TW201915872A - Method for producing personalized consumption information and a management system thereof - Google Patents

Abstract

The disclosure is related to a method for producing personalized consumption information and a management system thereof. In the method, the system obtains consumption data from every consumer's portable device for creating a personal consumption journey. Multiple personal consumption journeys form a database of personal consumption journeys. The consumption data of multiple consumers forms a big-data database. When the consumption data is analyzed and then undergoing classification, the system can acquire available data. For example, the system analyzes the interactions made by the consumers when they see the personalized consumption information so as to form a deep-learning database. The result of deep learning is used to modify the database of personal consumption journeys and the big-data database. When a consumption type with respect to the consumer is obtained through the analysis, a persona for the consumer can be created or modified. Personalized consumption information is therefore produced accordingly.

Description

 Personalized consumption information generation method and management system  

A method and system for generating consumer information, in particular, a method and related management system for generating personalized consumption information after obtaining data of personal consumption journeys through massive data analysis and deep learning.

Big data refers to the collection of activity data of a wide variety of users through various devices. For example, a web service provider can obtain data of a user browsing the web through records (such as cookies) in a web browser, so that the user's preferences and interests can be analyzed according to the attributes of the visited web page; the social media service provider (such as Facebook, Line, etc.) can easily obtain the user's activity data in the social media, in addition to the individual information, you can also analyze each group (such as gender, age, academic experience, region, country) The trend of community activities; operators who operate e-commerce can get the shopping materials of each consumer, so they can get the consumption trends of consumers in different groups to facilitate subsequent group marketing. Other applications include, if you can get long-term traffic data, you can let the traffic authorities predict the traffic conditions at each time, and even solve the traffic problems because of the pre-emptive measures; judge the government, from the people The overall huge amount of data can be analyzed to get public opinion trends, and more correct decisions can be made.

Obviously, huge amounts of data can provide users with a more convenient life, provide businesses to find business opportunities, and let the government make the right decisions. However, business organizations or government units with huge amounts of data can obtain the data they need, and huge amounts of data. Continue to bloom, but still hide a lot of information yet to be developed.

According to the embodiment of the method and management system for the localized consumption information disclosed in the disclosure, when the system obtains the consumer's consumption data through the software program in the consumer-side portable device, the system can obtain the consumer's individual according to the method. The consumer journey, in turn, achieves the overall consumption trend of massive data analysis, and further cooperates with deep learning technology to obtain the connection between consumers and back-end business activities, and can correct the trend analysis results in personal consumption journeys and huge amounts of data.

According to an embodiment of a method for generating personalized personal consumption information, the method includes obtaining, by a management system, consumption data from a portable device carried by a consumer, which may include location information, time information, and consumption content, and thus may be formed in the management system. A person's consumption journey, a plurality of personal consumption journeys can establish a database of human consumption journeys, in which a consumer description file corresponding to each consumer is recorded. Then, the consumption data is analyzed to obtain a consumption type. In addition to establishing or correcting the consumer description file of the corresponding consumer, the consumer profile can be judged from the consumer description file, and a personalized consumption information is generated according to the consumer feature.

Furthermore, after obtaining consumption data of a plurality of consumers, the system can establish a huge database, and the huge database is used to record the consumption data of multiple groups formed by multiple consumers obtained from the personal consumption journey database. Including the data generated by other non-consumer behaviors of consumers, the data generated by consumption data and non-consumption behaviors are analyzed by a data to obtain an overall trend, for example, the consumption trend of the business circle, the consumption analysis of the store, and an area. Consumer activity, and/or group consumption trends.

Then, the consumption data obtained from the huge amount of data, and/or the data generated by the non-consumer behavior, are classified after the data analysis, and the effective information is obtained, including analyzing the interaction behavior of each consumer with various personalized consumption information. Establish a deep learning database and use it to build a consumer description file, or feed back to the personal consumption journey database and a huge database according to the results of the deep learning in the deep learning database, so as to correct the corresponding consumers according to the consumption data at any time. Consumer description file.

