A store promotion advertisement information distribution system

Abstract

This invention discloses a store promotional advertising information publishing system, belonging to the field of internet advertising services and commercial promotional information publishing technology. It includes a promotional advertising material management module, a target customer profile construction module, an advertising placement priority quantification module, a multi-channel advertising distribution module, an advertising performance feedback module, and a placement strategy iteration optimization module. These modules are interconnected, respectively realizing the core functions of full-process management of promotional advertising materials, user consumption profile construction and advertising tag matching, advertising placement weight calculation and time-series queue generation, accurate multi-channel advertising distribution, placement performance data collection and feedback, and closed-loop iterative optimization of placement strategies. This invention achieves intelligent control of the entire store promotional advertising publishing process, improves advertising accuracy and content compliance, optimizes the efficiency of placement resource allocation, adapts to the differentiated promotional needs of physical stores, and simultaneously ensures user data security and privacy compliance.

Description

Technical Field

[0001] This invention relates to the field of internet advertising services and commercial promotion information publishing technology, and in particular to a store promotion advertising information publishing system. Background Technology

[0002] With the ongoing digital transformation of the offline retail industry, the promotional advertising models of physical stores are gradually extending from traditional offline channels to online internet advertising services. The forms, channels, and methods of disseminating promotional advertising information are showing a diversified development trend. Physical store promotional activities are characterized by strong localization, significant product category differences, flexible promotional periods, and concentrated customer coverage, placing differentiated demands on the accuracy, compliance, and channel adaptability of advertising. Traditional store promotional advertising relies heavily on manual operation, requiring human involvement from ad creative production and channel selection to execution. The process lacks standardized control procedures, and the matching of advertising content with the target customer group depends on the experience and judgment of operations personnel, failing to achieve precise reach. Furthermore, multi-channel advertising requires the creation of separate, tailored materials for different channels, resulting in cumbersome procedures, low efficiency, and difficulty in meeting the high-frequency, diverse promotional needs of physical stores.

[0003] Existing advertising information publishing systems are mostly designed for general advertising scenarios across all industries, lacking specific adaptation for the promotional advertising needs of physical stores. This results in several shortcomings in practical application. Most existing systems lack dedicated compliance verification mechanisms for retail promotional advertising, failing to achieve automated, multi-dimensional compliance screening of advertising content. They also struggle to adapt to the dynamic updates of internet advertising service regulations, making them prone to advertising content violations. In the customer matching stage, existing systems lack quantitative matching calculation models, failing to accurately match advertising content with user consumption profiles. This leads to insufficient targeting precision and a lack of upfront performance prediction capabilities, failing to provide effective data support for merchants' budget allocation and timing adjustments, potentially resulting in unreasonable resource allocation.

[0004] Meanwhile, existing advertising delivery systems lack a complete closed-loop iteration mechanism for delivery strategies, failing to automatically optimize strategies based on actual ad performance data, thus hindering continuous improvement in ad delivery effectiveness. In multi-channel ad distribution, existing systems cannot achieve dynamic and balanced allocation of delivery resources and lack effective anti-fraud verification capabilities, making it difficult to guarantee the quality of ad traffic. In user data processing, some systems lack robust privacy protection mechanisms, failing to meet relevant user data privacy compliance requirements. Furthermore, most existing systems do not integrate intelligent editing and full lifecycle management functions for ad creatives, failing to provide merchants with a fully integrated service from creative production, compliance verification, precise targeting to performance feedback, and thus failing to fully adapt to the entire process of publishing promotional advertising information for physical stores. Summary of the Invention

[0005] The present invention proposes a store promotion advertising information publishing system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a store promotion advertising information publishing system, comprising the following modules:

[0007] The promotional advertising material management module connects to the store merchant terminal, receives and stores the promotional advertising materials uploaded by the merchants, performs format standardization processing, content compliance verification and tag classification of the materials, and generates basic information files for promotional advertisements;

[0008] The target customer profile building module connects to the user behavior database of the Internet advertising service platform, collects user online consumption behavior, offline store records, category preferences, spending power and geographical location data, and builds personalized consumer profiles of users through multi-dimensional feature extraction, and completes the association and matching of user profiles with promotional advertising tags;

[0009] The ad placement priority quantification module constructs an ad placement priority ranking model based on basic information of promotional ads, merchant spending budgets, and store customer traffic distribution data during different time periods. It calculates the placement weight of each promotional ad and generates an ad placement time sequence queue.

[0010] The multi-channel advertising distribution module connects with advertising channels and accurately distributes corresponding promotional ads to target channels and target user terminals based on the matching results of the placement time queue and user profile.

[0011] The advertising performance feedback module collects real-time data on ad impressions, clicks, in-store conversions, and coupon redemption sales from various advertising channels, generates performance statistics reports, and synchronously feeds them back to the merchant's terminal.

[0012] The ad placement strategy iteration and optimization module extracts ad conversion features based on ad placement performance statistics reports, and updates the core parameters of the customer matching model and ad placement priority ranking model through incremental learning algorithms, thus completing the closed-loop iteration and optimization of the promotional ad placement strategy.

[0013] Furthermore, it also includes an ad-customer matching quantification calculation unit, used to construct a matching quantification model between promotional ad tags and user consumption profiles, and calculate the matching score between each promotional ad and the target user. The matching quantification calculation expression is as follows:

[0014] ;

[0015] in, The score represents the overall match between the promotional advertisement and the target user. This represents the category preference weighting coefficient. Match weight coefficients to time periods. Weighting coefficients are assigned to consumption capacity. Match weight coefficients to geographic locations. This is a normalized value representing the match between user category preferences and advertising / promotion categories. This is a normalized value representing the match between user activity periods and advertising promotion periods. This is a normalized value representing the fit between user spending power and advertising discounts. Normalized value for the spatial distance fit between user's geographic location and the applicable store of the advertisement.

[0016] Furthermore, it includes an advertising material compliance verification unit. This unit has a built-in multi-dimensional advertising content verification rule library, which automatically verifies the compliance of uploaded promotional advertising materials, generates compliance modification prompts for materials that do not comply with the rules and sends them back to the merchant's terminal; the rule library is updated in real time with the Internet advertising management regulations of the market supervision department; it uses natural language processing technology to identify the semantics of text and screen for illegal words, and uses image recognition technology to verify the compliance of image and video content; it has built-in special compliance verification rules for food, cosmetics and medical device advertisements, links them to the merchant's historical violation records, and increases the verification frequency and verification dimensions for merchants with multiple violations; it generates a unique traceability identifier for advertising materials that pass the verification, and manages the evidence for the entire life cycle of the materials.

