208 results about "Sales promotion" patented technology

Methods, systems, and media for timing presentation of content are provided. In some embodiments, the method comprises: causing a first video content item to be presented on a user device of a user; wherein the video content item is provided by a video sharing service; identifying a promotion for the video sharing service that is to be presented to the user during a time window; determining a probability that the user will select the promotion if the promotion is presented at a first time point within the time window in connection with the first video content item; determining whether to present the promotion at the first time point based on at least on the probability; in response to determining that the promotion is to be presented at the first time point, causing the promotion to be presented on the user device at the first time point in connection with the first video content item; and, in response to determining that the promotion is not to be presented at the first time point, causing the promotion to be presented on the user device at a second time point in connection with a second video content item, wherein the second time point is within the time window and wherein the second time point is after the first time point.

The invention relates to the technical field of e-commerce marketing, in particular to a product marketing service platform system and method, and the system comprises a user behavior tracking module, a consumption intention analysis module, a promotion time optimization module, an inventory monitoring and discount adjustment module, and a precision marketing push module. According to the method, the commodity access frequency and cross browsing data are extracted and analyzed in real time, the consumption trend of the user in a short period is rapidly identified, consumption tendency parameters are generated, potential purchase behaviors are accurately predicted by using browsing behaviors and an interaction mode, the recommendation accuracy is improved, the real-time inventory data are combined, a discount strategy and inventory management are dynamically adjusted, and the recommendation efficiency is improved. According to the method, the promotion activity is ensured to be closely matched with the inventory state, the resource utilization efficiency is improved, precise marketing pushing is performed according to the user equipment preference, the user interaction experience is enhanced, the sales growth is promoted, and the market response speed and the marketing effect are remarkably improved.

The invention belongs to the intelligent information technology, and discloses an e-commerce platform operation system and method based on intelligent decision and dynamic optimization. The system comprises a plurality of modules of multi-source data acquisition and preprocessing, user portrait and demand prediction, intelligent commodity recommendation and the like. A recommendation algorithm fusing a knowledge graph and reinforcement learning and an inventory prediction and optimization algorithm based on dynamic time sequence analysis are innovatively adopted, and a reward function formula and an inventory cost function formula are arranged respectively. According to the method, data acquisition, user portrait construction, business operation and system optimization are realized according to collaborative operation of the system and the modules. The system can accurately recommend commodities, intelligently manage inventory, efficiently plan promotion activities, collaboratively optimize a supply chain, and improve the operation efficiency and commercial competitiveness of an e-commerce platform.

An AI-powered augmented reality (AR) system for optimizing product placement and customer loyalty. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce and shopping areas within a public virtual technology space), with the unit analyzing consumer movement patterns, engagement metrics, eye-tracking and purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, underutilized areas and high-quality product placements using heatmap analysis, other spatial computer algorithms and the live movement of consumers; • A model-based product placement recommender based on machine learning, which uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through it to ensure a conversion. • a customer-centric, real-time AR visualization interface configured to allow customers to experience digitally enhanced product presentations directly via mobile AR applications, smart glasses, or in-store digital kiosks, enabling customers to preview product placements, rotate and resize objects, and visualize interactive advertising content before purchasing; • a customized advertising and sales promotion engine configured to generate augmented reality-based marketing content and provide users with tailored promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information on location data or consumer interaction history, wherein the customized advertising and sales promotion engine personalizes the configuration instructions for advertising and sales promotion to encourage consumer interaction and achieve higher sales revenues; • A secure, cloud-based data processing and analytics unit that uses an automated engagement model to link online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes based on a variety of programmatic retail trends; • a privacy-friendly AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while complying with global data protection regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI calculations, and implemented via dynamic voltage scaling and energy management techniques for seamless and sustainable operation; The system according to claim 1, wherein the user interaction data capture unit further comprises an eye-tracking and sentiment analysis sub-module, wherein the sub-module detects facial expressions, emotional reactions, and attention levels of consumers in real time to determine the effectiveness of product placements and advertising strategies.