In an embodiment of the management system, a computer system is included, which receives consumption data transmitted by a plurality of consumer portable devices through the network, and generates a personalized consumption information for each consumer; and includes a personal consumption journey database for recording The consumption data formed by the process of consumption by a plurality of consumers, each consumption data corresponds to a personal consumption journey of each consumer, and each consumption data includes at least one consumption time and one consumption location; including a huge amount of database for recording The consumption data of multiple groups formed by multiple consumers obtained by the personal consumption journey database further includes data generated by other non-consumer behaviors of consumers; the data generated by consumption data and non-consumption behaviors are analyzed by one data. The overall trend, including consumer trends in the business circle, consumer analysis of stores, consumer activity in a region, and/or group consumption trends; including a deep learning database to record consumption data from huge databases, And/or data generated by non-consumer behavior, establishing a consumer profile for each consumer And learn to give each consumer reaction to the personal consumer information, and to correct personal consumption data repository journey through the massive database of data analysis obtained.

Further, in the step of generating personalized consumption information by the foregoing computer system, the consumption data is also obtained from the portable device carried by each consumer, and after analyzing the consumption data, the consumption type of the consumer can be obtained to establish or modify the consumer description. The file, therefore, the system can determine the consumer characteristics from the consumer description file, and generate personalized consumption information according to the consumer characteristics.

In another embodiment, the management system can be coupled to a parking management system for obtaining parking information for the consumer.

The management system can be implemented by a computer system. The software module operating in the computer system mainly comprises a consumption data collection module for collecting consumption data transmitted by a plurality of consumer portable devices; a third party data collection module For obtaining non-consumer behavior data other than consumption data obtained by a plurality of consumers through the portable device; and a data analysis module for analyzing consumption data of the consumer in one or more consumptions for obtaining The path, location, time and consumption status of each consumption to establish a personal consumption journey database; analyze the consumption data of multiple consumers, establish a huge database to judge the overall or group consumption trends; and through a machine learning model The group drills down on consumer data with complex structures to build a deep learning database.

In order to further understand the technology, method and effect of the present invention in order to achieve the intended purpose, reference should be made to the detailed description and drawings of the present invention. The drawings are to be considered in all respects as illustrative and not restrictive

10‧‧‧ Personal Consumption Journey Database

101‧‧‧ Consumer Identification Information

102‧‧‧ Path

103‧‧‧Consumer location

104‧‧‧Amount of consumption

105‧‧‧Parking Information

12‧‧‧ huge database

121‧‧‧ overall trend

14‧‧‧Deep Learning Database

141‧‧‧ consumer description file

20‧‧‧ computer system

22‧‧‧Network

201,202,203‧‧‧ portable device

205‧‧‧ consumer description file

35‧‧‧Parking Management System

33‧‧‧Management system

30‧‧‧Network

31‧‧‧Portable device

301‧‧‧Store one

302‧‧‧Store 2

303‧‧‧Store 3

40‧‧‧Management system

411,412,413‧‧‧ portable device

403‧‧‧Consumer data collection module

402‧‧‧Data Analysis Module

408‧‧‧ machine learning module

404‧‧‧ Third-party data collection module

405‧‧‧Sales data collection module

406‧‧‧Advertising Demand Analysis Module

407‧‧‧Advertising Owners

401‧‧‧Decision Module

42‧‧‧Network

61‧‧‧ Consumer Images

62‧‧‧Consumer attributes

63‧‧‧ Consumer background

64‧‧‧Personal description

65‧‧‧Requirements/objectives

66‧‧‧Emotions/attitudes

67‧‧‧ Description of the behavior

Step S501~S519‧‧‧ Personalized consumption information generation process

Step S701~S713‧‧‧ Personalized consumption information generation process

Step S801~S807‧‧‧ Personalized consumption information generation process

1 is a diagram showing an embodiment of a database architecture of a management system for generating personalized personal consumption information; FIG. 2 is a schematic diagram showing a computer architecture embodiment of a management system; and FIG. 3 is a diagram showing an embodiment of a management system connection parking management system. FIG. 4 is a schematic diagram showing an embodiment of a management system; FIG. 5 is a flowchart showing an embodiment of a method for generating personalized consumption information; FIG. 6 is a view showing an embodiment of a consumer profile obtained by using a management system; A flow diagram of an embodiment for describing personalized consumption information that yields suitability; Figure 8 shows a flow diagram of one of the applications for generating personalized personal consumption information.