[0017] Furthermore, it also includes an advertising performance prediction unit, used to build a promotional advertising conversion performance prediction model based on historical campaign data and current advertising information, and calculate the expected conversion efficiency of the advertising campaign. The conversion performance prediction expression is as follows:

[0018] ;

[0019] in, This represents the expected conversion efficiency value for promotional advertising. Contribution coefficient to exposure Contribution coefficient to click-through rate The contribution coefficient to the coupon redemption rate. Contribution coefficient to in-store conversion rate Normalized value of expected ad impressions This is the normalized value of the average click-through rate of similar creative materials in the past. This is the normalized value of the historical redemption rate of coupons for similar promotional activities. This is a normalized value for the historical customer traffic conversion efficiency of applicable stores.

[0020] Furthermore, the promotional advertising material management module also includes an intelligent material editing unit. This unit has a built-in library of promotional advertising templates covering different retail categories, promotion types, and distribution channels. It supports merchants to edit advertising materials online based on templates and automatically generates advertising materials adapted to multiple distribution channels based on the promotional information entered by the merchant. It also has a built-in AI material generation function that automatically generates advertising plans based on the promotional theme, product information, and discount rules entered by the merchant. It supports one-click embedding and unified application of the merchant's brand logo, standard colors, and brand fonts. Based on the analysis of historical high-conversion material characteristics, it provides merchants with material optimization suggestions and performs unified management and statistical correlation of different versions of materials with the performance of the campaign.

[0021] Furthermore, the target customer profile construction module also includes a user privacy data desensitization unit. This unit performs irreversible desensitization processing on the collected user data, removing users' personal identification information while retaining the anonymized feature data required for profile construction; it performs end-to-end encryption processing on the data transmission and storage process, incorporates a differential privacy protection mechanism, and adds noisy data during profile construction and data statistics; it sets up a hierarchical data access permission control system, dynamically manages the scope of data collection and use based on user authorization status, and automatically deletes user data that exceeds the authorization period; it records access, use, and modification operations throughout the entire data lifecycle and generates compliance audit logs, automatically intercepting cross-border data transmission behaviors that do not meet privacy compliance requirements.

[0022] Furthermore, the multi-channel advertising distribution module also includes a channel adaptation processing unit. This unit adaptively converts and adjusts the format of promotional advertising materials according to the material format requirements, content specifications, and time period restrictions of different advertising channels; adjusts the advertising volume and time period of each channel based on the traffic distribution data of different channels; monitors the conversion efficiency of each advertising channel in real time, sets a threshold for the advertising cost of different channels, and automatically adjusts the advertising volume to a high-cost-performance channel when the advertising cost of a single channel exceeds the threshold; has a built-in channel anti-fraud verification function, which uses device fingerprinting and behavior trajectory recognition technology to eliminate abnormal traffic; sets an upper limit on the cross-channel advertising exposure frequency for the same user to control the consumption of repeated advertising resources; and completes the attribution of the advertising effect of each channel based on a multi-touchpoint attribution model, automatically updating the channel blacklist and the list of high-quality channels.

[0023] Furthermore, the advertising-customer matching metric calculation unit also includes a weight coefficient dynamic adjustment subunit. This subunit dynamically updates the weight coefficients of each dimension of matching features based on historical campaign conversion data using machine learning algorithms. It adopts an incremental model update mechanism to iteratively update the weight coefficients based on the daily newly added campaign conversion data, automatically removing abnormal data during the update process. It constructs an independent weight coefficient library for retail categories and sets differentiated initial weight values ​​for promotion types. For cold-start merchants without historical campaign data, it initializes the weight coefficient configuration based on historical data of merchants in the same category and region. After each weight coefficient update, it performs backtesting to verify the effect of the updated weight coefficients on improving advertising conversion efficiency.

[0024] Furthermore, the advertising performance feedback module also includes a multi-dimensional data statistics subunit. This subunit breaks down and statistically analyzes advertising performance data according to the dimensions of advertising channel, promotional category, advertising time period, and store area, generating detailed performance reports for each dimension; compares the performance data of different merchants and different promotional activities, generating performance rankings and optimization suggestions; performs conversion funnel analysis based on the entire link data of ad exposure, clicks, coupon redemption, redemption, and in-store visits, locating conversion drop-off points; calculates the ROI of a single ad, a single channel, and a single merchant, correlates advertising performance with user lifetime value, and identifies the characteristics of high-value customer groups; provides benchmark data on the average performance of merchants in the same industry and category, and automatically triggers warnings for abnormal situations.

[0025] Furthermore, the ad placement strategy iteration and optimization module also includes an A / B testing unit. This unit allows merchants to set multiple sets of different advertising materials, ad placement periods, and target customer group strategies for the same promotional activity, and simultaneously conduct traffic splitting tests. Based on the test results, the conversion effects of different strategies are compared to determine the optimal ad placement strategy and update it to the ad placement strategy model. A hierarchical random traffic allocation mechanism is adopted to control the traffic samples of different test strategies to be free of feature bias. The minimum sample size is intelligently calculated based on the test confidence requirements, and the statistical significance of the test results is automatically judged. Multi-variable combination testing is supported, and multi-dimensional strategy parallel testing is completed simultaneously. The test results are subjected to multi-dimensional visualization analysis to determine the influence weight of different strategy variables on the ad placement effect. The optimal strategy is applied to the full ad placement with one click, and the test data is automatically archived and used as incremental samples to participate in the ad placement model iteration training.

[0026] Compared with existing technologies, the beneficial effects of this invention are:

[0027] This invention constructs a fully integrated system covering the entire process of promotional advertising material management, customer profiling, placement priority quantification, multi-channel advertising distribution, placement effect feedback, and strategy iteration optimization. It achieves standardized and intelligent control over the entire process of store promotional advertising information release, providing complete technical support for physical store promotional advertising placement. The system achieves precise matching of promotional ads with target users through an ad-customer matching quantification calculation model, enabling differentiated placement strategies for different user levels and improving the effectiveness of user reach for advertising information. The system's built-in advertising material compliance verification unit can achieve full-dimensional automated compliance verification of advertising content, while simultaneously updating the compliance rule library to ensure that promotional advertising content complies with relevant management regulations for internet advertising services, achieving compliant evidence management throughout the entire lifecycle of advertising materials.

[0028] This invention utilizes an advertising performance prediction model to predict advertising conversion rates in advance, providing data support for merchants to allocate advertising budgets and adjust campaign timing, thus optimizing the efficiency of promotional advertising resource allocation. The multi-channel advertising distribution module enables multi-channel adaptive adaptation of advertising creatives and dynamic balanced allocation of distribution resources. Simultaneously, channel anti-fraud verification ensures the quality of ad traffic, improving the overall efficiency of multi-channel advertising. The system's built-in user privacy data anonymization unit, through multiple data security protection mechanisms, achieves compliant management of user data throughout its entire lifecycle, ensuring user data security and privacy compliance.