The invention discloses a business data intelligent analysis method and system based on data driving, and relates to the field of business data analysis. The business data intelligent analysis method based on data driving comprises the following steps: acquiring and preprocessing commodity purchase data, commodity sales data and commodity market data; analyzing the preprocessed commodity purchase data based on an association rule learning algorithm to obtain a commodity association degree matrix; according to the method, the association rule learning algorithm is applied to the commodity purchase data, so that the hidden relation between different commodities can be revealed, merchants are helped to optimize product layout and sales promotion strategies, the opportunity and revenue of cross sales are improved, and the commodity demand prediction efficiency is improved. Through deep demand prediction of sales data, merchants can predict future inventory demands more accurately, occurrence of excessive inventory and stockout conditions is avoided, the operation cost is reduced, and the resource utilization efficiency is improved.

The invention discloses an intelligent logistics supply chain management system, and the system comprises a block chain information sharing platform which is responsible for building an information sharing platform based on a block chain, inviting all participants on the supply chain to join, and storing and transmitting the data of each link of the supply chain through the distributed account book technology of the block chain; the product information tracing system is responsible for distributing a unique block chain ID for each fresh food and recording the circulation process of the fresh food in a supply chain; and the collaborative management platform is responsible for integrating and uniformly managing data of purchasing, warehousing, transportation and sales, predicting future sales trend and inventory demand change by using a random forest according to historical sales data and market trend information, and optimizing an inventory level, a reorder strategy, a sales strategy and a promotion activity plan in combination with a genetic algorithm, so as to achieve the purpose of sales promotion. The inventory cost is minimized, the sales demand is met, and the block chain information sharing platform and the collaborative management platform carry out data synchronization.

A digital promotion processing system may include a shopper device associated with a given shopper, and a promotion processing server. The promotion processing server may be configured to determine available digital promotions for a given product for purchase, each available digital promotion having different redemption terms. The promotion processing server may also be configured to cooperate with the shopper device to prompt the shopper to select one of the available digital promotions to apply toward purchase of the given product, store shopper promotion terms preference data based upon the selected digital promotion, and update the shopper promotion terms preference data based upon a plurality of new selections of available digital promotions. The promotion processing server may further be configured to generate and communicate a personalized digital promotion to the shopper device. The personalized digital promotion may have redemption terms based upon the updated shopper promotion terms preference data.

To provide an information display device capable of displaying various information helpful for customers in a gas station, etc., and widely taking approaches such as sales promotion and recommendation to the customers, and an information display system.SOLUTION: An information processing device 1 comprises: a vehicle photographing unit 10 which photographs a vehicle entering a facility; a vehicle number recognition unit 20 which recognizes a vehicle registration number from the image acquired by the vehicle photographing unit 10; a display device storage unit 30 which stores information on vehicle registration numbers and information for customers using the facility; an information display device 40 which can display information provided in the facility; a communication interface unit 50 which enables information transmission and reception with the outside; and a display device control unit 60 which controls ech unit. The display device control unit 60 selects or edits and displays the external provision information provided from an information providing source via the communication interface unit 50 in the information display unit 40 on the basis of information on vehicle registration numbers and information on customers who use the facility.SELECTED DRAWING: Figure 2

Methods and apparatus are disclosed regarding an e-commerce system that promotes sales of products based on product comparisons. Some embodiments may present a marketing message that justifies a price difference between presented products based on attribute differences between the presented products.

The invention relates to a data processing method for intelligent marketing products, and the method comprises the steps: obtaining the financial behavior data of a target user, carrying out the clustering through a machine learning algorithm, and selecting a target cluster through a preset threshold value; and defining a matching degree model to evaluate the comprehensive matching degree between the user and different loan products, and predicting the preference value of the target cluster. Then, sales promotion information is customized according to the sales target, and marketing influence factors of the sales promotion information are calculated; and calculating a recommendation score of each user for each loan product according to the matching degree and the marketing influence factor, calculating a comprehensive score according to the recommendation scores, dividing the loan products into priorities, and selecting a corresponding marketing strategy. And finally, collecting KPIs (Key Performance Indicators) related to marketing strategies, evaluating effects and adjusting strategy parameters. According to the invention, through accurate user data analysis and a personalized marketing strategy, the recommendation accuracy and marketing effect of loan products are improved, and the satisfaction and loyalty of users are enhanced.

