AI intelligent customer obtaining processing method and system

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an AI intelligent customer obtaining processing method and system, and the method comprises the following steps: collecting interaction data of a user and a platform, summarizing click, browsing duration and frequency information, extracting a user activity mode through an RNN and an LSTM network, analyzing the interest and behavior mode of the user, and generating a user behavior feature library. According to the method, the client acquisition process is optimized through data analysis and behavior prediction, the method does not depend on a traditional marketing means, user data are accurately analyzed through a sequence model, deep understanding of user behaviors is achieved, the accuracy of market segmentation is improved through refined analysis, it is ensured that marketing activities are matched with a target client group, and the marketing efficiency is improved. According to the method, the conversion rate is remarkably improved, the marketing means can be rapidly adjusted by means of intention recognition and real-time data analysis, synchronization with user requirements is ensured, the customer extension cost of the entity store is reduced by continuously integrating customer feedback and marketing data, and the problem that an entity store operator is difficult to obtain customers is solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an AI intelligent customer acquisition processing method and system. Background Art

[0002] The field of artificial intelligence technology covers a variety of technical sub-fields such as machine learning, deep learning and natural language processing, aiming to create systems that can perform intelligent judgments and responses. Research in this field has promoted the development of computer vision, speech recognition and decision-making systems, and at the same time promoted the advancement of intelligent algorithms. Algorithms can imitate or even surpass human cognitive and problem-solving capabilities. In commercial applications, AI is used to optimize operational processes, enhance user experience and realize automated decision-making, especially showing great potential in data analysis and customer behavior prediction.

[0003] Among them, the AI ​​intelligent customer acquisition method involves the use of artificial intelligence technology to automate and optimize the customer acquisition process, without relying on traditional marketing strategies such as making short videos, live broadcasts or hiring marketing teams. The system automatically identifies and attracts customers with consumption potential by analyzing data from surrounding merchants in the same city. This method uses AI's data processing and pattern recognition capabilities to help physical stores accurately target customers from a wide range of public domain traffic, significantly reducing customer development costs, while upgrading merchants' customer acquisition models, effectively solving the problem of customer acquisition difficulties, and fundamentally improving the efficiency of business operations.

[0004] In the customer acquisition process, the processing mode of existing technologies lacks sufficient adaptability and foresight. When processing complex user data, it is limited by fixed parameters and preset processes, and it is difficult to adapt to the rapid changes in user behavior in real time. Existing methods are insufficient in customer data analysis and market forecasting, which often leads to marketing methods being unable to respond to market changes in a timely manner, and the effectiveness of marketing activities and resource allocation efficiency are inefficient, which not only increases operating costs, but also reduces customer satisfaction and attractiveness. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an AI intelligent customer acquisition processing method and system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an AI intelligent customer acquisition processing method, comprising the following steps: S1: Collect the interaction data between users and the platform, summarize the click, browsing time and frequency information, use RNN and LSTM networks to extract user activity patterns, analyze user interests and behavior patterns, and generate a user behavior feature library; S2: Based on the user behavior feature library, screen user groups with high purchasing potential, refine the market customer group, use the target customer information for targeted marketing activities using the autoencoder, optimize the accuracy and conversion rate of the marketing activities, and generate the target market configuration; S3: Based on the target market configuration, target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using intent recognition mechanism to record feedback data of marketing activities, and conducting marketing effect analysis to generate marketing response plans; S4: Based on the marketing response plan, monitor and manage customer feedback information, including evaluation and purchase frequency, continuously optimize customer service experience, refine customer group stickiness, and generate service optimization configuration; S5: Based on the service optimization configuration, conduct customer behavior analysis, predict customers' future purchasing patterns, adjust inventory and marketing resources, match marketing needs, and generate resource allocation information; S6: Based on the resource allocation information, integrate marketing and customer service data, plan the business development process, analyze the data, refine the development direction, and generate a customer acquisition development plan.

[0007] As a further solution of the present invention, the user behavior feature library includes interaction type, activity frequency, and interest point analysis results; the target market configuration includes consumption potential index, target customer identification results, and market response variables; the marketing response plan includes feedback efficiency, promotion success rate, and customer satisfaction indicators; the service optimization configuration includes service response time and customer retention rate; the resource allocation information includes resource utilization, allocation efficiency, and marketing demand response speed; and the customer acquisition development plan includes key business indicators and long-term growth mechanisms.