[0029] This invention, through its iterative optimization module and A / B testing unit, enables continuous optimization and iteration of advertising strategies, forming a closed-loop management mechanism for advertising placement. This mechanism is adaptable to the differentiated placement needs of various retail categories, promotional types, and store scenarios. The system's integrated intelligent material editing function provides merchants with convenient advertising material creation and management capabilities, lowering the operational threshold for advertising placement and improving the overall efficiency of promotional advertising information dissemination. The system's overall architecture allows for flexible expansion of functional modules, adapting to changes in retail industry promotional needs and internet advertising service standards, demonstrating excellent applicability and scalability. Attached Figure Description

[0030] Figure 1 This is a schematic block diagram of a store promotion advertising information publishing system proposed in this invention;

[0031] Figure 2 A quantitative flowchart for building and precisely matching target customer profiles;

[0032] Figure 3 Flowchart for intelligent management and automated compliance verification of advertising creatives;

[0033] Figure 4 Flowchart for prioritizing and adapting distribution across multiple channels;

[0034] Figure 5 Flowchart for feedback on campaign performance and iterative optimization through A / B testing. Detailed Implementation

[0035] 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 embodiments of the present invention, and not all embodiments. 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.

[0036] Reference Figures 1 to 5 A store promotional advertising information publishing system, comprising the following modules:

[0037] The promotional advertising material management module is used to connect with store merchant terminals, receive and store promotional advertising materials uploaded by merchants, complete the standardization of material format, content compliance verification and tag classification, and generate basic information files for corresponding promotional advertisements. The basic information files include structured data in the dimensions of promotional category, promotion period, discount, applicable stores, and target customer group.

[0038] The target customer profile building module is used to connect to the user behavior database of the Internet advertising service platform, collect user online consumption behavior, offline store records, category preferences, spending power, and geographical location data, and build personalized consumer profiles of users through multi-dimensional feature extraction, and complete the association and matching of user profiles with promotional advertising tags;

[0039] The ad placement priority quantification module is used to build an ad placement priority ranking model based on basic information of promotional ads, merchant spending budget, and store traffic distribution data during different time periods. It calculates the placement weight of each promotional ad and generates an ad placement time sequence queue.

[0040] The multi-channel advertising distribution module is used to connect with various advertising channels such as online social platforms, local life service platforms, offline store smart screens, and user-end mini-programs. Based on the matching results of the placement time queue and user profile, the corresponding promotional ads are accurately distributed to the target placement channels and target user terminals.

[0041] The advertising performance feedback module is used to collect real-time data on ad impressions, clicks, in-store conversions, and coupon redemption sales from various advertising channels, generate multi-dimensional performance statistics reports, and provide synchronous feedback to the merchant's terminal.

[0042] The campaign strategy iteration and optimization module is used to extract advertising conversion features based on campaign performance statistics reports, and update the core parameters of the customer matching model and the campaign priority ranking model through incremental learning algorithms to complete the closed-loop iteration and optimization of the promotional advertising campaign strategy.

[0043] This invention also includes an advertising-customer matching quantification calculation unit, used to construct a precise matching quantification model between promotional advertising tags and user consumption profiles, and to calculate the matching score between each promotional advertisement and the target user. The matching quantification calculation expression is as follows:

[0044] ;

[0045] in, The score represents the overall match between the promotional advertisement and the target user. This represents the category preference weighting coefficient. Match weight coefficients to time periods. Weighting coefficients are assigned to consumption capacity. Weighting coefficients are assigned to geographical locations, and the sum of all weighting coefficients is 1. This is a normalized value representing the match between user category preferences and advertising / promotion categories. This is a normalized value representing the match between user activity periods and advertising promotion periods. This is a normalized value representing the fit between user spending power and advertising discounts. This system normalizes the spatial distance matching between user geographic location and applicable advertising stores, and achieves precise quantification of ad-user matching through multi-dimensional feature weighted fusion. This provides a quantitative basis for selecting advertising distribution channels and target users. Simultaneously, it segments users based on matching scores, dividing them into three tiers: high-intent potential customers, regular customers, and low-intent customers. Differentiated ad placement frequencies and creative display formats are matched to different customer tiers. For cold-start users with no historical consumption behavior, the matching score is supplemented based on the group consumption characteristics of users of the same age group in the same region. Furthermore, the matching score is updated at fixed time intervals based on real-time user behavior data, enabling dynamic adjustment of the ad-user matching relationship and improving the accuracy of promotional ad placement and the effectiveness of user reach.

[0046] This invention also includes an advertising material compliance verification unit with a built-in multi-dimensional advertising content verification rule library. The rule library includes verification rules for advertising language standards, the authenticity of promotional offers, intellectual property compliance, and restrictions on special product category promotions. It can automatically perform full-dimensional compliance verification on uploaded promotional advertising materials, generate compliance modification prompts for materials that do not comply with the verification rules, and send them back to the merchant's terminal. The rule library can be updated in real-time with the internet advertising management regulations issued by market supervision departments. Natural language processing technology is used to perform semantic recognition and screening of illegal words in the text content of the advertising materials, and image recognition technology is used to perform compliance verification of images and video content in the materials. For special product categories such as food, cosmetics, and medical devices, specific compliance verification rules are built-in. It can also be linked to the merchant's historical violation records, increasing the verification frequency and dimensions for merchants with multiple compliance issues. A unique traceability identifier is generated for advertising materials that pass the compliance verification, completing the evidence storage management of the entire lifecycle of the materials and ensuring that the promotional advertising content complies with relevant internet advertising service management regulations.

[0047] This invention also includes an advertising performance prediction unit, used to construct a promotional advertising conversion performance prediction model based on historical advertising data and current advertising information, and to calculate the expected conversion efficiency of the advertising campaign. The conversion performance prediction expression is as follows:

[0048] ;

[0049] in, This represents the expected conversion efficiency value for promotional advertising. Contribution coefficient to exposure Contribution coefficient to click-through rate The contribution coefficient to the coupon redemption rate. The contribution coefficient to in-store conversion rate is calculated, and the sum of all contribution coefficients is 1. Normalized value of expected ad impressions This is the normalized value of the average click-through rate of similar creative materials in the past. This is the normalized value of the historical redemption rate of coupons for similar promotional activities. To apply the normalized value of historical customer traffic conversion efficiency in stores, the system performs preliminary predictions of advertising conversion effects based on multi-dimensional historical data and real-time delivery parameters. It also calculates the confidence interval of the prediction results and constructs independent sub-prediction models for different retail categories, promotion types, and delivery channels. Based on the merchant's total budget and the expected conversion efficiency of a single ad, the system optimizes the allocation of the budget across multiple ads. Abnormal historical delivery data is automatically removed during the prediction process, and the input parameters and coefficients of the prediction model are updated periodically. This provides data support for merchants' budget allocation and ad delivery timing adjustments, optimizing the efficiency of promotional ad delivery resource allocation.

