107 results about "Precision marketing" patented technology

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

The invention discloses an intelligent recommendation precision marketing method and system based on a large model, and belongs to the technical field of artificial intelligence and retail, and the method comprises a data perception layer which is used for collecting user related information and comprises a face recognition terminal, an Internet of Things sensor, a CRM system and transaction POS data; the intelligent decision-making layer is used for performing fusion modeling and recommendation strategy generation on the sensing data and comprises a dynamic image engine, a demand prediction large model and a recommendation strategy generator; and the execution feedback layer is used for sending the recommendation result to the user in a multi-channel manner and recycling feedback data, and comprises a shop assistant PAD push module, an electronic price tag updating module and a self-service terminal guide module. The repurchase rate and customer unit price of a retail scene can be remarkably improved, and the technical problem that a traditional retail robot cannot adapt to complex requirements of customers is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an object portrait label set generation method, device and equipment and a medium, and the method comprises the steps: collecting and integrating multi-source data, generating a multi-modal data set, and cleaning and enhancing the data set; extracting a portrait label set based on the historical structured information of the object, obtaining a multi-modal cue word set, and constructing a unified representation sequence; inputting the unified representation sequence into a multi-modal model, obtaining fusion representation and extracting a derivative factor candidate set; and performing consistency verification on the cross-modal data to obtain a consistency factor set, and finally fusing the consistency factor set with the structured portrait label set to generate a unified portrait system and output an object portrait label set. According to the method, the accurate and comprehensive customer portrait is generated by combining multi-modal data and cross-modal consistency verification, the portrait accuracy is improved, and support is provided for accurate marketing and personalized service.

The invention relates to a financial customer precision marketing system and method based on big data and artificial intelligence in the field of financial science and technology and intelligent marketing cross technology, and provides a financial customer precision marketing system and method based on big data and artificial intelligence by integrating multi-dimensional customer data, constructing a dynamic customer portrait, predicting customer demands by using a deep learning model and combining the real-time reaching capability of enterprise WeChat ecology. According to the method, automatic execution and closed-loop optimization of a personalized marketing strategy are realized, and the customer conversion rate and loyalty of a financial institution are improved; the method is innovatively embodied in deep combination of an emotion recognition technology and a financial scene, reinforcement learning optimization of a dynamic rule engine and a privacy compliance data desensitization mechanism, and solves the problems of lagging customer demand recognition and low strategy matching efficiency in traditional marketing.

The invention provides a marketing system based on multi-modal literature data intelligent analysis, and the system comprises the steps: automatically recognizing and analyzing various formats of scientific research literatures through a literature analysis and preprocessing module, and extracting structured information; the AI enhancement analysis module deeply mines literature semantics by using a large model; the data processing and integration module cleans and integrates the information, constructs a researcher portrait including five dimensions of identity, interest, technology, equipment and demand prediction, and further generates a personalized marketing strategy; the cache management module optimizes result storage and retrieval efficiency, and the batch processing module coordinates a multi-file parallel processing flow; according to the system, the automatic conversion from the original literature to the precision marketing strategy is realized. According to the method, deep mining and precise marketing of the multi-source scientific research literature are realized, and the information extraction accuracy and the marketing conversion efficiency are remarkably improved.

The invention discloses a smart marketing system and device and a medium, and belongs to the field of big data analysis. The system comprises a data support layer used for collecting multi-source heterogeneous data including equipment data, user behavior data and third-party data, and forming data assets by preprocessing the multi-source heterogeneous data; the intelligent decision-making layer is used for identifying the life cycle stage of the current user based on an intelligent model matrix and the data assets, predicting the transition stage of the life cycle of the user and generating a precision marketing strategy and a channel selection scheme of each stage; and the business application layer is used for converting the precision marketing strategy and the channel selection scheme into a specific marketing task, monitoring marketing effect data and feeding back the marketing effect data to the data support layer and the intelligent decision-making layer. According to the system, the intelligent marketing process of full-link data fusion, dynamic life cycle management and automatic marketing closed loop can be realized.

