Private domain traffic automatic operation and customer expansion method and system

Abstract

The invention discloses a private domain traffic automatic operation and customer expansion method and system, relates to the technical field of intelligent marketing, analyzes multi-modal data through a deep learning algorithm, constructs a comprehensive customer portrait, and generates a personalized recommendation strategy. Meanwhile, the social graph analysis technology is used for mining the potential relation between customers, designing fission marketing activities, stimulating the customers to share and attracting more new customers to join in a private domain traffic pool, and rapid expansion of private domain traffic is achieved. According to the method, key clients and social relations can be accurately identified, the propagation effect and the conversion rate of marketing activities are improved, and thus the enterprise competitiveness and the market share are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent marketing, in particular to a private domain traffic automation operation and customer expansion method and system. BACKGROUND

[0002] With the rapid development of Internet technology, the private domain traffic automation operation and customer expansion method has gradually become an important means of enterprise marketing. Through the construction of customer portrait, personalized recommendation and viral marketing technology, enterprises can more accurately reach target customers and improve customer satisfaction and loyalty. However, with the intensification of market competition and the continuous progress of technology, the existing private domain traffic operation method gradually exposes some shortcomings.

[0003] The existing private domain traffic operation method mainly relies on traditional data mining and analysis technology, although it can achieve customer portrait and personalized recommendation to a certain extent, but there are obvious shortcomings in multi-modal data processing, dynamic strategy adjustment and viral marketing effect. For example, the traditional data preprocessing method cannot effectively process the noise and missing values in multi-modal data, resulting in inaccurate customer portrait. In addition, the existing personalized recommendation strategy lacks dynamic adjustment mechanism and cannot be optimized according to the real-time feedback of customers, affecting the recommendation effect and customer experience. In the aspect of viral marketing, the existing method is difficult to accurately identify key customers and social relationships, resulting in limited spread effect of marketing activities. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a private domain traffic automation operation and customer expansion method to solve the problems of inaccurate multi-modal data preprocessing, resulting in inaccurate customer portrait; the lack of dynamic adjustment mechanism in personalized recommendation strategy, which cannot be optimized according to the real-time feedback of customers; and how to accurately identify key customers and social relationships to improve the effect of viral marketing.

[0006] To solve the above technical problems, the present application provides the following technical scheme:

[0007] The present application provides a private domain traffic automation operation and customer expansion method, characterized in that it comprises the following steps:

[0008] S1. Collect multi-modal data of customers from multiple channels, including text, image and voice data, and preprocess the collected data.

[0009] S2. Analyze the preprocessed multi-modal data using deep learning algorithm to construct a comprehensive customer portrait.

[0010] S3. According to the constructed customer portrait, a personalized recommendation strategy is generated using a deep learning algorithm, and the recommendation strategy is dynamically adjusted according to the real-time feedback of the customer through a reinforcement learning mechanism.

[0011] S4. According to the personalized recommendation strategy, personalized information is pushed to the customer through a private domain traffic platform, and the interactive feedback of the customer is collected.

[0012] S5. Combined with the interactive feedback and historical behavior data of the customer, a deep learning algorithm is used to predict the future behavior of the customer, and the private domain traffic operation strategy is optimized according to the prediction result.

[0013] S6. Based on the optimized customer portrait and behavior prediction result, the potential relationship between customers is mined, a fission marketing activity is designed, customers are encouraged to share, more new customers are attracted to join the private domain traffic pool, and the rapid expansion of private domain traffic is realized.

[0014] As a preferred solution of the private domain traffic automatic operation and customer expansion method of the present application, in step S1, the preprocessing of the multi-modal data includes the following steps,

[0015] The text data is segmented and the stop words are removed to extract more valuable key information.

[0016] The image data is cropped and normalized to meet the format requirements of subsequent analysis.

[0017] The voice data is denoised and converted into text form for analysis with other text data.

[0018] The structured data is cleaned and standardized to remove errors and duplicate data, ensuring the accuracy and consistency of the data.