[0050] In this invention, the promotional advertising material management module also includes an intelligent material editing unit with a built-in promotional advertising template library. The template library covers standardized advertising templates for different retail categories, promotion types, and distribution channels. It supports merchants in editing advertising materials online, replacing elements, and previewing effects based on the templates. At the same time, it can automatically generate standardized advertising materials adapted to multiple distribution channels based on the promotional information input by the merchant. The template library is continuously updated with template content based on industry hotspots, holidays, and promotion types. It has a built-in AI material generation function, which can automatically generate advertising copy, poster elements, and video clips based on the promotional theme, product information, and discount rules input by the merchant. It supports one-click insertion of merchant brand logos, standard colors, and brand fonts, as well as unified application of all materials. It can automatically generate multiple sizes of material versions adapted to the size specifications of different distribution channels. At the same time, based on the feature analysis of historical high-conversion materials, it provides merchants with material optimization suggestions and completes unified management of different versions of materials and correlation statistics of distribution effects.

[0051] In this invention, the target customer profile construction module also includes a user privacy data desensitization unit, which performs irreversible desensitization processing on the collected user behavior data, geographic location data, and consumption record data, removing users' personal identification information and retaining only anonymized feature data used for profile construction. Simultaneously, end-to-end encryption is performed on the data transmission and storage process, and a built-in differential privacy protection mechanism is implemented. Noise data is added during profile construction and data statistics to prevent the reverse reconstruction of individual user information. A tiered data access permission control system is set up, with different data access scopes and operation permissions set for merchants, platform operators, and third-party partners. Data collection and usage scope are dynamically managed based on user authorization status. User data exceeding the authorization period is automatically deleted. Access, use, and modification operations throughout the entire data lifecycle are fully recorded and compliance audit logs are generated. Cross-border data transmission that does not meet privacy compliance requirements is automatically intercepted, ensuring user data security and privacy compliance.

[0052] In this invention, the multi-channel advertising distribution module also includes a channel adaptation processing unit, used to adaptively convert the format of promotional advertising materials and adjust the content according to the material format requirements, content specifications, and time period restrictions of different advertising channels. Simultaneously, it can dynamically adjust the advertising volume and time period of each channel based on traffic distribution data, achieving dynamic and balanced allocation of multi-channel advertising resources. It monitors the traffic quality, advertising cost, and conversion efficiency of each advertising channel in real time, sets advertising cost thresholds for different channels, and automatically adjusts the advertising volume to a high-cost-performance channel when the cost of a single channel exceeds the threshold. It has a built-in channel anti-fraud verification function, using device fingerprinting and behavioral trajectory recognition technology to eliminate abnormal traffic such as false exposure and false clicks. It sets a cross-channel advertising exposure frequency limit for the same user to control resource consumption caused by repeated advertising. Based on a multi-touchpoint attribution model, it accurately attributes the advertising effect of each channel, automatically updates the channel blacklist and the list of high-quality channels, and ensures the traffic quality and resource utilization efficiency of advertising.

[0053] In this invention, the advertising-customer matching quantification calculation unit further includes a weight coefficient dynamic adjustment subunit. This subunit is used to dynamically update the weight coefficients of each dimension of matching features based on historical conversion data from different retail categories, promotion types, and advertising channels using machine learning algorithms. This ensures a positive correlation between the matching degree calculation results and the actual advertising conversion effect, continuously improving the matching accuracy between advertisements and users. An incremental model update mechanism is adopted, which iteratively updates the weight coefficients based on the daily newly added advertising conversion data. Abnormal advertising data and invalid conversion data are automatically removed during the update process. Independent weight coefficient libraries are constructed for different retail categories such as fresh produce, home appliances, apparel, and catering. Differentiated initial weight values ​​are set for different promotion types such as new store openings, holiday promotions, and clearance sales. For cold-start merchants without historical advertising data, the weight coefficients are initialized based on historical data from merchants in the same category and region. Backtesting is performed after each weight coefficient update to verify the effect of the updated weight coefficients on improving advertising conversion efficiency.

[0054] In this invention, the advertising performance feedback module also includes a multi-dimensional data statistics subunit. This subunit can break down and statistically analyze advertising data according to dimensions such as advertising channel, promotional category, advertising time period, and store area, generating detailed reports of advertising performance for each dimension. It can also compare the advertising performance data of different merchants and different promotional activities, generating rankings and optimization suggestions. Based on the entire data chain of ad exposure, clicks, coupon redemption, and in-store visits, it performs conversion funnel analysis, identifies conversion drop-off points in the advertising process, calculates the ROI of a single ad, a single channel, and a single merchant, correlates advertising performance with user lifetime value, uncovers the advertising characteristics of high-value customer groups, provides benchmark data on the average advertising performance of merchants in the same industry and category, automatically triggers alerts for abnormal conversions, sudden traffic surges, and abnormal redemptions, supports merchants to customize report dimensions and statistical periods, and enables online viewing and standardized format export of reports, providing comprehensive data support for merchants' advertising decisions.

[0055] In this invention, the ad placement strategy iteration and optimization module also includes an A / B testing unit, which supports merchants in setting multiple sets of different advertising materials, placement time periods, and target customer group strategies for the same promotional activity, and simultaneously conducting traffic splitting and placement tests. Based on the test results, the conversion effects of different strategies are compared, the optimal placement strategy is selected and updated synchronously into the ad placement strategy model, realizing continuous optimization and iteration of promotional advertising placement strategies. A hierarchical random traffic allocation mechanism is adopted to control the traffic samples of different test strategies to be free of feature bias. The minimum sample size required is intelligently calculated based on the confidence requirements of the test, and the statistical significance of the test results is automatically judged to control strategy misjudgment caused by random fluctuations. Multi-variable combination testing is supported, and parallel testing of multi-dimensional strategies such as materials, time periods, customer groups, and channels can be completed simultaneously. The test results are subjected to multi-dimensional visualization analysis to clarify the influence weight of different strategy variables on the placement effect. The optimal strategy selected by the test can be implemented into the full-scale placement with one click. The test process data is automatically archived and used as incremental samples to participate in the iterative training of the placement model, forming a complete closed loop of strategy testing, effect verification, and model optimization.

[0056] The following two examples further illustrate the specific implementation of this system:

[0057] Example 1: Implementation of Advertising Information Release for Holiday Promotional Activities in Large Chain Supermarkets

[0058] This embodiment is applied to a holiday-themed promotional activity of a nationwide chain supermarket. The activity covers 36 offline stores in 12 provinces across the country. The promotional categories include three major categories: food and fresh produce, home furnishings and department stores, and home appliances and digital products. The promotion period is 14 days. The core requirements are to achieve unified management of multi-store promotional advertising, precise regional targeting, omni-channel distribution, and full-process control of the advertising effect. It fully implements all the functions of the store promotional advertising information publishing system of this invention. All technical contents are within the scope of the technical solution of this invention and there is no content beyond the scope of protection.

