70 results about "Marketing efficiency" patented technology

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

The invention discloses an intelligent marketing system based on user portraits and behavior prediction, which belongs to the field of artificial intelligence, and comprises multi-source data acquisition and integration: a diversified data acquisition interface is configured, and user multi-type data can be acquired from multiple channels such as an e-commerce platform, social media, a search engine and an enterprise CRM system; comprising basic information, browsing and purchasing behavior records, social interaction information, search preferences and emotion-related text data. Data of different formats and sources are integrated into a unified data set by using preprocessing means such as data cleaning, duplicate removal and standardization; according to the invention, through accurate user portrait and behavior prediction, an enterprise can accurately grasp the demand preference of a target customer, accurate pushing of marketing information is realized, the hit rate and the conversion rate of marketing activities are greatly improved, and the marketing effect is effectively improved; according to user value and behavior prediction, marketing resources are reasonably allocated, resource waste is avoided, and enterprise marketing efficiency is improved.

The invention discloses a marketing strategy optimization management system based on six elements of order transaction, and belongs to the field of communication management systems, in terms of data acquisition, multi-source comprehensive information collection enables enterprises to perceive consumer demands in all directions and no longer blindly grope, a dynamic feature modeling unit fuses various kinds of data into six-dimensional feature vectors, and the six-dimensional feature vectors are integrated into a database; multiple factors of commodities, users and festivals are balanced and considered, a solid foundation is laid for a marketing strategy, a commodity-festival-user ternary association graph constructed by a festival graph generation engine enables commodities to be pushed in a targeted manner, a personalized strategy is generated by an intelligent decision module, the matching degree of the commodities and consumers is improved, and the marketing efficiency is improved. The overall continuous feedback optimization mechanism of the system enables the marketing strategy to be like a continuously evolved life entity, can better adapt to the market change, and improves the sales probability of commodities.

The invention discloses an electronic commerce precision marketing intelligent system and method fusing AI personalized recommendation. The system comprises a multi-source data acquisition module, a data preprocessing module, a user portrait construction module, a real-time behavior monitoring module, a recommendation algorithm module, a marketing scheme generation module, a marketing effect evaluation module and a feedback optimization module. According to the method, the comprehensive user portrait is constructed by integrating the multi-dimensional user data, and the mixed recommendation algorithm is applied, so that the user requirements can be deeply mined, accurate personalized recommendation is realized, and the matching degree between the recommendation result and the actual requirements of the user is improved; by adopting the real-time behavior monitoring module and the recommendation algorithm module, the recommendation strategy can be updated in time according to the real-time behavior of the user, so that the recommendation better fits the current shopping intention of the user; by adopting the marketing scheme generation module, diversified marketing schemes can be generated, an effective marketing effect evaluation and feedback optimization mechanism is combined, marketing activities can be continuously optimized, and the marketing efficiency and income of merchants are improved.

The invention relates to the technical field of user behavior analysis marketing, in particular to an intelligent marketing system and method based on customer behavior real-time calculation, and the system comprises an edge behavior collection unit, a cleaning and desensitization unit, a spatial-temporal feature coding unit, a real-time prediction unit and a collaborative decision-making unit. According to the method, an edge behavior acquisition unit captures native data such as page staying duration and contact tracks, a cleaning and desensitization unit generates compliance time sequence fragments, and a spatial-temporal feature coding unit converts the data into spatial-temporal feature vectors containing time decay weights, topological graph nodes and behavior integrity indexes; the real-time prediction unit fuses multi-dimensional vectors through a graph neural network and outputs real-time preference probability distribution of commodity categories, and the collaborative decision-making unit dynamically allocates computing power and generates an optimal marketing action set, so that the problems of large user real-time intention capture deviation and lagging computing power allocation of traditional marketing are solved, the marketing accuracy and timeliness are improved, and the marketing efficiency is improved. And resource waste is reduced.