[0019] As a preferred solution of the private domain traffic automatic operation and customer expansion method of the present application, in step S2, the deep learning algorithm includes the following steps:

[0020] The convolutional neural network (CNN) is used to extract features from image data, extracting key features in images.

[0021] The recurrent neural network (RNN) or long short-term memory network (LSTM) is used to process text and voice-recognized text data, extracting time series features in text.

[0022] The fully connected neural network is used to process structured data to extract features.

[0023] The features of the above three kinds of data are fused to construct a comprehensive customer portrait.

[0024] As a preferred solution of the private domain traffic automation operation and customer expansion method described in the present application, wherein: the generation and dynamic adjustment of the personalized recommendation strategy includes the following steps:

[0025] Generate personalized recommendation strategies using the Transformer architecture, and predict products or services that customers may be interested in based on feature information in the customer portrait.

[0026] Generate a recommendation list based on the prediction results and sort them according to relevance.

[0027] Use the Proximal Policy Optimization (PPO) algorithm to dynamically adjust the recommendation strategy, and optimize the recommendation content and order based on real-time customer feedback.

[0028] Apply the adjusted recommendation strategy to the private domain traffic platform and push personalized information to customers.

[0029] As a preferred solution of the private domain traffic automation operation and customer expansion method described in the present application, wherein: in step S4, the push of personalized information and the collection of customer interaction feedback includes the following steps:

[0030] Push personalized information to customers through private domain traffic platforms such as WeChat public accounts, mini-programs, and enterprise WeChat.

[0031] Design diverse interactive methods such as questionnaires, comment interactions, online customer service consultations, etc., to encourage customers to provide feedback.

[0032] Use natural language processing technology to analyze customer feedback in real time and extract information such as customer sentiment, opinions, and suggestions.

[0033] Collect and store customer interaction feedback information for subsequent customer behavior prediction and operation strategy optimization.

[0034] As a preferred solution of the private domain traffic automation operation and customer expansion method described in the present application, wherein: in step S5, the customer behavior prediction and operation strategy optimization includes the following steps:

[0035] Combine customer interaction feedback and historical behavior data, and use an LSTM-based time series prediction model to predict future customer behavior.

[0036] Based on the prediction results, analyze factors such as customer purchase cycles, browsing habits, and responses to recommended content.

[0037] Optimize private domain traffic operation strategies based on the analysis results, such as adjusting recommended content and optimizing marketing activities.

[0038] The optimized strategy is applied to the private domain traffic platform to improve customer satisfaction and loyalty.

[0039] As a preferred solution of the private domain traffic automation operation and customer expansion method, in step S6, the design of the fission marketing activity and the expansion of the private domain traffic include the following steps:

[0040] Based on the optimized customer portrait and behavior prediction results, the potential relationships and social networks among customers are mined.

[0041] Key customers with high influence and propagation are identified as the starting point of fission marketing.

[0042] Design personalized fission marketing activities, such as providing exclusive sharing rewards, coupons, etc., to encourage key customers to share information, products or activities of the private domain traffic platform to their social circles.

[0043] Use social graph analysis technology to track the propagation path and effect after sharing, further optimize the fission marketing strategy, attract more new customers to join the private domain traffic pool, and realize the rapid expansion of private domain traffic.

[0044] The present application has the following advantages: through the innovative multi-modal data preprocessing, deep learning algorithm to construct customer portrait, dynamic adjustment of personalized recommendation strategy and precise fission marketing activity design, the operation effect of private domain traffic is significantly improved. First, the multi-modal data preprocessing technology effectively solves the problem of inaccurate data processing in the prior art. Through word segmentation, noise reduction, normalization and other operations, the quality of the data is ensured, providing a solid foundation for accurate customer portrait. Secondly, the customer portrait constructed by deep learning algorithm and multi-modal data can more comprehensively reflect the characteristics of customers, so as to realize more accurate personalized recommendation. In addition, the dynamic adjustment mechanism optimizes the recommendation strategy according to the real-time feedback of customers, improving the accuracy of recommendation and customer satisfaction. Finally, through social graph analysis to mine key customers and potential relationships, the designed fission marketing activity effectively improves the propagation effect and accelerates the expansion of private domain traffic. These innovations work together to not only solve the shortcomings of existing technologies, but also bring significant improvement and enhancement to private domain traffic operation. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0046] Figure 1Flowchart of the private domain traffic automation operation and customer expansion method in Example 1. DETAILED DESCRIPTION

[0047] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0048] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0049] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or selected from other embodiments.