The disclosure reveals a method and management system for localized consumer information generation, mainly through software programs (such as APP) installed on consumer portable devices (such as mobile phones) to obtain consumers in different locations, different times and specific situations. Consumption data, which may include events that record consumer driving or passenger vehicles arriving in a certain business district or consumer location, as well as consumption data generated at the destination, which can be comprehensively classified, analyzed, and combined with big data analysis. Appropriate personal consumption information.

The management system for generating the personalized personal consumption information is mainly constructed in several databases, including the embodiment of the database structure of the management system for generating the personalized personal consumption information shown in FIG. 1, which shows that the individual can be obtained in the first time. The personal consumption journey database 10 of the consumption data, the data in the personal consumption journey database 10 can be used to track the consumer's process of collecting and acquiring services, and collecting the data therein. Among them, the personal consumption journey database 10 is established by a plurality of personal consumption journeys, in which a consumer profile (persona) corresponding to each consumer is recorded.

According to an embodiment, the system may obtain the consumer identification information 101 through a consumer portable device (such as a software program executed therein) as part of the consumer identification information 101, such as a user ID of the login system, or It is a unique hardware code, phone number, email, etc. that consumers can use to identify various information about consumers. The system can also obtain the path 102 of the consumer's movement through the consumer portable device, including the path of driving, cycling, riding, walking, etc. to the consumer, and the path 102 formed by the continuous movement can be obtained by the positioning circuit in the portable device. . The system can obtain consumption data such as the consumption amount 104 reaching a certain consumption place 103 and completing consumption through the consumer portable device. One of the ways in which these consumption data is obtained is through a software program executed in the portable device, which assists the consumer in completing the consumption, and also records the consumption data such as time, path, place, amount, and the like. It is worth mentioning that, in an embodiment, the management system described in the disclosure can obtain the parking information 105 of the consumer because the data of the parking management system can be connected, including the parking time, the parking location and the time of leaving the parking space. The management system can obtain the events related to the consumption activities such as the parking time of the consumer, the parking location, and the like, and can be one of the consumption data for performing the analysis.

The management system includes a huge amount of database 12, which can collect consumption data generated by multiple consumption behaviors of multiple people (which can form multiple groups) executed at different times and in different places from the personal consumption journey database 10, in addition to the consumption data. Obtained from the personal consumption journey, including data generated by consumers in other non-consumer behaviors, such as consumer profiles (age, gender, academic experience, etc.), activities in the social network (such as posts, responses, By praise, etc., places that have been visited (such as punching locations, global positioning information), and preferences (such as the results of data analysis in the community). Through the data analysis of the data generated by the huge amount of database 12 for consumption data and non-consumption behavior, an overall trend 121 is obtained, and the overall trend 121 of the large-scale database is, for example, a consumption trend of a business circle, a consumption analysis of a store, and an area. Consumer activities, and/or group consumption trends, group of consumers identified by data such as race, age, gender, and academic experience can be derived from the analysis of huge amounts of data.

The management system further includes a deep learning database 14 for recording consumption data obtained from the huge amount of database 12, or adding data generated by non-consumer behavior, performing classification after data analysis, and obtaining effective information, including analyzing consumers. Faced with interactive behaviors of various personalized consumer information (such as advertising), such as ignoring advertising information (such as deleting advertisements), clicking on advertising information (such as entering advertising links), and displaying advertising messages, etc. Consumers can pay for advertisers for a variety of consumer information, including pay for performance (PFP) or pay for click. Even after obtaining the correct information after learning from the diverse information, the personal consumption journey database 10 and the data obtained by the data analysis in the huge database 12 can be corrected, and the consumer description file 141 is created in the personal consumption journey database 10, According to the results of the deep learning in the deep learning database, the personal consumption journey database 10 and the huge database 12 can be fed back, and the consumer description file 141 can be modified according to the consumption data at any time.

