178 results about "Digital marketing" patented technology

The invention relates to the technical field of digital marketing, in particular to an intelligent marketing data analysis system and method. According to the invention, through deep analysis of the user interaction data, the user behavior offset is accurately identified, so that the marketing strategy can be dynamically adjusted, and the putting accuracy is improved. By means of the change trend of the residence time sequence, the relative change rate is calculated in combination with the access preference, user interest hotspot distribution information is effectively extracted, and the pertinence of content recommendation is improved. Based on access path stability analysis of interest hotspots, a stable mode of user behaviors is extracted, and an advertisement putting interval is optimized, so that user loss caused by frequent putting is reduced. And user groups with fluency decline or obvious jumping behaviors are screened by utilizing change calculation of interaction fluency, so that the advertisement pushing rhythm is reasonably adjusted, and the advertisement experience is improved. The user access data is synthesized to analyze the conversion change of the user behavior after the advertisement is displayed, and the influence of different advertisement intervals on the user behavior is accurately evaluated.

The present invention relates to the field of intelligent marketing technology, specifically to a SAAS platform based on mobile digital marketing and its method, wherein the platform includes a behavior sequence module, an identity matching module, a conversion willingness evaluation module, a marketing link optimization module, and a reach strategy adjustment module. In the present invention, through the timestamp sorting and behavior pattern analysis of access data, it is possible to accurately identify the access stability between devices, effectively eliminate random interference, ensure the accuracy of identity recognition, classify unpaid behaviors, optimize marketing strategies, screen user interest preferences, match suitable marketing reach strategies in an automated manner, realize personalized push, improve user interaction rate, adjust push content and frequency, make marketing information more targeted, reduce user interference, improve click-through rate and stay time, improve user conversion rate, and optimize marketing links through a closed data loop to form a more efficient marketing strategy execution system.

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

The invention relates to an intelligent marketing content generation and optimization system, and belongs to the technical field of artificial intelligence and digital marketing. According to the system, through cooperative operation of the data acquisition module, the intelligent text generation module, the multi-modal synthesis module, the risk management and control module and the dynamic optimization module, whole-process closed-loop management of marketing content is realized. The data acquisition module captures unstructured data from a multi-source platform based on a user authorization protocol, semantic cleaning and structured processing are performed through a large model interface, and the problems of data dispersion and fragmentation are solved. The intelligent text generation module receives user parameters through an interactive interface, matches a preset prompt word bank, calls a large model interface to generate multi-version copywriting in batches, and supports concurrent processing of single-choice or multi-choice product lists. The multi-modal synthesis module extracts keywords according to copywriting semantics, matches pre-annotated visual materials and generates multilingual speech paraphrases, so as to ensure the consistency of graphic and text information. The risk management and control module intercepts sensitive content in real time based on an industry rule base, and guarantees output compliance. And the dynamic optimization module integrates the user behavior data to generate a delivery strategy, updates a prompt word bank and a generation model through a closed-loop feedback mechanism, and realizes dynamic adaptation of contents and market demands. Compared with a traditional method, the system has the advantages that the content generation efficiency, the cross-modal matching precision and the compliance control level are remarkably improved, and the system is suitable for the global digital marketing requirements of multiple industry scenes such as e-commerce, industrial equipment and cross-border trade.

The invention relates to the technical field of Internet of Things, in particular to a digital marketing management system and method for telecommunication services, and the system comprises a user behavior monitoring module, a path track extraction module, a dynamic work and rest modeling module, a behavior cycle analysis module and a user portrait generation module. According to the method, through group feature modeling of cellular communication data, accurate calculation of a behavior trend change rate, dynamic description of group behavior features and extraction of a high-frequency region node sequence, analysis of a behavior track is converted from a single-point discrete state to a path association mode, and recognition of a group activity mode is realized; based on group residence duration statistics and cross analysis of multi-dimensional behavior indexes, a time distribution model is constructed, a stable behavior interval and a typical mode are identified, the analysis capability of group behavior characteristics is improved, and in combination with holiday and festival characteristic identification and hot spot area distribution characteristics, marketing strategy matching is optimized, and the accuracy of a grouping recommendation strategy is improved.

