Intelligent customer acquisition and user behavior analysis system based on AI full ecology

Abstract

The invention discloses an intelligent customer acquisition and user behavior analysis system based on AI full ecology, and relates to the technical field of artificial intelligence and big data marketing, and the system comprises a multi-source data collection module which collects internal and external multi-channel data of an enterprise; the data cleaning module processes abnormal and missing values and unifies data formats; the user portrait construction module extracts multi-dimensional labels to describe user features; the intelligent customer obtaining module uses transfer learning to screen high-potential customers, and the user behavior analysis module mines behavior logic by means of Transform; the intelligent recommendation engine is fused with multiple algorithms to realize personalized recommendation, and the real-time decision module is combined with rules and reinforcement learning to optimize marketing. According to the method, multi-source data integration and deep analysis are realized, clients are accurately identified, behavior requirements are mined, the client obtaining efficiency and the conversion rate are improved, the marketing effect is optimized through personalized recommendation and intelligent decision, and a whole-process intelligent marketing solution from data acquisition to decision execution is provided for enterprises.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and big data marketing technology, and in particular to an intelligent customer acquisition and user behavior analysis system based on the entire AI ecosystem. Background Technology

[0002] In the wave of digital marketing, enterprises have an increasingly urgent need for intelligent customer acquisition and user behavior analysis, but existing technologies have many bottlenecks and are unable to meet the requirements of the rapidly changing market.

[0003] Traditional customer acquisition models rely on manual screening of customer leads and marketing outreach through methods such as telemarketing and mass email campaigns. This approach is not only inefficient but also wastes significant marketing resources and results in high customer acquisition costs due to a lack of accurate customer profiling and needs analysis. Furthermore, enterprise data is scattered across multiple independent systems. For example, Customer Relationship Management (CRM) systems record basic information and transaction data, e-commerce platforms store purchase behavior data, and social media platforms store user interests and social data. These data have varying formats and inconsistent standards, resulting in severe data silos. This makes it difficult for enterprises to integrate multi-dimensional information, hindering the formation of a comprehensive understanding of customers and limiting the implementation of targeted marketing.

[0004] In the field of user behavior analysis, existing technologies mostly remain at the level of basic data statistics. For example, they can only count superficial data such as page views, clicks, and dwell time, failing to delve into the underlying needs and behavioral logic behind user behavior. Traditional analysis methods are insufficient in handling complex temporal dependencies in user behavior sequences, making it difficult to predict users' next actions and providing forward-looking decision support for enterprises. Furthermore, most recommendation systems use a single recommendation algorithm, such as collaborative filtering or content-based recommendations, ignoring real-time changes and personalized characteristics of user behavior. The recommendation results lack targeting and diversity, leading to poor user experience and hindering effective user conversion and retention.

[0005] While artificial intelligence (AI) technology has been gradually applied to the marketing field, significant shortcomings remain. Some AI systems optimize only a single aspect, such as standalone customer prediction models or recommendation modules, lacking an ecosystem that integrates multiple technologies and thus failing to achieve end-to-end intelligent management from data collection and analysis to decision-making execution. Furthermore, with increasingly stringent data privacy regulations, cross-enterprise data collaboration faces the dual risks of legal compliance and privacy breaches. Existing technologies struggle to achieve value sharing of multi-party data while ensuring data security, limiting the depth and breadth of enterprises' customer insights and hindering the further development of intelligent marketing. Summary of the Invention

[0006] The present invention proposes an intelligent customer acquisition and user behavior analysis system based on the entire AI ecosystem to solve the problems mentioned in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] An intelligent customer acquisition and user behavior analysis system based on the entire AI ecosystem, comprising:

[0009] Multi-source data acquisition module: Deploys distributed acquisition nodes, connects with enterprise CRM and ERP systems through API interfaces to obtain customer data, uses SDK tracking technology to collect user interaction behavior on mobile devices, and uses incremental crawlers to crawl media data in a targeted manner;

[0010] Data cleaning and preprocessing module: Deduplication is performed using a Bloom filter. Outliers are marked when the average path length from a sample to the root node is less than the threshold μ-3σ, where μ is the average path length and σ is the standard deviation. After cleaning, the data is stored in the Hadoop Distributed File System in Parquet format.

[0011] User profile building module: One-Hot encoding vectorizes categorical features, quantile discretization processes continuous features, and the GraphSAGE algorithm maps entities to a 256-dimensional vector space. User similarity is calculated using a formula. u i u j For two different users, vec(u i ),vec(u j ) is the user feature vector;

[0012] Intelligent Customer Acquisition Module: The XGBoost algorithm initially screens leads based on user base, behavior, and external market data; it then uses DANN transfer learning technology to transfer customer characteristics from leading companies, and outputs the customer conversion probability (P) via the LightGBM model. convert =f(X) user , W), X user A user vector containing 128-dimensional features, where W is the weight matrix;

[0013] User behavior analysis module: The user behavior sequence is divided using the Session-Based method, and the behavior sequence modeling adopts the Transformer architecture;

[0014] The intelligent recommendation engine module: The content-based recommendation part uses the BERT-CNN model to extract product features, concatenates them with user profile features, and then inputs them into a multilayer perceptron to calculate the matching degree; the collaborative filtering part introduces a time decay factor. t represents the number of days since the action occurred;

[0015] Real-time decision-making and automated marketing module: A dual-driven framework of rule engine and reinforcement learning is built. The former supports rule configuration; the latter has a state space containing 50-dimensional information and an action space with 8 marketing channels.

[0016] Data visualization and report generation module: Based on ECharts and AntV, a component library is built to provide a drag-and-drop report designer. Users select indicators and dimensions from the metadata center, and the system automatically generates SQL query statements.

[0017] System management and optimization module: Deploys Prometheus + Grafana for monitoring, issues alerts when metrics exceed limits for 5 consecutive minutes, and uses an automated A / B testing framework to allocate traffic via Nginx.

[0018] Furthermore, it also includes a multimodal data fusion module: at the hardware level, a microphone array and camera module are deployed, and after removing silent segments using VAD, the data is input into the Wav2Vec2.0 model to extract 768-dimensional acoustic features; image data is processed by the MTCNN algorithm to detect face regions, and then ResNet50 is used to extract 2048-dimensional visual features; cross-modal alignment uses a Transformer-based alignment network, and the loss function is... In the middle, L align The cross-modal alignment loss value; N is the number of samples; i is the sample index; x i For the i-th text sample, f text f audio These are text and speech feature extraction functions, respectively.

[0019] Furthermore, it also includes a customer churn warning module: constructing an integrated learning warning model, integrating three base models: Random Forest, LightGBM, and XGBoost, and fusing the models through the Stacking method, adopting the Cox proportional hazards model in survival analysis, in the formula S(t|X)=exp(-Λ(t)exp(Xβ)), where S(t|X) is the user retention probability at time t, Λ(t) is the baseline risk function, X is a user vector containing 50-dimensional features, and β is the regression coefficient.

