A charging attribution processing method and system

Abstract

The present application relates to the field of data processing, and relates to a charging attribution processing method and system, wherein the method comprises the following steps: obtaining original conversion data through a three-party attribution platform, and then obtaining the conversion number required by charging; cleaning the original conversion data, including: eliminating unnecessary data and supplementing the data required by attribution; matching the cleaned conversion data with the offer data of the company's internal operation system, so as to attribute the conversion data to the corresponding offer, and realize original association; for the offer matched by the conversion data, combining the settlement type, charging event and charging unit price information of the offer, calculating the upstream income and downstream expenditure of the corresponding offer, wherein the upstream income is equal to the conversion number multiplied by the upstream unit price, and the downstream expenditure is equal to the conversion number multiplied by the downstream unit price. The method of the present application can effectively improve the efficiency, flexibility and accuracy of attribution charging; and can realize industry and finance integration in the MMP mode.

Description

Technical Field

[0001] This invention relates to the field of data processing. More specifically, this invention relates to a billing attribution processing method and system. Background Technology

[0002] Attribution-based billing refers to the process in advertising or performance marketing of accurately attributing user conversion behavior to the corresponding advertising channel, campaign, or promoter, and calculating advertising costs or revenue accordingly. In performance advertising, advertisers only pay for results (such as installs, registrations, and purchases). Because users may be exposed to multiple ads, attribution is needed to find the source that ultimately drives the conversion, thereby settling accounts with the media and paying commissions to agencies.

[0003] Current technologies typically begin by acquiring a large amount of conversion data from third-party attribution platforms, then calculate the conversion count based on this data, and finally perform attribution billing based on the conversion count and pre-defined billing rules. However, since there is usually only one pre-defined billing rule, hard-coded into the program logic and unable to be dynamically changed, and different types of offers often have different settlement models, existing attribution billing methods only support attribution billing for a few standard offers, resulting in poor flexibility. Furthermore, existing attribution billing methods are usually limited to attribution and expense calculation from an operational perspective, lacking support for upstream revenue, leading to a disconnect between upstream (advertiser) billing and downstream (media) billing; attribution behavior cannot drive complete financial settlement and reporting; and it is difficult to achieve business-finance integration. Summary of the Invention

[0004] To address the technical problems of poor flexibility and difficulty in achieving business-finance integration in existing billing attribution methods, this invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a billing attribution processing method, comprising:

[0006] Obtain raw conversion data through a third-party attribution platform, and then obtain the number of conversions needed for billing;

[0007] Cleaning the raw conversion data includes: removing unnecessary data and supplementing the data required for complete attribution; unnecessary data refers to data that does not actually contribute to attribution or billing or may cause interference.

[0008] The cleaned conversion data is matched with offer data from the company's internal operating system, thereby attributing the conversion data to the corresponding offer and realizing the original association.

[0009] For offers matched with conversion data, the upstream revenue and downstream expenditure of the corresponding offer are calculated by combining the offer's settlement type, billing event, and billing unit price information. The upstream revenue is equal to the number of conversions multiplied by the upstream unit price, and the downstream expenditure is equal to the number of conversions multiplied by the downstream unit price. The upstream is the advertiser, and the downstream is the media side.

[0010] Preferably, it further includes: writing raw conversion data with high real-time requirements into a first real-time processing channel, and writing raw conversion data with low real-time requirements into a first batch processing channel;

[0011] The process of cleaning the raw conversion data includes: using a stream processing engine to clean the raw conversion data in the first real-time processing channel, and writing the cleaned data into the second real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption. The process also includes periodically reading the raw conversion data in the first batch processing channel, performing corresponding batch cleaning operations on the read raw conversion data, and writing the cleaned data into the second batch processing channel.

[0012] Matching the cleaned conversion data with offer data from the company's internal operating system includes: using a stream processing engine to match the conversion data in the second real-time processing channel, and writing the conversion data that matches the offer into the third real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the second batch processing channel, performing corresponding batch matching operations on the read conversion data, and writing the conversion data that matches the offer into the third batch processing channel.

[0013] Calculating the upstream revenue and downstream expenditure of the corresponding offer includes: using a stream processing engine to calculate the conversion data in the third real-time processing channel to obtain the corresponding upstream revenue and downstream expenditure of the offer. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the third batch processing channel and performing corresponding batch calculation operations on the read conversion data to obtain the upstream revenue and downstream expenditure of the conversion data offer.

