AI-powered cross-platform user acquisition and growth promotion system for major companies

Abstract

This application discloses a cross-platform user acquisition and growth promotion system for large enterprises based on artificial intelligence, belonging to the field of monitoring and analysis technology. It includes a data acquisition and processing module for acquiring user cross-platform interaction behavior data and generating a set of multi-platform user behavior feature vectors; a data mining module for performing cross-platform user identification code matching processing on the multi-platform user behavior feature vector set to generate a user cross-platform behavior feature map; a promotion strategy generation module for constructing a dynamic acquisition strategy generation matrix based on the user cross-platform behavior feature map to generate user cross-platform traffic promotion strategies; and an optimization module for acquiring real-time user feedback data streams, constructing a dynamic correction model for acquisition strategies based on the real-time user feedback data streams, optimizing user cross-platform traffic promotion strategies, and deploying the cross-platform traffic promotion strategies to target platforms to perform user growth operations.

Description

Technical Field

[0001] This application relates to the field of monitoring and analysis technology, and in particular to a cross-platform user acquisition and growth promotion system for large companies based on artificial intelligence. Background Technology

[0002] In today's booming digital commerce landscape, it's increasingly common for large companies to operate across multiple platforms, making cross-platform user acquisition and growth promotion a key need. Today, various platforms such as social media, e-commerce, and news aggregators possess massive amounts of user data, containing rich information such as user interests, preferences, and behavioral habits. The continuous evolution of artificial intelligence (AI) technology has made it possible to effectively utilize user data for precise user acquisition and promotion. Leveraging AI algorithms and models, it's possible to deeply mine cross-platform user data. For example, machine learning can analyze user behavior and interaction content across platforms to accurately depict user profiles, thereby developing more targeted user acquisition and promotion strategies. Simultaneously, AI can monitor user dynamics in real time and adjust promotional methods accordingly to adapt to the ever-changing market environment and user needs.

[0003] In related technologies, traditional large companies' cross-platform user acquisition and growth promotion systems struggle to effectively integrate heterogeneous user behavior data across multiple platforms such as social media, e-commerce, and content, resulting in fragmented user profiles. Furthermore, they are unable to capture user interest migration patterns across platforms in real time, leading to strategy lag and consequently reduced efficiency in user acquisition and growth, resulting in wasted promotional resources. There are areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a cross-platform user acquisition and growth promotion system for large enterprises based on artificial intelligence.

[0005] Firstly, this application provides a cross-platform user acquisition and growth promotion system based on artificial intelligence, including:

[0006] The data acquisition and processing module is used to acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into the behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors.

[0007] The data mining module is used to perform cross-platform user identification code matching processing on the multi-platform user behavior feature vector set, and to establish a unified cross-platform user identity profile based on the processing results. Then, based on the unified cross-platform identity profile, the module performs platform behavior preference mining on the target user group and generates a cross-platform user behavior feature map.

[0008] The promotion strategy generation module is used to construct a dynamic traffic generation matrix based on the user cross-platform behavior feature map, and calculate the expected user retention growth value of each traffic generation strategy through a preset cross-platform benefit prediction model, and then generate user cross-platform traffic promotion strategy based on the expected user retention growth value of each traffic generation strategy.

[0009] The optimization module is used to acquire real-time user feedback data streams, construct a dynamic correction model for the traffic acquisition strategy based on the real-time user feedback data streams, optimize the user cross-platform traffic acquisition and promotion strategy, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

[0010] Preferably, the user's cross-platform interaction behavior data is input into a behavior feature deep extraction network for cross-platform feature association modeling to generate a multi-platform user behavior feature vector set, specifically including:

[0011] Establish a cross-platform data collection channel, and collect user cross-platform interaction behavior data on multiple platforms through encrypted data collection technology based on the cross-platform data collection channel. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data.

[0012] The collected user cross-platform interaction behavior data is aggregated and processed, and then a user interaction behavior feature set is generated based on the result of the aggregation and processing. The user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features.

[0013] A deep feature extraction network is constructed, and multi-dimensional feature calculations are performed on the user interaction behavior feature set based on the deep feature extraction network to generate a primary feature set.

[0014] Cross-platform feature association modeling is performed on the primary feature set to calculate the weight distribution corresponding to each platform, and then the weight distribution corresponding to each platform is fused to obtain a multi-platform user behavior feature vector set.

[0015] Preferably, the multi-platform user behavior feature vector set is subjected to cross-platform user identification code matching processing, and a unified cross-platform user identity profile is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map, specifically including:

[0016] Based on user device fingerprint features and cross-platform account association features, a user identity matching decision set is constructed. Feature transfer is performed on the user's cross-platform interaction behavior data corresponding to multiple platforms to generate a user identity verification feature set. Then, the user identity matching decision set is matched with the user identity verification feature set to establish a unified cross-platform identity profile of the user.

[0017] A dynamic preference prediction model is constructed to extract spatiotemporal features from the user's cross-platform unified identity profile and generate a behavioral preference prediction map.