[0050] Example 1, refer to Figure 1 For the first embodiment of the present application, the embodiment provides a private domain traffic automation operation and customer expansion method, characterized in that it comprises the following steps:

[0051] S1. Collecting multi-modal data of customers from multiple channels, including text, image and voice data, and preprocessing the collected data.

[0052] S2. Analyzing the preprocessed multi-modal data using deep learning algorithms to build a comprehensive customer portrait.

[0053] S3. According to the constructed customer portrait, using deep learning algorithms to generate personalized recommendation strategies, and dynamically adjusting the recommendation strategies according to the real-time feedback of the customers through reinforcement learning mechanism.

[0054] S4. According to the personalized recommendation strategy, pushing personalized information to the customers through the private domain traffic platform, and collecting the interactive feedback of the customers.

[0055] S5. Combining the interactive feedback and historical behavior data of the customers, using deep learning algorithms to predict the future behavior of the customers, and optimizing the private domain traffic operation strategy according to the prediction results.

[0056] S6. Based on the optimized customer portrait and behavior prediction results, mining the potential relationship between customers, designing fission marketing activities, encouraging customers to share, attracting more new customers to join the private domain traffic pool, and realizing the rapid expansion of private domain traffic.

[0057] It should be noted that the S1 step involves collecting multi-modal data of customers from multiple channels, including text, image and voice data. During the collection process, it is necessary to ensure the diversity and comprehensiveness of the data for subsequent processing. After the collection is completed, the data is preprocessed, including word segmentation processing of text data, removal of stop words, and extraction of more valuable key information. Image data is cropped and normalized to meet the format requirements of subsequent analysis. Noise reduction is performed on the voice data, and it is converted into text form for analysis with other text data. Structured data is cleaned and standardized to remove errors and duplicate data, ensuring data accuracy and consistency. This step provides high-quality raw data for building a comprehensive customer portrait.

[0058] Through the S1 step, comprehensive collection and high-quality preprocessing of multi-modal data of customers are achieved, providing a solid data foundation for subsequent customer portrait construction and personalized recommendation. This helps to improve the accuracy of customer portrait, thereby improving the accuracy of personalized recommendation and customer satisfaction.

[0059] In the S2 step, deep learning algorithms are used to analyze the preprocessed multi-modal data to build a comprehensive customer portrait. First, a convolutional neural network (CNN) is used to extract features from image data, extracting key features from images. Then, a recurrent neural network (RNN) or long short-term memory network (LSTM) is used to process text and voice-recognized text data, extracting time series features from text. Next, a fully connected neural network is used to process structured data, extracting features from it. Finally, the features of the three types of data are fused to build a comprehensive customer portrait.

[0060] Through the S2 step, deep analysis and feature fusion of multi-modal data are achieved, building a more comprehensive and three-dimensional customer portrait. This helps to better understand customer needs and preferences, providing strong support for personalized recommendation and marketing strategy formulation.

[0061] In the S3 step, according to the constructed customer portrait, a personalized recommendation strategy is generated using deep learning algorithms. The Transformer architecture is used to predict products or services that customers may be interested in based on the feature information in the customer portrait. A recommendation list is generated based on the prediction results and sorted by relevance. At the same time, the Proximal Policy Optimization (PPO) algorithm is used to dynamically adjust the recommendation strategy based on real-time customer feedback to optimize the content and order of recommendations. This step ensures the real-time and adaptability of the recommendation strategy.

[0062] Through S3, personalized recommendation strategy generation and dynamic adjustment based on customer portrait are achieved. This helps improve the accuracy of recommendations and customer satisfaction, while enhancing the flexibility and adaptability of the recommendation system, which can be optimized according to real-time customer feedback to improve customer experience.