The invention relates to an intelligent vending machine and a remote management system. The intelligent vending machine and the remote management system comprise the intelligent vending machine and a remote management platform. The intelligent vending machine is provided with a movable goods shelf or a mechanical arm used for storing and dynamically adjusting the positions of commodities. The sensor is used for monitoring commodity inventory, mechanical part states and environmental parameters, and is in bidirectional communication with the remote management platform through the communication module. The remote management platform comprises a commodity configuration module, generates a commodity position adjustment instruction based on real-time sales data, sales promotion activities or customer behavior analysis, and sends the instruction to the intelligent vending machine to realize real-time commodity layout adjustment. The system further comprises a fault prevention and self-repair module which analyzes sensor data through machine learning, predicts potential faults of the mechanical part and executes self-diagnosis. And the customer service module provides a video call function and realizes purchase suggestion, fault processing and refunding and changing services. According to the invention, intelligentization and adaptive optimization of commodity management are realized, and the operation efficiency and user experience of the vending machine are improved.

The invention discloses a new tea drink sales volume prediction method and system based on a graph neural network, and the method comprises the steps: building an algorithm model trained based on spatial features and time sequence features through employing the graph neural network; according to the invention, store sales volume prediction can be carried out in different festivals and holidays, sales promotion activities, weather conditions, store business districts and consumer groups. By constructing a graph network topology structure based on stores, modeling is performed on store information data by adopting an undirected weighted graph, and the influence of surrounding stores on the current store is fully considered, so that the sales volume of each store is predicted; the problem that a conventional method is inaccurate in prediction due to the industry characteristics of short commodity life cycle, old product degradation, high commodity renewal speed and the like in the new tea beverage industry is solved, the number of stockout times and the inventory cost of the commodities are reduced, and therefore the store income is increased.

To provide an information distribution apparatus, an information distribution method, and an information distribution program capable of distributing information (content) appropriate and highly usable for a user aboard a vehicle (e.g., train).SOLUTION: An information distribution apparatus includes: a boarding detection unit which detects a user aboard a vehicle; a user-related information acquisition unit which acquires user-related information related to the user; a vehicle-related information acquisition unit which acquires vehicle-related information related to the vehicle; and a sales promotion information distribution unit which distributes, when the user aboard the vehicle is detected, sales promotion information based on the user-related information and the vehicle-related information.SELECTED DRAWING: Figure 3

The invention relates to a supermarket fresh commodity sales volume prediction method based on big data analysis, and belongs to the technical field of commodity sales volume prediction, and the method comprises the following operation steps: 1, carrying out the data collection and preprocessing through sensor data, historical sales data and consumer behavior analysis; 2, summarizing the data of the three angles of the person and goods yard and processing the data; missing values and abnormal values are processed, and the abnormal values and features are selected. 3, establishing a data model; feature engineering is established, data segmentation is carried out, a prediction model is selected, and then prediction and evaluation are carried out. And 4, dynamically adjusting a pricing strategy based on the predicted demand quantity and supply condition. And 5, according to the predicted sales volume data, planning sales promotion activities, order quantities and distribution arrangements in advance. And the accuracy and the real-time performance of sales volume prediction are improved. The inventory loss is effectively reduced, and the sales management efficiency of fresh commodities is improved.

Individual consumer NFT behavior is collected and analyzed. Various offers from retailers are targeted to certain consumer NFT behavior along with other characteristics, such as demographics, location, time, and the like. Once a match is identified, an individualized coupon offer is made to the consumer. By offering relevant and personalized promotions, businesses can increase customer loyalty, engagement, enhance user experience and potentially increase sales.