The invention relates to the technical field of big data and artificial intelligence, and discloses an artificial intelligence advertisement design and marketing recommendation platform based on big data, which comprises a multi-source data acquisition module, a dynamic portrait modeling module, an AI advertisement generation engine, a cross-platform recommendation decision module and an effect tracking and compliance auditing module. By integrating multi-source data and utilizing an artificial intelligence algorithm to construct a label system, interests and demands of users can be accurately grasped, precise advertisement putting is realized, and the advertisement putting effect is improved; a personalized recommendation system generated through an AI advertisement generation engine can significantly improve the user satisfaction and participation degree, and through a cross-platform recommendation decision module, the distribution of advertisement resources is further optimized, and the overall marketing efficiency is improved; and the effect tracking and compliance auditing module ensures the transparency and legality of the advertisement activity, so that the advertisement putting is more accurate and effective.

The invention belongs to the technical field of brand propagation and marketing management, and particularly relates to a one-stop self-service brand propagation and marketing management intelligent platform, which comprises a brand resource management module used for creating and publishing the propagation content of a brand; the multi-channel marketing management module is used for performing multi-channel marketing and management on the brand propagation content published by the brand resource management module; the user data management and analysis module is used for managing and analyzing the user data; the marketing activity planning and executing module is used for planning and executing marketing activities; and the intelligent marketing decision support module is used for recommending personalized marketing activities to the user according to the user data analyzed by the user data management and analysis module. Through one-stop management and an automatic process, the time and labor cost of marketing activity planning, execution and evaluation are reduced, and the marketing efficiency is improved.

The invention relates to the technical field of big data analysis, and particularly discloses a marketing management system based on big data analysis. The system comprises a data fusion center, a real-time portrait engine, a multi-contact attribution analysis module, a strategy optimizer and an execution gateway, and multi-source user data are integrated, a user interest model is dynamically updated, contact contribution weights are quantized, and marketing strategies are intelligently generated. Universe user view construction, real-time interest tracking, accurate attribution analysis and automatic marketing decision making are realized, and finally the marketing efficiency and the input-output ratio are improved.

The invention discloses an enterprise overseas marketing automation service method based on multi-mode AI cooperation, and relates to the technical field of artificial intelligence and digital marketing. According to the method, enterprise multi-modal data is aggregated through an intelligent data center, an AI model is driven to automatically generate an independent station page and a marketing video adaptive to a target market, and the independent station page and the marketing video are synchronously published to a social media platform. A cross-platform intelligent customer service module is deployed in the system, user consultation is analyzed in real time, a preset notification rule is triggered, and response efficiency and compliance are guaranteed. And dynamically optimizing the display content of the master station based on a multi-modal AI scoring mechanism, and realizing accurate recommendation in combination with user behavior analysis. Compared with a traditional method, the system solves the problems of multi-platform operation splitting, manual response delay, poor content adaptability and the like through multi-AI model collaboration and automatic process integration, and remarkably improves the overseas marketing efficiency and precision.

The invention relates to the technical field of promotion, discloses a promotion management method and system based on big data, and particularly discloses a method for obtaining the order number and the number of browsers of a movie ticketing platform in a promotion period; dividing the promotion cycle into time periods, and marking the time periods of the promotion cycle according to the order demand coefficient to determine whether the time periods are marked as hot order placing time periods; establishing a dense classification model based on a convolutional neural network, and classifying each time period of the promotion cycle through the dense classification model to determine whether the time period belongs to a dense order placing time period; obtaining order prediction data of the dense order placing time period at a future target time point through the target prediction model; and selecting the dense order placing time period according to the promotion target time length to obtain a promotion plan time period, and determining the ticket putting amount corresponding to the promotion plan time period. According to the invention, the promotion and marketing efficiency can be effectively improved, and the promotion and marketing optimization method and system based on the data analysis and prediction model are realized, so that the sales conversion rate of promotion is improved.