There is a data computing system behind each database, which is mainly implemented by a computer system. Refer to the schematic diagram of the computer architecture of the management system shown in Figure 2. In this example, the personal consumption journey database 10, huge amount The database 12 and the deep learning database 14 are connected to a computer system 20, and the computer system 20 can be implemented in a large-scale computer in a cluster manner, or can be a distributed computing system, or a specific servo system, which is responsible for computing and generating appropriate personal consumption. Information consumption data.

The communication circuit of the computer system 20 obtains data generated by each consumer portable device (201, 202, 203) via the network 22. The portable devices 201, 202, 203 can be various forms of personal computer devices, wherein the software program for communicating with the management system is executed, and the software program can be executed. Provide consumers with information on various consumer needs, such as parking information, information on consumer sites (offers, prices, new products, etc.), other locality information, and even solutions for mobile payment. Consumers are also required to log in to the system. The management system can obtain the consumer identification information through the consumer portable device, and obtain the personal consumption data. After the computer system 20 performs the analysis and analysis, the consumer's personal consumption history can be obtained to establish the personal consumption journey database 10 .

The personal consumption history of the consumer recorded in the personal consumption journey database 10. For example, the data shows that the consumer arrives at the business circle A at a certain time on a certain day of the month of the year; according to the data of the parking management system, the parking position B of the vehicle that the consumer drives or rides can be known; the data display When the consumer goes to the restaurant C to eat, the system can obtain consumption data including consumption items, time and amount; then go to the store D to purchase, the system obtains the consumption item, time and amount; then goes to the shopping center E to shop at a certain shop. F shopping, even if there is no consumption amount, but the system also has a stay time; after a coffee shop G point a cup of coffee; after the system based on location information to know that consumers leave the coffee shop G to drive, from the parking management system The time when the vehicle leaves the parking position B. The above consumption data includes location information A, B, C, D, E, F, G, etc. of a number of consumer activities, plus consumption items, time, amount and other information, can comprehensively determine the consumer's current consumption journey. If combined with consumer history records, or may include relevant interactive materials of the consumer social media, the computer system 20 can analyze the consumer's consumption habits (which can be derived from places of habitual consumption, consumption items, consumption categories), preferences. (Analysis of shopping and shopping projects), economic ability (from the average amount of consumption), so that the management system can summarize the consumer's personal consumption data, become part of the consumer profile.

The personal consumption journey database 10 provides a large amount of personal consumption data, which establishes a huge amount of database 12, and the data of the huge database 12 can be calculated by the computer system 20 to obtain an overall consumption trend, such as a certain region and a business district. Consumer activities within the group, or group consumption trends, such as a certain age group, gender, professional consumer spending at a certain time. For example, from the huge amount of data analysis, the consumption power of certain ethnic groups can be derived. After the location analysis, it can be concluded which group has a higher consumption power in a certain business circle, and can analyze the distribution of daily consumption activities in a week. .

Then, the deep learning database 14 takes over, and the computer system 20 can analyze the huge amount of data obtained. For example, the multiple personal consumption history of a certain consumer and the consumption history of the group of the consumer can exclude some accidents. The occurrence of consumer behavior, even the wrong information, and the acquisition of effective data, can be combined with other non-consumer behavior data of consumers to form a data that correctly describes a consumer and establish a consumer description file 205. Moreover, through machine learning, the operations and analysis are repeated repeatedly, and the data of the personal consumption journey database 10 and the huge database 12 can be corrected to correct the consumer description file 205.

It is worth mentioning that when the management system obtains consumption data from the software program executed by the portable device 201, 202, 203, the consumption data can be immediately transmitted to the computer system 20 via the network 20, and the personal consumption journey, huge amount of data and deep learning are continuously established. In the process, the consumer information (such as an advertisement) operator can derive the characteristics of the consumer according to the consumer attribute in the established consumer profile 205, and broadcast the consumption information (such as an advertisement) through the management system, especially the appropriate place. Sexual consumer consumption information, that is, the management system can grasp the position of the consumer, and broadcast the appropriate consumption information according to the marketing needs of the consumer information industry. Through the software programs in the portable devices 201, 202, 203, the consumer can further obtain the response of the consumer information, for example, to obtain further actions of the consumer, including closing the push message, clicking on the push message, and even having the next step. action. These actions are part of the consumer data, and continue to build personal consumption journeys, analysis of huge amounts of data, and deep learning.