[0008] As a further solution of the present invention, the steps of collecting the interaction data between users and the platform, summarizing the click, browsing time and frequency information, extracting the user activity pattern by using RNN and LSTM network, and analyzing the user interest and behavior pattern to generate the user behavior feature library are as follows: S101: Collect the interaction data between users and the platform, including the number of clicks, browsing time and access frequency, classify and organize the data to obtain the user behavior data set; S102: Based on the user behavior data set, use RNN and LSTM networks to filter active user data, perform time series analysis on the user's access frequency and duration, identify periodic behavior patterns, and obtain a behavior pattern feature set; S103: Based on the behavior pattern feature set, a feature storage structure is established, the extracted behavior features are classified and encoded, the availability and security of the data are verified, and a user behavior feature library is obtained.

[0009] As a further solution of the present invention, based on the user behavior feature library, user groups with high purchasing potential are screened, market customer groups are refined, and the target customer information is used for targeted marketing activities using an autoencoder to optimize the accuracy and conversion rate of the marketing activities. The steps of generating a target market configuration are specifically as follows: S201: Based on the user behavior feature library, perform cluster analysis on the data using an autoencoder to identify and mark user groups with high purchase potential, and obtain a potential customer feature set through comparative analysis of user behavior and purchase history; S202: Based on the potential customer feature set, perform coding processing, extract and optimize the features of target customer information, adjust the targeting of marketing activities, and obtain optimized customer information; S203: Based on the optimized customer information, the information is applied to marketing activities, marketing methods are adjusted, customer feedback information is analyzed, and marketing effects are dynamically optimized to obtain target market configuration.

[0010] As a further solution of the present invention, based on the target market configuration, the target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using an intention recognition mechanism to record feedback data of marketing activities, and performing marketing effect analysis, and the steps of generating a marketing response plan are specifically as follows: S301: Based on the target market configuration, implement activity promotion, send preferential information and product recommendations via email and social media platforms, record and analyze reception rate and user interaction data, and obtain a market response data set; S302: Based on the market response data set, an intention recognition mechanism is used to analyze customer interaction information obtained from the response data, including click-through rate and reply time, to identify the customer's purchase intention and participation, and to adjust the marketing information content to obtain activity feedback optimization results; S303: Based on the optimization results of the activity feedback, optimize the marketing information and promotion channels, and generate a marketing response plan by comparing the effects of differential promotions, refining the matching between the promotion plan and the target customers.

[0011] As a further solution of the present invention, the intention recognition mechanism is according to the formula: ; Calculate the customer's purchase intention, where represents the purchase intention of the i-th customer, represents the click rate of the i-th customer, represents the response time of the i-th customer, and coefficients representing click-through rate and response time, is the number of customers.

[0012] As a further solution of the present invention, based on the marketing response plan, customer feedback information is monitored and managed, including evaluation and purchase frequency, customer service experience is continuously optimized, and customer group stickiness is refined. The steps of generating service optimization configuration are specifically as follows: S401: Based on the marketing response plan, monitor the customer's online evaluation and purchase frequency, identify key problems in the service, optimize the user experience, and obtain customer feedback monitoring results; S402: Based on the customer feedback monitoring results, adjust the customer service process, including optimizing the purchase process and response speed, and evaluate the service improvement effect according to the customer satisfaction survey to obtain the service process improvement result; S403: Based on the service process improvement results, customized services and value-added activities are implemented to optimize customer group stickiness and obtain service optimization configuration.

[0013] As a further solution of the present invention, based on the service optimization configuration, customer behavior analysis is performed to predict the customer's future purchase pattern, adjust inventory and marketing resources, match marketing needs, and generate resource allocation information in the following steps: S501: Based on the service optimization configuration, analyze the customer's historical purchase data and interaction records, identify consumption patterns and product preferences, predict future purchase trends, and update inventory in real time to obtain purchase trend prediction information; S502: Optimize inventory allocation according to the purchase trend prediction information, dynamically adjust the inventory of high-liquidity products, and reallocate marketing resources to obtain inventory resource data; S503: Based on the inventory resource data, potential hot-selling products are promoted, market response is optimized through targeted advertising and promotional activities, marketing activities are adjusted, and resource allocation information is generated.

[0014] As a further solution of the present invention, based on the resource allocation information, the marketing and customer service data are integrated to plan the business development process, analyze the data, refine the development direction, and generate the customer acquisition development plan in the following steps: S601: Based on the resource allocation information, integrate the marketing and customer service data of multiple channels, analyze the data, identify the improvement points and growth points in the business process, and obtain a business data analysis record; S602: Based on the business data analysis record, plan the business development process, determine the key business development direction and priority, determine the priority and resource allocation, and obtain a business optimization plan; S603: Based on the business optimization plan, refine the action steps, implement targeted marketing and customer relationship management, optimize customer acquisition and maintenance efficiency, and obtain a customer acquisition development plan.