[0014] Preferably, the attribution event is App installation, user registration, or first payment.

[0015] Preferably, the original conversion data includes core conversion information, user / device information, and media source information. The core conversion information includes one or more of the following: currency type, advertising campaign ID, unique ad click ID, business offer ID, user app installation time, conversion event occurrence time, conversion event type, and unique identifier of the ad click event. The user / device information includes one or more of the following: device identifier, user's IP address at the time of click or conversion, browser or device UA, geographic location, and operating system version. The media source information includes one or more of the following: channel name, advertising campaign ID, ad group ID, creative ID, and sub-channel name.

[0016] Preferably, the number of conversions required to obtain billing includes:

[0017] Filter out conversion events that meet the billing requirements from the raw conversion data;

[0018] The selected conversion events are deduplicated;

[0019] Normalize the conversion events after deduplication, including: determining whether the conversion event matches the time range, country, channel, etc. of the offer. If it matches, record it as the attribution event.

[0020] The validity of each attribution event is determined, including: whether the attribution event meets the billing conditions, and if so, it is recorded as a valid attribution event;

[0021] The total number of valid attribution events is used as the transformation number.

[0022] Preferably, the unnecessary data to be removed includes user-defined events, data with abnormal formats, test data, expired data, and data without offer mapping. Among them, data with abnormal formats includes data with missing fields, non-standard timestamps, and incorrect ID formats; test data refers to install / click record data from the internal testing environment; expired data refers to data that has exceeded the attribution validity period; and data without offer mapping refers to data that has not been configured with corresponding activities by third-party channels and cannot enter the attribution process.

[0023] Preferably, the fields of the data required for attribution include: campaign ID, offer ID, conversion event type, currency type, and conversion event occurrence time.

[0024] Preferably, matching the cleaned conversion data with offer data from the company's internal operating system includes:

[0025] The click_id exact matching method is used to match conversion data with click logs from the company's internal operating system, and the data that matches successfully is recorded as the first conversion data;

[0026] The first conversion data is matched with the offer data in the company's internal operating system using preset matching rules. The matching rules include: the first conversion data and the offer data have the same advertising campaign ID, the first conversion data and the offer data have the same media source, the first conversion data and the offer data have the same geographic region, and the first conversion data and the offer data have the same event type.

[0027] Preferably, the third-party attribution platform is AppsFlyer and / or Adjust.

[0028] In a second aspect, the present invention provides a billing attribution processing system, including a memory and a processor, wherein the memory stores computer program instructions that, when executed by the processor, implement the billing attribution processing method of the present invention.

[0029] The beneficial effects of this invention are as follows: This invention no longer uses pre-designed fee rules when performing attribution billing. Instead, it matches offer data from the company's internal operating system with cleaned conversion data, and links the successfully matched conversion data with information such as the settlement type, billing event, and billing unit price of the corresponding offer, automatically performing income / expense calculations. This achieves an automated and standardized expense accounting process, reduces manual intervention, and effectively improves the efficiency, flexibility, and accuracy of attribution billing. Furthermore, the method of this invention can achieve seamless integration of upstream and downstream billing in MMP mode, realizing business-finance integration. Attached Figure Description

[0030] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0031] Figure 1 This is a schematic flowchart illustrating a billing attribution processing method according to an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram illustrating a billing attribution processing system according to an embodiment of the present invention. Detailed Implementation

[0033] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0035] Example of a billing attribution processing method:

[0036] like Figure 1 As shown, the billing attribution processing method of the present invention includes:

[0037] S101. Obtain raw conversion data through a third-party attribution platform, and then obtain the number of conversions required for billing;

[0038] In this embodiment, the third-party attribution platform is AppsFlyer and / or Adjust. In other embodiments, other suitable mobile attribution platforms may also be used.

[0039] AppsFlyer is a mobile attribution and marketing analytics platform, and one of the most mainstream MMPs in the industry. It provides advertisers, app developers, and marketing teams with tools to help them accurately track user origins, evaluate ad performance, optimize marketing campaigns, and detect fraud.