[0063] In S4, according to the personalized recommendation strategy, personalized information is pushed to customers through private domain traffic platforms. This includes information push through WeChat public accounts, mini programs, enterprise WeChat, etc. At the same time, design diversified interactive ways such as questionnaire survey, comment interaction, online customer service consultation, etc., encourage customers to feedback. Use natural language processing technology to analyze customer feedback in real time, extract customer sentiment, opinions and suggestions, etc. This step aims to collect customer interactive feedback to provide data support for subsequent customer behavior prediction and operation strategy optimization.

[0064] Through S4, precise push of personalized information and real-time collection of customer interactive feedback are achieved. This helps to understand customer feedback and needs in a timely manner, providing a basis for optimizing private domain traffic operation strategy, while also enhancing customer engagement and satisfaction.

[0065] In S5, combined with customer interactive feedback and historical behavior data, deep learning algorithms are used to predict future customer behavior. Use LSTM-based time series prediction model to predict customer purchase cycle, browsing habits and response to recommended content, etc. According to the prediction results, analyze customer purchase intention and risk of loss, optimize private domain traffic operation strategy, such as adjusting recommended content, optimizing marketing activities, etc. This step aims to predict and analyze customer behavior in advance to develop targeted operation strategies to improve customer satisfaction and loyalty.

[0066] Through S5, precise prediction of customer future behavior and optimization of operation strategy are achieved. This helps to identify potential customer needs and risks in advance and develop more targeted marketing strategies to improve customer satisfaction and loyalty, enhancing the effectiveness of private domain traffic operations.

[0067] In S6, based on the optimized customer portrait and behavior prediction results, potential relationships between customers are explored, and fission marketing activities are designed. Identify key customers with high influence and communication power as the starting point for fission marketing. Design personalized fission marketing activities such as providing exclusive sharing rewards, coupons, etc. to encourage key customers to share private domain traffic platform information, products or activities with their social circles. Use social graph analysis technology to track the spread path and effect after sharing, further optimize fission marketing strategy, attract more new customers to join the private domain traffic pool, and achieve rapid expansion of private domain traffic.

[0068] Through step S6, the design and optimization of the fission marketing activity based on customer social relationships are realized. This helps to quickly spread the information of the private domain traffic platform through the customer's social network, attract more new customers to join, and thus realize the rapid expansion and growth of the private domain traffic. At the same time, through accurate identification of key customers and optimization of fission strategies, the propagation effect and conversion rate of the marketing activity are improved.

[0069] Specifically, in step S1, the preprocessing of the multi-modal data includes the following steps,

[0070] The text data is segmented and the stop words are removed to extract more valuable key information.

[0071] The image data is cropped and normalized to meet the format requirements of subsequent analysis.

[0072] The speech data is denoised and converted into text form for analysis with other text data.

[0073] The structured data is cleaned and standardized to remove errors and duplicate data, ensuring data accuracy and consistency.

[0074] It should be noted that in step S1, the preprocessing of multi-modal data is the key basis for building accurate customer portraits. The specific operation includes: first, the text data is segmented and the stop words are removed to extract more valuable key information. Then, the image data is cropped and normalized, cropping is to remove irrelevant parts of the image, and normalization is to scale the image data to a uniform size to meet the format requirements of subsequent analysis. In addition, the speech data is denoised and converted into text form through speech recognition technology for analysis with other text data. Finally, the structured data is cleaned and standardized to remove errors and duplicate data, ensuring data accuracy and consistency.

[0075] Through these steps, comprehensive and high-quality preprocessing of multi-modal data is realized, providing a solid data foundation for subsequent deep learning and analysis. The role and use of this is to help improve the accuracy of customer portraits, thereby improving the accuracy of personalized recommendations and customer satisfaction. The beneficial effects are that through accurate preprocessing steps, data quality can be significantly improved, reducing noise and errors in subsequent analysis, making customer portraits more realistic and reliable, and providing more accurate data support for personalized marketing and customer service.

[0076] Specifically, in step S2, the deep learning algorithm includes the following steps:

[0077] The image data is processed using a convolutional neural network (CNN) to extract key features from the images.