To a digital marketing support system for a small and medium-sized enterprise in which the small and medium-sized enterprise maximizes effects of a website and an advertisement to develop an appropriate strategy.SOLUTION: A digital marketing support system for a small and medium-sized enterprise includes: information analysis means of the small and medium-sized enterprise by AI; means for providing a strategy and advice suitable for the small and medium-sized enterprise; and corresponding means considering characteristics of the small and medium-sized enterprise and a user.SELECTED DRAWING: Figure 1

The invention discloses a multi-agent cooperative driven AI marketing content generation method, and belongs to the technical field of artificial intelligence and digital marketing, the whole process of marketing content from planning to generation and auditing is completed through cooperative work of a plurality of AI agents with different functions, and the method comprises the steps that S1, a strategy planning agent plans the marketing content direction and key points, generating a content creative outline or scheme; s2, creating a specific content material by the creative generation agent according to the content creative outline or scheme; s3, the content auditing agent continuously monitors the generated content and audits the content; s4, the creative generation agent reads the modification suggestions and modifies the modification suggestions; s5, after the content auditing agent passes auditing, final marketing content is output; according to the multi-agent collaborative driven AI marketing content generation method provided by the invention, the AI agents with different functions cooperate with each other to jointly complete the whole process from content planning to generation and auditing, so that the generation quality and efficiency of the marketing content are remarkably improved.

The invention discloses a cross-border advertisement accurate delivery method, system and device based on a user portrait, relates to the technical field of cross-border digital marketing, and discloses the cross-border advertisement accurate delivery method, system and device based on the user portrait, which generates an accurate user portrait by integrating cross-regional behavior data. An advertisement putting strategy is dynamically calculated in combination with exchange rate, tax and logistics economic parameters, and compliance adaptation is performed based on legal and cultural rules, so that the problems of single data dimension, inaccurate user portraits and lack of economic variables in the prior art are solved; the method has the advantages that the cross-border advertisement putting accuracy and conversion effect are improved, and the compliance risk is reduced.

The invention relates to the field of digital marketing, and discloses a member loss prediction-based marketing promotion method, which comprises the following steps of: 1, acquiring behavior data, transaction data, social interaction data and advertisement putting data of members; step 2, preprocessing the data, including data cleaning, feature selection and data standardization; 3, establishing a member loss prediction model, and predicting whether the members are lost or not; 4, establishing a promotion channel effect prediction model, and evaluating the effect of each promotion channel; 5, calculating the weight value of each promotion channel, wherein the weight value reflects the cost performance of the promotion channel; and step 6, sorting the promotion channels based on the weight values. Through accurate analysis based on member loss prediction, corresponding delivery budget can be allocated to each promotion channel, and reasonable allocation of resources of different channels is ensured, so that promotion activities can be more accurately matched with target audiences, and resource waste is avoided.

The invention discloses a data processing method, device and system, and relates to the technical field of digital marketing. A specific embodiment of the method comprises the following steps: acquiring a rule comprising a trigger condition and a disposal strategy, generating an execution script corresponding to the rule, and determining an execution logic of a state machine instance corresponding to the rule by utilizing the execution script; in response to the monitored behavior event of the target user, transmitting the behavior event and a historical feature vector of the target user to a state machine instance, updating the historical feature vector of the target user to a current feature vector based on the state machine instance and the behavior event, and transmitting the current feature vector to a preset stream calculation engine; matching the current feature vector with a trigger condition in the rule based on a stream calculation engine; and under the condition that the matching is successful, executing the processing strategy in the matching rule on the target user. According to the embodiment, the user behavior event can be responded in real time, so that business requirements are met.

The invention discloses a multi-modal generation-based agricultural product brand digital marketing method and system, and the method and system collect multi-source data related to a target agricultural product, carry out the preprocessing, and construct an agricultural product culture knowledge graph; based on a graph convolutional network, a potential diffusion model and a multi-modal fusion Transform prediction network, constructing a visual generation model, receiving an agricultural product culture knowledge graph and a specific demand description statement input by a user by the visual generation model, and outputting a prediction value of a key marketing index; and performing parameter updating and iterative optimization on the visual generation model to form a closed loop, and applying the optimized visual generation model to complete the generation and effect prediction of the agricultural product marketing image-text. According to the method, a complete process from cultural element mining, visual content automatic generation, market effect prediction to strategy closed-loop optimization is constructed, and the problems of visual generation blindness and effect evaluation hysteresis in agricultural product brand digital marketing are solved.