[0020] Furthermore, in the intelligent customer acquisition module, a horizontal federated learning architecture is built based on TensorFlow Federated. Each participant trains a LightGBM model locally using its own customer data, and only uploads the model gradient parameters to the central server. The central server updates the global model parameters through a weighted aggregation algorithm. The objective function is... Where K is the number of participating companies, ω i L represents the percentage of data volume for the i-th enterprise. i (θ) is the loss function of the i-th enterprise local model. The system has a built-in differential privacy mechanism, adding Laplace noise before parameter upload. ∈ represents the privacy budget, and Δf represents the sensitivity.

[0021] Furthermore, in the user behavior analysis module, the formula for calculating the conversion rate of the behavior path is: N start N represents the initial number of actions. end To determine the number of actions to be terminated; a cause-and-effect graph of user behavior is constructed based on the Do-Calculus theory, and the formula ACE = Σ is adjusted through a backdoor. x′ ∑ w P(Y|do(X=x),W=w)P(W=w)-Σ x′ ∑ w P(Y|do(X=x′),W=w)P(W=w)

[0022] The average causal effect of the behavioral intervention is calculated, where ACE is the average causal effect, X is the intervention variable, Y is the outcome variable, and W is the set of confounding variables. The system automatically detects backdoor paths in the causal graph. When there are unblocked confounding factors, bias is eliminated by propensity score matching. The analysis results are used to guide product iteration.

[0023] Furthermore, in the intelligent recommendation engine module, the weights of the two recommendation results are adjusted through an attention mechanism, using the following formula: h i W is the eigenvector. a A 128×128 learnable matrix is ​​used, where n is the total number of feature vectors. A knowledge graph containing entities is constructed based on Neo4j, and node embeddings are learned through a graph neural network GraphSAGE. The recommendation model inputs the knowledge graph embedding vector vec(G), the user behavior sequence vector vec(u), and the product feature vector vec(i) into a multilayer perceptron, and outputs a recommendation score r. u，i =f(vec(u), vec(i), vec(G)), the system supports interpretable display and generates recommendation reasons through knowledge graph path search.

[0024] Furthermore, considering the eight marketing channels as the arms of a multi-armed slot machine, the Thompson sampling algorithm is used to dynamically select the delivery strategy, and the posterior distribution of the conversion rate for each arm is modeled as a Beta distribution θ. a ~Beta(α) a ,β a ), where α a Increment the success rate by 1, β a The number of failures is incremented by 1, the posterior distribution parameters are updated every hour, and the marketing budget is allocated based on the sampling results.

[0025] Furthermore, in the data visualization and report generation module, **natural language generation** technology is used to automatically generate analysis reports. Based on the pre-trained T5-Base model, it is fine-tuned on the dataset. The system encodes data indicators, dimensional information, and business rules into input sequences, generates natural language text through beamsearch, and introduces a template fusion mechanism to combine the generated text with preset report templates, supporting multi-language output.

[0026] Furthermore, in the system management and optimization module, an automated operation and maintenance system is built. Based on Prometheus, system hardware, software and business metrics are collected and visualized using Grafana. The LSTM-Seq2Seq model is used to predict system load. Inputting 7 days of historical minute-level metric data, the system predicts the load situation for the next hour. In terms of model optimization, the model monitoring module detects data drift and concept drift. When drift is detected, new data is automatically extracted from the data lake for incremental training.

[0027] Furthermore, during the data acquisition phase, differential privacy technology is used to add Laplace noise to user behavior data, with the noise intensity adjusted according to data sensitivity. During the data storage phase, homomorphic encryption technology is used to encrypt sensitive data. During the model training phase, a secure multi-party computation protocol is used for joint data modeling. The system dynamically allocates permissions based on user roles, data tags, and operation scenarios through a federated identity authentication and access control mechanism.

[0028] Compared with existing technologies, the beneficial effects of this invention are:

[0029] In terms of data processing, the system achieves efficient collection and deep integration of multi-source heterogeneous data, breaking down the limitations of data silos. Through a unified data cleaning and preprocessing workflow, data quality is effectively improved, laying a solid foundation for subsequent precise analysis. Dynamic user profiles built upon this high-quality data can comprehensively depict user characteristics from over 300 dimensions, enabling enterprises to gain a deep understanding of user needs and preferences.

[0030] In the field of intelligent customer acquisition, the system utilizes advanced algorithms such as transfer learning and ensemble learning to accurately identify high-potential customers, transforming the traditional extensive customer acquisition model. By accurately predicting customer conversion probabilities, businesses can concentrate their marketing resources on the most valuable customer groups, significantly improving customer acquisition efficiency and reducing marketing costs. Simultaneously, personalized marketing outreach strategies can effectively enhance customer attention and response rates.

[0031] The user behavior analytics module, leveraging the Transformer architecture and causal inference technology, can not only process large-scale time-series behavioral data but also uncover causal relationships between user behaviors, helping businesses accurately grasp the evolving trends of user needs. This enables businesses to specifically optimize product design, improve user experience, and adjust marketing strategies, thereby increasing user satisfaction and loyalty.

[0032] The intelligent recommendation engine integrates multiple recommendation algorithms and knowledge graph technologies, enabling it to provide diverse and interpretable recommended content based on users' real-time behavior and personalized characteristics. This not only enhances user engagement and experience but also effectively promotes user conversion, increasing enterprise sales and profits.

[0033] The real-time decision-making and automated marketing module enables intelligent triggering of marketing campaigns and dynamic strategy optimization. The system can automatically adjust marketing channels, content, and timing based on changes in user status and market environment to maximize marketing ROI. The application of privacy-preserving computation and federated learning technologies ensures secure and compliant data sharing across enterprises, expanding the depth and breadth of enterprise customer insights.

[0034] Furthermore, the system's data visualization and automated operation and maintenance functions lower the barrier to entry and maintenance costs, enabling enterprises to more easily monitor marketing effectiveness and optimize system performance. This application's technology provides enterprises with a full-chain intelligent solution from data collection and analysis to decision execution, comprehensively enhancing their marketing competitiveness and market responsiveness. Attached Figure Description

[0035] Figure 1 This is a schematic block diagram of the intelligent customer acquisition and user behavior analysis system based on the entire AI ecosystem proposed in this invention;

[0036] Figure 2 A radar chart comparing the effectiveness of traditional customer acquisition methods with the intelligent customer acquisition system of this system;

[0037] Figure 3 A combined graph showing the comparison of data processing throughput and model inference latency before and after optimization;

[0038] Figure 4 A line chart comparing the dynamic optimization of marketing channel ROI between multi-armed slot machine algorithms and fixed-budget strategies. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0041] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0042] Reference Figures 1 to 4 A smart customer acquisition and user behavior analysis system based on the entire AI ecosystem, comprising the following modules:

[0043] Multi-source data acquisition module: The system achieves multi-channel data acquisition through a distributed acquisition architecture. For internal enterprise system integration, a RESTful API interface is used to connect with the CRM system, synchronizing structured data such as customer basic information and transaction records hourly; ETL tools (such as Apache NiFi) are used to extract related data such as orders and inventory from the ERP system. For mobile applications, a self-developed SDK is embedded to collect user behavior data, covering 23 types of interaction behaviors including click coordinates, swipe trajectories, and page jump paths, with real-time reporting and data format conforming to the JSON standard. For external data acquisition, a distributed crawler cluster is deployed, and a targeted crawler is developed based on the Scrapy framework. By setting up a User-Agent pool and an IP proxy pool to bypass website anti-crawling mechanisms, unstructured data such as product reviews from e-commerce platforms and social media topic discussions are automatically crawled every morning at midnight. The system builds a message queue (Kafka) on an Alibaba Cloud ECS server cluster as a buffer layer for data acquisition. Each Topic corresponds to one data type, and the number of partitions is set to 8 to meet high throughput requirements. The data collection nodes push data to Kafka in real time. When the data traffic of a single node exceeds 5MB / s, the load balancer (Nginx) automatically distributes the data collection tasks to the backup nodes to ensure that the system can stably process 120,000 data streams per second.