[0018] By projecting a unified cross-platform identity profile and behavioral preference prediction map of users into a three-dimensional spatiotemporal map, a cross-platform behavioral feature map of users is generated.

[0019] Preferably, a dynamic traffic generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic generation strategy is calculated through a preset cross-platform benefit prediction model. Then, a user cross-platform traffic promotion strategy is generated based on the expected user retention growth value of each traffic generation strategy, specifically including:

[0020] A dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map, and then an initial traffic acquisition strategy candidate set is generated based on the dynamic traffic acquisition strategy generation matrix.

[0021] The expected user retention growth value of each candidate strategy in the initial traffic acquisition strategy candidate set is calculated by using a pre-set cross-platform benefit prediction model. The calculation results are then filtered, and a user cross-platform traffic acquisition and promotion strategy is generated based on the filtering results.

[0022] Preferably, the process involves acquiring real-time user feedback data streams, constructing a dynamic correction model for the user referral strategy based on these data streams, optimizing the cross-platform user referral promotion strategy, and deploying the optimized strategy to the target platform to perform user growth operations. Specifically, this includes:

[0023] Real-time user feedback data streams are acquired through a real-time data acquisition gateway to construct a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response latency distribution data, conversion funnel parameter data, and sentiment tendency index data.

[0024] Based on the dynamic monitoring index system, abnormal patterns are identified in the real-time user feedback data stream to generate strategy failure warning signals and preference shift detection reports.

[0025] A dynamic correction model for the user acquisition strategy is constructed based on the strategy failure early warning signal and preference drift detection report. The user cross-platform traffic acquisition and promotion strategy is optimized based on the dynamic correction model to generate an optimized traffic acquisition and promotion strategy. The optimized traffic acquisition and promotion strategy is then integrated with the user cross-platform traffic acquisition and promotion strategy to generate a final cross-platform traffic acquisition and promotion strategy. Finally, the final cross-platform traffic acquisition and promotion strategy is synchronized to the target platform to perform user growth operations through a distributed deployment mechanism.

[0026] Preferably, the construction process of the dynamic correction model for traffic generation strategy specifically includes:

[0027] A behavior response correction factor is generated based on the behavior response delay distribution data in the real-time user feedback data stream. A user preference deviation detection model is constructed by combining conversion funnel parameter data and sentiment tendency index data. Based on the user preference deviation detection model, abnormal patterns in the real-time user feedback data stream are classified and identified, a strategy failure warning signal and a preference deviation detection report are generated, and the preference deviation correction matrix is ​​confirmed based on the preference deviation detection report.

[0028] The design of the strategy gradient update module defines the strategy optimization objective based on the reward function, thereby generating gradient update rules. Then, by integrating the behavior response correction factor, preference offset correction matrix and gradient update rules, a dynamic correction model for the referral strategy is generated.

[0029] Secondly, this application provides a method for large companies to attract and promote users across platforms based on artificial intelligence, including the following steps:

[0030] Acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors;

[0031] The multi-platform user behavior feature vector set is subjected to cross-platform user identity code matching processing, and a unified cross-platform identity profile of users is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map.

[0032] A dynamic traffic generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic generation strategy is calculated through a preset cross-platform benefit prediction model. Then, a user cross-platform traffic promotion strategy is generated based on the expected user retention growth value of each traffic generation strategy.

[0033] Acquire real-time user feedback data streams, construct a dynamic correction model for traffic acquisition strategies based on the real-time user feedback data streams, optimize the cross-platform traffic acquisition and promotion strategy for users, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

[0034] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute any of the above-described AI-based cross-platform user acquisition and growth promotion system for large enterprises.

[0035] In summary, this application includes the following beneficial technical effects:

[0036] This application provides an AI-based cross-platform user acquisition and growth promotion system for large enterprises. It acquires user cross-platform interaction behavior data, generates a multi-platform user behavior feature vector set, and performs cross-platform user identification code matching on this set to generate a user cross-platform behavior feature map. This effectively analyzes user cross-platform interaction behavior data, reducing fragmented user profiles. A dynamic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map. A pre-set cross-platform benefit prediction model calculates the expected user retention growth value for each acquisition strategy. Based on the expected user retention growth value, a user cross-platform traffic promotion strategy is generated. Real-time user feedback data streams are acquired, and a dynamic correction model for the acquisition strategy is constructed based on these streams to optimize the cross-platform traffic promotion strategy. The optimized strategy is then deployed to the target platform to perform user growth operations. This effectively reduces strategy lag caused by the inability to capture user cross-platform interest migration patterns in real time, thus significantly improving the efficiency of user acquisition and growth and minimizing the waste of promotional resources. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a system schematic diagram of an embodiment of this application.

[0039] Figure 2 This is a flowchart of a method embodiment of this application. Detailed Implementation Example 1

[0040] This application discloses an AI-based cross-platform user acquisition and growth promotion system for large enterprises.