The invention provides an electric power marketing digitalization method based on a business platform, and the method comprises the steps: recognizing an electric power marketing business scene, and generating a scene feature identifier; calling a capability matching engine of a service platform according to the scene feature identifier; matching a target atomic power set in an atomic power library of a business platform through the capability matching engine; dynamically arranging and generating a digital marketing process instance based on the target atomic power set; executing the digital marketing process instance, and collecting process operation data in real time; inputting the process operation data into a quality monitoring module of a service platform for anomaly detection; according to the anomaly detection result, triggering a process optimization instruction to a capability matching engine; and updating the atomic power matching strategy based on the process optimization instruction and re-executing the matching operation. The risk management and control capability and the operation efficiency of power marketing can be improved, and the economic benefits and the service stability of power enterprises are guaranteed.

The invention relates to the technical field of artificial intelligence-driven digital marketing, in particular to an AI-based new media intelligent marketing method, which comprises the following steps of: 1, carrying out cross-platform user behavior fusion modeling; 2, constructing a multi-modal content gene pool: analyzing a visual focus area and a text emotional tendency of historical content, and generating a content gene vector; 3, dynamic content generation and real-time optimization, wherein initial marketing content is generated according to the space-time behavior matrix retrieval gene bank; user interaction behaviors are monitored in real time, elements in a visual focus area are dynamically replaced, and the emotion intensity of the copywriting is adjusted; and step 4, closed-loop strategy optimization: a dynamic effect index is calculated by fusing multi-platform indexes, a gene variation mechanism is triggered when the index continuously decreases, and cross-platform compensation content is generated based on failed user behaviors. By analyzing the cross-platform behavior and the content preference of the user, the user demand can be accurately predicted, and the content more conforming to the personalized preference of the user is provided.

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 a construction scheme of a consumer financial marketing system. The construction scheme comprises the following steps: constructing a user full-life-cycle digital marketing system; constructing a customer data platform to integrate customer data; a data-driven closed-loop marketing whole process is realized; a marketing automation technology is applied; and an end-to-end marketing integration capability is constructed. According to the method, the reach rate is improved, and by constructing a user full-life-cycle digital marketing system, precise marketing and personalized services can be realized, so that the reach rate of marketing activities is remarkably improved.

The invention relates to the technical field of digital people, and provides a method and equipment for generating a digital people video, which can be applied to a digital marketing scene. According to the method, the generation of the digital human video is divided into an audio generation process and a video generation process, when the audio is generated, a target text, an emotion parameter and a reference audio are taken as input, and the target audio of the target text is broadcasted by the sound of a reference person in the reference video and a set emotion, so that sound cloning is realized, the degree of violation of the generated digital human video is reduced, and the user experience is improved. And the text features are extracted from two dimensions of characters and pronunciation, so that the pronunciation accuracy during sound cloning is ensured. When the video is generated, the cloned target audio and the cloned reference video are used as input, and the fidelity of the digital human video is improved through expression and mouth shape matching, so that a reference person can quickly and efficiently generate vivid digital human videos with different broadcast contents only by shooting one reference video without shooting one by one, thereby reducing the manufacturing cost of the digital human video, and improving the user experience. And the video production efficiency is improved.

The invention relates to the field of digital marketing, and discloses a video understanding-oriented hierarchical spatio-temporal semantic extraction method, which analyzes video content through a three-stage progressive analysis framework: firstly, detecting an object instance in a key frame sequence and constructing a spatio-temporal trajectory to ensure object identity continuity; modeling an object state evolution law by using a recurrent neural network, and quantifying an interaction relationship between objects; and finally, constructing an event causal graph based on the state mutation points, and generating a directed causal chain through a statistical causal test algorithm. Video content is abstracted into event causal logic from pixel features, a semantic path of'track-state-causal 'is formed, structured semantic description of decisions can be directly driven, video semantics and user behavior data are coupled, user preference portraits are dynamically constructed, and personalized material generation is guided.

To provide a system for solving the problem of sales improvement of a customer in consulting sales of digital marketing.SOLUTION: A system includes an AI advisor that proposes a pattern of sales improvement to a customer in consulting sales of digital marketing.SELECTED DRAWING: Figure 1

The invention provides a digital marketing method and system, a storage medium and a computer, and the method comprises the steps: obtaining multi-dimensional data of a customer group, encrypting the data, and integrating the encrypted data; carrying out feature extraction on the integrated data based on a distributed big data calculation framework; performing label setting on customers in the customer group based on the extracted features to form a to-be-screened customer group, and performing intelligent product matching according to the customers in the to-be-screened customer group to form a matching white list of the products and the customers; and a marketing model is constructed, the matched white list is evaluated and analyzed according to the marketing model, so that the purchase intention of the customer is quantified, and the intention customer is screened out for product marketing recommendation. According to the digital marketing method provided by the invention, the target intention customer can be accurately obtained, and the marketing conversion rate is improved.