[0044] Data Cleaning and Preprocessing Module: The data cleaning process adopts a streaming architecture, building a processing pipeline based on Apache Flink. First, a Bloom filter is used to remove duplicate data. The Bloom filter uses 6 hash functions and a 1024KB bit array. For numerical data (such as transaction amount and dwell time), anomaly detection is performed using the Isolation Forest algorithm. The number of trees is set to 100, and the subsample size is 256. A sample is marked as an outlier when the average path length from the sample to the root node is less than μ-3σ, and normal values ​​calculated using box plots are used to replace it. In the missing value handling stage, SparkSQL is used to fill missing values ​​by statistical mode for categorical data (such as gender and region); time-series data (such as visit time series) is filled using an LSTM-AE model. The model's input layer has 128 neurons, and the hidden layers use two LSTM layers (64 units per layer). During training, the batch size is set to 32, the learning rate is 0.001, and the training lasts for 100 epochs. The cleaned data is converted to Parquet format, stored in the Hadoop Distributed File System (HDFS), and a three-tiered architecture is built in the Hive data warehouse: the raw data layer (ODS) retains the complete raw data, the cleaned data layer (DWD) stores the cleaned data, and the lightly aggregated data layer (DWS) aggregates statistical indicators by day, week, and month.

[0045] User profile construction module: In the feature engineering phase, the data is processed using Python's Scikit-learn library. Categorical features are converted into sparse vectors using One-Hot encoding; continuous features are discretized into 5 intervals using quantiles. A GraphSAGE model is built based on the DGL (DeepGraphLibrary) framework to learn node embeddings, mapping entities such as users, products, and brands to a 256-dimensional vector space. During model training, the neighbor sampling number is set to 10, the aggregation function is mean pooling, the optimizer is Adam, the learning rate is 0.002, and the training period is 7 days.

[0046] User similarity calculations combine cosine similarity and the Jaccard coefficient. The cosine similarity formula is as follows: Used to measure the distance between users in a vector space, where u i u j For two different users, vec(u i ),vec(u j The user feature vector is mapped to a 256-dimensional vector space using the GraphSAGE algorithm; the Jaccard coefficient calculates the user label overlap, and the formula is... Where A and B are user u i and u j The profile tagging system comprises 326 dimensions. Dynamic tags (such as real-time interests and preferences) are updated every 15 minutes via Flink stream computation, while static tags (such as demographic information) are automatically calibrated via batch processing tasks at midnight on the 1st of each month.

[0047] Intelligent Customer Acquisition Module: Constructs a three-tiered potential customer screening system: Initial screening uses the XGBoost model to filter massive amounts of leads. Input features include basic user information (age, gender, etc., 12 dimensions), historical behavioral data (visit frequency, dwell time, etc., 18 dimensions), and external market data (industry growth rate, competitor activity popularity, etc., 10 dimensions). During model training, the number of trees is set to 500, the learning rate is 0.05, the maximum depth is 6, and parameters are adjusted using 5-fold cross-validation to select the top 30% of leads for the next round.

[0048] The secondary screening employed Domain-Adversarial Neural Network (DANN) for transfer learning, transferring the characteristics of high-value customers from leading companies to the target customer group. The source domain data contained 100,000 high-value customer samples, while the target domain consisted of 500,000 clues after the initial screening. The discriminator used a 3-layer fully connected network (128 neurons per layer), with the adversarial loss weight set to 0.5 during training, selecting the top 20% of clues with the highest similarity to the source domain features.

[0049] Use the LightGBM model to output the customer conversion probability P. convert =f(X) user , W), where X user Let W be a user vector containing 128-dimensional features, and let W be the weight matrix obtained from model training. The model uses the GOSS (Gradient-based One-Side Sampling) algorithm to handle the sample imbalance problem, with a learning rate of 0.1, 300 trees, a maximum depth of 8, and customers with a predicted probability greater than 0.6 defined as high-potential customers.

[0050] For high-potential customers, the system uses the Freemarker template engine to generate personalized marketing copy, which includes dynamic variables (such as user name and browsed product names). Marketing channel selection employs a multi-armed slot machine algorithm (see the real-time decision module for details). The optimal time for sending messages is determined through historical data analysis (e.g., pushing app messages between 8-10 PM on weekdays), and A / B testing is used to compare the effectiveness of different copy and channel combinations.

[0051] The user behavior analysis module is a real-time behavior analysis engine built on the Flink streaming computing framework. It uses a session-based approach to segment user behavior sequences, considering a session ended when a user remains inactive for 30 minutes. Behavior sequence modeling employs a Transformer architecture with a 12-layer encoder, 8 attention heads, and 512 embedding dimensions. When calculating the behavior path conversion rate, a directed acyclic graph (DAG) is used to construct the user behavior trajectory. The formula for calculating the behavior path conversion rate is as follows: Where N start N represents the initial number of actions. end The number of ending actions, N start With N end Real-time statistics are obtained through the Redis counting service.

[0052] Specifically, a real-time behavior analysis platform is built based on Apache Flink, employing a session-based approach to segment user behavior sequences, with a session timeout set to 30 minutes. Behavior sequence modeling uses a Transformer architecture, containing a 12-layer encoder with 8 attention heads per layer and an embedding dimension of 512. Input data undergoes positional encoding before entering the model; the positional encoding formula is as follows: Where pos is the position, i is the dimension index, and d is the dimension index. modelFor embedding dimensions, the behavioral path conversion rate is calculated using a directed acyclic graph (DAG), with Redis's HyperLogLog data structure used to count the number of users at each node in real time. Shapley values ​​are introduced for attribution analysis to quantify the contribution of each behavioral node to the final conversion. When a node's contribution exceeds 30%, the system automatically generates optimization suggestions: for example, if a high drop-off rate is found in the "add to cart" to "checkout" stage, it is recommended to optimize the payment process and reduce the number of steps required.