According to the above embodiment, the management system can obtain the parking information of the consumer in combination with the parking management system. For an embodiment, reference may be made to the system architecture diagram shown in FIG. 3.

The management system 33 is connected to the parking management system 35 via the network 30. The company operating the parking management system 35 can employ a manual registration of parking spaces near the parking space, including recording whether the parking location has a vehicle, as well as the vehicle number, parking time, departure time, etc. Or get parking information for each parking space through various positioning technologies managed by the parking lot. In this way, the management system 33 can stop to obtain the parking information in the specific business district or the vicinity of the consumer from the parking management system 35, and compare the location information generated by the consumer portable device 31 with the vehicle information established through the login management system. By getting parking information related to the consumer, it is possible to accurately determine the parking activities of the consumer in the vicinity of the business district or the consumer.

In a particular embodiment, the vehicle information is set by the software program executed in the portable device 31, with the user identification code corresponding to one or more vehicle information. Once the consumer is combined with the parking information, the management system can grasp the consumer's consumption activities in the business circle, and then cooperate with nearby stores (store one 301, store two 302 and store three 303) to provide preferential treatment for the consumption deduction of parking fees. This can attract consumers to come to spend.

The parking management system 35 can provide the latest parking space information to the management system 33 at any time. When the consumer holds the portable device 31 and drives to the store 301, when the vehicle stops at a nearby parking space, the servo system of the parking management system 35 can be The above method obtains parking information, including obtaining the vehicle number, parking location and parking time. At this time, the user goes to the store 301 to consume, and the service provided by the management system 33 in combination with the parking management system 35 allows the consumer to deduct all or part of the parking fee at the store 301. If the consumer continues to have other consumption, the consumer data is similarly transmitted to the management system 33, and the management system 33 receives one or more consumer information against the user identification code from the consumer's portable device 31 via the network 30. The system collects consumption information. The consumption information may include the amount of consumption, items, time, entity information, etc., and any combination of one or more kinds of information may be the basis for forming a deduction of parking fees.

Moreover, once the parking information of a certain business circle or a consumer place can be obtained according to the data of the parking management system 35, the management system can judge the consumption habits of the deeply-learned consumers according to the consumer description file, and actively provide the consumers. Parking information that can be parked near a business district or a consumer location, or parking information near a residence where you return to your home from a consumer location. Even parking space navigation services are available.

In the embodiment architecture diagram of the management system shown in FIG. 4, the work performed by the management system 40 can be implemented by a hardware circuit in a computer system with a software module. In the hardware part, mainly including executing various software programs. The processor and the memory also include a database for establishing huge amounts of data for individuals or groups, and data of consumption activities are obtained from the portable devices 411, 412, 413 of the consumer through communication technology and data collection technology. Preferably, the portable devices 411, 412, 413 are managed. The specific software program provided by the system 40 allows the consumer to log in to the system and agrees that the management system 40 collects data generated by the consumer's consumption activities. Thus, the management system 40 can provide consumers with better consumption information through data analysis. The personalized consumption information disclosed in the disclosure can provide consumers with more meaningful and appropriate services if they cooperate with the commercial activities of the consumer places, business districts and stores.

In this example, the management system 40 retrieves the consumption data from the portable device 411, 412, 413 via the network 42. When the consumer carries the portable device 411, 412, 413, the software program executed in the portable device 411, 412, 413 starts collecting data, and the software model in the management system 40 is used. Component processing. According to this embodiment, the software modules are distinguished by functions, and the programmer can combine or separate the functions by various program methods.

The management system 40 can obtain the consumer data in a plurality of ways, and can also directly request the consumer to fill out the questionnaire, and further includes a consumption data collection module 403. When the management system 40 receives the data generated by each terminal portable device 411, 412, 413, the consumption data is collected. The module 403 is configured to collect data, and perform preliminary analysis to filter out data that is determined to be meaningless; the data analysis module 402 then takes over the analysis of the data, mainly according to the requirements of the management system 40, and the parameters are set by the administrator. Meaningful data. For example, when a consumer carries a portable device that executes a software program provided by the system, the positioning circuit in the portable device can provide location information of the portable device, and can also determine whether to stay or not stay at a certain location according to the location movement information. In addition, it is judged that the store, the business district or the area where the consumer consumes, and if it is combined with the means of action payment, the consumption quota can be obtained. For the message of the credit amount, the management system 40 can obtain the consumption data in cooperation with the store or the payment service provider.