[0015] An AI intelligent customer acquisition processing system, the AI ​​intelligent customer acquisition processing system is used to execute the above-mentioned AI intelligent customer acquisition processing method, the system comprises: The user behavior analysis module collects the interaction data between users and the platform, including the number of clicks, browsing time and frequency, and uses recurrent neural networks and long short-term memory networks to process the data, analyze user interests and behavior patterns, and obtain a user behavior feature library; The target market positioning module screens user groups with high purchasing potential based on the user behavior feature library, refines the market customer groups, uses autoencoders to optimize the information processing flow of target customers, optimizes the accuracy and conversion rate of marketing activities, and generates target market configurations; The marketing activity optimization module executes targeted promotion activities based on the target market configuration, including sending preferential information and product recommendations, using the intent recognition mechanism to record and analyze the feedback of the marketing activities, and generate a marketing response plan; The customer service improvement module monitors and manages customer feedback information, including evaluation and purchase frequency, based on the marketing response plan, continuously optimizes customer service experience, refines customer group stickiness, and generates service optimization configuration; The resource and plan integration module optimizes the configuration of the services, performs customer behavior analysis, predicts customers' future purchasing patterns, adjusts inventory and marketing resources, integrates marketing and customer service data, plans business development processes, and generates customer acquisition development plans.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the customer acquisition process is optimized through data analysis and behavior prediction. It does not rely on traditional marketing methods, but accurately analyzes user data through sequence models to achieve an in-depth understanding of user behavior. This refined analysis improves the accuracy of market segmentation, ensures that marketing activities match the target customer group, and significantly improves the conversion rate. By using intent recognition and real-time data analysis, marketing methods can be quickly adjusted to ensure synchronization with user needs. By continuously integrating customer feedback and marketing data, the customer development cost of physical stores is reduced, solving the problem of difficulty in customer acquisition for physical store operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A detailed flow chart of step S1 of the present invention; Figure 3 A detailed flow chart of step S2 of the present invention; Figure 4 A detailed flow chart of step S3 of the present invention; Figure 5A detailed flow chart of step S4 of the present invention; Figure 6 A detailed flow chart of step S5 of the present invention; Figure 7 A detailed flow chart of step S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise specified.

[0020] Embodiment 1 See also Figure 1 The present invention provides a technical solution: an AI intelligent customer acquisition processing method, comprising the following steps: S1: Collect the interaction data between users and the platform, summarize the click, browsing time and frequency information, use RNN and LSTM networks to extract user activity patterns, analyze user interests and behavior patterns, and generate a user behavior feature library; S2: Based on the user behavior feature library, screen user groups with high purchasing potential, refine the market customer group, use the autoencoder to use the target customer information for targeted marketing activities, optimize the accuracy and conversion rate of marketing activities, and generate target market configuration; S3: Based on the target market configuration, target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using the intent recognition mechanism to record feedback data of marketing activities, and conducting marketing effect analysis to generate marketing response plans; S4: Based on marketing response planning, monitor and manage customer feedback information, including evaluation and purchase frequency, continuously optimize customer service experience, refine customer group stickiness, and generate service optimization configuration; S5: Based on service optimization configuration, conduct customer behavior analysis, predict customers' future purchasing patterns, adjust inventory and marketing resources, match marketing needs, and generate resource allocation information; S6: Based on resource allocation information, integrate marketing and customer service data, plan the business development process, analyze the data, refine the development direction, and generate a customer acquisition development plan.

[0021] The user behavior feature library includes interaction type, activity frequency, and interest point analysis results. The target market configuration includes consumption potential index, target customer identification results, and market response variables. The marketing response plan includes feedback efficiency, promotion success rate, and customer satisfaction indicators. The service optimization configuration includes service response time and customer retention rate. Resource allocation information includes resource utilization, allocation efficiency, and marketing demand response speed. The customer acquisition development plan includes key business indicators and long-term growth mechanisms.