[0053] The intelligent recommendation engine module employs a hybrid architecture. The content-based recommendation component uses the BERT-CNN model. The BERT pre-trained model utilizes Google's open-source Chinese model, freezing the parameters of the first six layers in downstream tasks while fine-tuning the last six. The CNN part consists of three convolutional layers (kernel sizes of 3, 4, and 5, with 128 channels), concatenating product text descriptions with user profile features before inputting them into the model to calculate the matching degree. The collaborative filtering part uses the matrix factorization (MF) algorithm and incorporates a time decay factor. (t is the number of days since the behavior occurred), to reduce the impact of outdated behaviors, the model is trained using stochastic gradient descent (SGD) with a learning rate of 0.01 for 100 epochs.

[0054] The formula fuses two recommendation results through an attention mechanism. In the middle, h i W is the eigenvector. a The learningable matrix is ​​128×128, and the parameters are optimized using the cross-entropy loss function during training. Recommendation results are processed using a cold-start strategy: new users are recommended based on popular listings (ranked by product sales), and new products are recommended based on semantics using a knowledge graph (finding related users for similar products). The final recommendation list is returned via a Redis cache service with a cache expiration time of 10 minutes, achieving a QPS of 5000+.

[0055] Real-time decision-making and automated marketing module: The rules engine is developed based on the Drools framework and supports a visual rules configuration interface. Rule types include time-triggered (e.g., automatically sending coupons on holidays), event-triggered (pushing a reminder 1 hour after a user adds items to their cart but doesn't pay), and condition-triggered (giving points for purchases over 500 yuan). Rule matching uses the Rete algorithm, and the system can process 100,000 rule matching requests per second.

[0056] The reinforcement learning part uses a deep Q-network (DQN) to optimize marketing decisions. The state space contains 70 dimensions of information, including user profile features (50 dimensions), historical marketing campaign data (20 dimensions), and the current time. The action space includes eight marketing channels, such as SMS, email, and push notifications. The reward function R... t =αC t +βR′ t-γC cost In this context, α = 0.4 (immediate conversion rate weight), β = 0.3 (long-term value weight), γ = 0.3 (cost weight), R t Let C be the reward value at time t. t R is the instantaneous conversion rate at time t. t Let ′ be the long-term value at time t, and Ccost be the marketing cost at time t. The model adopts an experience replay mechanism with a replay buffer size of 100,000 records, and updates the target network every 100 steps.

[0057] The marketing calendar module allows for campaign planning up to 6 months in advance, with execution orchestrated via the Airflow task scheduler. When a campaign's ROI falls below 15% of expectations, the system automatically triggers strategy adjustments: for example, reducing budgets for inefficient channels and increasing spending on high-conversion channels; or adjusting copywriting and finding the optimal version through A / B testing.

[0058] Data visualization and report generation module: The visualization components are developed based on ECharts 5.0 and AntVG6, providing 28 chart types. Sankey diagrams are used to display user behavior paths, with node sizes automatically adjusted according to the number of users; heatmaps can display click distribution by region and time; funnel charts support custom conversion steps and calculate conversion rates in real time. The report designer uses a drag-and-drop interface. After users select metrics (such as GMV, UV, conversion rate) and dimensions (time, region, channel) from the metadata center, the system automatically generates SQL query statements, connecting to the ClickHouse columnar database. The response time for complex reports (containing more than 10 aggregate calculations) is controlled within 3 seconds.

[0059] The Natural Language Generation (NLG) module is based on the T5-Base model and fine-tuned on a dataset containing 100,000 enterprise analysis reports. Input data includes indicator values, year-on-year / month-on-month changes, and dimensional information, which are then used to generate text via beamsearch (beamsize=3). A template fusion mechanism is introduced, for example: "This month's \({region}'s \){indicator} is \({value}, year-on-year \){percentage}, mainly due to ${reason}", where variables are automatically filled from the data. Multilingual output in Chinese, English, and Japanese is supported, with machine translation via Google Translate API followed by human review.

[0060] System Management and Optimization Module: The monitoring system is built on Prometheus + Grafana, and the collected metrics are divided into three categories:

[0061] Hardware metrics: CPU utilization, memory bandwidth, disk I / O;

[0062] Software metrics: Interface QPS, model inference latency, number of database connections;

[0063] Business metrics: Daily active users, conversion rate, GMV.

[0064] Set up a three-level alarm rule: send an alert SMS when the indicator exceeds the threshold by 80%; trigger an email notification when it exceeds 90%; and automatically suspend non-critical tasks and notify operations and maintenance personnel by phone when it exceeds 95%.

[0065] Automated A / B testing utilizes an Nginx reverse proxy for traffic allocation, with 90% of traffic going to the baseline version and 10% to the experimental version by default. Bayesian statistical methods are used to evaluate experimental performance; when the confidence level of the new solution improves the metrics by more than 95%, traffic is automatically switched to the new solution. For model optimization, data drift is detected by calculating the Kolmogorov-Smirnov (KS) distance between training and real-time data. When the KS value exceeds 0.2, incremental training is performed using new data from the past 7 days extracted from HDFS. Training employs a hybrid strategy of model parallelism (layer-based partitioning) and data parallelism (batch size set to 128). On an 8-GPU NVIDIA A100 cluster, the incremental training time for the LightGBM model was reduced from 4 hours to 1.5 hours.

[0066] This invention also includes a multimodal data fusion module: at the hardware level, a microphone array (6 microphones) and a camera module (4K resolution) are integrated on the Rockchip RK3588 development board. Voice data acquisition uses an 8kHz sampling rate and 16-bit quantization precision. After removing silent segments using the VAD algorithm, it is input into the Wav2Vec2.0 model to extract 768-dimensional acoustic features. Image data is processed using the MTCNN algorithm to detect face regions, and then ResNet50 extracts 2048-dimensional visual features.

[0067] Cross-modal alignment uses a Transformer-based alignment network, with a loss function... Where f text f is the text feature extraction function. audio Let be the speech feature extraction function, i be the sample index, N be the total number of samples involved in the calculation, and x be the number of samples. i For the i-th text data sample, y i Let be the i-th voice data sample. During training, the batch size is set to 16, the learning rate is 0.0001, and the training duration is 50 epochs. The multimodal features are mapped to a 1024-dimensional unified semantic space. The fused data is used to enhance user profiles. For example, by analyzing the consistency between the tone of voice and the emotion in the text of user comments, the true attitude of the user can be more accurately determined. If the tone of voice is angry but the text is neutral, the user's emotion label is set to "potential dissatisfaction".

[0068] This invention also includes a customer churn early warning module: the early warning model adopts an ensemble learning framework, integrating three base models: Random Forest (RF), LightGBM, and XGBoost, and using a stacking method for model fusion. The first layer of the model is trained independently, and the second layer uses logistic regression to perform secondary training with the output of the first layer as features. Input features include 68 dimensions of data, such as RFM metrics (recent purchase time, purchase frequency, purchase amount), service satisfaction rating, and number of complaints. The churn probability is calculated using the Cox proportional hazards model from survival analysis, in the formula S(t|X)=exp(-Λ(t)exp(Xβ)), where S(t|X) is the user retention probability at time t, Λ(t) is the baseline risk function, X is a user vector containing 68 dimensions of features, and β is the regression coefficient. Model training uses Python's lifelines library, solving for parameters through maximum likelihood estimation. When the predicted churn probability exceeds a threshold of 0.6, the system automatically triggers a three-level retention strategy:

[0069] Beginner: Send a personalized coupon for ¥20 off orders over ¥100;

[0070] Intermediate: A dedicated customer service representative will follow up within 24 hours.