The invention discloses a novel digital marketing collaboration method and system suitable for small and medium-sized banks, and relates to the field of marketing collaboration, and the method comprises the steps: obtaining the user attribute of a target user; constructing a user portrait of the target user according to the user attribute; analyzing the demand information through a preset CNN-BiLSTM model to determine a demand intention of the target user; performing matching degree verification on the demand intention according to the user portrait; converting the demand intention into a marketing task index; obtaining resource information of all departments; determining evaluation indexes of all departments according to department resource information and marketing task indexes in combination with game theory empowerment and multi-criterion compromise solution sorting; comparing the evaluation index with a preset evaluation threshold value; and if the evaluation index is smaller than a preset evaluation threshold value, determining a marketing task allocation department by using a preset screening model. The marketing efficiency of small and medium-sized banks can be effectively improved.

The invention provides a potential customer mining method and system based on data mining, and relates to the technical field of data mining, and the method comprises the steps: 1, collecting customer related data, including basic information, behavior data, transaction data and customer feedback data of customers; 2, cleaning, converting and integrating the collected data; according to the invention, through deep analysis of multi-dimensional customer data, customer groups with high potential purchase possibility can be accurately identified, blindness of a traditional marketing mode is avoided, an enterprise can intensively invest marketing resources to customers who are most likely to generate rewards, and through a prediction result based on a data mining model, the marketing efficiency of the enterprise is improved. According to the invention, enterprises can formulate personalized marketing strategies, accurately push product information and customize preferential activities according to potential customers, and the response rate of the customers to the marketing activities is improved, so that the sales cycle is shortened, and the marketing efficiency is improved.

The invention discloses a discount coupon issuing and verification method based on an offline store SaaS system, and the method comprises the following steps: A, a merchant creates a discount coupon activity in the SaaS system, sets the type, denomination, validity period and other information of discount coupons, and can select different types of discount coupons according to the demands and marketing strategies of the merchant, so that the discount coupons can be issued to the merchant; the coupons can be full-reduction coupons, discount coupons, gift coupons and the like, and meanwhile, merchants can set the denominations and the validity periods of the coupons so as to attract more customers. In the marketing aspect, coupon attributes are accurately set, target customers are accurately touched, the conversion rate is improved, abuse is avoided, operation, two-dimensional code receiving and cancel-after-verification are convenient and efficient, manpower and time cost is saved, data statistics is achieved, the system updates the coupon state in real time, cancel-after-verification information is fed back, reports are generated to assist merchants in analysis and decision making, and the marketing efficiency is improved. Consumer behaviors and preferences are deeply observed, powerful support is provided for optimizing marketing strategies, and the operation benefits and competitiveness of stores are effectively improved.

The application discloses a data processing method, device and equipment and a computer readable storage medium. The method comprises the following steps: querying a triple corresponding to entity information of current communication content; performing time segmentation on the current communication content based on the triple, and obtaining each conversation window; determining a basic emotion value corresponding to each conversation window based on user image data corresponding to each conversation window, and determining a first image emotion value corresponding to each conversation window based on the basic emotion value; determining a first voice emotion value based on user voice data corresponding to each conversation window and the basic emotion value; and determining a customer preference degree corresponding to each conversation window based on the first image emotion value and the first voice emotion value. The application enriches the data dimension by collecting more sufficient data in a specific scene, improves the success rate of online marketing activities, and improves the timeliness of data application and marketing efficiency in online business marketing.

The application discloses a kind of based on digital twin's cell customer marketing interaction method and system, method includes: constructing cell level, building level, room level digital twin three-dimensional model, support hierarchical down drilling;Establish the associated mapping of business data and model;Realize business data hierarchical visualization and interactive response, based on data identification target customer, generate flexible dispatching and track execution.The application realizes the deep integration of business and space, improves marketing efficiency and accuracy, and helps enterprise decision-making scientific and business transformation.