[0059] In this embodiment, the promotional advertising material management module first connects to the merchant terminal of the chain supermarket headquarters to receive the basic information and original materials of the holiday promotion uploaded by the headquarters. The built-in intelligent material editing unit provides suitable holiday promotion templates for this event. The template library matches standardized advertising templates for supermarket retail categories and holiday themes. Merchants complete the editing of brand elements, promotional product information and discount rules based on the templates. The built-in AI material generation function automatically generates corresponding advertising copy and poster elements according to the input promotional theme and product information. At the same time, it automatically generates multiple specifications of material versions for different delivery channels such as online social platforms, local life service platforms, offline store smart screens, and user-end mini programs. After the materials are edited, the advertising material compliance verification unit initiates a full-dimensional compliance verification. The built-in rule base is synchronized in real time with the Internet advertising management regulations issued by the market supervision department. Natural language processing technology is used to screen for illegal words in the text content, and image recognition technology is used to verify the compliance of the image content. Specific compliance verification rules are implemented for food and home appliance categories. For materials that do not comply with the verification rules, modification prompts are generated and sent back to the merchant's terminal. For materials that pass the verification, a unique traceability identifier is generated, and the materials are tagged and classified to generate a basic information file for promotional advertisements that includes dimensions such as promotional category, promotional period, discount level, applicable stores, and target customer group.

[0060] The target customer profile building module connects to the user behavior database of the internet advertising service platform, collecting user online supermarket consumption behavior, offline store visit records, category preferences, spending power, and geographical location data. The module's built-in user privacy data anonymization unit performs irreversible anonymization processing on all collected data, removing users' personal identification information and retaining only anonymized feature data. End-to-end encryption is performed on the data transmission and storage process, and a differential privacy protection mechanism is built-in. A hierarchical data access permission control system is set up, dynamically managing the scope of data use based on user authorization status, automatically deleting data that exceeds the authorization period, and generating a complete compliance audit log. After data anonymization, the module constructs personalized consumer profiles for users through multi-dimensional feature extraction. The ad-customer matching metric calculation unit calculates the comprehensive matching score between each promotional ad and the target user. Based on the matching score, users are segmented into three levels: high-intent potential customers, regular customers, and low-intent customers. Differentiated ad placement frequencies and creative display formats are matched for different customer levels. For cold-start users with no historical consumption behavior, the matching score is supplemented based on the group consumption characteristics of users of the same age group in the same region. The matching score is updated at a fixed interval of 2 hours based on real-time user behavior data, completing the association and matching between user profiles and promotional ad tags. The built-in dynamic adjustment subunit for weight coefficients in the advertising-customer matching metric calculation unit dynamically updates the weight coefficients of each dimension of matching features based on historical campaign conversion data for supermarket retail categories through machine learning algorithms. It adopts an incremental model update mechanism, completing iterative updates of weight coefficients based on newly added campaign conversion data each day. During the update process, abnormal campaign data and invalid conversion data are automatically removed. Independent weight coefficient libraries are built for different categories such as food and fresh produce, home appliances and digital products, and home furnishings. Differentiated initial weight values ​​are set for different promotional types such as holiday promotions and new store openings. Backtesting is performed after each weight coefficient update to verify the effect of the updated weight coefficients on improving the efficiency of advertising conversion.

[0061] The ad placement priority quantification module constructs an ad placement priority ranking model based on basic information of promotional ads, the merchant's total budget, and customer traffic distribution data for each store during different time periods. It calculates the placement weight of each promotional ad and generates an ad placement time sequence queue by combining the promotional focus of each store and the characteristics of the regional customer base. The ad placement effect prediction unit constructs a category-specific conversion effect prediction sub-model based on historical holiday promotional data from supermarkets and the basic information of this current ad campaign. It calculates the expected conversion efficiency of each ad and the confidence interval of the prediction results. Based on the total budget and the expected conversion efficiency of a single ad, it optimizes the allocation of the budget among multiple ads and stores, and updates the input parameters and coefficients of the prediction model on a daily basis.

[0062] The multi-channel advertising distribution module connects to four types of advertising channels: online social platforms, local life service platforms, smart screens in 36 offline stores, and mini-programs for supermarket users. The module's built-in channel adaptation processing unit adaptively converts and adjusts advertising materials according to the material format requirements, content specifications, and time restrictions of different channels. Based on real-time traffic distribution data of each channel, it dynamically adjusts the advertising volume and time of each channel, monitors the traffic quality, cost, and conversion efficiency of each channel in real time, sets cost thresholds for different channels, and automatically adjusts the distribution volume to high-cost-performance channels when the cost of a single channel exceeds the threshold. It uses device fingerprint and behavior trajectory recognition technology to eliminate abnormal traffic such as false exposure and false clicks, sets a daily exposure frequency limit for cross-channel ads for the same user to control the resource consumption caused by repeated ads, and completes accurate attribution of the advertising effect of each channel based on a multi-touchpoint attribution model. It automatically updates the channel blacklist and the list of high-quality channels, and accurately distributes the corresponding promotional ads to the target advertising channels and target user terminals based on the matching results of the ad placement time queue and user profile.

[0063] The advertising performance feedback module collects real-time data on ad impressions, clicks, in-store conversions, and coupon redemption sales from various advertising channels. The module's built-in multi-dimensional data statistics sub-unit breaks down and analyzes data by advertising channel, promotional category, advertising time period, and store region, generating detailed performance reports for each dimension. Based on the entire data chain of impressions, clicks, coupon redemption, and in-store visits, it performs conversion funnel analysis, identifies conversion drop-off points in the advertising process, calculates the ROI of a single ad, channel, and store, correlates advertising performance with customer lifetime value, identifies the characteristics of high-value customer groups, provides benchmark data on average advertising performance from supermarkets of the same industry and product category, automatically triggers alerts for anomalies in the advertising data, supports merchants to customize report dimensions and statistical periods, allows online viewing and standardized format export of reports, and synchronously feeds multi-dimensional advertising performance statistics reports back to supermarket headquarters and individual store merchant terminals.

[0064] The ad placement strategy iteration and optimization module extracts ad conversion characteristics based on ad performance statistics reports. It updates the core parameters of the customer matching model and the ad placement priority ranking model through incremental learning algorithms. The module's built-in A / B testing unit sets up multiple sets of different ad creatives, ad placement time periods, and target customer strategies for this promotional campaign, and conducts simultaneous traffic splitting tests. A tiered random traffic allocation mechanism ensures that the test samples are free of feature bias. The minimum sample size is calculated based on confidence requirements, and the statistical significance of the test results is automatically judged. The test results are analyzed from multiple dimensions to clarify the impact weight of different strategy variables on ad placement performance. The optimal strategy selected is implemented in the full campaign with one click. The test data is automatically archived throughout the entire process and used as incremental samples to participate in model iteration training, completing the closed-loop iterative optimization of the ad placement strategy for this promotional campaign.

[0065] Table 1: Performance Comparison of This System and Traditional Advertising System in Chain Supermarket Holiday Promotion Scenarios

[0066]

[0067] Table 1 shows the data derived from actual measurements during the entire campaign's delivery cycle, comprehensively covering all channels for the 14-day promotional period. Traditional advertising systems, in multi-store promotional scenarios within supermarkets, suffer from incomplete compliance verification, insufficient customer matching accuracy, low material adaptation efficiency, and lagging strategy iteration. They also exhibit significant shortcomings in user privacy compliance management. This invention's system, through intelligent end-to-end management, achieves full-process compliance coverage of advertising materials, significantly improves customer matching accuracy and material production adaptation efficiency, reduces user churn during conversion, shortens the strategy iteration cycle, and simultaneously achieves full-process compliance management of user privacy data, fully adapting to the advertising needs of multi-store holiday promotions in chain supermarkets.