The invention discloses an intelligent delivery targeted penetration decision-making method for precision marketing, particularly relates to the technical field of intelligent delivery targeted penetration decision-making, and comprehensively identifies potential anomalies through unified processing of a standardized index set and dynamic association construction of multi-level thresholds; by means of the constraint of a delivery strategy oscillation index on a delivery behavior, dynamic bidding, material optimization and flow distribution can be smoothly adjusted in a fluctuating environment, the controllability of budget consumption and the continuity of a delivery effect are ensured, and a multi-dimensional penetration strategy of competition, scenes, crowds and materials is combined, so that the delivery efficiency is improved. According to the method, high-value user groups are more accurately reached, the material matching degree is improved, the return on investment and the conversion rate are stably kept increased, oscillation and resource waste in the putting process are reduced, and the cross-platform collaborative optimization capability and the risk prevention and control level are improved.

The invention discloses a user behavior analysis and precision marketing system based on deep learning, and relates to the technical field of computers, and the system comprises the steps: S1, carrying out data access and management, collecting a user behavior log, a session sequence, position information, an end-side event and cross-domain Internet of Things data, combining a commodity portrait, a commercial tenant portrait and multi-modal content, and carrying out the recognition of the user behavior log, the session sequence, the position information, the end-side event and the cross-domain Internet of Things data; time alignment, anomaly repair, label completion and privacy desensitization are completed, and a unified data basis is formed. According to the method, seamless integration and high-quality processing of multi-source heterogeneous data are realized through unified data access and treatment capability, a solid data foundation is laid for subsequent deep learning and precision marketing, the problems of data dispersion and poor quality of an existing system are effectively solved, and the method has a good application prospect through multi-modal representation learning and feature construction. Text, image and video features are extracted by using a depth encoder, and cross-modal feature alignment and complex relation modeling are realized in combination with sequence modeling and a graph neural network.

The invention belongs to the technical field of user portrait analysis, and mainly relates to a cross-border e-commerce user portrait analysis method based on big data, and the method comprises the steps: extracting a user demand tag through multi-source data collection and standardization processing in combination with a semantic understanding model, and constructing a compliance knowledge graph of a target market; in the construction of user portraits, a framework based on cultural cognition is adopted, and preferences of users on types and components of health care products under different cultural backgrounds are deeply mined; a semantic network between a culture background and health care product attributes is constructed through a knowledge graph technology, and the recognition ability of the model to culture sensitive features is further improved; by dynamically updating the user portraits and combining marketing feedback data, commodity recommendation and marketing strategies are continuously optimized, so that the accurate marketing effect of cross-border e-commerce is improved on the premise of guaranteeing compliance; the user experience and the conversion rate of the cross-border e-commerce platform can be effectively improved, and the risk of violation is reduced.

The invention discloses a precision marketing label screening method and system based on high-dimensional interactive screening. The method comprises the following steps: S1, a user determines a core index variable y for measuring a service effect; s2, a user determines a to-be-screened label range and preliminarily selects a proper customer group to obtain a sample set x; s3, constructing a hierarchical screening model; and S4, the user selects a screening hierarchical mode: weak hierarchical structure screening and strong hierarchical structure screening. The system comprises an interactive display module, a data storage module, a screening analysis module and a feedback optimization module. According to the method, reference can be provided for bank marketing personnel when the marketing objects are determined, the situation that labels with information values are omitted when the labels are selected from numerous labels is avoided, and hidden interaction information among the labels can be explored.

The application discloses a cost-optimized out-of-season clothing renovation design method and an intelligent processing system, relates to the field of clothing intelligent design systems, and has the technical scheme as follows: a data acquisition module, which collects original data of out-of-season clothing and popular trend matching accurate marketing landing; a data analysis and prediction module, which evaluates potential risks and feasibility in the renovation process based on the collected data; a design scheme generation module, which automatically generates multiple feasible renovation design schemes according to the data analysis results and personalized requirements input by a user, and determines the best design scheme; and the application integrates a complete closed-loop process from data acquisition to evaluation, combines an artificial intelligence deep learning algorithm to complete demand prediction, design scheme generation and optimization, and significantly improves production efficiency and reduces artificial dependence.