The invention discloses a panoramic VR and offline retail combined technical service platform. The technical service platform comprises the following steps of panoramic image capture, virtual shopping guide system, interactive experience, data analysis and multi-platform compatibility. The method has the beneficial effects that the technical service platform combines a panoramic VR technology with offline retail to provide immersive shopping experience for consumers, and the users can enter virtual shops through VR equipment, browse commodities and in-shop environments in a 360-degree panoramic manner, even interact with virtual shopping guide and understand commodity details and sales promotion information, so that the shopping experience of the consumers is improved. A panoramic VR technology is combined with an offline retail system, a brand new shopping mode is provided, through personalized recommendation and immersive experience, the shopping experience and satisfaction of a user are enhanced, retailers are helped to optimize operation management, improve the sales conversion rate and expand the market range, the system has remarkable commercial value, the user stickiness and the shopping experience are enhanced, and the shopping experience of the retailers is improved. And retailers are helped to stand out in fierce market competition.

The invention provides a remote control method for wirelessly relieving various alarms, and relates to the technical field of wireless communication, the remote controller comprises a shell, a mainboard and a battery, the mainboard is provided with an RF antenna, an RF circuit and a single chip microcomputer, the battery of the remote controller provides a power supply for working of a circuit board, when the remote controller works, an A anti-theft device receiving instruction is preferentially sent, and the A anti-theft device receiving instruction is sent to the single chip microcomputer. According to the remote control method for wirelessly releasing the multiple alarms, the operation efficiency of a commodity retail store is remarkably improved, the remote controller only needs to press a button once, and the alarm of the multiple alarms can be released through one-time button pressing, so that the alarm of the multiple alarms can be released through one-time button pressing, and the remote control can be realized through one-time button pressing and one-time button pressing and one-time button pressing and one-time button pressing and one-time button pressing and one-time button pressing. According to the technical scheme, the releasing instructions can be sent to the different burglar alarms at the same time only when the burglar alarms are released, the alarm processing time is greatly shortened, sales promotion personnel can rapidly solve the alarm problem and put more time and energy into sales promotion activities, and therefore the overall operation efficiency of a store is improved.

The invention relates to the technical field of market analysis, in particular to a footwear market trend prediction system and method, and the system comprises a sales volume change analysis module, a time period influence analysis module, a promotion influence analysis module and a sales volume prediction evaluation module. According to the method, the change rate of the sales volume of the shoes can be effectively evaluated by analyzing the association between the sales volume data and the economic indexes, the specific influence of seasons and festivals on the sales volume is considered, the sales volume prediction is more suitable for the actual market condition by calculating the time period influence factors, and the sales promotion activity effect is analyzed. According to the method, the influence of the sales promotion activity on the sales volume can be evaluated, data support is provided for making an effective sales promotion strategy, the sales volume change trend is predicted through multi-dimensional analysis, the accuracy of market trend prediction is improved, resource allocation is optimized, an enterprise can more flexibly cope with market changes, and thus the market competitiveness and profitability are improved.

The invention relates to the field of artificial intelligence shopping, in particular to a shopping mall system of an intelligent search interest recommendation algorithm, which comprises a registration and login module used for selecting a login identity and a login mode and inputting login information; the factory cloud supply module is used for updating and deploying the commodity supply information in real time; the merchant service module is used for managing commodity orders, counting service of buyers and analyzing sold commodities; the promotion and distribution module is used for carrying out advertisement putting and promotion activity promotion based on a digital transmission channel, and is also used for helping merchants to carry out management setting on distributors and formulate distribution levels, regional distribution and shareholder bonuses; the financial module is used for providing balance and points of shop members for merchant users and providing commission calculation and commission withdrawal; according to the invention, the enterprise operation efficiency is improved, the cost is reduced, the brand influence and market competitiveness are improved, and an efficient, intelligent and integrated business management system is realized.

The invention discloses an online marketing coupon and promotion activity data tracking and optimizing system, and belongs to the technical field of marketing data processing, and the system comprises a data storage module which is used for storing data related to promotion activities; the data acquisition module is used for acquiring consumption record information of the system; the range setting module is used for analyzing data related to promotion activities to obtain a time interval and an activity effect threshold value; the promotion analysis module is used for analyzing the newly generated consumption records with cards and coupons according to the time interval and the activity effect threshold value to obtain an evaluation result of the promotion activity effect; and the optimization early warning module is used for carrying out early warning on the user according to the evaluation result so as to adjust the promotion strategy. According to the method, key indexes such as participation conditions, conversion effects and the like of cards, coupons and sales promotion activities are tracked by adopting an advanced data processing algorithm, the effects of different sales promotion strategies can be accurately evaluated, and real-time acquisition, efficient storage, intelligent analysis and dynamic optimization of online marketing data are realized.