[0022] See also Figure 2 , collect the interaction data between users and the platform, summarize the click, browsing time and frequency information, use RNN and LSTM networks to extract user activity patterns, and analyze user interests and behavior patterns. The specific steps to generate a user behavior feature library are as follows: S101: Collect the interaction data between users and the platform, including the number of clicks, browsing time and access frequency, classify and organize the data to obtain the user behavior data set; Based on the interaction data between users and the platform, detailed analysis is conducted, including the collection of specific data such as the number of clicks, browsing time, and access frequency. By integrating the data, a complete set of user behavior data sets can be constructed. The data sets provide a basic basis for subsequent behavior analysis. The construction of user behavior data sets is completed through a variety of data processing technologies, including data cleaning, data classification and other steps. Through a series of processing, a clear and usable user behavior data set is obtained. The data set provides an accurate input source for subsequent user behavior analysis and pattern recognition.

[0023] S102: Based on the user behavior data set, use the RNN and LSTM networks to filter active user data, perform time series analysis on the user's access frequency and duration, identify periodic behavior patterns, and obtain a behavior pattern feature set; In the above content, RNN and LSTM networks are used to filter active user data, according to the formula Calculate the behavior pattern feature set, where SY represents the score of the behavior pattern feature set, represents the user behavior data at time t, is the weight parameter at time t, T is the total number of time steps; considering 10 time steps, the user's behavior data at each time step is [1, 2, 3, 4, 2, 1, 5, 6, 2, 1], the weight parameter is set to a decreasing weight, decreasing from 1.0 to 0.1, and the calculation of the behavior pattern feature set is: ; This indicates that the user's behavioral characteristic score after considering the time weight is 14 points, reflecting the time sensitivity and activity level of his behavior.

[0024] S103: Based on the behavior pattern feature set, a feature storage structure is established, the extracted behavior features are classified and encoded, the availability and security of the data are verified, and a user behavior feature library is obtained; Based on the behavioral pattern feature set that has been screened and analyzed, establishing a feature storage structure is a process involving a lot of technical details. In this process, the behavioral features are classified and encoded. The classification process uses the clustering algorithm in machine learning, and the encoding converts the user behavior features into a format that is easier for computers to process based on the clustering results. In addition, verifying the availability and security of the data is also an important part of this step. This process ensures the validity of the data in subsequent applications and the security of data protection. The obtained user behavior feature library provides a reliable foundation for further data application and analysis.

[0025] See also Figure 3 Based on the user behavior feature library, we screen user groups with high purchasing potential, refine the market customer base, use the autoencoder to use the target customer information for targeted marketing activities, optimize the accuracy and conversion rate of marketing activities, and generate the target market configuration in the following steps: S201: Based on the user behavior feature library, the autoencoder is used to perform cluster analysis on the data, identify and mark user groups with high purchase potential, and obtain a potential customer feature set through comparative analysis of user behavior and purchase history; In the above content, the autoencoder is used to perform cluster analysis on the data, according to the formula Calculate the potential customer feature set, where U represents the cumulative score of the potential customer feature set, represents the behavioral feature vector of customer i, represents the cluster center j, is the decay parameter, m is the number of cluster centers; if there are two cluster centers, the decay parameter , customer behavior feature vector , cluster center and , the calculation of potential customer feature set is: ; This indicates that the cumulative similarity score between this customer and the two cluster centers is 1.765, indicating high purchase potential.

[0026] S202: Based on the potential customer feature set, perform coding processing, extract and optimize the features of the target customer information, adjust the targeting of the marketing activities, and obtain optimized customer information; Based on the feature set of potential customers, the detailed process of performing encoding processing includes feature extraction and optimization of target customer information, using autoencoders for dimensionality reduction to more accurately capture key features, and then adjusting marketing strategies based on the features to ensure that marketing activities are more targeted. For example, by analyzing purchase history and user behavior, the most effective promotion methods and communication channels can be determined, and then the responses of different customer groups can be predicted through machine learning models to optimize the content and format of promotional information. The optimized customer information obtained will be directly used to improve the accuracy and conversion rate of marketing activities.

[0027] S203: Based on the optimized customer information, apply it to marketing activities, adjust marketing methods, analyze customer feedback information, and dynamically optimize marketing effects to obtain target market configuration; Based on the optimized customer information, the execution of marketing activities includes the adjustment of marketing methods and the analysis of customer feedback information. By regularly analyzing the marketing activity data, we can identify which strategies are most effective and which need to be improved. By comparing the effects of different marketing information, we can adjust the advertising strategy and content in real time. We collect and analyze customer feedback, such as satisfaction surveys and online reviews, to dynamically optimize marketing effects. The generated target market configuration not only reflects the current market demand, but also indicates future marketing trends, thereby more accurately positioning the market and customers.