[0041] Reference Figure 1 A cross-platform user acquisition and growth promotion system based on artificial intelligence, including:

[0042] The data acquisition and processing module is used to acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into the behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors.

[0043] The data mining module is used to perform cross-platform user identification code matching processing on the multi-platform user behavior feature vector set, and to establish a unified cross-platform user identity profile based on the processing results. Then, based on the unified cross-platform identity profile, the module performs platform behavior preference mining on the target user group and generates a cross-platform user behavior feature map.

[0044] The promotion strategy generation module is used to construct a dynamic traffic generation matrix based on the user cross-platform behavior feature map, and calculate the expected user retention growth value of each traffic generation strategy through a preset cross-platform benefit prediction model, and then generate user cross-platform traffic promotion strategy based on the expected user retention growth value of each traffic generation strategy.

[0045] The optimization module is used to acquire real-time user feedback data streams, construct a dynamic correction model for the traffic acquisition strategy based on the real-time user feedback data streams, optimize the user cross-platform traffic acquisition and promotion strategy, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

[0046] It should be noted that the user cross-platform interaction behavior data is input into a behavior feature deep extraction network for cross-platform feature association modeling, generating a multi-platform user behavior feature vector set, specifically including:

[0047] Establish a cross-platform data collection channel, and collect user cross-platform interaction behavior data on multiple platforms through encrypted data collection technology based on the cross-platform data collection channel. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data.

[0048] The collected user cross-platform interaction behavior data is aggregated and processed, and then a user interaction behavior feature set is generated based on the result of the aggregation and processing. The user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features.

[0049] A deep feature extraction network is constructed, and multi-dimensional feature calculations are performed on the user interaction behavior feature set based on the deep feature extraction network to generate a primary feature set.

[0050] Cross-platform feature association modeling is performed on the primary feature set to calculate the weight distribution corresponding to each platform, and then the weight distribution corresponding to each platform is fused to obtain a multi-platform user behavior feature vector set.

[0051] Specifically, a cross-platform data collection channel is established, and based on this channel, encrypted tracking technology is used to collect user cross-platform interaction behavior data across multiple platforms. This cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application navigation path data. Connection channels with multiple platforms are established, for example, connecting e-commerce platforms, social media platforms, and news platforms. Through encrypted tracking technology, data collection points are set on the pages and applications of each platform. When a user clicks a product link on an e-commerce platform, the encrypted tracking records real-time click trajectory data, including the click location and time. When a user browses a page on a social media platform, page dwell time data is recorded. When a user navigates from a news platform to an e-commerce platform, cross-application navigation path data is recorded.

[0052] Secondly, the collected user cross-platform interaction behavior data is aggregated and processed, and then a user interaction behavior feature set is generated based on the aggregation results. The user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features. The collected user cross-platform interaction behavior data is aggregated, for example, by integrating the click trajectory data, page dwell time data, and cross-application jump path data of the same user on different platforms. By analyzing the above data, user device fingerprint features are extracted, and a unique device fingerprint is generated based on the user device's hardware information, software configuration, etc., to identify the user's device. At the same time, cross-platform account association features are established, and the user's accounts on multiple platforms are associated through the user's registration information, login information, etc. on different platforms to understand the user's behavior association on different platforms. In addition, behavior time series parameter features, such as the user's operation time sequence and operation frequency on different platforms, are analyzed to generate a user interaction behavior feature set that comprehensively reflects the user's behavior patterns on multiple platforms.

[0053] Then, a deep feature extraction network is constructed to perform multi-dimensional feature calculations on the user interaction behavior feature set, thereby generating a primary feature set. Using deep learning techniques, a deep feature extraction network, such as a convolutional neural network or a recurrent neural network, is constructed. The user interaction behavior feature set is input into the deep feature extraction network for multi-dimensional feature calculations. For example, in a convolutional neural network, local and global features in the user interaction behavior feature set are extracted through convolutional and pooling layers; in a recurrent neural network, the time-series pattern of user behavior is learned based on the behavioral temporal parameter features, and a primary feature set is generated through multi-dimensional feature calculations.

[0054] Finally, cross-platform feature association modeling is performed on the primary feature set to calculate the weight distribution for each platform. Then, the weight distributions of each platform are merged to obtain a multi-platform user behavior feature vector set. Based on the primary feature set, a cross-platform feature association model is established. For example, the association between user behavior characteristics on e-commerce platforms and behavior characteristics on social and news platforms is analyzed. By calculating the weight distribution for each platform, the degree of influence of different platform behavior characteristics on overall user behavior is determined. For instance, in some cases, user purchasing behavior on e-commerce platforms may be related to recommendation information on social platforms. By calculating the weight distribution, the influence weight of social platforms on e-commerce platform behavior is determined. By merging the weight distributions of each platform, the features in the primary feature set are integrated according to their weights to generate a multi-platform user behavior feature vector set. This multi-platform user behavior feature vector set comprehensively reflects the user's behavior characteristics on multiple platforms and their interrelationships, providing rich data support for subsequent user behavior analysis and applications.