[0040] Adjust is also a mobile attribution and marketing analytics platform, and like AppsFlyer, it's a typical MMP (Mobile Measurement Partner). It helps advertisers and app developers accurately track ad performance, analyze user behavior, and provides fraud prevention and marketing automation features. Adjust is an attribution analytics and marketing monitoring tool for mobile apps, primarily used to track user behavior, evaluate ad channel performance, and prevent ad fraud.

[0041] S102. Clean the original conversion data, including: removing unnecessary data and supplementing the data required for complete attribution; unnecessary data refers to data that does not actually contribute to attribution or billing or may cause interference;

[0042] S103. Match conversion data with offer data, specifically including: matching the cleaned conversion data with offer data from the company's internal operating system, thereby attributing the conversion data to the corresponding offer and realizing the original association;

[0043] The company's internal operating system refers to a set of business platforms or data systems built to support the company's business processes such as advertising placement, traffic operation, channel management, and billing calculation. It is used to manage, configure, track, and analyze all core information and data related to advertising activities (offers).

[0044] S104. Calculate the upstream revenue and downstream expenditure of the offer. Specifically, for the offer matched by the conversion data, combine the settlement type, billing event, and billing unit price information of the offer to calculate the upstream revenue and downstream expenditure of the corresponding offer. The upstream revenue is equal to the number of conversions multiplied by the upstream unit price, and the downstream expenditure is equal to the number of conversions multiplied by the downstream unit price. The upstream is the advertiser, and the downstream is the media side.

[0045] The method of this invention first cleans the raw conversion data to ensure that it is accurate and reliable before calculating the upstream revenue and downstream expenditure of the offer.

[0046] Because different offers have different settlement types (CPI / CPA / CPS / retention, etc.), each rule corresponds to a different pricing method. If a conversion event is not bound to the correct billing unit price or settlement method, it will lead to errors in cost calculation, which will affect revenue, gross profit, and even financial statements. This invention no longer uses pre-designed fee rules when performing attribution billing. Instead, it matches offer data from the company's internal operating system with cleaned conversion data. The successfully matched conversion data is linked with the corresponding offer's settlement type, billing event, billing unit price, and other information, and revenue / expense calculations are automatically executed. This achieves an automated and standardized cost accounting process, reduces manual intervention, and effectively improves the efficiency, flexibility, and accuracy of attribution billing. In addition, the method of this invention can achieve full-link integration of upstream and downstream billing in MMP mode, realizing business and finance integration. Furthermore, since the original conversion data may have missing fields, incorrect field formats, or field conflicts, cleaning the original conversion data can unify the data format, thereby ensuring the accuracy, completeness, and reliability of subsequent attribution, billing, and analysis processes, thus guaranteeing the accuracy of attribution billing results. The cleaned and transformed data has a unified structure, making it easy to integrate with data warehouses, BI systems, or downstream accounting platforms.

[0047] In this embodiment, the MMP model refers to a service model in which enterprises collaborate with professional mobile attribution measurement partners (MMPs) to achieve data tracking, user behavior analysis, and attribution analysis for advertising campaigns.

[0048] In one embodiment, the method further includes: writing raw conversion data with high real-time requirements into a first real-time processing channel, and writing raw conversion data with low real-time requirements into a first batch processing channel.

[0049] The process of cleaning the raw conversion data includes: using a stream processing engine to clean the raw conversion data in the first real-time processing channel, and writing the cleaned data into the second real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption. The process also includes periodically reading the raw conversion data in the first batch processing channel, performing corresponding batch cleaning operations on the read raw conversion data, and writing the cleaned data into the second batch processing channel.

[0050] Matching the cleaned conversion data with offer data from the company's internal operating system includes: using a stream processing engine to match the conversion data in the second real-time processing channel, and writing the conversion data that matches the offer into the third real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the second batch processing channel, performing corresponding batch matching operations on the read conversion data, and writing the conversion data that matches the offer into the third batch processing channel.

[0051] Calculating the upstream revenue and downstream expenditure of the corresponding offer includes: using a stream processing engine to calculate the conversion data in the third real-time processing channel to obtain the corresponding upstream revenue and downstream expenditure of the offer. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the third batch processing channel and performing corresponding batch calculation operations on the read conversion data to obtain the upstream revenue and downstream expenditure of the conversion data offer.