[0028] See also Figure 4 Based on the target market configuration, target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using the intention recognition mechanism to record the feedback data of marketing activities, and conducting marketing effect analysis. The specific steps for generating a marketing response plan are as follows: S301: Based on the target market configuration, implement activity promotion, send preferential information and product recommendations via email and social media platforms, record and analyze the reception rate and user interaction data, and obtain the market response data set; Promotion based on target market configuration involves the process of sending preferential information and product recommendations via email and social media platforms. It requires setting up a precise target group screening mechanism, determining the characteristics of potential customers such as age, interests and purchase history based on existing market data analysis, and then using marketing automation tools to create and send marketing content. At the same time, setting tracking codes to monitor user clicks and interactive behaviors. After data collection, cluster analysis is required through data analysis tools to determine which information or products receive more attention, and then adjust future marketing activity strategies to ensure that the content provided is better matched with user needs, thereby improving market response rate and the effectiveness of user interaction data, and obtaining a more comprehensive market response data set.

[0029] S302: Based on the market response data set, the intention recognition mechanism is used to analyze the customer interaction information obtained from the response data, including click-through rate and reply time, to identify the customer's purchase intention and participation, and adjust the marketing information content to obtain the activity feedback optimization results; Intent recognition mechanism, according to the formula: ; Calculate the customer's purchase intention, where represents the purchase intention of the i-th customer, represents the click rate of the i-th customer, represents the response time of the i-th customer, and coefficients representing click-through rate and response time, is the number of customers; set up and , taking the data of three customers as an example, we calculate: Client 1: , Second Client 2: , Second Client 3: , Second Calculate the purchase intention of each customer: ; The calculation results show each customer's purchase intention based on the interaction data. This result can provide data support for further marketing strategies and optimize customer feedback by adjusting the information content.

[0030] S303: Based on the optimization results of activity feedback, optimize marketing information and promotion channels, and by comparing the effects of differential promotions, refine the matching between promotion plans and target customers, and generate a marketing response plan; The process of optimizing marketing information and promotion channels based on activity feedback optimization results includes comparing the effectiveness of different promotion strategies through A / B testing. First, design two or more different versions of marketing information, such as different titles or content layouts, and then randomly assign different versions to different target customer groups. Use data tracking tools to record the performance of each version, such as click-through rate and conversion rate. After data analysis, identify the best version and then fully adopt the best strategy. At the same time, continue to monitor market feedback and dynamically adjust the promotion plan based on real-time customer feedback to ensure maximum marketing effectiveness and match with target customers, and generate a marketing response plan.

[0031] See also Figure 5 Based on the marketing response plan, customer feedback information is monitored and managed, including evaluation and purchase frequency, to continuously optimize customer service experience and refine customer group stickiness. The specific steps for generating service optimization configuration are as follows: S401: Based on marketing response planning, monitor customers’ online reviews and purchase frequency, identify key issues in services, optimize user experience, and obtain customer feedback monitoring results; In the above content, monitor the customer's online evaluation and purchase frequency, according to the formula Calculate the customer feedback monitoring results, where CX represents the average score of the customer feedback monitoring results. represents the evaluation level of the i-th customer, Represents the corresponding purchase frequency, is the total number of customers who evaluated; the evaluation levels of the five customers are 4, 3, 5, 2 and 4, and the corresponding purchase frequencies are 1, 3, 2, 1 and 2. The total number of customers , then the calculation of customer feedback monitoring results is: ; It shows that the average score based on customers' online reviews and purchase frequency is 6.6 points.

[0032] S402: Based on the customer feedback monitoring results, adjust the customer service process, including optimizing the purchase process and response speed, and evaluate the service improvement effect based on the customer satisfaction survey to obtain the service process improvement results; Based on the customer feedback monitoring results, the service process is adjusted, including optimizing the purchase process and improving the response speed. The adjustment is based on a detailed customer satisfaction survey. Through the survey results, the service team can identify and solve the shortcomings in the service, thereby improving the overall service quality. The improved service process can respond to customer needs more quickly, reduce customer waiting time, and enhance customer purchasing experience. Through service process adjustment, the service process improvement results obtained can significantly improve the overall customer satisfaction.

[0033] S403: Based on the service process improvement results, customized services and value-added activities are implemented to optimize customer group stickiness and obtain service optimization configuration; Based on the results of service process improvement, customized services and value-added activities are implemented. The measures are aimed at optimizing customer group stickiness. By providing more personalized services and targeted value-added activities, customer loyalty and satisfaction can be effectively improved. Services and activities are customized according to customer feedback and purchase history to ensure that each service can accurately meet customer needs, thereby forming a differentiated competitive advantage in the market. The resulting optimized service configuration is optimized through continuous market feedback and service adjustments, which enhances the company's responsiveness to market dynamics and the degree to which it meets customer needs.