[0071] Advanced: Offers free upgrades to your membership level.

[0072] In this invention, the intelligent customer acquisition module utilizes a horizontal federated learning framework built upon TensorFlow Federated (TFF), supporting data collaboration from up to 50 enterprises. Each enterprise trains a LightGBM model locally using its own customer data, with training parameters consistent with those in the intelligent customer acquisition module. The central server receives the model gradient parameters uploaded by each enterprise and updates the global model using a weighted aggregation algorithm. In this context, K represents the number of participating companies, and ω i L represents the percentage of data volume for the i-th enterprise. i (θ) represents the loss function of the i-th enterprise-local model. The system has a built-in differential privacy mechanism, adding Laplace noise before parameter upload. ∈ represents the privacy budget (default value 0.5), and Δf represents the sensitivity (set to 1). After each training round, the central server verifies the validity of each enterprise's parameter updates through a secure multi-party computation (MPC) protocol to prevent malicious attacks.

[0073] In this invention, causal inference technology is introduced into the user behavior analysis module to identify the true causal relationships of user behavior. A causal graph of user behavior is constructed based on Do-Calculus theory, and the formula ACE = Σ is adjusted via a backdoor. x′ ∑ w P(Y|do(X=x),W=w)P(W=w)-Σx′ ∑ w P(Y|do(X=x′),W=w)P(W=w)

[0074] The average causal effect of the behavioral intervention is calculated, where ACE is the average causal effect, X is the intervention variable (e.g., page redesign), x and x′ are different values ​​of the intervention variable X, Y is the outcome variable (e.g., conversion rate), W is the set of confounding variables, and w is the value of the confounding variable W.

[0075] The system automatically detects backdoor paths in the cause-effect graph. When unblocked confounding factors exist, it eliminates bias using propensity score matching (PSM). For example, when analyzing the impact of page redesign on conversion rates, if user activity is identified as a confounding factor, PSM is used to match users with similar activity levels between the redesigned and unredesigned groups to ensure the reliability of the results. The analysis results are used to guide product iteration, such as extending a solution that improved conversion rates by 18% by changing the position of a button to other pages.

[0076] In this invention, a knowledge graph-enhanced recommendation mechanism is employed in the intelligent recommendation engine module. A knowledge graph is constructed based on Neo4j 5.0, containing entities such as users, products, brands, categories, and attributes, with a total of 12 million nodes and 58 million relationship edges. Node embedding learning is performed using a graph neural network (GraphSAGE). A two-layer aggregator is configured, with neighbor sampling numbers of 15 and 10 respectively. Training uses the Adam optimizer with a learning rate of 0.003 and a training period of 10 days.

[0077] The recommendation model inputs the knowledge graph embedding vector vec(G), the user behavior sequence vector vec(u), and the product feature vector vec(i) into a three-layer fully connected network (256 neurons per layer), and outputs a recommendation score r. u,i =f(vec(u),vec(i),vec(G)). The system supports interpretable display, searching for recommended paths in the knowledge graph using Cypher statements, for example: "MATCH(u:User)-[:BOUGHT]->(p1:Product)-[:BELONGS_TO]->(c:Category)<-[:BELONGS_TO]-(p2:Product)WHEREu.id={userId}RETUR Np2", generating the recommendation reason "Because you have purchased sports shoes of brand A, we recommend the new model of brand B in the same category, both of which belong to the breathable and shock-absorbing series".

[0078] In this invention, a multi-armed slot machine algorithm is used to optimize marketing resource allocation in the real-time decision-making and automated marketing module. Eight marketing channels are considered as the "arms" of the multi-armed slot machine, and the Thompson sampling algorithm is used to dynamically select the delivery strategy. The posterior distribution of the conversion rate for each arm is modeled as a Beta distribution θ. a ~Beta(α) a ,β a ), where α a Increment the success rate by 1, β a Increment the failure count by 1. Initially, the α value for each arm... a and β a Set to 1 to represent a uniform prior distribution. The system updates the posterior distribution parameters hourly and allocates the marketing budget based on the sampling results. The specific implementation steps are as follows:

[0079] Sampling phase: Each channel a samples a conversion rate from its Beta distribution.

[0080] Selection Phase: Select the channel with the highest sampling conversion rate.

[0081] Update phase: Execute marketing campaigns on the selected channels; if successful... otherwise

[0082] When a channel's sampling score is below 20% of the average for three consecutive times, its budget allocation is automatically reduced by 5%. To avoid cold start issues, new channels are initially allocated a 2% budget allocation and participate in normal Thompson sampling only after accumulating 100 executions. Through this algorithm, the system can dynamically adapt to changes in channel performance and optimize resource allocation.

[0083] In this invention, the **Natural Language Generation (NLG)** technology is used to automatically generate analysis reports in the data visualization and report generation module. Based on a pre-trained T5-Base model, fine-tuning is performed on a dataset containing 100,000 enterprise analysis reports. The system encodes data metrics, dimensional information, and business rules into input sequences and generates natural language text via beamsearch (beamsize=3). A template fusion mechanism is introduced to combine the generated text with preset report templates, such as "From a regional perspective, East China's GMV contribution reached 45%, a year-on-year increase of 30%, mainly due to newly developed online channels." Multilingual output is supported, automatically converting reports into English, Japanese, and other languages ​​via a translation API to meet the needs of multinational corporations.

[0084] In this invention, an automated operation and maintenance system is constructed within the system management and optimization module. System hardware, software, and business metrics are collected using Prometheus and visualized and monitored using Grafana. An LSTM-Seq2Seq model is employed to predict system load. Inputting seven days of historical minute-level metric data, the system predicts the load for the next hour. When the predicted load exceeds a threshold of 80%, Kubernetes is automatically triggered to scale up containers. For model optimization, a model monitoring module detects data drift (using the Maximum Mean Difference (MMD) test) and concept drift (based on changes in the model's predicted probability distribution) in real time. When drift is detected, new data is automatically extracted from the data lake for incremental training, and the online model is updated using a blue-green deployment approach to ensure uninterrupted service.

[0085] In this invention, the system implements privacy protection measures throughout the entire data lifecycle. During the data acquisition phase, differential privacy technology is used to add Laplace noise to user behavior data. For numerical data (such as dwell time), noise is added. Where N is Laplace noise. The distribution follows a Laplace distribution, ∈ represents the privacy budget (∈ = 0.3 for transaction data, ∈ = 0.5 for browsing data), and Δf represents the sensitivity (set to 1). For categorical data (such as product categories), perturbation is applied using a random response mechanism with probability... Return the true value (p is the probability of returning the true value), otherwise randomly select another category.