In this way, the data analysis module 402 can analyze the relevant data of the consumer in one or more consumptions, including the path, location, time and consumption status, so that the personal consumption journey database 10 can be established, and the consumption of each consumer can be established. A persona describes a description of the attributes, background, preferences, etc. of the consumer after analysis. When the management system 40 obtains the consumption data of the majority consumer through the consumption data collection module 403, the data analysis module 402 can establish a huge database 12 for determining the overall or group consumption trend. Then, the processed data of the data analysis module 402, through a machine learning module 408, deeply analyzes the consumption data with complex structure, and converts the data through linear or non-linear transformation in multiple processing layers (linear or non -linear transform), taking out features representing individual or overall consumer consumption data characteristics, the decision module 401 can derive information helpful to the decision based on the data analysis module 402, especially to assist the advertising owner 407 to follow up the business. The decision of behavior (such as advertising). When performing deep learning calculus, the applied techniques, such as the mathematical model of the neural network, can achieve the best results after gradually optimizing the data analysis by the algorithms that can be implemented by the specific domain. These results will establish a deep learning database14. The management system 40 obtains valid and accurate information from the deep learning database 14, which can provide subsequent commercial services, and can further modify the data in the personal consumption journey database 10 and the huge database 12.

Moreover, the management system 40 can also obtain, through the third-party data collection module 404, information other than the consumption data obtained by the consumer through the portable devices 411, 412, 413, such as data generated by non-consumption behavior. For example, the management system 40 connects the open data to the related event data of the time, place or region through the third-party data collection module 404; and the third-party data collection module 404 can be connected to the social media. Data to obtain consumer activity data in social media; can connect to the parking management system (Figure 3, 35) to obtain consumer parking information, and then learn about the activities of consumers in certain areas; An external servo system such as a store, a payment service provider, or a bank that provides consumption amount data can obtain the amount of consumption of the consumer; if the sales data collection module 405 is used, the overall consumption trend of the entire mass or group can be obtained, instead of being limited to The data that the system can get. As such, the data obtained by the third-party data collection module 404 or the sales data collection module 405 can be used to obtain the consumer's personal (consumer profile) or the management system 40 more completely. The data of the group to which it belongs.

When the management system 40 establishes the personal consumption journey database 10, the huge database 12 and the deep learning database 14, and simultaneously analyzes the demand of the advertisement owner 407 (the strategy of marketing advertisement) through the advertisement demand analysis module 406, the decision module 401 The data analysis module 402 and the advertisement demand analysis module 406 can be used to obtain information that is helpful for decision-making, thereby generating decisions, particularly for commercial activities (such as placing advertisements), to provide accurate marketing services.

For example, the advertisement owner 407 inputs the advertisement demand through the user interface provided by the management system 40, and the advertisement demand analysis module 406 can analyze the promotion plan of a certain store in a nearby business district. The parameters of this requirement. The management system 40 can query the consumer description file from the personal consumption journey database 10 to obtain a hidden list of interest in the business circle or the store promotion program, once the location information of the consumer's portable device is displayed near the business circle. The personalized consumption information sent by the management system 40 according to the needs of the advertising owner 407 can be received, so that the consumer can go to the consumption. In another example of accurate marketing, if the management system 40 knows that the consumer is currently shopping in another shopping district based on the location information generated by the consumer portable device, after analysis, the consumer should have a promotion plan for the store. The person who is interested, and the business circle of the place where the business circle advertises for the advertisement owner 407 is the same, the management system 40 can broadcast the personalized consumption information of the promotion plan of a certain store in a certain business circle to the consumer.

Figure 5 then shows a flow chart of one embodiment of a method of generating personalized consumption information.