The invention relates to the technical field of rural e-commerce marketing, and discloses a rural e-commerce precision marketing method based on big data, and the method comprises the steps: dividing user data sets of all regions based on the geographic positions and consumption records of users, including purchase and browsing behavior data; determining a user type according to the purchase frequency and the commodity preference; generating a marketing strategy set and determining an execution sequence; generating recommended commodities and a promotion activity list in combination with user types, stock and the like; and associating the data to generate a precision marketing scheme. The method further relates to user data boundary determination, user type subdivision, marketing strategy dynamic adjustment, consumption potential grade division, personalized recommendation strategy generation, marketing scheme verification and correction and the like. According to the method, accurate classification and regional differentiation marketing of users are realized through big data mining, the strategy is dynamically optimized, the marketing effect and the resource utilization rate are improved, and the method is suitable for accurate operation of a rural e-commerce platform.

Transaction and communication systems and methods are disclosed for vendors and promoters. Some embodiments disclosed herein provide a deal manager system comprising transaction data associated with the promotional activities of one or more promoters, such as promoters registered with the deal manager system. The deal manager system can process the transaction data and provide compensation to promoters for their promotional activities based at least partly thereon. In certain embodiments, at least a portion of the transaction data can be provided to one or more vendors. A vendor may direct that benefits or promotional material be provided to a subset of promoters based at least in part on the received transaction data. The deal manager system may include promoter contact information, and materials provided by the vendor may be conveyed by the deal manager system to certain promoters based on the same.

The invention relates to the technical field of electronic commerce, in particular to a digital marketing method and system based on a payment two-dimensional code and a mobile terminal.The method comprises the following steps that based on user behavior data, the two-dimensional code scanning frequency and the user retention time are calculated, and a primary user behavior analysis result is obtained; and two-dimensional code content parameters including discount strength, the type of promotion information and timeliness are adjusted, and optimized two-dimensional code parameter configuration is generated. According to the invention, through refining user behavior data such as two-dimensional code scanning frequency and user retention time, a marketing strategy is adjusted in real time, market demands are accurately reflected, rapid response of the strategy to market changes is enhanced, discount strength and sales promotion timeliness are optimized, and user participation degree is improved. The optimal position and time of the two-dimensional code are determined, the advertisement exposure rate and the marketing effect are improved, the cost and the effect are effectively balanced through real-time data-driven Bayesian optimization model adjustment and the use of a multi-objective optimization algorithm, activity scheduling is optimized, and the marketing return on investment is remarkably improved.

The invention discloses a cloud service system and method applied to logistics docking, and relates to the technical field of cloud services, and the method comprises the steps: receiving order data sent by an e-commerce platform and a dealer in real time, and obtaining the inventory data of each warehouse; analyzing order data, extracting communication features, analyzing association between order placing time and sales promotion activities, and obtaining an emergency order proportion in a specific time period of the target region; associating the order communication characteristics with the warehouse inventory data of the corresponding region, and generating an inventory allocation trigger signal when the order quantity exceeds an order quantity surge threshold value and the emergency order proportion exceeds a threshold value and is lower than an inventory safety threshold value within a preset duration after the promotion activity of the target region starts; based on the inventory allocation trigger signal, screening out surrounding warehouses, and sending a scheduling instruction to start emergency replenishment; and calling historical same-period promotion order data, analyzing an order growth trend and a replenishment amount, predicting a subsequent replenishment amount, and notifying related warehouses to prepare in advance.

The invention relates to the technical field of artificial intelligence and machine learning, in particular to an intelligent retail shelf monitoring system based on artificial intelligence image recognition. Comprising a data acquisition unit, an image recognition unit, a data processing and analysis unit, a feedback and decision support unit and a communication unit, wherein the data acquisition unit is used for acquiring state information of commodities on a shelf through a camera and a sensor which are mounted on the shelf. Multi-modal information is fused through the image recognition unit, a lightweight model is developed, a meta-learning mechanism is introduced, the recognition efficiency and precision are improved, the computing resource requirement is reduced, the model adaptability is enhanced, prediction, self-adaptive adjustment and customer subdivision of the future sales trend are generated by using a time sequence analysis model and other external factors, and the market competitiveness is improved. The sales data value is mined, the trend is accurately predicted, the inventory is dynamically optimized, retailers are helped to better plan purchasing plans and sales promotion activities, and the market competitiveness is improved.