[0078] The text data is processed using a recurrent neural network (RNN) or long short-term memory network (LSTM) to extract time-series features from the text.

[0079] The structured data is processed using a fully connected neural network to extract features from the data.

[0080] The features extracted from the three types of data are combined to construct a comprehensive customer profile.

[0081] It should be noted that in step S2, the deep learning algorithm includes the following steps: using a convolutional neural network (CNN) to extract features from image data, extracting key features in images. This process involves applying a series of convolutional layers and pooling layers to image data to identify features such as edges, textures and objects in images. CNN can automatically learn and extract image features through its deep structure, without the need for manual design of feature extraction algorithms, thus simplifying the image processing process and improving the accuracy and efficiency of feature extraction. The text data after text and speech recognition is processed using a recurrent neural network (RNN) or long short-term memory network (LSTM) to extract time-series features from the text. RNN and LSTM are particularly suitable for processing sequential data and can capture long-term dependencies in text, which is crucial for understanding the content and context of the text. In this way, meaningful time-series features can be extracted from text data, providing rich information for subsequent customer profile construction.

[0082] The structured data is processed using a fully connected neural network to extract features from the data. The fully connected neural network can process various types of input data, including structured data, through its dense connection mode. It can learn the complex relationships between input data and extract features useful for customer profile construction. The features extracted from the three types of data are combined to construct a comprehensive customer profile. This step involves integrating the features extracted from images, text and structured data to form a unified customer profile. This multi-modal feature fusion method can provide more comprehensive and accurate customer information, helping to better understand customer needs and behavior patterns.

[0083] Through the S2 step, deep analysis and feature fusion of images, text, and structured data are achieved, and a more comprehensive and three-dimensional customer portrait is constructed. The role and purpose of this step is to extract key information from multiple types of data and integrate it into a unified view, providing a solid foundation for subsequent personalized recommendations and marketing strategies. Ultimately, it achieves the beneficial effects of improving the accuracy of customer portraits and the effectiveness of personalized services, which helps enterprises more accurately reach target customers and improve customer satisfaction and loyalty.

[0084] Specifically, in step S3, the generation and dynamic adjustment of the personalized recommendation strategy include the following steps:

[0085] A Transformer architecture is used to generate personalized recommendation strategies, which predict products or services that customers may be interested in based on feature information in the customer portrait.

[0086] A recommendation list is generated according to the prediction results, and sorted according to relevance.

[0087] The Proximal Policy Optimization (PPO) algorithm is used to dynamically adjust the recommendation strategy, optimizing the recommendation content and order based on real-time customer feedback.

[0088] The adjusted recommendation strategy is applied to the private domain traffic platform to push personalized information to customers.

[0089] It should be noted that the Transformer architecture is used to generate personalized recommendation strategies. This architecture can handle sequential data and capture complex dependencies in customer behavior data. Based on the feature information in the customer portrait, the Transformer model predicts products or services that customers may be interested in. Then, a recommendation list is generated according to the prediction results, and sorted according to relevance to ensure that the recommended content matches the customer's interests. In addition, the Proximal Policy Optimization (PPO) algorithm is used to dynamically adjust the recommendation strategy, which is a reinforcement learning algorithm that can optimize the recommendation content and order based on real-time customer feedback, thereby adapting to changes in customer preferences. Finally, the adjusted recommendation strategy is applied to the private domain traffic platform to push personalized information to customers, achieving precise marketing.

[0090] Benefits: By adopting the Transformer architecture, in-depth analysis and understanding of customer behavior data are achieved, and the generated personalized recommendation strategy more accurately reflects the interests and needs of customers. The sorting process ensures the relevance of the recommendation list, improving the attractiveness and click rate of the recommendations. The introduction of the PPO algorithm enables the recommendation system to respond to customer feedback in real time, dynamically adjusting the recommended content and enhancing the flexibility and adaptability of the system. Finally, applying these optimized recommendation strategies to the private domain traffic platform not only improves customer satisfaction and loyalty but also increases conversion rates and sales, achieving efficient use of private domain traffic and maximizing customer value.