[0052] The method in this embodiment no longer uses a linear processing approach when performing attribution analysis on conversion data from third-party platforms. Instead, it differentiates the real-time nature of the raw conversion data, dividing it into data with high real-time requirements (i.e., high-priority data) and data with low real-time requirements (i.e., low-priority data). High-priority data is processed in real-time using a stream processing engine for cleaning, matching, and billing operations. Low-priority data is processed in batches periodically. This ensures timely attribution and billing for data with high real-time requirements, balancing the real-time and non-real-time needs of conversion data and reducing the data processing pressure on the attribution and billing system.

[0053] In one embodiment, the attribution event is an app installation, user registration, or first payment.

[0054] In one embodiment, the original conversion data includes core conversion information, user / device information, and media source information. The core conversion information includes one or more of the following: currency type, advertising campaign ID, unique ad click ID, business offer ID, user app installation time, conversion event occurrence time, conversion event type, and unique identifier of ad click event. The user / device information includes one or more of the following: device identifier, user IP address at the time of click or conversion, browser or device UA, geolocation, and operating system version. The media source information includes one or more of the following: channel name, advertising campaign ID, ad group ID, creative ID, and sub-channel name.

[0055] In one embodiment, the number of conversions required to obtain billing includes:

[0056] Filter out conversion events that meet the billing requirements from the raw conversion data;

[0057] Conversion events that meet the billing requirements are the attribution events set by the advertiser on the attribution platform.

[0058] The selected conversion events are deduplicated;

[0059] Deduplication can be performed using a unique key. For a set of converted data, certain field combinations serve as "unique identifiers." When multiple records with the same unique identifier appear in a set of converted data, only one is retained, and the rest are considered "duplicate records" and discarded, thereby achieving data uniqueness control. In this embodiment, the field combination can be device_id + event_type + offer_id.

[0060] Normalize the conversion events after deduplication, including: determining whether the conversion event matches the time range, country, channel, etc. of the offer. If it matches, record it as the attribution event.

[0061] The validity of each attribution event is determined, including: whether the attribution event meets the billing conditions, and if so, it is recorded as a valid attribution event;

[0062] The total number of valid attribution events is used as the transformation number.

[0063] The method described in this embodiment can extract the conversion number from the original conversion data relatively accurately.

[0064] In one embodiment, the unnecessary data to be removed includes user-defined events, data with abnormal formats, test data, expired data, and data without offer mappings. Among them, data with abnormal formats includes data with missing fields, non-standard timestamps, and incorrect ID formats; test data refers to install / click record data from the internal testing environment; expired data refers to data that has exceeded the attribution validity period; and data without offer mappings refers to data that cannot enter the attribution process because the third-party channel has not configured the corresponding activity.

[0065] In one embodiment, the fields of the data required for attribution include: campaign ID, offer ID, conversion event type, currency type, and conversion event occurrence time.

[0066] In one embodiment, matching the cleaned conversion data with offer data from the company's internal operating system includes:

[0067] The click_id exact matching method is used to match conversion data with click logs from the company's internal operating system, and the data that matches successfully is recorded as the first conversion data;

[0068] The first conversion data is matched with the offer data in the company's internal operating system using preset matching rules. The matching rules include: the first conversion data and the offer data have the same advertising campaign ID, the first conversion data and the offer data have the same media source, the first conversion data and the offer data have the same geographic region, and the first conversion data and the offer data have the same event type.

[0069] The method described in this embodiment enables high-precision matching of third-party data with internal company offers, avoiding invalid conversions that "cannot be attributed to business functions" and effectively improving the accuracy of data attribution.

[0070] Example of a billing attribution processing system:

[0071] This invention also provides a billing attribution processing system. For example... Figure 2 As shown, the billing attribution processing system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a billing attribution processing method according to the first aspect of the present invention.