At the beginning of the process, in step S501, the consumer carries a portable device that executes a specific software program, and the software program can obtain consumption data generated by the portable device, including location information, time information, and consumption content, and the consumption content may include a consumer item, The personal consumption data such as the consumption location and the amount of payment for some consumption, in step S503, the management system obtains one or more consumption data generated by the terminals through the network. In step S505, the data analysis means in the management system may classify the consumption data, for example, analyze and determine the consumption locations (such as business districts, store locations, shopping centers) and consumption types (such as food and clothing) included in the consumption data. , living, traveling, entertainment, etc., and can judge the consumer's spending power according to the time, date and amount of consumption.

Then, in step S507, analyzing the meaningful information in the consumption data through the mechanism of data analysis and deep learning in the management system, and learning the data of each consumer or the group according to the analysis result of the plurality of data, and correcting the past The data that has been obtained, as in step S509, is used to create a database and generate a consumer profile. In step S511, the consumer profile can be determined from the consumer profile, and the features will become the basis for the management system to provide personalized consumption information. In step S513, the management system generates and broadcasts personalized consumption according to the demand and consumer characteristics. News. Personalized consumer information such as advertising marketing information.

In step S515, when the management system broadcasts the personalized consumption information through a specific communication means, the next step is to obtain the consumer's reaction to the personalized consumption information. For example, the management system identifies the potential marketing consumers according to the needs of the advertising owner, and broadcasts the advertising message to the consumer under the condition (such as the time and place conditions), when the consumer receives the advertising message, There may be several situations. For example, the first case is to delete the advertisement immediately or after a short time; the second case is to delete the advertisement after a long period of time; the third situation is to read the advertisement and click on the advertisement to link further information. For the management system, as in step S517, the management system can obtain these consumer responses and perform data analysis. For example, the above case 1 indicates that the consumer does not want to know the content recorded in the advertisement, and can also initially judge that the consumer may have no interest in such products; the second case may indicate that the consumer does not exclude the content recorded in the advertisement, but has no further understanding. The willingness; the third case indicates that the consumer is interested in the goods or services recorded in the advertisement, and even has the willingness to consume. Thus, in step S519, these messages can be used to modify the database, as well as the consumer profile.

If the management system continues to repeat the above steps, after analyzing the consumer response and deep learning, and getting consumers' behaviors on various consumer information, they can more and more accurately understand the preferences and interests of consumers, and thus provide more accurate marketing information. In turn, it provides services for advertisers to improve the effectiveness of advertising. Conversely, perhaps consumers may not be able to see relevant information that is not of interest. The management system can still learn to consume consumer habits in this situation, and can develop it by pushing advertisements at specific times and places. Consumer interest in other aspects of the consumer.

Figure 6 is a diagram showing an embodiment of a consumer profile.

The management system establishes a personal consumption journey database, a huge database, and a deep learning database according to the consumer's consumption data, wherein the process can establish a consumer description file, which is a description file defining the consumer attribute, which allows the system to quickly obtain consumption. Characteristics of the person. Consumer description files are mainly composed of data and structured words, which record the needs and behaviors of consumers, or can be summarized according to these data, and finally form a readable description content, in addition to the system-related software. Programs are derived directly from consumer behavior, including consumption data in stores, business districts, or online malls, and the system can also get a variety of data from consumer community activities. Any combination of the following enumerated items may be recorded in the consumer description file, and may include some or all of them, such as the consumer image 61, the consumer attribute 62, and the consumer background 63 (describe the character file, including the age, location, title, and Character type), personal description 64 (specifically describe the most prominent difference between this person type and others), demand or goal 65 (recorded in the use of field services maximum expectations and goals? What are the needs or desires?), emotions Or Attitude 66 (what is the expected value and service expected to enter the field? What are the opinions of the service, company brand, product quality, etc.) and the behavior description 67 (recording the thoughts and behaviors of the store before and during consumption).