[0055] It should be noted that cross-platform user identity code matching processing is performed on the multi-platform user behavior feature vector set, and a unified cross-platform user identity profile is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map, specifically including:

[0056] Based on user device fingerprint features and cross-platform account association features, a user identity matching decision set is constructed. Feature transfer is performed on the user's cross-platform interaction behavior data corresponding to multiple platforms to generate a user identity verification feature set. Then, the user identity matching decision set is matched with the user identity verification feature set to establish a unified cross-platform identity profile of the user.

[0057] A dynamic preference prediction model is constructed to extract spatiotemporal features from the user's cross-platform unified identity profile and generate a behavioral preference prediction map.

[0058] By projecting a unified cross-platform identity profile and behavioral preference prediction map of users into a three-dimensional spatiotemporal map, a cross-platform behavioral feature map of users is generated.

[0059] Specifically, a user identity matching decision set is constructed based on user device fingerprint features and cross-platform account association features. Feature transfer is performed on user cross-platform interaction behavior data corresponding to multiple platforms to generate a user identity verification feature set. Then, the user identity matching decision set is matched with the user identity verification feature set to establish a unified cross-platform user identity profile. The user device fingerprint features contain hardware information of the user device, such as device model, operating system version, sensor data, etc., which can accurately identify the user device. The cross-platform account association features associate the user's accounts on multiple platforms through the user's registration information, login information, and behavioral data on different platforms. For example, the mobile phone number used by the user on the e-commerce platform is bound to the account on the social platform. Through this association, the user's behavioral data on different platforms can be integrated. Based on the above features, a user identity matching decision set is constructed, matching rules and strategies are formulated, and feature transfer is performed on user interaction behavior data across multiple platforms. The behavioral features of different platforms are integrated and transformed. For example, the browsing behavior features of users on information platforms are correlated with the purchasing behavior features of e-commerce platforms to generate a user identity verification feature set. The user identity matching decision set is matched with the user identity verification feature set, and a unified cross-platform identity profile of users is established based on the matching results. The unified cross-platform identity profile of users includes the user's basic information, device information, and behavioral patterns on multiple platforms, comprehensively reflecting the user's identity features on different platforms.

[0060] Secondly, a dynamic preference prediction model is constructed to extract spatiotemporal features from the unified cross-platform user profile and generate a behavioral preference prediction map. The dynamic preference prediction model employs deep learning algorithms, such as recurrent neural networks or long short-term memory networks. These algorithms can process time-series data, learn the dynamic changes in user behavior, and extract spatiotemporal features from the unified cross-platform user profile. This allows for the analysis of user behavior patterns at different times and in different locations, such as differences in user behavior at different time periods and geographical locations. By analyzing data such as user purchase times on e-commerce platforms, activity times on social media platforms, and browsing times on news platforms, temporal features are extracted. Similarly, by analyzing user behavior data in different regions, spatial features are extracted. Based on these spatiotemporal features, the dynamic preference prediction model generates a behavioral preference prediction map, which displays user behavioral preferences at different times and in different locations, such as user preferences for certain products at specific time periods and their attention to different information in different regions.

[0061] By projecting a unified cross-platform user identity profile and behavioral preference prediction map into a 3D spatiotemporal model, a cross-platform user behavioral feature map is generated. This 3D spatiotemporal projection displays user identity and behavioral preference information in a 3D space, using time as one dimension and spatial location as the other two. It integrates information from the unified cross-platform identity profile and the behavioral preference prediction map. For example, it combines user behavioral preferences at different times and locations with user identity characteristics, displaying user behavior across multiple platforms in 3D space. This generates a cross-platform user behavioral feature map that visually displays user behavior patterns, preferences, and their correlation with identity characteristics across multiple platforms. For instance, the cross-platform user behavioral feature map can show the frequency and behavioral preferences of a user using different platforms at a specific time and in a specific region, providing more accurate user behavior analysis and helping to optimize services and recommendation systems.

[0062] It should be noted that a dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic acquisition strategy is calculated through a preset cross-platform benefit prediction model. Then, based on the expected user retention growth value of each traffic acquisition strategy, a user cross-platform traffic promotion strategy is generated, specifically including:

[0063] A dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map, and then an initial traffic acquisition strategy candidate set is generated based on the dynamic traffic acquisition strategy generation matrix.

[0064] The expected user retention growth value of each candidate strategy in the initial traffic acquisition strategy candidate set is calculated by using a pre-set cross-platform benefit prediction model. The calculation results are then filtered, and a user cross-platform traffic acquisition and promotion strategy is generated based on the filtering results.