The invention discloses a marketing management method and system based on machine learning, and relates to the field of digital marketing, and the method comprises the steps: collecting consumer transaction records and user interaction data, constructing a data transmission channel through a distributed message module, and carrying out the abnormal value detection and field standardization processing, and obtaining structured data; inputting the structured data into a feature calculation engine to obtain basic purchase behavior features and geographic spatial-temporal features, and generating a multi-dimensional behavior feature matrix through sliding window statistics and a time sequence decomposition algorithm; and inputting the multi-dimensional behavior feature matrix into a graph neural network for behavior pattern mining, and generating a user embedding vector. According to the method, the anti-fact data set is constructed through tendency score matching, the causal forest is utilized to quantify the grouping heterogeneous processing effect, the real increment income and the RO I of the strategy are accurately calculated, the deviation problem of traditional mean value evaluation is solved, and dynamic optimization of budget is realized.

The present invention provides a technology for precisely identifying the interest of an Internet user and dynamically adjusting an advertisement display according to the interest. That is, the present invention proposes a method and a system for advertisement utilizing AI gaze analysis, the method including the steps of: collecting image or video data including a user face through a user terminal (including a camera) to which an Internet advertisement is exposed so that the collected image or video data can be processed by the AI analysis tool installed in the user terminal or an advertisement server; extracting information related to the user's gaze from the collected image or video data by an analysis tool, and determining, on the basis of the user's gaze information, whether the user's gaze overlaps an advertisement display area in which the advertisement is posted; and changing an exposure method of the advertisement in the user terminal when the user's gaze is determined to be oriented toward the advertisement display area. Furthermore, the present invention also proposes a method capable of maximizing advertisement profits on the basis of analysis of a customer's gaze at a digital advertisement.

The invention relates to the technical field of digital marketing, and discloses a digital marketing system based on big data, which comprises a multi-modal data acquisition module used for collecting original behavior data of a user on an e-commerce platform, a social media platform and a search platform; the cross-modal feature alignment module is used for performing space-time alignment and feature mapping on texts, images and behavior sequences in the original behavior data to generate cross-platform alignment features; and the dual-channel generation type interest modeling module is used for extracting a time sequence mode through a dominant behavior channel based on cross-platform alignment features, generating hidden interest representation in combination with a potential vector channel, and separating long-term interest from short-term dynamic interest by utilizing orthogonal constraints. According to the method, a double-channel generation type interest modeling module is adopted, the hidden interest detection rate is increased, and compared with a static label matching method based on collaborative filtering in the prior art, the technical bottleneck that potential demands of users are not completely captured, and recommendation deviation is caused by long-term and short-term interest coupling is solved.

The invention discloses an intelligent video editing optimization system and method based on target audience prediction, and belongs to the technical field of artificial intelligence and digital marketing. According to the method, dynamic analysis of target audience features and real-time adaptation of a video editing strategy are realized through a programmed automatic process, and the problems of high manual dependency, poor cross-culture adaptability, strategy updating lagging and the like in the traditional technology are solved. The method specifically comprises the following steps: calling a third-party platform application program interface to obtain user behavior data, and generating a dynamic audience portrait by using an artificial intelligence large model; shot selection, duration distribution and culture adaptation of video content are optimized through a reinforcement learning algorithm on the basis of portraits, a shot selection module dynamically distributes technical parameter close-up and cost comparison content according to user role types, and a culture adaptation module calls a taboo library to automatically replace sensitive elements; a synchronous test is carried out by generating multiple versions of videos, closed-loop feedback iteration is realized based on a user click rate and a conversion rate index, and an editing strategy is continuously optimized. Compared with a traditional method, the system has the advantages that the cross-culture adaptation precision, the strategy response speed and the user conversion effect of the video content are remarkably improved through cooperation of the artificial intelligence model and the programmed decision engine, and the system is suitable for precision marketing requirements of multiple scenes such as cross-border e-commerce and industrial equipment.

The invention discloses a digital marketing management system based on amount and cost analysis. Relates to power systems. Comprising a data uploading unit, a data processing unit, a data analysis unit and a user interface, the data uploading unit is used for transmitting data to the data processing unit, the data processing unit is used for enabling the processed data to be used by the data analysis unit, and the data analysis unit is used for displaying an analysis result to a user through the user interface. And the user interface is used for a user to operate the interface and check and store the required analysis result. According to the invention, the electric charge accounting and issuing are no longer the end point of the marketing process, but the starting point of the front-end marketing link is fed back and supplemented; the defects of insufficient self-checking and self-correcting capability and demand side response lag caused by one-way transmission of the marketing process are overcome, reverse thinking and source tracing are achieved, and quality-effect re-improvement of each link of the marketing process is deduced.