[0086] During the data storage phase, homomorphic encryption (CKKS scheme) is used to encrypt sensitive data. For numeric fields (such as transaction amounts), hierarchical homomorphic encryption is employed, supporting addition and multiplication operations in the ciphertext state. For example, when calculating the average of ciphertext data, summation and division operations can be performed directly on the ciphertext, and the correct result is obtained after decryption.

[0087] During the model training phase, the Garbled Circuit technique from the Secure Multi-Party Computation (MPC) protocol is employed. Participants convert the raw data into Boolean circuits, perform joint computations by exchanging encrypted circuit gates, and ultimately obtain encrypted model parameters. In the federated learning scenario, each participant only uploads the encrypted gradient parameters; the central server decrypts and aggregates them before distributing updates, ensuring that the original data is not leaked.

[0088] The system uses an attribute-based access control (ABAC) mechanism to dynamically assign permissions based on user roles, data tags, and operational scenarios. For example, customer service personnel can only view anonymized user consultation records, analysts can access encrypted raw data but require approval, and administrators can perform full data operations, but these operations are subject to auditing.

[0089] Example 1: Smart Customer Acquisition and Repeat Purchase Improvement Scenarios on E-commerce Platforms

[0090] After a comprehensive e-commerce platform integrated this system, it was applied to new user acquisition and repeat purchases by existing users. The multi-source data acquisition module connects to the platform's order system via API (averaging 100,000 orders per day), collects click / add-to-cart behavior within the app via SDK (averaging 3 million interactions per day), and crawls competitor prices and user reviews (50,000 entries per day). The data cleaning module uses Bloom filters for deduplication (removing 8% of duplicate data daily), identifies abnormal orders using an isolated forest algorithm (e.g., abnormal purchases exceeding 100,000 RMB per transaction), and fills in missing user geographic information using IP address mapping (92% accuracy).

[0091] The user profile building module generates 326-dimensional tags, with dynamic tags such as "price-sensitive" and "brand-loyal" updated every 15 minutes. The intelligent customer acquisition module transfers the characteristics of high-value customers in leading 3C product categories through transfer learning. Using the LightGBM model, it identifies 50,000 potential customers with a conversion rate exceeding 60% and pushes personalized coupons of "20% off first order for new customers" to them. The preferred channel for reaching these customers is App Push (which has been verified by the multi-armed slot machine algorithm to have the highest ROI).

[0092] The user behavior analysis module found that the conversion rate for the "homepage search → product details → add to cart → checkout" path was only 12%. Through causal inference, "slow loading of the checkout page" was identified as the key bottleneck, and after optimization, the conversion rate increased to 28%. The intelligent recommendation engine, integrating BERT-CNN and collaborative filtering, recommended accessories from the same brand to users who had browsed the phone, increasing the click-through rate by 40%. The real-time decision-making module automatically triggered discount activities during promotional periods and dynamically adjusted budgets for various channels using reinforcement learning, ultimately reducing new customer acquisition costs by 35% and increasing repeat purchase rates among existing users by 22%.

[0093] Example 2: Targeted Marketing Scenarios for Financial Products

[0094] A bank's wealth management platform uses this system to optimize the marketing of wealth management products. The multi-source data acquisition module integrates customer asset data from the CRM system (500,000 users), online banking logs (200,000 daily operations), and external financial news browsing records (30,000 articles crawled daily). During the data cleaning phase, box plots were used to replace isolated forest-marked abnormal transactions (such as single-day transfers exceeding 5 million yuan), and missing risk assessment data was filled using an LSTM-AE model (error rate <5%).

[0095] The user profile building module extracts core tags such as "risk preference" and "investment cycle," and calculates similarity using the GraphSAGE algorithm. It finds that 80% of users in the "conservative" cluster are interested in bond products. The intelligent customer acquisition module utilizes federated learning, combining data from three branches to train a model, outputting the conversion probability of high-potential customers. It then pushes a "4.2% annualized principal-protected wealth management product" to the top 20% of customers based on their ratings, targeting them at 10:00 AM on weekdays (historical data shows the highest open rate at this time).

[0096] The user behavior analysis module, through Transformer analysis of behavior sequences, discovered that the conversion rate of the "view product manual → calculate returns → consult customer service" path was three times that of other paths. Based on this, customer service scripts were optimized to guide users through this path, increasing the conversion rate by 15%. The intelligent recommendation engine, combined with a knowledge graph, recommends new products managed by the same fund manager to users who have purchased funds, improving the explainability of recommendations by 60% (user feedback: "the reasons are clear"). The real-time decision-making module dynamically allocates SMS and telephone marketing resources through Thompson sampling, increasing marketing ROI by 28%. Compliance audits show that the system, through differential privacy and homomorphic encryption technologies, fully complies with data security regulations.

[0097] Example 1 (E-commerce Platform) Effect Comparison Table

[0098] index Before system application After system application Increase New customer acquisition cost (RMB / person) 85 55 35％ Repeat purchase rate of existing users (%) 18 22 22％ Recommended click-through rate (%) 3.5 4.9 40％ Critical path conversion rate (%) 12 28 133％ Abnormal data processing efficiency (data items / second) 5000 12000 140％

[0099] The table shows that after implementing the system on the e-commerce platform, core marketing metrics improved across the board. New customer acquisition costs decreased by 35%, thanks to the intelligent customer acquisition module's precise screening of high-potential customers through transfer learning, reducing ineffective marketing investment. The repurchase rate of existing users increased by 22%, reflecting the effectiveness of the user behavior analysis module's optimization of the repurchase path; for example, after the checkout page loading speed improved, the key path conversion rate jumped from 12% to 28%. The recommendation click-through rate increased by 40%, confirming the effectiveness of the intelligent recommendation engine integrating multiple algorithms, and the interpretability of recommendations combined with knowledge graphs enhances user trust. The efficiency of abnormal data processing doubled, demonstrating the synergistic advantages of the Bloom filter and the Isolation Forest algorithm in the data cleaning module, laying a high-quality data foundation for subsequent analysis and improving the platform's overall marketing ROI.

[0100] Example 2 (Financial Management Platform) Effect Comparison Table

[0101]

[0102] This table showcases the significant achievements of the financial platform application system. Marketing ROI improved by 28%, thanks to the Thompson sampling algorithm in the real-time decision-making module's dynamic resource allocation across SMS and telephone channels. The accuracy rate for identifying high-potential customers reached 82%, benefiting from federated learning integrating data from multiple branches and enhancing the model's generalization ability. The explainability score for recommendations improved by 60%, as recommendations based on knowledge graphs, such as "same fund manager," were more easily understood by users. The compliance audit pass rate was 100%, validating the effectiveness of differential privacy and homomorphic encryption technologies and meeting financial data security requirements. Customer service conversion rate improved by 188%, reflecting the user behavior analysis module's accurate identification of high-value paths. Optimized communication guided users to complete conversions, comprehensively improving the marketing efficiency of wealth management services.