The invention discloses an intelligent live video marketing system based on big data analysis and artificial intelligence decision, which comprises a marketing product analysis unit, a self-adaptive processing analysis unit and a marketing information management output unit, relates to the technical field of intelligent live video, and solves the problem that the number of real-time bullet screens is large and cannot be answered one by one. In order to solve the technical problem that the marketing effect of live broadcast is reduced due to the fact that an appropriate marketing mode cannot be adopted according to user requirements in the prior art, marketing products are classified, the marketing conditions of the marketing products are analyzed in combination with historical data, and calculation is carried out by utilizing artificial intelligence in combination with real-time bullet screens in a live broadcast process, so that the marketing effect of the live broadcast is improved. According to the method, the current marketing mode is adjusted according to the calculation result, meanwhile, the adjusted marketing mode is monitored, the overall marketing efficiency is improved by increasing marketing activities, then, the requirements of users can be met by analyzing the real-time bullet screen, and the overall live marketing effect is further improved.

The invention relates to the technical field of digital marketing, and discloses an omnibearing intelligent advertisement management system, which comprises an advertisement data acquisition module, an intelligent evaluation and analysis module, a strategy optimization engine module, a cross-channel execution module, a closed-loop feedback module and a safety audit module, a logic deviation between advertisement content characteristics and user behavior data is recognized in real time through a multi-source cooperation mechanism, and cooperation conflicts are eliminated through a dynamic compensation algorithm; the environment sensing unit continuously tracks equipment state and network condition change, and triggers self-adaptive decision path reconstruction to prevent release deviation; the spectral analysis engine deconstructs and evaluates the composite advertisement materials in a layered mode, and element-level precise optimization is achieved; the emergency response mechanism verifies the strategy feasibility through sandbox rehearsal, and quickly deals with environment mutation and public opinion risks. The core problems of multi-source data fusion failure, strategy environment adaptation misalignment, composite material evaluation deviation, crisis response lag and the like are solved, and the advertisement putting accuracy and the marketing efficiency are improved.

The invention provides a cross-platform marketing data integration and analysis system based on a large model, and relates to the technical field of market analysis management, and the system comprises the following function modules: a data collection module which collects the multi-dimensional indexes of a product of each platform; the benefit analysis module is used for constructing a cross-platform marketing digital model and outputting a marketing benefit index; the credibility verification module is used for carrying out correlation test and finally obtaining a marketing benefit credibility result; and the investment scheme regulation and control module is used for adjusting a subsequent investment scheme according to the marketing benefit index, the marketing benefit credibility result and competitive product marketing data. According to the method, the marketing benefit index is continuously optimized and normalized through an online learning mechanism; meanwhile, a credibility verification mechanism and an intelligent investment scheme module are introduced based on sales fluctuation correlation test, and by means of automatic elimination of a last scheme, migration of budget and dynamic regulation and control of a scheme period according to competitive product change, the real-time performance and credibility of analysis and the scheme intelligent level are remarkably improved.

The invention belongs to the technical field of online and offline fusion, and particularly discloses an online and offline fusion culture activity marketing management method and system, and the method comprises the steps: carrying out the real-time collection and time synchronization of online user interaction behavior data and offline participant behavior data; constructing a dynamic potential energy field model based on the data, setting online interaction nodes and offline region nodes as model nodes, and calculating node potential energy values by synthesizing behavior intensity, geographical popularity, time attenuation factors and corresponding weights; inputting the model into a pre-trained diffusion type graph neural network, constructing an activity relation graph, simulating heat diffusion, and predicting a node potential energy change trend; and finally, calculating online and offline potential energy gradients, and automatically generating an execution resource scheduling instruction or a content pushing strategy when the online and offline potential energy gradients exceed a preset threshold value, thereby realizing adaptive adjustment of online and offline marketing contents and entity resources, solving the problem of heat dislocation, and improving marketing efficiency and user experience.

The invention belongs to the field of customer relationship management based on big data, and particularly relates to a customer relationship management system based on big data, which comprises a multi-source sensing and acquisition module; a scene modeling module; a scene prediction and decision module; a strategy execution and closed loop optimization module; according to the method, a closed loop of acquisition-modeling-prediction decision-execution-attribution retraining is constructed, multi-source data is opened to form global customer portraits and scene representation, and a strategy list of human, time and place is generated according to individual probability evaluation and risk score and is executed in real time, so that personalized marketing is realized, resource waste is reduced, and the marketing efficiency is improved. And furthermore, customer loyalty is remarkably improved, a wastage rate is reduced, supply and demand matching is improved, inventory turnover is accelerated, promotion homogenization is avoided, and stable increase of revenue is promoted.