[0072] The billing attribution processing system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0073] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0074] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0075] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A billing attribution processing method, characterized in that, include: Obtain raw conversion data through a third-party attribution platform, and then obtain the number of conversions needed for billing; Cleaning the raw conversion data includes: removing unnecessary data and supplementing the data required for attribution; unnecessary data refers to data that does not actually contribute to attribution or billing or may cause interference. The cleaned conversion data is matched with offer data from the company's internal operating system, thereby attributing the conversion data to the corresponding offer and realizing the original association. For offers matched with conversion data, the upstream revenue and downstream expenditure of the corresponding offer are calculated by combining the offer's settlement type, billing event, and billing unit price information. The upstream revenue is equal to the number of conversions multiplied by the upstream unit price, and the downstream expenditure is equal to the number of conversions multiplied by the downstream unit price. The upstream is the advertiser, and the downstream is the media side. It also includes: writing raw conversion data with high real-time requirements into the first real-time processing channel, and writing raw conversion data with low real-time requirements into the first batch processing channel; The process of cleaning the raw conversion data includes: using a stream processing engine to clean the raw conversion data in the first real-time processing channel, and writing the cleaned data into the second real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption. The process also includes periodically reading the raw conversion data in the first batch processing channel, performing corresponding batch cleaning operations on the read raw conversion data, and writing the cleaned data into the second batch processing channel. Matching the cleaned conversion data with offer data from the company's internal operating system includes: using a stream processing engine to match the conversion data in the second real-time processing channel, and writing the conversion data that matches the offer into the third real-time processing channel. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the second batch processing channel, performing corresponding batch matching operations on the read conversion data, and writing the conversion data that matches the offer into the third batch processing channel. Calculating the upstream revenue and downstream expenditure of the corresponding offer includes: using the stream processing engine to calculate the conversion data in the third real-time processing channel to obtain the corresponding upstream revenue and downstream expenditure of the offer. The data input mechanism of the stream processing engine is asynchronous consumption; periodically reading the conversion data in the third batch processing channel and performing corresponding batch calculation operations on the read conversion data to obtain the upstream revenue and downstream expenditure of the conversion data. The number of conversions required to obtain billing includes: Filter out conversion events that meet the billing requirements from the raw conversion data; The selected conversion events are deduplicated; Normalization matching is performed on the deduplicated conversion events, including: determining whether the conversion event matches the time range, country, and channel dimensions of the offer. If it matches, it is recorded as an attribution event. The validity of each attribution event is determined, including: whether the attribution event meets the billing conditions, and if so, it is recorded as a valid attribution event; The total number of valid attribution events is used as the transformation number.

2. The billing attribution processing method as described in claim 1, characterized in that, The attribution events are App installation, user registration, or first payment.

3. The billing attribution processing method as described in claim 1, characterized in that, The original conversion data includes core conversion information, user / device information, and media source information. The core conversion information includes one or more of the following: currency type, advertising campaign ID, unique ad click ID, business offer ID, user app installation time, conversion event occurrence time, conversion event type, and unique identifier of ad click event. The user / device information includes one or more of the following: device identifier, user IP address at the time of click or conversion, browser or device UA, geolocation, and operating system version. The media source information includes one or more of the following: channel name, advertising campaign ID, ad group ID, creative ID, and sub-channel name.

4. The billing attribution processing method as described in claim 1, characterized in that, The unnecessary data removed includes user-defined events, data with abnormal formats, test data, expired data, and data without offer mappings. Among them, data with abnormal formats includes data with missing fields, non-standard timestamps, and incorrect ID formats; test data refers to install / click record data from the internal testing environment; expired data refers to data that has exceeded the attribution validity period; and data without offer mappings refers to data that cannot enter the attribution process because the corresponding activity is not configured in the third-party channel.

5. The billing attribution processing method as described in claim 1, characterized in that, The fields required for attribution data include: campaign ID, offer ID, conversion event type, currency type, and conversion event occurrence time.

6. The billing attribution processing method as described in claim 4, characterized in that, Matching the cleaned conversion data with offer data from the company's internal operating system also includes: The click_id exact matching method is used to match conversion data with click logs from the company's internal operating system, and the data that matches successfully is recorded as the first conversion data; The first conversion data is matched with the offer data in the company's internal operating system using preset matching rules. The matching rules include: the first conversion data and the offer data have the same advertising campaign ID, the first conversion data and the offer data have the same media source, the first conversion data and the offer data have the same geographic region, and the first conversion data and the offer data have the same event type.

7. The billing attribution processing method as described in any one of claims 1 to 6, characterized in that, The third-party attribution platforms are AppsFlyer and / or Adjust.

8. A billing attribution processing system, comprising a memory and a processor, wherein the memory stores computer program instructions, characterized in that, When the computer program instructions are executed by the processor, the billing attribution processing method according to any one of claims 1 to 7 is implemented.

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