[0065] Specifically, a dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavioral feature map. Then, an initial candidate set of traffic acquisition strategies is generated based on this matrix. The user cross-platform behavioral feature map details user behavior patterns, preferences, and identity characteristics across multiple platforms. Based on this, information such as user behavior conversion patterns, active periods, and interests across different platforms is analyzed. For example, it is found that a user's attention to certain types of content on social platforms is correlated with their purchasing behavior on e-commerce platforms, or that changes in user behavior on other platforms after obtaining information on news platforms are observed. Based on this information, a dynamic traffic acquisition strategy generation matrix is ​​constructed. The elements of the dynamic traffic generation matrix include strategy-related parameters such as traffic direction, traffic time, and traffic content across different platforms. For example, the rows of the dynamic traffic generation matrix can represent different platforms, and the columns can represent different traffic generation strategy dimensions. The values ​​in the dynamic traffic generation matrix can be traffic weights or probabilities, etc. Based on the dynamic traffic generation matrix, an initial traffic generation strategy candidate set is generated. The initial traffic generation strategy candidate set contains various possible combinations of traffic generation strategies, such as pushing product information related to e-commerce platforms on social platforms, or guiding users to access specific content on other platforms on news platforms.

[0066] The model uses a pre-defined cross-platform benefit prediction model to calculate the expected user retention growth value of each candidate strategy in the initial user acquisition strategy candidate set. The calculation results are then filtered to generate a cross-platform user acquisition and promotion strategy. This cross-platform benefit prediction model is a data-driven model that comprehensively considers factors such as user behavior data across multiple platforms, platform operational data, and market environment. For each candidate strategy in the initial user acquisition strategy candidate set, the model predicts the potential user retention growth value based on historical data and current user behavior characteristics. For example, for the candidate strategy of "pushing e-commerce platform product information on social media platforms," ​​the model analyzes the effects of similar strategies in the past, combines factors such as current user activity on social media platforms, interest in products, and user retention on e-commerce platforms, to calculate the potential user retention growth value. The calculation results of all candidate strategies are then sorted and filtered, and a threshold for retention growth value is set to retain strategies with higher expected retention growth values. Based on the screening results, a cross-platform user referral and promotion strategy is generated. This strategy can more effectively guide users to interact across multiple platforms, thereby improving user retention and activity. For example, the selected strategies may include pushing content related to popular products on e-commerce platforms on social platforms during specific time periods, or guiding users to visit relevant social groups or e-commerce pages based on their interests and preferences on information platforms.

[0067] It should be noted that the process involves acquiring real-time user feedback data streams, constructing a dynamic correction model for the user acquisition strategy based on these data streams, optimizing the cross-platform user acquisition and promotion strategy, and deploying the optimized strategy to the target platform to perform user growth operations. Specifically, this includes:

[0068] Real-time user feedback data streams are acquired through a real-time data acquisition gateway to construct a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response latency distribution data, conversion funnel parameter data, and sentiment tendency index data.

[0069] Based on the dynamic monitoring index system, abnormal patterns are identified in the real-time user feedback data stream to generate strategy failure warning signals and preference shift detection reports.

[0070] A dynamic correction model for the user acquisition strategy is constructed based on the strategy failure early warning signal and preference drift detection report. The user cross-platform traffic acquisition and promotion strategy is optimized based on the dynamic correction model to generate an optimized traffic acquisition and promotion strategy. The optimized traffic acquisition and promotion strategy is then integrated with the user cross-platform traffic acquisition and promotion strategy to generate a final cross-platform traffic acquisition and promotion strategy. Finally, the final cross-platform traffic acquisition and promotion strategy is synchronized to the target platform to perform user growth operations through a distributed deployment mechanism.

[0071] Specifically, real-time user feedback data streams are acquired through a real-time data acquisition gateway to construct a dynamic monitoring indicator system. The real-time user feedback data stream includes behavioral response latency distribution data, conversion funnel parameter data, and sentiment index data. The real-time data acquisition gateway is deployed at key nodes on various platforms to collect various behavioral data of users on the platform in real time. Taking an e-commerce platform as an example, behavioral response latency distribution data records the time distribution from when a user clicks on a product to when the page loads. Conversion funnel parameter data reflects the conversion rate at each stage from browsing products to placing an order. Sentiment index data measures users' emotional attitudes towards the platform and products by analyzing user comments, likes, complaints, and other behaviors. After collecting the above data, a dynamic monitoring indicator system is constructed based on the characteristics of different data and analysis needs. This dynamic monitoring indicator system covers indicators of different dimensions, such as the average response latency, the trend of conversion rate changes, and the positive and negative values ​​of sentiment, comprehensively monitoring users' behavior and emotional state on the platform.