[0068] Example 2: Implementation of Daily Promotional Advertising Information Release for Regional Community Fresh Food Chains

[0069] This embodiment is applied to the daily high-frequency promotional activities of a regional community fresh food chain brand. The activities cover 28 community stores in the target city, and the promotional categories are mainly seasonal fresh food, meat, poultry, eggs and dairy products, and grains, oils and non-staple foods. The promotion period is 7 days. The core requirements are to adapt to the localized, short-cycle and high-frequency promotional characteristics of community stores, to achieve precise reach to the surrounding community customers, to complete the lightweight management and intelligent distribution of multi-store promotional advertisements across all channels, and to fully implement all the functions of the store promotional advertising information publishing system of this invention. All technical contents are within the scope of the technical solution of this invention and there is no content beyond the scope of protection.

[0070] In this embodiment, the promotional advertising material management module first connects with the fresh food chain brand merchant terminals and various community store terminals to receive daily promotional information and basic materials uploaded by each store. The module's built-in intelligent material editing unit contains standardized advertising templates covering fresh food retail categories and daily promotional scenarios. For community stores without professional design capabilities, it supports quick editing of promotional product information, prices, and discount rules based on the templates. The built-in AI material generation function can automatically generate advertising copy and materials adapted to community communication scenarios based on the fresh food product information and daily promotional activities input by the stores. At the same time, it automatically generates multiple versions of materials adapted to different delivery channels such as local life service platforms, community social groups, store smart screens, and brand user-end mini-programs, completing unified management of different versions of materials and correlation statistics of delivery effects. After the materials are edited, the advertising material compliance verification unit initiates a full-dimensional compliance verification. The built-in rule base is synchronized in real time with the Internet advertising management regulations and special requirements for fresh food advertising issued by the market supervision department. Natural language processing technology is used to screen for illegal words and false advertising content in the copywriting, and image recognition technology is used to verify the compliance of the material content. Special compliance verification rules are applied to the fresh food category. For materials that do not comply with the verification rules, modification prompts are generated and sent back to the store terminal. For advertising materials that pass the verification, a unique traceability identifier is generated, and the materials are tagged and classified to generate a basic information file for promotional advertisements that includes dimensions such as promotional category, promotion period, discount, applicable stores, and target customer group.

[0071] The target customer profile building module connects to the user behavior database of an internet advertising service platform, collecting data on fresh food consumption behavior, offline store visit records, category preferences, spending power, and geographical location of users within a 3-kilometer radius of the store. The module's built-in user privacy data anonymization unit performs irreversible anonymization processing on all collected data, removing users' personal identification information and retaining only anonymized feature data used for profile building. End-to-end encryption is performed on the data transmission and storage process, with a built-in differential privacy protection mechanism and a hierarchical data access control system. The system dynamically manages the scope of data collection and use based on user authorization status, automatically deletes user data that exceeds the authorization period, fully records access, use, and modification operations throughout the entire data lifecycle and generates compliance audit logs, and automatically intercepts data transmission behaviors that do not meet privacy compliance requirements. After data anonymization, the module constructs personalized consumer profiles for users through multi-dimensional feature extraction. The advertising-customer matching quantification unit calculates the comprehensive matching score between each store promotional ad and surrounding users. Based on the matching score, users are segmented, and differentiated ad delivery frequencies and creative formats are matched for different user segments. For new community users with no historical consumption behavior, the matching score is supplemented based on the group consumption characteristics of users of the same age group in the same community. The matching score is updated at a fixed interval of 1 hour based on real-time user behavior data, thus completing the association and matching between user profiles and store promotional ad tags. The advertising-customer matching metric calculation unit has a built-in dynamic weight coefficient adjustment subunit. Based on historical campaign conversion data for fresh food retail categories, it dynamically updates the weight coefficients of each dimension of matching features through machine learning algorithms. It adopts an incremental model update mechanism, completing the iterative update of weight coefficients based on the daily newly added campaign conversion data. It builds independent weight coefficient libraries for different categories such as seasonal fresh food, meat, poultry, eggs and dairy products, and grains, oils and non-staple foods. It sets differentiated initial weight values ​​for different promotion types such as daily promotions, new store openings, and clearance sales. For newly opened stores without historical campaign data, it completes the initial configuration of weight coefficients based on historical data of stores of the same category in the same region. Backtesting is performed after each weight coefficient update.

[0072] The advertising priority quantification module constructs an advertising priority ranking model based on basic information of promotional ads for each store, single-store advertising budget, and community customer traffic distribution data. It calculates the weight of each promotional ad and generates an advertising time sequence queue by combining the promotional focus of each store with the characteristics of the surrounding customer base. The advertising performance prediction unit constructs conversion performance prediction sub-models by category and store based on historical daily promotional data of the fresh food brand and the basic information of this advertising campaign. It calculates the expected conversion efficiency of each ad and the confidence interval of the prediction results. Based on the total single-store advertising budget and the expected conversion efficiency of a single ad, it achieves optimal allocation of the advertising budget and updates the input parameters and coefficients of the prediction model on a fixed daily cycle.

[0073] The multi-channel advertising distribution module connects to four types of advertising channels: local life service platforms, community social platforms, smart screens in 28 offline stores, and brand user-end mini-programs. The module's built-in channel adaptation processing unit adaptively converts and adjusts advertising materials according to the material format requirements, content specifications, and community user activity times of different channels. Based on real-time traffic distribution data of each channel, it dynamically adjusts the advertising volume and time of each channel, monitors the traffic quality, cost, and conversion efficiency of each channel in real time, sets single-store advertising cost thresholds for different channels, and automatically adjusts the advertising volume to high-cost-performance channels when the cost of a single channel exceeds the threshold. It has a built-in channel anti-fraud verification function, which uses device fingerprinting and behavior trajectory recognition technology to eliminate abnormal traffic such as false exposure and false clicks. It sets a daily exposure frequency limit for cross-channel advertisements for the same community user to control the resource consumption caused by repeated advertising. Based on a multi-touchpoint attribution model, it completes accurate attribution of the advertising effect of each channel, automatically updates the channel blacklist and the list of high-quality channels, and accurately distributes the corresponding store's promotional advertisements to the target advertising channels and target user terminals around the store based on the advertising time sequence queue and user profile matching results.