[0034] See also Figure 6 , based on service optimization configuration, conduct customer behavior analysis, predict customers' future purchasing patterns, adjust inventory and marketing resources, match marketing needs, and generate resource allocation information in the following specific steps: S501: Based on the service optimization configuration, analyze the customer's historical purchase data and interaction records, identify consumption patterns and product preferences, predict future purchase trends, and update inventory in real time to obtain purchase trend forecast information; In the above content, based on the service optimization configuration analysis of customer historical purchase data and interaction records, according to the formula Calculate the purchase trend forecast information, where PE represents the purchase trend forecast information, Represents the weighted analysis results of historical purchase data, Represents the weighted analysis results of the interaction records, and is the influence weight of historical purchase data and interaction records, and The value of is obtained through statistical regression analysis of historical data. and , historical purchase data 50, interactive records is 30, the calculation of PE for buying trend forecast information is: ; Indicates that based on the given historical purchase and interaction data, the purchase trend prediction information score is 42 points.

[0035] S502: Optimize inventory allocation based on purchase trend forecast information, dynamically adjust inventory for high-liquidity products, and reallocate marketing resources to obtain inventory resource data; Based on the purchase trend forecast information, inventory allocation optimization is carried out, including dynamic inventory adjustment of high-liquidity goods. The adjustment is based on detailed data analysis. By updating the inventory system in real time, it ensures that the inventory is consistent with market demand. At the same time, marketing resources are reallocated to better adapt to market changes and improve resource utilization efficiency.

[0036] S503: Promote potential hot-selling products based on inventory resource data, optimize market response through targeted advertising and promotional activities, adjust marketing activities, and generate resource allocation information; Based on inventory resource data, we carried out promotions for potential hot-selling products, including optimizing market response through targeted advertising and promotional activities. The adjustment of marketing activities was based on in-depth market analysis and customer behavior research. Through targeted marketing strategies, we were able to more effectively reach the target customer group, increase product penetration and sales, and thus achieve optimal resource allocation.

[0037] See also Figure 7 Based on resource allocation information, integrate marketing and customer service data, plan the business development process, analyze the data, refine the development direction, and generate the customer acquisition development plan. The specific steps are: S601: Based on resource allocation information, integrate multi-channel marketing and customer service data, analyze the data, identify improvement points and growth points in business processes, and obtain business data analysis records; The process of integrating multi-channel marketing and customer service data based on resource allocation information includes data collection and summarization, determining the type of data to be integrated, including sales data, customer feedback, online interaction records, etc., and then using data warehouse technology to merge data from different channels, using data cleaning technology to remove duplicate or erroneous records, and classifying and labeling data for better analysis. Through data analysis tools, such as SQL queries and Python data analysis libraries, in-depth data analysis is performed to identify improvement points and growth points in business processes, thereby forming business data analysis records, which will provide decision support for subsequent business development.

[0038] S602: Based on the business data analysis records, plan the business development process, determine the key business development direction and priority, determine the priority and resource allocation, and obtain the business optimization plan; In the above content, the business development process is planned based on business data analysis and records, according to the formula Calculate the business optimization plan, where BH represents the comprehensive score of the business optimization plan, represents the weight of the ith business direction, represents the score of the ith business direction, is the number of business directions; there are three business directions, and their weights , , , the first business direction score , the second business direction score , the third business direction score , the calculation of business optimization planning is: ; It shows that based on the given weight and business direction score, the business optimization planning score is 63 points, providing a decision-making basis for further action steps.

[0039] S603: Based on the business optimization plan, refine the action steps, implement targeted marketing and customer relationship management, optimize customer acquisition and maintenance efficiency, and obtain a customer acquisition development plan; Based on the business optimization plan, analyze the key action directions determined in the business optimization plan, such as enhancing customer service experience or increasing product market penetration, and then design specific marketing activities based on the directions, such as email marketing, social media advertising, etc. At the same time, use the CRM system to optimize customer information management to ensure that feedback and improvement points can be collected for each customer interaction. Through data analysis, continuously monitor the effectiveness of marketing activities, adjust strategies in a timely manner, form a customer acquisition development plan, and ensure the optimal allocation and utilization of resources.