The invention provides an e-commerce integrated order management system, and relates to the technical field of e-commerce, and the system comprises an order processing module which is used for receiving, splitting, merging and updating order states, automatically optimizing order circulation, improving the order processing efficiency, guaranteeing the real-time synchronization of the order states, and reducing the processing delay. In the invention, the data analysis module analyzes user behavior data (such as browsing, purchasing and returning) and market sales data in real time and predicts future commodity demands and sales trends by using the prediction model, and the system can automatically provide a real-time report for an operation team based on the data, so that the user experience is improved. The module helps to adjust the inventory replenishment cycle, sales promotion activities and commodity shelving plans, and has the core advantages that market changes are identified through big data analysis, response measures are automatically generated, the inventory management precision is improved, and the inventory overstock and stockout conditions are reduced.

To enhance convenience of promotions utilizing generative AI for customers using mobile terminals.SOLUTION: An information processing apparatus according to an embodiment comprises a first acquisition unit, a second acquisition unit, a first generation unit, a second generation unit, and an output control unit. The first acquisition unit acquires customer information related to a customer who enters a facility. The second acquisition unit acquires position information representing a position of the customer in the facility. The first generation unit generates first text information including the customer information and the position information. The second generation unit, by inputting the first text information into generative AI which generates a text related to a product handled in the facility in response to the input of the first text information, generates, based on a generative text generated by the generative AI, second text information for a promotion of the product described in the generative text. The output control unit outputs the second text information to a terminal device used by the customer.SELECTED DRAWING: Figure 3

According to one embodiment, an information processing device includes a control unit and a communication unit to communicate with an edge device. The control unit is configured to acquire an attribute of a customer visiting a store and generate an inquiry prompt for providing a promotional recommendation of merchandise items for sale at the store to the customer. The inquiry prompt is generated based on condition information including at least the attribute of the customer. The control unit inputs the inquiry prompt to a functionalized generative AI to generate a response, then extracts a merchandise item corresponding to description information in the generated response from among the various merchandise items available for sale at the store, and generates sales promotion information for the extracted merchandise item based on merchandise information of the extracted merchandise item and the description information in the generated response.

The invention belongs to the technical field of SaaS (Software as a Service) systems, and particularly relates to an SaaS system for interactive services, a merchant user side module comprises a merchant general situation display module, a commodity list module, an order list module, a member display module, a shop statistics module, a shop setting module and a commodity retrieval module; the master station management end module is used for a system administrator to manage and check the merchant user end module; the merchant general situation display module is used for displaying shop data, common functions, marketing activities and application tools of merchants; the SaaS system is high in applicability, can be suitable for various merchants such as fruits, vegetables, food, specialties, take-out, hotels and the like, is complete in function, improves interactivity among customers through sales promotion functions of various shops, can improve the sales volume of commodities, enables the merchants to check the sales state of the commodities at multiple angles at the background of the system, and improves the sales quality of the commodities. And merchants can conveniently and timely find problems existing in the commodities or possibly occurring problems.

The present invention relates to the field of retail data analysis technology, and specifically to a sales performance attribution analysis system and method based on store retail terminals. The data acquisition module provided by the present invention collects comprehensive data from multiple key data sources to ensure that no important information is missed, and provides a comprehensive and in-depth sales performance attribution analysis for store retail terminals. By accurately quantifying the contribution of customer factors, product factors and store operation factors to sales performance, store managers can more effectively allocate market promotion resources and design personalized marketing strategies based on accurate data, optimize product combinations, adjust pricing strategies and improve display methods to improve product sales efficiency and gross profit contribution. In addition, in terms of store operations, evaluating factors such as promotional activities and employee performance is helpful to reasonably arrange promotion plans and motivate employees, thereby improving the overall operation efficiency of stores.