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 belongs to the technical field of advertisement putting, and discloses an accurate customer obtaining and advertisement putting system and method based on big data. The method comprises the following steps: S1, multi-source data acquisition: acquiring Internet public data, enterprise private domain data and third-party compliance data to form a comprehensive data pool; s2, data structured storage: the collected data are classified and stored, and an index is established. According to the method, through the collaborative application of multiple technologies such as integrated distributed crawlers, generative adversarial networks, Kalman filtering, edge calculation, multiple linear regression and Lasso regularization, full-link optimization from data acquisition to effect landing is realized, the core problems of inaccurate customer acquisition and poor advertisement effect in the prior art are solved, and the service life of the advertisement is prolonged. Manual intervention cost is reduced through an automatic mechanism, the comprehensive goals of improving customer obtaining precision, optimizing advertisement effect, reducing marketing cost and guaranteeing long-term effectiveness are finally achieved, and a more systematic and more efficient technical support is provided for enterprise digital marketing.

The invention relates to a digital marketing rights and interests activity publishing method and system supporting agile configuration, and the method comprises the following steps: creating a marketing activity, and setting an activity standard condition; managing the rights and interests, newly adding the rights and interests, maintaining the rights and interests of various types, and associating and binding the created marketing activities with the existing rights and interests; by means of an existing data source and a script engine, multiple customer group rules are configured in a user-defined mode, and the created marketing activity is associated with the configured customer group rules; and when the user logs in the online platform, displaying a marketing activity interface and carrying out qualification judgment on the activity through a customer group rule on the marketing activity created on the online platform, and when the customer reaches an activity standard condition, sending corresponding rights and interests to the customer.

The invention relates to the technical field of digital marketing management, and discloses a Malus hupehensis cloud digital marketing service management platform. The system comprises a marketing service management module, a marketing data stability analysis module, a marketing strategy configuration module and a marketing execution optimization module. Wherein the marketing service management module collects global marketing data and constructs a multi-dimensional historical marketing data set; a marketing data stability analysis module traverses the data set for stability analysis, and outputs product, channel and user behavior stability factors; the marketing strategy configuration module takes the stability factors as indexes, and searches and determines a target marketing strategy decision value in a marketing strategy configuration space in a centralized manner; the marketing execution optimization module generates an execution scheme based on the decision value and optimizes execution process data in real time through a marketing strategy analyzer. According to the platform, the whole-process systematic management of marketing data is realized, the marketing management efficiency and strategy accuracy are improved, and enterprises are assisted to adapt to the change of a digital marketing environment.

The invention provides a digital marketing effect analysis method and system based on big data, and the method comprises the steps: obtaining browsing behavior data of a user for different marketing contents under different marketing channels, and integrating and storing the browsing behavior data; analyzing the browsing behavior data on the basis of the integration and storage results, determining progressive conversion rates of different marketing contents in each marketing stage under different marketing channels, performing correlation analysis on the progressive conversion rates of each marketing content in each marketing stage, and determining user conversion contributions under different marketing channels; and determining the return on investment of different marketing channels based on the user conversion contribution, and performing tabulation visualization display on the return on investment of different marketing channels. According to the method, the user can intuitively and effectively understand the marketing effects of different marketing channels, and meanwhile, the reliability and the accuracy of digital marketing effect analysis are improved.

The invention discloses a digital marketing content interaction generation method adaptive to multi-scene delivery of a large model, and relates to the technical field of interaction generation. According to the method, by setting an envelope boundary set modeling mechanism, behavior transition characteristics of a user in different content paths can be accurately described, high-value interaction nodes are effectively identified, and blindness and redundancy of content display are avoided; meanwhile, a double-channel feedback mechanism divides real-time behaviors of the user into high-speed feedback and verification feedback, collaborative linkage of high-frequency response and long-term optimization is achieved, and instantaneity of content scheduling and stability of a strategy are both considered. And in combination with a dynamic exposure frequency matrix and an ordering and rearranging strategy, content delivery has structural self-adaption and multi-scene generalization capabilities, and personalized accuracy and user response efficiency of digital marketing driven by a large model are remarkably improved.