[0040] See also Figure 8 , an AI intelligent customer acquisition processing system, the system includes: The user behavior analysis module collects the interaction data between users and the platform, including the number of clicks, browsing time and frequency, and uses recurrent neural networks and long short-term memory networks to process the data, analyze user interests and behavior patterns, and obtain a user behavior feature library; The target market positioning module screens user groups with high purchasing potential based on the user behavior feature library, refines the market customer base, uses autoencoders to optimize the information processing flow of target customers, optimizes the accuracy and conversion rate of marketing activities, and generates target market configurations; The marketing campaign optimization module executes targeted promotional activities based on target market configuration, including sending preferential information and product recommendations, using the intent recognition mechanism to record and analyze the feedback of marketing activities, and generate marketing response plans; The customer service improvement module monitors and manages customer feedback information, including evaluation and purchase frequency, based on marketing response planning, continuously optimizes customer service experience, refines customer group stickiness, and generates service optimization configuration; The resource and planning integration module conducts customer behavior analysis based on service optimization configuration, predicts customers' future purchasing patterns, adjusts inventory and marketing resources, and integrates marketing and customer service data to plan business development processes and generate customer acquisition development plans.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An AI intelligent customer acquisition processing method, characterized in that: The following steps are involved: Collect the interaction data between users and the platform, summarize the click, browsing time and frequency information, use RNN and LSTM networks to extract user activity patterns, analyze user interests and behavior patterns, and generate a user behavior feature library; Based on the user behavior feature library, screen user groups with high purchasing potential, refine the market customer group, use the autoencoder to use the target customer information for targeted marketing activities, optimize the accuracy and conversion rate of marketing activities, and generate target market configuration; Based on the target market configuration, target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using intent recognition mechanism to record feedback data of marketing activities, and conducting marketing effect analysis to generate marketing response plans; Based on the marketing response plan, monitor and manage customer feedback information, including evaluation and purchase frequency, continuously optimize customer service experience, refine customer group stickiness, and generate service optimization configuration; Based on the service optimization configuration, conduct customer behavior analysis, predict customers' future purchasing patterns, adjust inventory and marketing resources, match marketing needs, and generate resource allocation information; Based on the resource allocation information, marketing and customer service data are integrated to plan the business development process, analyze the data, refine the development direction, and generate a customer acquisition development plan.

2. The AI ​​intelligent customer acquisition processing method according to claim 1 is characterized in that: The user behavior feature library includes interaction type, activity frequency, and interest point analysis results; the target market configuration includes consumption potential index, target customer identification results, and market response variables; the marketing response plan includes feedback efficiency, promotion success rate, and customer satisfaction indicators; the service optimization configuration includes service response time and customer retention rate; the resource allocation information includes resource utilization, allocation efficiency, and marketing demand response speed; the customer acquisition development plan includes key business indicators and long-term growth mechanisms.

3. The AI ​​intelligent customer acquisition processing method according to claim 1 is characterized in that: Collect the interaction data between users and the platform, summarize the click, browsing time and frequency information, use RNN and LSTM networks to extract user activity patterns, and analyze user interests and behavior patterns. The specific steps to generate a user behavior feature library are as follows: Collect data on user interaction with the platform, including clicks, browsing time, and access frequency, and classify and organize the data to obtain a user behavior data set; Based on the user behavior data set, the RNN and LSTM networks are used to filter active user data, perform time series analysis on the user's access frequency and duration, identify periodic behavior patterns, and obtain a behavior pattern feature set; Based on the behavior pattern feature set, a feature storage structure is established, the extracted behavior features are classified and encoded, the availability and security of the data are verified, and a user behavior feature library is obtained.

4. The AI ​​intelligent customer acquisition processing method according to claim 1 is characterized in that: Based on the user behavior feature library, user groups with high purchasing potential are screened, market customer groups are refined, and target customer information is used for targeted marketing activities using autoencoders to optimize the accuracy and conversion rate of marketing activities. The specific steps for generating target market configuration are as follows: Based on the user behavior feature library, cluster analysis is performed on the data using an autoencoder to identify and mark user groups with high purchase potential, and a potential customer feature set is obtained through comparative analysis of user behavior and purchase history; Based on the potential customer feature set, perform coding processing, extract and optimize the features of target customer information, adjust the targeting of marketing activities, and obtain optimized customer information; Based on the optimized customer information, it is applied to marketing activities, marketing methods are adjusted, customer feedback information is analyzed, and marketing effects are dynamically optimized to obtain target market configuration.