[0074] The advertising performance feedback module collects real-time data on ad impressions, clicks, in-store conversions, and coupon redemption sales from various advertising channels. The module's built-in multi-dimensional data statistics sub-unit breaks down and analyzes advertising data by channel, promotional category, time period, and store, generating detailed performance reports for each dimension. Based on the entire data chain of ad impressions, clicks, coupon redemption, and in-store visits, it performs conversion funnel analysis, identifies conversion drop-off points during the campaign, calculates the ROI for a single ad, channel, and store, correlates advertising performance with customer lifetime value, identifies the characteristics of high-value customer groups in the community, provides benchmarking data on average performance of similar fresh food stores in the same industry, and automatically triggers alerts for abnormal conversions, traffic surges, and redemption anomalies. It supports stores and brand headquarters in customizing report dimensions and statistical periods, enabling online viewing and standardized format export of reports, and synchronously feeding back multi-dimensional performance statistics reports to brand headquarters and various community store terminals.

[0075] The ad placement strategy iteration and optimization module extracts ad conversion characteristics based on ad placement performance statistics reports. It updates the core parameters of the customer group matching model and the ad placement priority ranking model through incremental learning algorithms. The module's built-in A / B testing unit allows each store to set multiple sets of different ad creatives, placement time periods, and target customer group strategies for the same promotional activity, and conduct simultaneous traffic splitting tests. A layered random traffic allocation mechanism ensures that the traffic samples of different test strategies are free from feature bias. Based on the test confidence requirements, it intelligently calculates the minimum sample size required and automatically judges the statistical significance of the test results. It supports multi-variable combination testing and can simultaneously complete parallel testing of multi-dimensional strategies such as creatives, time periods, customer groups, and channels. The test results are subjected to multi-dimensional visualization analysis to clarify the impact weight of different strategy variables on ad placement performance. The optimal strategy selected from the test can be implemented in the full-scale ad placement of stores with one click. The test process data is automatically archived and used as incremental samples to participate in the iterative training of the ad placement model, forming a complete closed loop of strategy testing, effect verification, and model optimization.

[0076] Table 2: Comparison of the effectiveness of this system and traditional methods in daily promotional scenarios for fresh produce in communities.

[0077]

[0078] Table 2 data comes from the statistical results of omnichannel advertising placement tests conducted by 28 community stores during the 7-day promotional period of this embodiment. Traditional manual placement methods in daily promotional scenarios for community fresh food stores suffer from low efficiency in creating promotional materials, limited reach to surrounding customer groups, poor advertising conversion rates, and high labor costs for store placement, making them unsuitable for the high-frequency, short-cycle promotional needs of community fresh food stores. The system of this invention lowers the operational threshold for stores through lightweight intelligent material editing functions, improves community user reach and conversion rates through localized and precise customer group matching, significantly reduces the labor costs for store placement, and fully adapts to the advertising needs of daily promotional activities in community fresh food retail stores.

[0079] refer to Figure 1 This diagram illustrates the closed-loop process of the system, from uploading creative materials to merchants to final strategy optimization. The system first standardizes creative materials through management and integrates multi-dimensional user data through a customer profiling module. Then, a priority quantification module generates a campaign queue based on budget and customer traffic, which is then precisely targeted by a multi-channel distribution module. After campaign execution, a performance feedback module collects conversion data in real time, and finally, an iterative optimization module uses incremental learning to update system parameters, continuously improving campaign accuracy.

[0080] Reference Figure 2This diagram details how anonymized user characteristics are quantitatively matched with ad tags. The system first anonymizes the collected online and offline data to extract features such as category preferences and spending power to construct user profiles. Through a matching quantification model, a comprehensive score is calculated for both ads and users, categorizing users into three levels: high intent, regular intent, and low intent. A dynamic weight adjustment unit continuously adjusts the influence weights of dimensions such as category and location based on historical conversion data, ensuring that the matching relationship is dynamically updated in response to market changes.

[0081] Reference Figure 3 This diagram highlights the standardized process from creative creation to distribution of advertising materials. Merchants can quickly edit materials using AI-generated content and template libraries, with the system automatically adapting to various channel sizes. Subsequently, the compliance verification unit calls upon a multi-dimensional rule base, utilizing NLP and image recognition technologies to review text and images. Materials that pass verification will be assigned a unique traceability identifier and stored in an archive, ensuring that all content delivered is both consistent with brand identity and complies with internet advertising regulations.

[0082] Reference Figure 4 This diagram illustrates how the system scientifically allocates resources. The performance prediction unit forecasts conversion efficiency based on historical data, assisting merchants in optimizing budget allocation. The system generates a delivery timeline queue based on ad weight, store traffic, and user matching. During distribution, the channel adaptation processing unit monitors traffic quality in real time and automatically eliminates fake clicks. When the cost of a certain channel is too high, the system automatically redirects resources to a more cost-effective channel, achieving cross-channel traffic balance and maximizing resource utilization.

[0083] refer to Figure 5 This diagram illustrates the closed-loop logic of the system's self-evolution. Multi-dimensional statistical sub-units perform funnel analysis on the entire data chain to calculate ROI. To verify the effectiveness of new strategies, the system uses an A / B testing unit to randomly distribute traffic and compare the significance of different creative materials or customer group strategies. The high-quality strategies generated from the tests, along with the statistical results, serve as incremental samples. Incremental learning algorithms are used to update the core model parameters, ensuring the system can continuously learn and adapt to changes in user behavior.

[0084] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A store promotional advertising information publishing system, characterized in that, Includes the following modules: The promotional advertising material management module connects to the store merchant terminal, receives and stores the promotional advertising materials uploaded by the merchants, performs format standardization processing, content compliance verification and tag classification of the materials, and generates basic information files for promotional advertisements; The target customer profile building module connects to the user behavior database of the Internet advertising service platform, collects user online consumption behavior, offline store records, category preferences, spending power and geographical location data, and builds personalized consumer profiles of users through multi-dimensional feature extraction, and completes the association and matching of user profiles with promotional advertising tags; The ad placement priority quantification module constructs an ad placement priority ranking model based on basic information of promotional ads, merchant spending budgets, and store customer traffic distribution data during different time periods. It calculates the placement weight of each promotional ad and generates an ad placement time sequence queue. The multi-channel advertising distribution module connects with advertising channels and accurately distributes corresponding promotional ads to target channels and target user terminals based on the matching results of the placement time queue and user profile. The advertising performance feedback module collects real-time data on ad impressions, clicks, in-store conversions, and coupon redemption sales from various advertising channels, generates performance statistics reports, and synchronously feeds them back to the merchant's terminal. The ad placement strategy iteration and optimization module extracts ad conversion features based on ad placement performance statistics reports, and updates the core parameters of the customer matching model and ad placement priority ranking model through incremental learning algorithms, thus completing the closed-loop iteration and optimization of the promotional ad placement strategy.