[0072] Secondly, based on a dynamic monitoring indicator system, abnormal patterns are identified in the real-time user feedback data stream, generating strategy failure warning signals and preference shift detection reports. The dynamic monitoring indicator system provides the data foundation for abnormal pattern identification. By analyzing various data points in the real-time user feedback data stream and comparing them with the normal range in the indicator system, for example, when the average behavior response latency exceeds the normal range, or when the conversion rate at a certain stage of the conversion funnel drops significantly, it is determined to be an abnormal pattern. For sentiment index data, if users' negative sentiment suddenly increases, it is also considered an abnormal situation. Based on the abnormal patterns, strategy failure warning signals are generated, indicating that there may be problems with the platform's current traffic acquisition strategy. At the same time, through in-depth analysis of user behavior data, preference shift detection reports are generated. For example, it is found that users' preferences for a certain type of product have changed, previously popular products are no longer attracting attention, and new product types are becoming popular. The report describes in detail the changes in user preferences, providing a basis for subsequent strategy adjustments.

[0073] A dynamic correction model for traffic acquisition strategies is constructed based on strategy failure warning signals and preference drift detection reports. This model is then used to optimize cross-platform traffic acquisition and promotion strategies, generating optimized strategies. These optimized strategies are then integrated with the existing cross-platform traffic acquisition and promotion strategies to generate a final cross-platform strategy. This final strategy is then synchronized to the target platform via a distributed deployment mechanism to execute user growth operations. The dynamic correction model, combining strategy failure warning signals and preference drift detection reports, analyzes the shortcomings of the current traffic acquisition strategy. For example, based on the issue of excessively long behavioral response latency identified in the strategy failure warning signal, the dynamic correction model may adjust the traffic acquisition strategy, optimize page loading speed, or adjust the timing of pushed content. Regarding preference drift... The dynamic correction model for user preferences for products, as detected in the monitoring report, adjusts the content of user traffic acquisition based on the new preference trends, pushing product information that aligns with these new preferences. Based on this model, the model optimizes cross-platform user traffic acquisition strategies, generating an optimized strategy. This optimized strategy is then integrated with the original cross-platform user traffic acquisition strategy, taking into account the advantages of both strategies to generate a final cross-platform traffic acquisition strategy. Through a distributed deployment mechanism, this final strategy is synchronized to all target platforms, ensuring accurate execution across different platforms and achieving user growth goals. For example, the optimized strategy can be executed simultaneously on social media and e-commerce platforms, guiding users to interact between the two platforms and improving user activity and retention rates.

[0074] Furthermore, the construction process of the dynamic correction model for traffic generation strategies specifically includes:

[0075] A behavior response correction factor is generated based on the behavior response delay distribution data in the real-time user feedback data stream. A user preference deviation detection model is constructed by combining conversion funnel parameter data and sentiment tendency index data. Based on the user preference deviation detection model, abnormal patterns in the real-time user feedback data stream are classified and identified, a strategy failure warning signal and a preference deviation detection report are generated, and the preference deviation correction matrix is ​​confirmed based on the preference deviation detection report.

[0076] The design of the strategy gradient update module defines the strategy optimization objective based on the reward function, thereby generating gradient update rules. Then, by integrating the behavior response correction factor, preference offset correction matrix and gradient update rules, a dynamic correction model for the referral strategy is generated.

[0077] Specifically, behavior response correction factors are generated based on the behavior response latency distribution data in the real-time user feedback data stream. The real-time user feedback data stream contains various user behavior data on the platform. Among them, the behavior response latency distribution data reflects the time relationship between user operation and system response. For example, in an e-commerce platform, this could be the time from when a user clicks on a product details page to when the page is fully loaded, or the time from when an order is submitted to when the system confirms the order. By analyzing the behavior response latency distribution data, statistical quantities such as average latency and latency standard deviation are calculated. Based on the statistical information, behavior response correction factors are generated. For example, if the average latency is too long, a correction factor greater than 1 may be generated for subsequent adjustments to the traffic acquisition strategy to optimize the user experience and reduce the negative impact of latency.

[0078] A user preference shift detection model is constructed by combining conversion funnel parameter data and sentiment index data. The conversion funnel parameter data shows the conversion rate of users at each stage of the platform from initial browsing behavior to final conversion behavior (such as purchasing goods, registering for membership, etc.). The sentiment index data quantifies users' emotional attitudes towards the platform, products, or services by analyzing users' comments, ratings, likes, etc. For example, on social platforms, users' likes and comments on specific content can reflect their sentiment inclinations. By combining conversion funnel parameter data and sentiment index data and using machine learning algorithms, such as neural networks or decision tree algorithms, a user preference shift detection model is constructed. The user preference shift detection model can learn users' behavioral patterns and emotional changes at different stages, thereby identifying user preference shifts. For example, if the conversion rate at a certain stage of the conversion funnel suddenly drops, and the sentiment index shows a negative change, it can be determined that user preferences have shifted, possibly because some functions or products on the platform do not meet user needs.