[0091] Specifically, in step S4, the pushing of personalized information and the collection of customer interaction feedback include the following steps:

[0092] Push personalized information to customers through WeChat public numbers, mini-programs, enterprise WeChat, and other private domain traffic platforms.

[0093] Design diverse interaction methods such as questionnaires, comment interactions, online customer service consultations, etc., to encourage customer feedback.

[0094] Use natural language processing technology to analyze customer feedback in real time, extracting information such as customer sentiment, opinions, and suggestions.

[0095] Collect and store customer interaction feedback information for subsequent customer behavior prediction and operation strategy optimization.

[0096] It should be noted that first, personalized information is pushed to customers through WeChat public numbers, mini-programs, enterprise WeChat, and other private domain traffic platforms. This step requires the operation team to carefully design and customize the push content based on customer portraits and behavior data, ensuring the relevance and attractiveness of the information. Next, diverse interaction methods such as questionnaires, comment interactions, online customer service consultations, etc. are designed to improve customer participation and feedback diversity. Then, natural language processing technology is used to analyze customer feedback in real time, extracting information such as customer sentiment, opinions, and suggestions. The application of this technology can quickly identify key information from a large amount of text data, providing support for subsequent analysis. Finally, customer interaction feedback information is collected and stored for subsequent customer behavior prediction and operation strategy optimization. This step ensures the systematicity and usability of the data, providing a foundation for continuous improvement.

[0097] Through the S4 step, the accurate push of personalized information and the real-time collection of customer interaction feedback are achieved, which helps to understand customer feedback and needs in a timely manner, provides a basis for optimizing private domain traffic operation strategies, and also enhances customer participation and satisfaction. Pushing personalized information to customers through WeChat public number, applet, enterprise WeChat, and other private domain traffic platforms can improve the arrival rate and reading rate of information, thereby improving customer participation and loyalty. Designing various interactive ways to encourage customers to provide feedback helps to collect more extensive customer opinions, providing references for product improvement and service quality improvement. Real-time analysis of customer feedback using natural language processing technology can quickly identify customer needs and problems, and timely adjust service strategies to improve customer satisfaction. Collecting and storing customer interaction feedback information provides data support for subsequent customer behavior prediction and operation strategy optimization, which helps to achieve precise marketing and personalized services, and ultimately achieves the beneficial effects of improving customer experience and business growth.

[0098] Specifically, in step S5, the customer behavior prediction and operation strategy optimization includes the following steps:

[0099] Combining customer interaction feedback and historical behavior data, a time series prediction model based on LSTM is used to predict future customer behavior.

[0100] According to the prediction results, analyze customer purchase cycle, browsing habits, and response to recommended content, etc.

[0101] According to the analysis results, optimize private domain traffic operation strategies, such as adjusting recommended content, optimizing marketing activities, etc.

[0102] Apply the optimized strategies to the private domain traffic platform to improve customer satisfaction and loyalty.

[0103] It should be noted that by integrating customer interaction feedback and historical behavior data, a time series prediction model based on LSTM is used to predict future customer behavior. This process involves collecting customer purchase history, interaction records, and behavior trajectory data on private domain traffic platforms. Then, deep learning algorithms, especially LSTM models, are used to analyze these time series data to identify behavior patterns and trends. LSTM models are particularly important in this step because they can capture long-term dependencies, helping to predict future customer purchase behavior or response to marketing activities. Then, according to the prediction results, analyze customer purchase cycle, browsing habits, and response to recommended content, etc. These analysis results will be used to optimize private domain traffic operation strategies, such as adjusting recommended content, optimizing marketing activities, etc. Finally, apply the optimized strategies to the private domain traffic platform to improve customer satisfaction and loyalty.

[0104] Beneficial effects: Through this step, accurate prediction of future customer behavior is achieved, which helps enterprises to develop targeted marketing strategies in advance, thereby improving marketing efficiency and customer conversion rate. At the same time, by analyzing the customer's purchase cycle and browsing habits, more accurate recommended content can be adjusted to provide products and services that better meet customer needs, enhancing customer experience. In addition, optimized marketing activities can more effectively attract and retain customers, improving customer satisfaction and loyalty, which is of great significance to the long-term development and brand building of enterprises. Ultimately, these optimization measures help improve the operation effect of private domain traffic, achieving higher business value and customer value.