5. The AI ​​intelligent customer acquisition processing method according to claim 1 is characterized in that: Based on the target market configuration, target customers are matched through targeted promotion activities, including sending preferential information and product recommendations, using the intent recognition mechanism to record feedback data of marketing activities, and conducting marketing effect analysis. The specific steps for generating a marketing response plan are as follows: Based on the target market configuration, implement promotion activities, send preferential information and product recommendations via email and social media platforms, record and analyze reception rates and user interaction data, and obtain market response data sets; Based on the market response data set, an intent recognition mechanism is used to analyze customer interaction information obtained from the response data, including click-through rate and reply time, to identify customer purchase intention and engagement, and to adjust marketing information content to obtain activity feedback optimization results; Based on the optimization results of the activity feedback, optimize the marketing information and promotion channels, and generate a marketing response plan by comparing the effects of differential promotions, refining the matching of promotion plans and target customers.

6. The AI ​​intelligent customer acquisition processing method according to claim 5 is characterized in that: The intent recognition mechanism is based on the formula: ； Calculate the customer's purchase intention, where represents the purchase intention of the i-th customer, represents the click rate of the i-th customer, represents the response time of the i-th customer, and coefficients representing click-through rate and response time, is the number of customers.

7. The AI ​​intelligent customer acquisition processing method according to claim 1, characterized in that: Based on the marketing response plan, customer feedback information is monitored and managed, including evaluation and purchase frequency, customer service experience is continuously optimized, and customer group stickiness is refined. The specific steps for generating service optimization configuration are as follows: Based on the marketing response plan, monitor customers' online reviews and purchase frequency, identify key issues in the service, and optimize user experience to obtain customer feedback monitoring results; Based on the customer feedback monitoring results, adjust the customer service process, including optimizing the purchase process and response speed, and evaluate the service improvement effect based on customer satisfaction surveys to obtain service process improvement results; Based on the service process improvement results, customized services and value-added activities are implemented to optimize customer group stickiness and obtain optimized service configuration.

8. The AI ​​intelligent customer acquisition processing method according to claim 1, characterized in that: Based on the service optimization configuration, the steps of conducting customer behavior analysis, predicting customers' future purchasing patterns, adjusting inventory and marketing resources, matching marketing needs, and generating resource allocation information are as follows: Based on the service optimization configuration, analyze the customer's historical purchase data and interaction records, identify consumption patterns and product preferences, predict future purchase trends, and update inventory in real time to obtain purchase trend forecast information; According to the purchase trend forecast information, optimize inventory allocation, dynamically adjust inventory for high-liquidity products, and reallocate marketing resources to obtain inventory resource data; Based on the inventory resource data, potential hot-selling products are promoted, market response is optimized through targeted advertising and promotional activities, marketing activities are adjusted, and resource allocation information is generated.

9. The AI ​​intelligent customer acquisition processing method according to claim 1, characterized in that: Based on the resource allocation information, integrate marketing and customer service data, plan the business development process, analyze the data, refine the development direction, and generate the customer acquisition development plan in the following steps: Based on the resource allocation information, integrate multi-channel marketing and customer service data, analyze the data, and identify improvement points and growth points in the business process to obtain business data analysis records; Based on the business data analysis records, plan the business development process, determine the key business development direction and priority, determine the priority and resource allocation, and obtain the business optimization plan; Based on the business optimization plan, refine the action steps, implement targeted marketing and customer relationship management, optimize customer acquisition and maintenance efficiency, and obtain a customer acquisition development plan.

10. An AI intelligent customer acquisition processing system, characterized in that: According to the AI ​​intelligent customer acquisition processing method according to any one of claims 1 to 9, the system comprises: The user behavior analysis module collects the interaction data between users and the platform, including the number of clicks, browsing time and frequency, and uses recurrent neural networks and long short-term memory networks to process the data, analyze user interests and behavior patterns, and obtain a user behavior feature library; The target market positioning module screens user groups with high purchasing potential based on the user behavior feature library, refines the market customer groups, uses autoencoders to optimize the information processing flow of target customers, optimizes the accuracy and conversion rate of marketing activities, and generates target market configurations; The marketing activity optimization module executes targeted promotion activities based on the target market configuration, including sending preferential information and product recommendations, using the intent recognition mechanism to record and analyze the feedback of the marketing activities, and generate a marketing response plan; The customer service improvement module monitors and manages customer feedback information, including evaluation and purchase frequency, based on the marketing response plan, continuously optimizes customer service experience, refines customer group stickiness, and generates service optimization configuration; The resource and plan integration module optimizes the configuration of the services, performs customer behavior analysis, predicts customers' future purchasing patterns, adjusts inventory and marketing resources, integrates marketing and customer service data, plans business development processes, and generates customer acquisition development plans.

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