[0103] Example 3: Smart Customer Acquisition and Repeat Purchase Improvement Scenarios on E-commerce Platforms

[0104] A certain offline chain supermarket (20 stores) is facing the problem of inefficiency in traditional customer acquisition models: relying on methods such as flyer distribution and offline promotions, the cost of customer acquisition is high and the conversion rate is less than 5%; user behavior data is scattered in the POS system, membership registration and mini-program, which is difficult to integrate and analyze, resulting in an inability to accurately grasp consumer preferences, poor targeting of promotional activities, and a continuous decline in the repurchase rate of old customers.

[0105] To address the aforementioned issues, the supermarket integrated an AI-based intelligent customer acquisition and user behavior analysis system, with the following applications: Data Collection and Processing: A multi-source data collection module connects to the store's POS system via API (30,000 transactions per day), collects product browsing and coupon redemption behavior via a mini-program SDK (500,000 interactions per day), and uses incremental web crawlers to capture promotional information from nearby competitors (20,000 entries per day). The data cleaning module uses Bloom filters for deduplication (removing 7% of duplicate redemption records per day), identifies abnormal consumption through an isolated forest algorithm (e.g., non-group-buying orders of 50 bottles of the same beverage), and fills in missing user age information through consumer category clustering (89% accuracy).

[0106] User profiling and customer acquisition: The user profiling module extracts 310+ dimension tags, including dynamic tags such as "frequent fresh food purchases" and "weekend family shopping" (updated every 30 minutes). The GraphSAGE algorithm maps users to a 256-dimensional vector space, calculating similarity to segment consumer groups. The intelligent customer acquisition module utilizes DANN transfer learning to transfer the characteristics of high-value customers from core stores. The LightGBM model filters out 20,000 potential customers with a conversion probability exceeding 62%, pushing "new customers get ¥50 off for purchases over ¥200" coupons. The primary channel for reaching these customers is community WeChat groups (verified by the multi-armed slot machine algorithm for the highest ROI).

[0107] Behavioral Analysis and Recommendation: The user behavior analysis module uses a session-based approach to segment behavior sequences. Through the Transformer architecture, it was found that the conversion rate for the path "scanning for coupons → lingering in the fresh produce section → abandoning payment at the checkout" was only 8%. Causal inference identified "excessively high coupon usage thresholds" as the key bottleneck. After optimization, the coupon was changed to "30 off for purchases over 150," increasing the conversion rate to 23%. The intelligent recommendation engine integrates knowledge graphs to recommend related products (such as baby wipes and complementary foods) to users who purchase infant formula, increasing the click-through rate by 45%.

[0108] Real-time decision-making and results: The real-time decision-making module combines a rule engine and reinforcement learning to automatically trigger the "gift for members who spend a certain amount" activity during holidays, dynamically adjust the resource allocation of store broadcasts and sales guide recommendations, and ultimately reduce the cost of acquiring new customers by 40% and increase the repurchase rate of old customers by 28%.

[0109] Effect Comparison Table

[0110] index Before system application After system application Increase New customer acquisition cost (RMB / person) 60 36 40％ Promotional activity conversion rate (%) 5 12 140％ Repeat purchase rate of existing customers (%) 18 23 28％ Recommended product click-through rate (%) 3.2 4.6 45％

[0111] The comparison table clearly demonstrates the system's optimization effectiveness in retail scenarios: new customer acquisition costs decreased by 40%, thanks to the intelligent customer acquisition module's precise screening of high-potential customers through transfer learning, reducing ineffective marketing investment; promotional activity conversion rates increased by 140%, reflecting the optimization effect of the user behavior analysis module on the consumption path, such as adjusting coupon thresholds to solve conversion bottlenecks; the increase in repeat purchase rate of existing customers and click-through rate of recommended products confirms the effectiveness of the intelligent recommendation engine combined with knowledge graphs to achieve personalized recommendations, enhancing user stickiness and willingness to consume.

[0112] Example 4: User Retention and Paid Conversion Scenarios on Online Entertainment Platforms

[0113] An online entertainment platform (providing film, games, and reading services) faces user retention challenges: the 7-day retention rate of registered users is less than 20%, and paid conversion relies heavily on homepage recommendations. However, the recommended content is highly homogenized, and the simplistic recommendation logic based on "popularity" results in low user interest matching, with a paid conversion rate of only 3%. Furthermore, user behavior data (such as viewing time, click patterns, and comment interactions) is not deeply analyzed, making it impossible to identify high-potential paying users, leading to significant waste of marketing resources. After integrating an AI-based intelligent customer acquisition and user behavior analysis system, the following applications were observed:

[0114] Data Acquisition and Processing: The multi-source data acquisition module collects user behavior data within the app via SDK (1 million clicks and 300,000 comments daily), integrates with the membership system and payment records via API, and crawls content popularity data from industry competitors. The data cleaning module removes duplicate click records using Bloom filters (processing 12% daily), identifies abnormal accounts using Isolation Forest (e.g., accounts that switch between 10+ regions in a short period to generate fake clicks), and fills in missing user interest tags using an LSTM-AE model (error rate <6%).

[0115] User profiling and customer acquisition: The user profiling module generates 340+ dimension tags, including "suspense drama preference" and "mobile game payment willingness," etc. Features are processed using One-Hot encoding and quantile discretization, and user similarity is calculated using the GraphSAGE algorithm. The intelligent customer acquisition module uses XGBoost to initially screen leads, integrates data from three partner platforms using federated learning, and outputs 15,000 users with a paid conversion probability exceeding 70% through the LightGBM model. These users are then targeted with a "first month membership half price" promotion, reaching them between 8-10 PM (historical peak activity time).

[0116] Behavioral Analysis and Recommendation: The user behavior analysis module, through Transformer, parses behavioral sequences and discovers that the conversion rate for the path "preview episodes → add to watchlist → check paid packages" is only 5%. Using Do-Calculus theory, "unclear package descriptions" were identified as the key factor. After optimization, adding a "pay per episode" option increased the conversion rate to 18%. The intelligent recommendation engine introduces a time decay factor. Combining knowledge graphs to recommend works by the same director improves recommendation accuracy by 52%.

[0117] Real-time decision-making and results: The real-time decision-making module dynamically adjusts recommendation slot resources through reinforcement learning, increasing the exposure of high-potential users by 30%, ultimately increasing the platform's 7-day retention rate to 35% and the paid conversion rate to 8%.

[0118] Effect Comparison Table

[0119] index Before system application After system application Increase 7-day user retention rate (%) 20 35 75％ User payment rate (%) 3 8 167％ High-potential user identification accuracy (%) 55 78 42％ Recommended content matching degree (%) 40 61 52％

[0120] The improved performance of online entertainment platforms stems from the system's in-depth analysis and precise operation of user behavior: the significant improvement in 7-day user retention rate and recommended content matching is attributed to the intelligent recommendation engine's introduction of a time decay factor and integration of knowledge graphs, enabling dynamic matching of content with user interests; the improvement in user payment rate and the accuracy of identifying high-potential users reflects the user profile building module's accurate characterization of multi-dimensional tags, as well as the role of the intelligent customer acquisition module in enhancing the model's generalization ability through federated learning, making marketing resources more focused on high-value users.

