ad ID, ad type), display environment information (display time, device model, display location), and interaction behavior information (interaction type, interaction time, interaction result, network at the time of interaction), while duplicate "device model" fields are removed; association status identifiers are added: "Association Status: Success" and "Association Time: June 20, 2024, 19:05:29" (the time when data association was completed) are added; the integrated information is stored in a structured format: the integrated information is sorted in the order of "ad ID → ad type → display time → display location → device model → interaction type → interaction time → interaction result → network at the time of interaction → association status → association time" to generate the final association tracking data, which is stored in the App's backend database and archived by "ad ID" for easy retrieval of all interaction data by ad dimension.

[0082] The final generated correlation tracking data can be clearly described as follows: "The shampoo feed ad with ad ID VS_Shampoo_Feed_20240620_123 was displayed in the second position of the feed on the right side of the video playback page on the X100 device at 19:05:20 on June 20, 2024. Eight seconds later (19:05:28), the user clicked on the ad under the 5G network and was successfully redirected to the product live broadcast room. The data correlation status was successful, and the correlation completion time was 19:05:29."

[0083] In one embodiment, the step of filtering out the system-level responses corresponding to the interaction in the system call chain based on the feature information further includes: if it is an Android platform, filtering out system-level responses that are consistent with the package name information in the feature information from the system-level response chain corresponding to the interaction; if it is an iOS platform, filtering out system-level responses containing the irrelevant class identifier in the system-level response chain based on the predefined irrelevant class identifier in the feature information through a configuration file, and retaining the system-level responses corresponding to the interaction with the promotional content.

[0084] The Android platform chooses "package name information" as the filtering criterion because interactions such as "launching external applications" and "opening specific functions" in the Android system are all bound to the unique package name of the target application (such as the package name of an app, "com.xxx.shop"). The feature information of promotional content interactions (such as clicking on an ad to jump to an app) will contain the package name of the target application in advance. By "package name matching", the response related to the interaction can be directly locked. The iOS platform chooses "irrelevant class identifier filtering" because the iOS system has stricter control over application calls. The system response triggered by the interaction may contain multiple system classes (such as "UIAlertController" is a pop-up class and "SKStoreProductViewController" is an app store preview class). Some of these classes are irrelevant to the interaction with the promotional content (such as system pop-up classes). By setting the identifier of these irrelevant classes in the configuration file, invalid responses can be filtered directly, and core interaction responses (such as app store preview class responses) can be retained.

[0085] In one embodiment, a promotional content data tracking device is provided. For example... Figure 2 As shown, it includes a data acquisition module 21, a capture module 22, an extraction module 23, and a correlation module 24. Detailed descriptions of each functional module are as follows: The data collection module 21 is used to collect basic attribute data of the promotional content in response to the display of promotional content; Capture module 22 is used to capture the system-level response generated after the promotional content is triggered and interacted with; Extraction module 23 is used to extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response; The association module 24 is used to associate and bind the basic attribute data of the promotional content with the target behavior feedback data to obtain association tracking data.

[0086] This invention also provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned promotional content data tracking method; to avoid repetition, this will not be described again here. Alternatively, the electronic device may implement the functions of each module in this embodiment of the promotional content data tracking device; this will not be described again here.

[0087] This invention also provides a readable storage medium storing a program that, when executed by a processor, implements the aforementioned promotional content data tracking method. To avoid repetition, this will not be described further here. Alternatively, when executed by a processor, the program may implement the functions of each module in this embodiment of the promotional content data tracking device, which will also not be described further here.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

[0089] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for tracking promotional content data, characterized in that, include: In response to displaying promotional content, basic attribute data of the promotional content is collected; For the aforementioned promotional content, capture the system-level response generated after the interaction is triggered; Extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response; The basic attribute data of the promoted content is associated and bound with the target behavior feedback data to obtain associated tracking data.

2. The method for tracking promotional content data according to claim 1, characterized in that, After obtaining the associated tracking data, the process includes: The associated tracking data is sent to the server, and the associated tracking data is used by the server to verify the authenticity of the performance conversion data provided by the promotion content advertiser.

3. The method for tracking promotional content data according to claim 1, characterized in that, In response to displaying promotional content, the basic attribute data of the promotional content is collected, including: When the promotional content is detected to start rendering on the display interface, the basic attribute data collection process associated with the promotional content is started. The basic attribute data is collected in real time according to the lifecycle of the promotional content.

4. The method for tracking promotional content data according to claim 1, characterized in that, The capture of system-level responses generated after the promotional content is triggered and interacted with includes: During the display of the promotional content, hook points related to the interaction with the promotional content are pre-set at the system layer; When the promotional content is detected to be interacted with, the system call chain triggered by the interaction is intercepted through the Hook point, and the system-level response generated in the chain is captured.

5. The method for tracking promotional content data according to claim 4, characterized in that, When the promotional content is detected to be interacted with, the system call chain triggered by the interaction is intercepted through the Hook point, and the system-level response generated in the chain is captured, including: Obtain the characteristic information of the system interface call during this interaction; Based on the feature information, the system-level response corresponding to the interaction in the system call chain is selected.

6. The method for tracking promotional content data according to claim 1, characterized in that, The step of associating and binding the basic attribute data of the promoted content with the target behavior feedback data to obtain associated tracking data includes: Extract a unique identifier for the promotional content from the basic attribute data, and extract a unique identifier for the interaction behavior from the target behavior feedback data; By using preset identifier mapping rules, and by comparing the association between the unique identifier of the promotional content and the unique identifier of the interactive behavior, the association between the basic attribute data and the target behavior feedback data is established. The basic attribute data and the target behavior feedback data are associated and integrated according to a preset format to generate associated tracking data and store it.

7. The method for tracking promotional content data according to claim 5, characterized in that, The step of filtering out the system-level response corresponding to the interaction in the system call chain based on the feature information further includes: If it is an Android platform, filter out the system-level responses that match the package name information in the feature information from the system-level response chain corresponding to the interaction; If it is an iOS platform, based on the predefined irrelevant class identifier in the feature information, the system-level responses containing the irrelevant class identifier in the system-level response chain are filtered out through the configuration file, and the system-level responses corresponding to the interaction with the promotional content are retained.

8. A promotional content data tracking device, characterized in that, include: The data collection module is used to collect basic attribute data of the promotional content in response to its display. The capture module is used to capture the system-level response generated after the promotional content is triggered and interacted with. The extraction module is used to extract target behavior feedback data corresponding to the interaction with the promotional content from the system-level response; The association module is used to associate and bind the basic attribute data of the promotional content with the target behavior feedback data to obtain association tracking data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the promotional content data tracking method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the promotional content data tracking method as described in any one of claims 1 to 7.

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