[0079] The user preference shift detection model classifies and identifies abnormal patterns in real-time user feedback data streams, generating strategy failure warning signals and preference shift detection reports. Based on these reports, a preference shift correction matrix is ​​determined. The model monitors real-time user feedback data streams and identifies abnormal patterns. For example, when users exhibit abnormal browsing and purchasing behavior on e-commerce platforms—such as frequent browsing but infrequent purchases, or concentrated purchasing behavior on a few products—the model classifies these abnormal patterns. Based on the classification results, it generates strategy failure warning signals, indicating potential problems with the platform's current traffic acquisition strategy. Simultaneously, it generates a preference shift detection report, detailing the shift in user preferences, including the direction and degree of the shift. Based on this report, the model analyzes the reasons for the user preference shift and determines the preference shift correction matrix. For instance, if user preferences shift from one product category to another, the correction matrix can include adjustment strategies for different product categories, such as adjusting product recommendation algorithms or optimizing page layout.

[0080] The design incorporates a strategy gradient update module. Based on a reward function, it defines strategy optimization objectives and generates gradient update rules. This module then integrates behavioral response correction factors, preference offset correction matrices, and gradient update rules to create a dynamic correction model for the traffic acquisition strategy. This module optimizes traffic acquisition strategies by defining a reward function to measure the effectiveness of the strategy. The reward function can consider metrics such as user conversion rate, retention rate, and activity level. Based on the reward function, the optimization objectives are determined, such as improving conversion rate or increasing user activity. Gradient update rules are generated according to these objectives, and algorithms like gradient descent are used to adjust the parameters of the traffic acquisition strategy. The behavioral response correction factor, preference offset correction matrix, and gradient update rules are integrated into the dynamic correction model. For example, when the behavioral response latency is too long, the behavioral response correction factor prompts the model to adjust strategy parameters. Combined with adjustment strategies for user preference offsets in the preference offset correction matrix, and further optimized according to the gradient update rules, this generates a more effective traffic acquisition strategy and improves the platform's operational efficiency. Example 2

[0081] Reference Figure 2 The following are the steps of a large company's cross-platform user acquisition and growth promotion method based on artificial intelligence:

[0082] Acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors;

[0083] The multi-platform user behavior feature vector set is subjected to cross-platform user identity code matching processing, and a unified cross-platform identity profile of users is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map.

[0084] A dynamic traffic generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic generation strategy is calculated through a preset cross-platform benefit prediction model. Then, a user cross-platform traffic promotion strategy is generated based on the expected user retention growth value of each traffic generation strategy.

[0085] Acquire real-time user feedback data streams, construct a dynamic correction model for traffic acquisition strategies based on the real-time user feedback data streams, optimize the cross-platform traffic acquisition and promotion strategy for users, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

[0086] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0087] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A cross-platform user acquisition and growth promotion system for large enterprises based on artificial intelligence, characterized by: include: The data acquisition and processing module is used to acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into the behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors. The user cross-platform interaction behavior data is input into a behavior feature deep extraction network for cross-platform feature association modeling, generating a multi-platform user behavior feature vector set, specifically including: Establish a cross-platform data collection channel, and collect user cross-platform interaction behavior data on multiple platforms through encrypted data collection technology based on the cross-platform data collection channel. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data. The collected user cross-platform interaction behavior data is aggregated and processed, and then a user interaction behavior feature set is generated based on the result of the aggregation and processing. The user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features. A deep feature extraction network is constructed, and multi-dimensional feature calculations are performed on the user interaction behavior feature set based on the deep feature extraction network to generate a primary feature set. Cross-platform feature association modeling is performed on the primary feature set to calculate the weight distribution corresponding to each platform, and then the weight distribution corresponding to each platform is fused to obtain a multi-platform user behavior feature vector set. The data mining module is used to perform cross-platform user identification code matching processing on the multi-platform user behavior feature vector set, and to establish a unified cross-platform user identity profile based on the processing results. Then, based on the unified cross-platform identity profile, the module performs platform behavior preference mining on the target user group and generates a cross-platform user behavior feature map. The promotion strategy generation module is used to construct a dynamic traffic generation matrix based on the user cross-platform behavior feature map, and calculate the expected user retention growth value of each traffic generation strategy through a preset cross-platform benefit prediction model, and then generate user cross-platform traffic promotion strategy based on the expected user retention growth value of each traffic generation strategy. The optimization module is used to acquire real-time user feedback data streams, construct a dynamic correction model for the traffic acquisition strategy based on the real-time user feedback data streams, optimize the user cross-platform traffic acquisition and promotion strategy, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

2. The AI-based cross-platform user acquisition and growth promotion system for large enterprises according to claim 1, characterized in that, The multi-platform user behavior feature vector set is subjected to cross-platform user identification code matching processing, and a unified cross-platform user identity profile is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map, specifically including: Based on user device fingerprint features and cross-platform account association features, a user identity matching decision set is constructed. Feature transfer is performed on the user's cross-platform interaction behavior data corresponding to multiple platforms to generate a user identity verification feature set. Then, the user identity matching decision set is matched with the user identity verification feature set to establish a unified cross-platform identity profile of the user. A dynamic preference prediction model is constructed to extract spatiotemporal features from the user's cross-platform unified identity profile and generate a behavioral preference prediction map. By projecting a unified cross-platform identity profile and behavioral preference prediction map of users into a three-dimensional spatiotemporal map, a cross-platform behavioral feature map of users is generated.