[0121] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart customer acquisition and user behavior analysis system based on the entire AI ecosystem, characterized in that, include: Multi-source data acquisition module: Deploys distributed acquisition nodes, connects with enterprise CRM and ERP systems through API interfaces to obtain customer data, uses SDK tracking technology to collect user interaction behavior on mobile devices, and uses incremental crawlers to crawl media data in a targeted manner; Data cleaning and preprocessing module: Deduplication is performed using a Bloom filter. Outliers are marked when the average path length from a sample to the root node is less than the threshold μ-3σ, where μ is the average path length and σ is the standard deviation. After cleaning, the data is stored in the Hadoop Distributed File System in Parquet format. User profile building module: One-Hot encoding vectorizes categorical features, quantile discretization processes continuous features, and the GraphSAGE algorithm maps entities to a 256-dimensional vector space. User similarity is calculated using a formula. u i u j For two different users, vec(u i ),vec(u j ) is the user feature vector; Intelligent Customer Acquisition Module: The XGBoost algorithm initially screens leads based on user base, behavior, and external market data; it then uses DANN transfer learning technology to transfer customer characteristics from leading companies, and outputs the customer conversion probability (P) via the LightGBM model. convert =f(X) user ,W), X user A user vector containing 128-dimensional features, where W is the weight matrix; User behavior analysis module: The user behavior sequence is divided using the Session-Based method, and the behavior sequence modeling adopts the Transformer architecture; The intelligent recommendation engine module: The content-based recommendation part uses the BERT-CNN model to extract product features, concatenates them with user profile features, and then inputs them into a multilayer perceptron to calculate the matching degree; the collaborative filtering part introduces a time decay factor. t represents the number of days since the action occurred; Real-time decision-making and automated marketing module: A dual-driven framework of rule engine and reinforcement learning is built. The former supports rule configuration; the latter has a state space containing 50-dimensional information and an action space with 8 marketing channels. Data visualization and report generation module: Based on ECharts and AntV, a component library is built to provide a drag-and-drop report designer. Users select indicators and dimensions from the metadata center, and the system automatically generates SQL query statements. System management and optimization module: Deploys Prometheus + Grafana for monitoring, issues alerts when metrics exceed limits for 5 consecutive minutes, and uses an automated A / B testing framework to allocate traffic via Nginx.

2. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, It also includes a multimodal data fusion module: deploying a microphone array and camera module at the hardware level, removing silent segments through VAD, and then inputting it into the Wav2Vec2.0 model to extract 768-dimensional acoustic features; Image data is processed using the MTCNN algorithm to detect face regions, and then ResNet50 is used to extract 2048-dimensional visual features. Cross-modal alignment is performed using a Transformer-based alignment network, with a loss function... In the middle, L align The cross-modal alignment loss value; N is the number of samples; i is the sample index; x i For the i-th text sample, f text f audio These are text and speech feature extraction functions, respectively.

3. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, It also includes a customer churn warning module: constructing an integrated learning warning model, integrating three base models: random forest, LightGBM, and XGBoost, and fusing the models through the stacking method. It adopts the Cox proportional hazards model in survival analysis. In the formula S(t|X)=exp(-Λ(t)exp(Xβ)), S(t|X) is the user retention probability at time t, Λ(t) is the baseline risk function, X is a user vector containing 50-dimensional features, and β is the regression coefficient.

4. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, In the intelligent customer acquisition module, a horizontal federated learning architecture is built based on TensorFlow Federated. Each participant trains a LightGBM model locally using its own customer data, and only uploads the model gradient parameters to the central server. The central server updates the global model parameters through a weighted aggregation algorithm. The objective function is... Where K is the number of participating companies, ω i L represents the percentage of data volume for the i-th enterprise. i (θ) is the loss function of the i-th enterprise local model. The system has a built-in differential privacy mechanism, adding Laplace noise before parameter upload. ∈ represents the privacy budget, and Δf represents the sensitivity.

5. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, In the user behavior analysis module, the formula for calculating the conversion rate of the behavior path is: N start N represents the initial number of actions. end To determine the number of actions to be terminated; a cause-and-effect graph of user behavior is constructed based on the Do-Calculus theory, and the formula ACE = ∑ is adjusted through a backdoor. x′ ∑ w P(Y|do(X=x),W=w)P(W=w)-Σ x′ Σ w P(Y|do(X=x′),W=w)P(W=w) The average causal effect of the behavioral intervention is calculated, where ACE is the average causal effect, X is the intervention variable, Y is the outcome variable, and W is the set of confounding variables. The system automatically detects backdoor paths in the causal graph. When there are unblocked confounding factors, bias is eliminated by propensity score matching. The analysis results are used to guide product iteration.

6. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, In the intelligent recommendation engine module, the weights of the two recommendation results are adjusted through an attention mechanism, using the following formula: h i W is the eigenvector. a A 128×128 learnable matrix is ​​used, where n is the total number of feature vectors. A knowledge graph containing entities is constructed based on Neo4j, and node embeddings are learned through a graph neural network GraphSAGE. The recommendation model inputs the knowledge graph embedding vector vec(G), the user behavior sequence vector vec(u), and the product feature vector vec(i) into a multilayer perceptron, and outputs a recommendation score r. u，i =f(vec(u), vec(i), vec(G)), the system supports interpretable display and generates recommendation reasons through knowledge graph path search.

7. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, Treating the eight marketing channels as the arms of a multi-armed slot machine, the Thompson sampling algorithm is used to dynamically select the delivery strategy. The posterior distribution of the conversion rate for each arm is modeled as a Beta distribution θ. a ~Beta(α) a ,β a ), where α a Increment the success rate by 1, β a The number of failures is incremented by 1, the posterior distribution parameters are updated every hour, and the marketing budget is allocated based on the sampling results.

8. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, In the data visualization and report generation module, **natural language generation** technology is used to automatically generate analysis reports. Based on the pre-trained T5-Base model, it is fine-tuned on the dataset. The system encodes data indicators, dimensional information and business rules into input sequences, generates natural language text through beamsearch, and introduces a template fusion mechanism to combine the generated text with preset report templates, supporting multi-language output.

9. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, In the system management and optimization module, an automated operation and maintenance system is built. Based on Prometheus, system hardware, software and business metrics are collected and visualized using Grafana. The LSTM-Seq2Seq model is used to predict system load. Inputting 7 days of historical minute-level metric data, the system predicts the load situation for the next hour. In terms of model optimization, the model monitoring module detects data drift and concept drift. When drift is detected, new data is automatically extracted from the data lake for incremental training.

10. The AI-based intelligent customer acquisition and user behavior analysis system according to claim 1, characterized in that, During the data acquisition phase, differential privacy technology is used to add Laplace noise to user behavior data, with the noise intensity adjusted according to data sensitivity. During the data storage phase, homomorphic encryption technology is used to encrypt sensitive data. During the model training phase, a secure multi-party computation protocol is used for joint data modeling. The system dynamically allocates permissions based on user roles, data tags, and operation scenarios through a federated identity authentication and access control mechanism.

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