The invention discloses an AI-based tobacco consumer behavior analysis and personalized marketing method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting a multi-modal real-time behavior data stream; performing dynamic noise filtering on the multi-modal real-time behavior data stream to obtain an effective behavior sequence; extracting a plurality of spatio-temporal feature vectors of the effective behavior sequence, fusing the spatio-temporal feature vectors and compressing dimensions to obtain a target feature tensor; inputting the target feature tensor into an incremental learning engine for incremental learning; according to the public opinion event, performing matching in a preset marketing strategy library to obtain alternative actions, and then pushing differentiated marketing strategies; feedback behavior data is collected, and then a strategy utility index is calculated according to the feedback behavior data, and the strategy utility index is transmitted back to the incremental learning engine to serve as a regularization constraint term of next round of updating. According to the method, the incremental learning engine, the public opinion event and the marketing strategy mapping mechanism are combined, and dynamic optimization and precise marketing of the incremental learning engine are achieved.

The invention discloses a consumption loan marketing method, apparatus and device, and a computer program product. The method comprises the steps of obtaining multi-dimensional basic information and consumption loan behavior data of a customer; determining a customer group type to which the customer belongs according to the multi-dimensional basic information; according to the consumption loan behavior data of the customer, determining a life cycle stage and a limit use condition of the customer; determining a target consumption loan preferential strategy corresponding to the customer by using a dynamic consumption loan preferential strategy according to the customer group type of the customer, the life cycle stage of the customer and the limit use condition; and according to the target consumption loan preferential strategy, contacting the customer by using the preferential contacting channel of the customer. According to the method and the system, the customers are subjected to refined grouping and life cycle stage division, and a dynamic preferential strategy and an intelligent contact channel decision are combined, so that personalized preferential strategies can be provided for different customer groups and stages, accurate marketing of coupons in bank consumption loan business is realized, and the conversion rate of consumption loan customers is improved.

The invention discloses a machine learning recommendation method for precision marketing and potential customer mining. The method comprises the following steps: S1, data acquisition: establishing a user tag table based on historical data and acquiring user behavior characteristics; s2, data preprocessing: preprocessing the data, including removing abnormal values, filling missing values, and converting category features into numeric features; s3, automatic box separation: performing variable analysis and automatic box separation on the preprocessed data; s4, feature processing: converting a data table obtained by binning into a numerical value form; s5, dividing a data set: dividing the processed data set into a training set and a test set; s6, stepwise regression is carried out to select features: stepwise logistic regression is used to select model key variables; s7, manual binning and modeling data set preparation are carried out; the method has the characteristics of high automation level and high decision-making efficiency.

A training method of a marketing prediction model for eliminating time deviation and a precision marketing method relate to the technical field of data processing, samples are grouped by setting time stamps and marketing scheme stamps, experimental samples and contrast samples in the same time group are ensured to be comparable in average value, and the accuracy of marketing prediction is improved. Therefore, the time deviation problem when historical samples are adopted is eliminated, meanwhile, purchase rate target pre-training is carried out on the model to solve the problem that high-latitude features cannot be effectively utilized due to the fact that the unbiased sample size is small, and finally the overall loss is calculated in a multi-task training mode, so that the accuracy of the model is improved. The marketing prediction model can achieve better prediction accuracy under the condition that biased samples and a small number of unbiased samples are adopted, so that the sampling cost caused by only using the unbiased samples is greatly reduced, and both the cost and the effect are achieved. By using the marketing prediction model, accurate putting of marketing schemes can be further realized, and the marketing prediction model has a good practical value.