[0105] Specifically, in step S6, the design of the fissile marketing campaign and the expansion of private domain traffic includes the following steps:

[0106] Based on the optimized customer portrait and behavior prediction results, the potential relationships and social networks among customers are mined.

[0107] Identify key customers with high influence and transmission capacity as the starting point for fissile marketing.

[0108] Design personalized fissile marketing activities, such as providing exclusive sharing rewards, coupons, etc., to encourage key customers to share private domain traffic platform information, products or activities with their social circles.

[0109] Use social graph analysis technology to track the propagation path and effect after sharing, further optimize fissile marketing strategies, attract more new customers to join the private domain traffic pool, and achieve rapid expansion of private domain traffic.

[0110] It should be noted that based on the optimized customer portrait and behavior prediction results, data mining techniques are used to identify and analyze customer interaction patterns and social network structures. This includes using algorithms in graph theory to discover potential relationship chains and community structures, revealing the mutual influence and information propagation path among customers, and identifying key customers with high influence and transmission capacity in these social networks. This usually involves calculating the influence score of customers, which may be based on their number of connections, interaction frequency, and the range of their information transmission, etc., and designing personalized fissile marketing activities to provide exclusive sharing rewards, coupons, etc. for these key customers. These activities need to be tailored to the characteristics and preferences of key customers to maximize their participation and sharing willingness. Use social graph analysis technology to track the propagation path and effect after sharing, which includes monitoring the breadth and depth of information transmission and the conversion rate of new customers. Based on these data, further optimize fissile marketing strategies, such as adjusting the reward mechanism or changing the trigger point of information transmission.

[0111] Through the above steps, the rapid expansion of private domain traffic pool and the growth of customer base are achieved. First, by mining the potential relationships and social networks among customers, the customer group with greater influence on marketing activities can be more accurately identified. Second, identifying key customers as the starting point of fission marketing can more effectively utilize limited marketing resources and achieve higher return on investment. Third, designing personalized fission marketing activities can not only improve the participation of key customers, but also attract more new customers through their social networks, thereby achieving the rapid expansion of private domain traffic. Finally, using social graph analysis technology to track and optimize fission marketing strategies can maximize the effectiveness of marketing activities, and also allow timely adjustments to strategies based on real-time feedback, improving the flexibility and adaptability of marketing activities. These steps work together to not only improve marketing efficiency, but also enhance customer loyalty and satisfaction, ultimately achieving the beneficial effect of improving enterprise competitiveness and market share.

[0112] In summary, the present application significantly improves the operation effect of private domain traffic by: through innovative multi-modal data preprocessing, deep learning algorithm to build customer portrait, dynamic adjustment of personalized recommendation strategy and precise fission marketing activity design. First, the multi-modal data preprocessing technology effectively solves the problem of inaccurate data processing in the prior art, through word segmentation, noise reduction, normalization and other operations, ensuring the high quality of data and providing a solid foundation for accurate customer portrait. Second, the customer portrait constructed by deep learning algorithm fusion of multi-modal data can more comprehensively reflect customer characteristics, thereby achieving more accurate personalized recommendation. In addition, the dynamic adjustment mechanism optimizes the recommendation strategy according to real-time feedback from customers, improving the accuracy of recommendations and customer satisfaction. Finally, through social graph analysis to mine key customers and potential relationships, the designed fission marketing activities effectively improve the propagation effect and accelerate the expansion of private domain traffic. These innovations work together not only to solve the shortcomings of existing technologies, but also to bring significant improvements and enhancements to private domain traffic operations.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. A method for automated operation and customer development of private domain traffic, characterized in that: The following steps are involved: S1. Collect multimodal data from customers from multiple channels, including text, image, and voice data, and preprocess the collected data. S2. Use deep learning algorithms to analyze pre-processed multimodal data and build a comprehensive customer profile. S3. Based on the constructed customer profile, use deep learning algorithms to generate personalized recommendation strategies, and dynamically adjust the recommendation strategies based on real-time customer feedback through reinforcement learning mechanisms. S4. Based on personalized recommendation strategies, push personalized information to customers through private domain traffic platforms and collect customer interaction feedback. S5. Combine customer interaction feedback and historical behavior data, use deep learning algorithms to predict future customer behavior, and optimize private domain traffic operation strategies based on the prediction results. S6. Based on the optimized customer profile and behavior prediction results, explore potential relationships between customers, design fission marketing activities, incentivize customers to share, attract more new customers to join the private domain traffic pool, and achieve rapid expansion of private domain traffic.