2. The store promotion advertising information publishing system according to claim 1, characterized in that, It also includes an ad-customer matching quantification calculation unit, used to build a matching quantification model between promotional ad tags and user consumption profiles, and calculate the matching score between each promotional ad and the target user. The matching quantification calculation expression is: ； in, The score represents the overall match between the promotional advertisement and the target user. This represents the category preference weighting coefficient. Match weight coefficients to time periods. Weighting coefficients are assigned to consumption capacity. Match weight coefficients to geographic locations. This is a normalized value representing the match between user category preferences and advertising / promotion categories. This is a normalized value representing the match between user activity periods and advertising promotion periods. This is a normalized value representing the fit between user spending power and advertising discounts. Normalized value for the spatial distance fit between user's geographic location and the applicable store of the advertisement.

3. The store promotion advertising information publishing system according to claim 1, characterized in that, It also includes an advertising material compliance verification unit, which has a built-in multi-dimensional advertising content verification rule library to automatically verify the compliance of uploaded promotional advertising materials, generate compliance modification prompts for materials that do not comply with the rules and send them back to the merchant's terminal; The rule base is updated in real time with the internet advertising management regulations of the market supervision department; it uses natural language processing technology to identify the semantics of text and screen for illegal words, and uses image recognition technology to verify the compliance of image and video content; it has built-in special compliance verification rules for food, cosmetics and medical device advertisements, links them to the merchant's historical illegal placement records, and increases the verification frequency and verification dimensions for merchants with multiple violations; A unique traceability identifier is generated for verified advertising materials, and evidence storage management is carried out throughout the entire lifecycle of the materials.

4. A store promotional advertising information publishing system according to claim 1, characterized in that, It also includes an advertising performance prediction unit, which is used to build a promotional advertising conversion performance prediction model based on historical advertising data and current advertising information, and calculate the expected conversion efficiency of the advertising campaign. The conversion performance prediction expression is as follows: ； in, This represents the expected conversion efficiency value for promotional advertising. Contribution coefficient to exposure Contribution coefficient to click-through rate The contribution coefficient to the coupon redemption rate. Contribution coefficient to in-store conversion rate Normalized value of expected ad impressions This is the normalized value of the average click-through rate of similar advertising materials throughout history. This is the normalized value of the historical redemption rate of coupons for similar promotional activities. This is a normalized value for the historical customer traffic conversion efficiency of applicable stores.

5. A store promotional advertising information publishing system according to claim 1, characterized in that, The promotional advertising material management module also includes an intelligent material editing unit. This unit has a built-in library of promotional advertising templates covering different retail categories, promotion types, and distribution channels. It supports merchants to edit advertising materials online based on templates and automatically generates advertising materials adapted to multiple distribution channels based on the promotional information entered by the merchant. It also has a built-in AI material generation function that automatically generates advertising plans based on the promotional theme, product information, and discount rules entered by the merchant. It supports one-click embedding and unified application of the merchant's brand logo, standard colors, and brand fonts. Based on the analysis of historical high-conversion material characteristics, it provides merchants with material optimization suggestions and performs unified management and correlation statistics of different versions of materials.

6. A store promotional advertising information publishing system according to claim 1, characterized in that, The target customer profile building module also includes a user privacy data desensitization unit, which performs irreversible desensitization processing on the collected user data, removes the user's personal identification information, and retains the anonymized feature data required for profile building. End-to-end encryption is applied to the data transmission and storage process, with a built-in differential privacy protection mechanism. Noisy data is added during the profile building and data statistics process. A hierarchical data access permission control system is set up to dynamically manage the scope of data collection and use based on the user's authorization status, and automatically delete user data that exceeds the authorization period. Access, use and modification operations throughout the entire data lifecycle are recorded and compliance audit logs are generated, and cross-border data transmission that does not meet privacy compliance requirements is automatically blocked.

7. A store promotional advertising information publishing system according to claim 1, characterized in that, The multi-channel advertising distribution module also includes a channel adaptation processing unit. This unit adaptively converts and adjusts the format of promotional advertising materials according to the material format requirements, content specifications, and time period restrictions of different advertising channels; adjusts the advertising volume and time period of each channel based on traffic distribution data of different channels; monitors the conversion efficiency of each advertising channel in real time, sets a cost threshold for different channels, and automatically adjusts the advertising volume to a high-cost-performance channel when the cost of a single channel exceeds the threshold; has a built-in channel anti-fraud verification function, which uses device fingerprinting and behavior trajectory recognition technology to eliminate abnormal traffic; sets a cross-channel advertising exposure frequency limit for the same user to control the consumption of duplicate advertising resources; and completes the attribution of the advertising effect of each channel based on a multi-touchpoint attribution model, automatically updating the channel blacklist and the list of high-quality channels.

8. A store promotional advertising information publishing system according to claim 2, characterized in that, The ad-customer matching metric calculation unit also includes a weight coefficient dynamic adjustment subunit. This subunit dynamically updates the weight coefficients of each dimension of matching features based on historical campaign conversion data using machine learning algorithms. It adopts an incremental model update mechanism, completing iterative updates of weight coefficients based on daily new campaign conversion data, and automatically removing abnormal data during the update process. It builds an independent weight coefficient library for retail categories and sets differentiated initial weight values ​​for promotion types. For cold-start merchants without historical campaign data, it initializes the weight coefficient configuration based on historical data of merchants in the same category and region. After each weight coefficient update, it performs backtesting to verify the effect of the updated weight coefficients on improving ad conversion efficiency.

9. A store promotional advertising information publishing system according to claim 1, characterized in that, The advertising performance feedback module also includes a multi-dimensional data statistics sub-unit. This sub-unit breaks down and statistically analyzes advertising performance data according to the dimensions of advertising channel, promotional category, advertising time period, and store area, and generates detailed reports on the performance of the corresponding dimensions. It also compares the performance data of different merchants and different promotional activities to generate performance rankings and optimization suggestions. Based on the full-link data of ad impressions, clicks, coupon redemption, and in-store visits, we complete conversion funnel analysis to locate conversion drop-off points; calculate the ROI of a single ad, a single channel, and a single merchant; correlate ad performance with user lifetime value to identify the characteristics of high-value customer groups; provide benchmark data on the average performance of merchants in the same industry and category; and automatically trigger alerts for abnormal situations.

10. A store promotional advertising information publishing system according to claim 1, characterized in that, The ad placement strategy iteration and optimization module also includes an A / B testing unit, which allows merchants to set up multiple sets of different advertising materials, ad placement time periods and target customer group strategies for the same promotional activity, and conduct simultaneous traffic splitting and ad placement tests. Based on the test results, the conversion effects of different strategies are compared to determine the optimal strategy and updated to the strategy model. A hierarchical random traffic allocation mechanism is adopted to control the traffic samples of different testing strategies to be free of feature bias; the minimum sample size is intelligently calculated based on the test confidence requirement, and the statistical significance of the test results is automatically judged. It supports multi-variable combination testing and parallel testing of multi-dimensional strategies; it performs multi-dimensional visualization analysis of test results to determine the influence weight of different strategy variables on the campaign effect; it applies the optimal strategy to the full campaign with one click, and automatically archives the test data throughout the entire process and uses it as incremental samples to participate in the iterative training of the campaign model.

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