The invention relates to the technical field of insurance information integration, and discloses an insurance information integration method. The method comprises the following steps: reading annual insurance premium and total claim settlement amount of a customer, calculating and normalizing an extreme value, and eliminating dimensional difference; constructing a customer-insurance policy-risk three-layer edge set, generating a risk label set, calculating a similarity matrix according to an intersection-to-union ratio, and adaptively setting a community merging threshold value by using a global average value; clients are initialized into communities, high-similarity communities are iteratively merged, and the number of groups does not need to be preset; aggregating the average insurance premium and claim settlement of each community, and calculating a risk coverage ratio and homogeneity; and counting cross-community over-threshold similar customer pairs, comparing the cross-community over-threshold similar customer pairs with a mean value to identify potential association, and marking high-premium and high-claim communities. The process unifies and disperses data, eliminates dimensional differences, self-adapts threshold values and groups, generates community portraits, and supports precision marketing and joint risk control.

The invention discloses a bank financial cloud customer potential tapping method based on machine learning. The mining method comprises the steps of S1, defining sample data; s2, performing customer portraying according to the sample data; s3, carrying out data processing on the customer portrait; s4, carrying out model construction and verification; s5, the model is applied; and step S6, evaluating the model. Through a LightGBM ensemble learning algorithm, a complex relationship between customer characteristics and purchase tendencies can be automatically learned, a response probability of a customer is output, and accurate marketing of the customer is realized. And customer omission caused by potential customer mining through artificial experience is avoided.

This application relates to the interdisciplinary fields of digital marketing and electronic information technology, and specifically to a method for intelligent demand mining and precision marketing for small and medium-sized enterprises (SMEs) in the electronic information industry. The method includes: generating anonymized simulated customer data for SMEs in the electronic information industry, with the customer data generated based on customer characteristics in the electronic information industry, including customer attribute information and demand expression information; constructing a mapping model between customer attributes and product characteristics based on the customer data; analyzing the customer data based on the mapping model to mine potential customer needs and quantify the intensity of those needs; and generating marketing solutions adapted to the electronic information industry scenario based on these potential needs. This solves the problems of difficulty in demand mining, extensive marketing methods, and lack of digital marketing capabilities among SMEs in the electronic information industry. While ensuring data compliance, it lowers the technical application threshold and achieves accurate demand identification, automatic generation of marketing solutions, and continuous model evolution.

This invention discloses an intelligent identification method for street-front shops based on an improved TF-IDF model, belonging to the field of intelligent shop identification technology. The method includes: extracting and normalizing internal customer information from the operator to obtain a local customer pool; performing a three-dimensional matching between a clean shop pool and the local customer pool to obtain a first pool of matched shops and a second pool of unmatched shops; forming text pairs using corresponding fields from the second shop pool and the local customer pool, calculating semantic similarity using an improved TF-IDF model, and identifying shops with similarity greater than a threshold as model-matched local shops; merging the first shop pool with the model-matched local shops to form the final local shop pool, and removing them from the clean shop pool to obtain a pool of shops from other networks; adding industry and product tags to the final local and other network shop pools to generate a shop profile library and outputting it to the marketing end. This invention can improve the accuracy of shop identification, reduce the cost of manual visits, and achieve one-time separation of local and other network shops for precise marketing.

The embodiment of the application provides a kind of wisdom shop data processing method and device based on multidimensional data analysis, and the alignment and mapping of space-time feature sequence are innovatively realized by collecting sensor, intelligent terminal and social media data.Multiple dimensional data fusion is carried out based on feature weight coefficient, and a commodity recommendation model is constructed using convolutional neural network and recurrent neural network.The system continuously optimizes the model performance through incremental learning, realizes personalized commodity recommendation and shop operation trend analysis.Introduce marketing decision optimization model, intelligently evaluate and dynamically optimize marketing strategy, and continuously update the model through feedback data.The method breaks through the limitation of traditional single data analysis, and provides a comprehensive data processing solution for precision marketing of wisdom shop.