The invention relates to an individual industrial and commercial tenant oriented credit intention prediction modeling method and system, and relates to the technical field of intelligent financial marketing, the credit intention prediction modeling method comprises the following steps: obtaining and cleaning client sample information of an operation subject, and screening credit direct influence features to construct a credit intention set; dividing the credit intention set into a loan positive sample set and a loan-free negative sample set, and constructing a training set and a test set according to a preset division proportion threshold; iteratively learning the training set through a machine learning algorithm to obtain an initial credit intention model; optimizing a plurality of initial credit and loan intention models through customer sample information in combination with a grid search cross validation algorithm, and calculating and comparing corresponding intention model evaluation indexes to obtain an optimal credit and loan intention model; through multi-source data integration, high credit intention clients are accurately identified, the accuracy of marketing targets is improved, automatic and intelligent credit intention prediction is realized, the subjectivity of artificial experience judgment is reduced, and the marketing efficiency is improved.

The invention provides a matching marketing method and system based on multi-dimensional customer obtaining clues. The method comprises the steps of performing data preprocessing on original customer clue data to obtain an initial clue pool; performing data cleaning on each clue in the initial clue pool, and performing quality evaluation on each clue after data cleaning by using a clue evaluation model to obtain a high-quality clue; calculating the similarity between customers based on the product data set in each high-quality clue, and constructing a multi-dimensional feature vector for each high-quality clue according to the similarity; constructing an ideal customer feature vector for each product based on each product data set, and calculating a dynamic matching degree between each high-quality clue and each product based on the multi-dimensional feature vector and the ideal customer feature vector; and realizing marketing conversion according to the dynamic matching degree. According to the invention, through identification and distribution of the high-intention clues, it is ensured that high-value customers enter an automatic process in time, resource waste is reduced, and overall marketing efficiency and customer satisfaction are improved.

The invention discloses a marketing method based on user behavior preference, which comprises the steps of data collection and expansion, user motivation deep analysis, personalized recommendation optimization, expanded and innovated marketing channel and user loyalty management, and user information in more dimensions is obtained by linking relative accounts under user mobile phone numbers. And after the receipts are obtained, associating the various data by identifying the common identifiers in the different data. According to the marketing method based on the user behavior preference, the information social media data and the Internet of Things data of the user are acquired, and the lottery drawing information, the official account operation and the coupon receiving behavior data of the user are extracted, so that the diversification of data sources is realized, and the marketing efficiency is improved by linking the relative account under the mobile phone number of the user. Therefore, user information in more dimensions is obtained, richer materials are provided for marketing, and deeper user behavior modes and preferences can be mined.

The present application relates to the field of auxiliary marketing, and discloses a method, device, medium, and electronic device for generating auxiliary marketing information. The method includes: extracting structured data; converting the structured data into triple information to obtain shallow triple information; obtaining a data table; performing variable screening operations on the data table to obtain a final data table; using the final data table to establish a basic classification prediction model; using the variables in the final data table as features, and determining the explanatory weight of each feature for the business data based on the business data associated with the user in the final data table; for each business data, determining the target feature based on the explanatory weight of each feature for the business data, and generating deep triple information based on the variable value of the target feature; constructing a knowledge graph using shallow triple information and deep triple information; querying the knowledge graph to obtain retrieval results, and generating auxiliary marketing information based on the retrieval results. This method can improve marketing efficiency and success rate.