3. The AI-based cross-platform user acquisition and growth promotion system for large enterprises according to claim 1, characterized in that, A dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic acquisition strategy is calculated through a preset cross-platform benefit prediction model. Then, based on the expected user retention growth value of each traffic acquisition strategy, a user cross-platform traffic promotion strategy is generated, specifically including: A dynamic traffic acquisition strategy generation matrix is ​​constructed based on the user cross-platform behavior feature map, and then an initial traffic acquisition strategy candidate set is generated based on the dynamic traffic acquisition strategy generation matrix. The expected user retention growth value of each candidate strategy in the initial traffic acquisition strategy candidate set is calculated by using a pre-set cross-platform benefit prediction model. The calculation results are then filtered, and a user cross-platform traffic acquisition and promotion strategy is generated based on the filtering results.

4. The AI-based cross-platform user acquisition and growth promotion system for large enterprises according to claim 1, characterized in that, Acquire real-time user feedback data streams, construct a dynamic correction model for traffic acquisition strategies based on these data streams, optimize the cross-platform user traffic acquisition and promotion strategies, and deploy the optimized cross-platform traffic acquisition and promotion strategies to the target platform to perform user growth operations. Specifically, this includes: Real-time user feedback data streams are acquired through a real-time data acquisition gateway to construct a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response latency distribution data, conversion funnel parameter data, and sentiment tendency index data. Based on the dynamic monitoring index system, abnormal patterns are identified in the real-time user feedback data stream to generate strategy failure warning signals and preference shift detection reports. A dynamic correction model for the user acquisition strategy is constructed based on the strategy failure early warning signal and preference drift detection report. The user cross-platform traffic acquisition and promotion strategy is optimized based on the dynamic correction model to generate an optimized traffic acquisition and promotion strategy. The optimized traffic acquisition and promotion strategy is then integrated with the user cross-platform traffic acquisition and promotion strategy to generate a final cross-platform traffic acquisition and promotion strategy. Finally, the final cross-platform traffic acquisition and promotion strategy is synchronized to the target platform to perform user growth operations through a distributed deployment mechanism.

5. The AI-based cross-platform user acquisition and growth promotion system for large enterprises according to claim 4, characterized in that, The construction process of the dynamic adjustment model for traffic generation strategies specifically includes: A behavior response correction factor is generated based on the behavior response delay distribution data in the real-time user feedback data stream. A user preference deviation detection model is constructed by combining conversion funnel parameter data and sentiment tendency index data. Based on the user preference deviation detection model, abnormal patterns in the real-time user feedback data stream are classified and identified, a strategy failure warning signal and a preference deviation detection report are generated, and the preference deviation correction matrix is ​​confirmed based on the preference deviation detection report. The design of the strategy gradient update module defines the strategy optimization objective based on the reward function, thereby generating gradient update rules. Then, by integrating the behavior response correction factor, preference offset correction matrix and gradient update rules, a dynamic correction model for the referral strategy is generated.

6. A method for attracting and promoting users across platforms based on artificial intelligence, applied to the artificial intelligence-based cross-platform user attraction and growth promotion system for large enterprises as described in any one of claims 1-5, characterized in that, Includes the following steps: Acquire user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a set of multi-platform user behavior feature vectors; The user cross-platform interaction behavior data is input into a behavior feature deep extraction network for cross-platform feature association modeling, generating a multi-platform user behavior feature vector set, specifically including: Establish a cross-platform data collection channel, and collect user cross-platform interaction behavior data on multiple platforms through encrypted data collection technology based on the cross-platform data collection channel. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data. The collected user cross-platform interaction behavior data is aggregated and processed, and then a user interaction behavior feature set is generated based on the result of the aggregation and processing. The user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features. A deep feature extraction network is constructed, and multi-dimensional feature calculations are performed on the user interaction behavior feature set based on the deep feature extraction network to generate a primary feature set. Cross-platform feature association modeling is performed on the primary feature set to calculate the weight distribution corresponding to each platform, and then the weight distribution corresponding to each platform is fused to obtain a multi-platform user behavior feature vector set. The multi-platform user behavior feature vector set is subjected to cross-platform user identity code matching processing, and a unified cross-platform identity profile of users is established based on the processing results. Then, based on the unified cross-platform identity profile, platform behavior preference mining is performed on the target user group to generate a cross-platform user behavior feature map. A dynamic traffic generation matrix is ​​constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic generation strategy is calculated through a preset cross-platform benefit prediction model. Then, a user cross-platform traffic promotion strategy is generated based on the expected user retention growth value of each traffic generation strategy. Acquire real-time user feedback data streams, construct a dynamic correction model for traffic acquisition strategies based on the real-time user feedback data streams, optimize the cross-platform traffic acquisition and promotion strategy for users, and deploy the optimized cross-platform traffic acquisition and promotion strategy to the target platform to perform user growth operations.

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