The invention provides a marketing suggestion generation method, device and equipment for a target community and a storage medium, and the method comprises the steps: selecting a full-field user feature data stream of a target user group, which accords with a preset rule, from a user portrait system of the target community, reading a data compression operator combination corresponding to a marketing report type from an operator configuration library, each operator defines a specific data extraction and compression rule. And sequentially executing each data compression operator, performing multi-dimensional analysis and information extraction on the user feature data, generating a compressed feature description text fragment, and compressing the original mass data of millions of tokens to the scale of hundreds of tokens. According to a predefined template structure, all feature description text segments are combined in order to form a user portrait abstract, the user portrait abstract is embedded into a complete cue word template based on a feed-shot cue word construction method, it is ensured that LLM can generate a precise marketing strategy based on complete and refined user information, and the problems of data truncation and attention loss are effectively avoided.

This application discloses a modeling method and apparatus for customer conversion prediction, a customer marketing method and apparatus, equipment, and computer program product. The method includes: constructing dynamic panel data of sample customers; performing feature engineering processing on the dynamic panel data of sample customers using a preset feature engineering strategy to obtain multi-dimensional modeling feature data of sample customers; and performing adaptive modeling using a preset classification model based on the multi-dimensional modeling feature data of sample customers to obtain a customer conversion prediction model. This application effectively captures the impact of changes in customer behavior over time on marketing conversion by constructing a dynamic panel data structure and utilizing its "intra-group correlation and inter-group independence" characteristics. Feature engineering analysis and mining based on panel data ensure the effectiveness and robustness of the mined features. Through refined adaptation of different models and data structures, the model's accuracy in identifying high-potential conversion customers is improved, providing reliable support for precision marketing.

This invention provides a digital media promotion method and system, relating to the field of media promotion technology. By meticulously collecting population and consumption habit information of the target area and using correlation coefficients or cluster analysis to accurately classify the information correlation of the area, this invention achieves precise targeting of the audience. Simultaneously, by scientifically dividing the target area and identifying core target areas, resource allocation is optimized, enabling promotional resources to be more concentrated and effectively deployed in key areas. Furthermore, this method categorizes and deploys media information according to regional characteristics and sets flexible deployment strategies, improving promotional effectiveness and achieving precise marketing and continuous optimization. This provides strong support for enterprises to utilize digital media for promotion.

The invention discloses a multi-dimensional feature-based customer portrait grade construction method and device. The method comprises the following steps: step 10, collecting feature data of a customer; the feature data comprises basic information feature data, credit evaluation feature data and behavior feature data; step 20, preprocessing the feature data to obtain a feature vector; and step 30, based on the feature vector, obtaining a portrait grade result of the customer by using a multi-level decision model. According to the customer portrait level construction method and device based on the multi-dimensional features, the accuracy of risk management and precision marketing is improved.

The embodiment of the invention relates to the field of data processing, and provides a marketing method based on a customer portrait and a related device, and the method comprises the steps: obtaining customer behavior information of a target customer during consumption, carrying out the construction of the customer portrait according to the customer behavior information, and obtaining a target customer portrait, therefore, marketing activity preference information can be generated according to the target customer portrait, target marketing activity information corresponding to the target customer is further determined according to the marketing activity preference information, and the target marketing activity information can be sent to a terminal corresponding to the target customer. Precise marketing based on customer consumption behaviors can be realized, and the matching degree of marketing activities and customer demands and the customer response rate can be improved.

The invention relates to a two-dimensional code interaction system based on an outer package or a bottle cap of a beverage bottle. The method comprises the steps that a first-time interaction module obtains a two-dimensional code image, analyzes the two-dimensional code image to obtain a unique bottle body identifier and a server address, and generates a first-time interaction request in combination with an equipment encryption hardware identifier and user identity data; the mapping construction module verifies request validity and extracts core data to generate a mapping relation table; the user authentication module retrieves the mapping table through the re-scanned subsequent request, verifies the consistency of the device identifier and then generates a user identity authentication token; the recommendation generation module generates a user portrait based on the token associated social data, and generates an exclusive recommendation list in combination with historical interaction data. All the modules cooperate to provide more accurate services for users, and effective technical support is provided for enterprises to achieve accurate marketing and user management based on consumption scenes.