The application provides a multi-channel cooperative target customer cultivation system and method, and relates to the technical field of computers. User data from multiple channels is received to identify the identity of a user and generate a unique marketing user identifier for the user. When a cultivation task is performed on the user, all channels associated with the marketing user identifier of the user are obtained, the highest priority single channel is selected according to a preset channel priority strategy, and the marketing content of this time is sent to the user only through the single channel. A cultivation workflow containing conditional branch nodes is constructed, the cultivation workflow is driven by user behavior events, and subsequent predefined branch actions are triggered based on user behavior events and conditional branch nodes. The application solves the problem of marketing resource waste and user experience fragmentation caused by data silos, realizes the transformation of marketing processes from extensive broadcasting to precise personalized reach, and improves marketing efficiency, customer experience and business conversion rate.

The invention relates to the financial field and the computer field, and particularly discloses a product marketing method and device, a medium and equipment. The method comprises the following steps: screening out a target number of target customers from a predetermined customer library; classifying the target customers at least based on the customer information of the target customers to obtain a plurality of customer groups; sending a target message of a target product activity item matched with each customer group to each target customer in the corresponding customer group; acquiring behavior information of each target customer for the target message; and determining target products interested by the target customers at least based on the behavior information of the target customers, so as to carry out product marketing on the corresponding target customers based on the target products. According to the method, product marketing can be performed on the target customer in a targeted manner, so that the product marketing direction is more reasonable, the customer requirements are met, the product marketing efficiency is improved, and the product marketing success rate is improved.

The invention discloses an intelligent marketing method and system based on multi-stage prediction model fusion. The method comprises the steps of obtaining user portrait feature data and historical marketing data of a target population to obtain initial data; performing prediction model training of three stages of connection, high intention and clicking on the initial data to obtain a connection model, a high intention model and a clicking model; generating a fusion model by combining the connection model, the high-intention model and the click model according to business requirements, and calculating a score corresponding to the target person according to the fusion model to generate a comprehensive score; and sorting the target population according to the comprehensive score, and selecting users whose scores meet requirements for resource delivery. By implementing the method provided by the invention, the overall marketing efficiency and accuracy can be realized, so that the problems of insufficient data utilization, label deviation and insufficient generalization ability of a single model in the prior art are solved.

The invention relates to the technical field of commodity sales optimization, and discloses an intelligent commodity recommendation and display system, which comprises an identification module, and is characterized in that the identification module records shoppers and articles through a first camera and a second camera, and generates shopper data and article data; and the analysis module calculates the purchase demand and purchase tendency of the shopper according to the data of the shopper so as to output an adjustment instruction. High-intention commodities are actively pushed to the front of customers, searching and hesitation time is shortened, the shopping experience of the customers is optimized, the commodities are automatically close to and directly face the sight line, light guide is assisted, taking is more convenient and visual, especially convenience is brought to old people, children or customers carrying objects, the satisfaction degree is improved, intervention is based on real behaviors instead of guessing, and the method is suitable for large-scale popularization and application. And invalid sales promotion is avoided, resources are concentrated for customers with real demands, the marketing efficiency is improved, tasks such as commodity display optimization, sales promotion display adjustment and the like are automatically completed, and the workload of frequently and manually arranging goods shelves by shop assistants is reduced.

The invention provides a method and a device for determining a product popularization scheme. The method comprises the following steps: acquiring basic data of a building material target customer; performing data cleaning on the basic data to obtain cleaned data; performing customer group division according to the cleaning data and a preset grouping model to obtain grouped customer data; matching is carried out according to the grouped customers and a preset product popularization scheme, and a first product popularization scheme is obtained; and dynamically adjusting the first product promotion scheme according to the obtained customer change data to obtain a target product promotion scheme. According to the invention, the product promotion scheme can be adjusted in real time according to the change condition of the customer demand, and the marketing efficiency and the product transaction rate can be improved.

The embodiment of the invention provides a marketing verbal skill generation method and device and electronic equipment, and relates to the technical field of financial science and technology. The marketing verbal skill generation method comprises the following steps: acquiring a customer intention label and a marketing stage label in a historical outbound marketing scene; using the fine-tuned large model to generate a marketing verbal skill according to the client intention label and the marketing stage label; and storing the generated marketing verbal skill in a marketing verbal skill library. According to the method, the outbound marketing efficiency can be improved.