2. The method for automated private domain traffic operation and customer development according to claim 1, characterized in that: In step S1, the preprocessing of the multimodal data includes the following steps: Perform word segmentation on text data and remove stop words to extract more valuable key information. The image data were cropped and normalized to meet the format requirements for subsequent analysis. The speech data is denoised and converted into text for analysis along with other text data. Clean and standardize structured data to remove errors and duplicate data and ensure data accuracy and consistency.

3. The method for automated operation and customer development of private domain traffic according to claim 2, characterized in that: In step S2, the deep learning algorithm includes the following steps: Convolutional Neural Network (CNN) is used to perform feature extraction on image data and extract key features from the image. Use recurrent neural networks (RNN) or long short-term memory networks (LSTM) to process text data after text and speech recognition and extract time series features from the text. Use a fully connected neural network to process structured data and extract its features. Combine the characteristics of the above three types of data to build a comprehensive customer portrait.

4. The method for automated private domain traffic operation and customer development according to claim 3, characterized in that: In step S3, the generation and dynamic adjustment of the personalized recommendation strategy includes the following steps: The Transformer architecture is used to generate personalized recommendation strategies, and based on the characteristic information in the customer profile, it predicts the products or services that the customer may be interested in. Generate a recommendation list based on the prediction results and sort them by relevance. The Proximal Policy Optimization (PPO) algorithm is used to dynamically adjust the recommendation strategy and optimize the recommendation content and order based on real-time feedback from customers. Apply the adjusted recommendation strategy to the private domain traffic platform to push personalized information to customers.

5. The method for automated operation and customer development of private domain traffic according to claim 4, characterized in that: In step S4, the pushing of personalized information and the collection of customer interaction feedback include the following steps: Push personalized information to customers through private domain traffic platforms such as WeChat official accounts, mini programs, and corporate WeChat. Design a variety of interactive methods, such as questionnaires, comment interactions, online customer service consultations, etc., to encourage customers to provide feedback. Use natural language processing technology to analyze customer feedback in real time and extract information such as customer sentiment, opinions, and suggestions. Collect and store customer interaction feedback information for subsequent customer behavior prediction and operation strategy optimization.

6. The method for automated operation and customer development of private domain traffic according to claim 5, characterized in that: In step S5, the customer behavior prediction and operation strategy optimization includes the following steps: Combining customer interaction feedback and historical behavior data, an LSTM-based time series prediction model is used to predict customers' future behavior. Based on the prediction results, analyze factors such as the customer's purchasing cycle, browsing habits, and response to recommended content. Optimize private domain traffic operation strategies based on analysis results, such as adjusting recommended content and optimizing marketing activities. Apply the optimized strategy to private domain traffic platforms to improve customer satisfaction and loyalty.

7. The method for automated operation and customer development of private domain traffic according to claim 6, characterized in that: In step S6, the design of the fission marketing campaign and the expansion of private domain traffic include the following steps: Based on the optimized customer profiles and behavior prediction results, potential relationships and social networks between customers are explored. Identify key customers with high influence and communication power as the entry point for fission marketing. Design personalized fission marketing activities, such as providing exclusive sharing rewards, coupons, etc., to encourage key customers to share information, products or activities of the private domain traffic platform with their social circles. Use social graph analysis technology to track the dissemination path and effect after sharing, further optimize the fission marketing strategy, attract more new customers to join the private domain traffic pool, and achieve rapid expansion of private domain traffic.

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