The application provides an intelligent marketing method and system driven by an ai large model, which comprises the following steps: extracting a behavior feature vector based on the micro interaction behavior of a user by using a multi-scale behavior mode coding network; extracting the core marketing interest of the user by using an adaptive multi-layer interest clustering, and generating a behavior label; analyzing the real influence of different marketing strategies on the purchase decision of the user by using double-layer causal reasoning, and outputting an individualized marketing effect label; scoring the applicability of the marketing strategy based on the individualized marketing effect label, and optimizing the recommended strategy by using a reinforcement learning framework; pushing the individualized marketing content to the user according to the individualized marketing strategy distribution result, and optimizing the recommended strategy by using a dynamic closed-loop feedback optimization model based on the real-time feedback of the user. The application overcomes the limitations of the existing precision marketing technology by introducing innovative technologies such as fine-grained behavior analysis, causal reasoning optimization and dynamic marketing strategy adjustment, and improves the precision of the recommendation and the marketing ROI.

The application is specifically an e-commerce analysis and diagnosis method based on artificial intelligence, comprising: behavior data collection; behavior analysis; comprehensive judgment; intention diagnosis and evaluation.In the application, a multi-dimensional analysis system is constructed, the behavior data of a user from the whole link of contacting a commodity to browsing details, comments and the like is comprehensively collected, a foundation is laid for accurate analysis, two core indexes of a matching coefficient and an interest coefficient are introduced, the former quantifies the matching degree of the user and the commodity information from the angles of browsing fluency, information acquisition efficiency, key information attention and the like, the latter combines three dimensions of comment interaction, historical portrait matching and picture browsing to mine the potential interest of the user, finally, the intention evaluation coefficient is calculated and graded by comprehensively adding the purchase behavior and the collection behavior, the purchase intention of the user can be more accurately judged, the waste of marketing resources is avoided, the conversion rate of marketing activities and the input-output ratio are improved, and a scientific basis is provided for accurate marketing of e-commerce.

The invention discloses a label data query method, system and device and a storage medium. The method comprises the steps that a query statement which is input by a user side and contains a label combination is received; analyzing the query statement based on a preset global tag key database to generate an abstract syntax tree; mapping nodes of the abstract syntax tree into bitmap operation and generating a bitmap execution plan, and executing the bitmap execution plan based on the two-dimensional bitmap matrix to obtain a bitmap result; and converting the user index in the bitmap result into a target user identifier list corresponding to the query statement through a user space bidirectional mapping model, and outputting the target user identifier list. Millisecond-level response of complex label combination query is realized, query efficiency and real-time performance are remarkably improved, system resource occupation is optimized, high-frequency query requirements of business scenes such as user portraits, precision marketing and real-time recommendation are effectively supported, and rapid and reliable technical support is provided for fine operation decision making of enterprises.

The invention provides an aquatic product merchant wholesale management interaction method and system, electronic equipment and a medium, and relates to the technical field of intelligent agricultural trade, and the method comprises the steps: integrating five controls of aquatic product information editing, income and expenditure record query, goods in and out management, supply chain management and customer management through a wholesale management interaction main interface. In the aspect of information management, aquatic product traceability information can be systematically input, income and expenditure records can be conveniently inquired, and a traceability system and financial management can be conveniently constructed. In the aspect of inventory management, inventory can be monitored in real time and early warning can be performed when the inventory is lower than a threshold value, so that inventory problems are avoided. In supply chain management, suppliers can be effectively managed, replenishment is performed according to inventory requirements, and stable supply is guaranteed. In terms of customer relationship management, customer information and transaction records can be comprehensively managed, and the satisfaction degree can be improved through accurate marketing. On the whole, a plurality of key management links are integrated, a one-stop solution is provided, the process is simplified, the efficiency is improved, and digital and intelligent development of aquatic wholesale business is promoted.