For example, after establishing a personal consumption journey, massive data analysis, and deep learning of a consumer's consumption data, the final analysis results will be recorded in the consumer profile, which records: Consumer attribute (62) is " Single white-collar office workers"; consumer background (63) is "(1) female, 30 years old, unmarried, childless, professional secretary; (2) most often purchase clothing and jewelry on e-commerce platforms; (3) About one or more e-commerce platforms will be visited every day, and the products will be mainly women's clothing and household goods; (4) consumers who are demand-oriented, the price of goods purchased will be based on the income and expenditure balance, and finally according to their own needs. The decision to purchase"; personal description (64) is "(1) not blind shopping, shopping according to their own needs, and will decide to buy according to different e-commerce platforms; (2) will lock specific brands, take the initiative to different The commodity information source obtains the consumption information and evaluates it according to the demand; (3) it also asks the physical store for the information collected; (4) pays attention to the details of the e-commerce after-sales service, such as service evaluation; Compared with the return service, different shopping channels, hope to obtain better service; (6) after comparing different e-commerce platforms, then demand and price are the priority factors; demand or target (65) is "(1) Apparel locks specific brands, mainly low-priced and low-priced brand products; (2) expects to find products that meet their needs, and the price is affordable; emotions or attitudes (66) are "(1) despite the specific e-commerce platform Inquire about the same item at a higher price, but you may still decide to purchase it because you have better service and online evaluation, and carefully read the information such as after-sales service, first collect the information; (2) If you go to the physical store, it will be specific The brand conducts enquiries, if it is tried on the clothing or try it out, and actively consults the service personnel's introduction and suggestions"; the behavior description (67) records "(1) still prefer to shop on a specific well-known e-commerce platform, because of good after-sales service, Special shipment and return procedures are simple; (2) think that you can avoid the information on the Internet by asking the information collected from the physical store.

In another example, the consumer description file records: consumer attribute (62) is "home-sitting SOHO family"; consumer background (63) is "(1) male, 45 years old, married, with 2 Children, professional for translation; (2) most often shopping in 3C stores or e-commerce platforms, the main purchase of goods for digital goods, such as 3C; (3) about 1-3 times a week to browse e-commerce platform, buy goods to Computer peripherals, mobile peripherals, and trendy household items are the mainstays; (4) accustomed to browsing the products of certain well-known large-scale e-commerce platforms, as well as shopping in well-known electronic streets or shopping malls, mainly relying on online evaluation and use experience to decide to shop, see compliance The product will still hesitate for a period of time before deciding to place an order"; personal description (64) is "(1) does not often purchase 3C goods, mainly based on research specifications and functions, not the main purchaser at home, mainly based on home Need to buy household goods, followed by personal needs to buy goods of interest; (2) most often use the lunch break to search for 3C products, often after the order is decided at night, can be picked up every other day; (3) like to search the same on the Internet The price of the goods Price, but still usually in a few e-commerce platforms, the purchase of goods is very hesitant; (4) focus on the safety of the purchase pipeline, on a few e-commerce platforms with credit card consumption; (5) long-term use of goods, not pursue Have the latest products"; demand or target (65) is "because of the safety and after-sales service considerations, only on a few well-known e-commerce platforms, occasionally buy 3C products in physical stores"; mood or attitude (66) is "(1) I like to study specifications and price comparisons, and I will refer to online evaluation and open-box texts; (2) no brand considerations, mainly functional considerations; (3) the most durable goods are most important to purchase"; behavior description (67) ) records "(1) customary travel to a few e-commerce platforms; (2) occasionally through the physical store to ask for product content, will not be affected by the store introduction; (3) purchase of goods is not impulsive, or Very hesitant."

The consumer description files described above may be corrected after each analysis and deep learning of consumption data, and the interests and habits of each consumer may change over time, and the management system will correct the contents of the database at any time.

FIG. 7 continues to describe a flow chart of an embodiment of generating personalized consumption information. In this process, in step S701, the system first obtains a consumer profile for a consumer, and derives consumer preferences from the user. S703. The management system continues to obtain various consumption data from the consumer portable device, for example, the time when the consumption is performed, such as step S705, obtaining the consumption location, and the like. The management system can make a decision according to the consumption data. First, according to step S707, the data analysis of the obtained consumption data is performed, including step S709, determining the type of consumption, for example, from the consumption data, the consumer activity being performed by the consumer is catering, shopping. Still entertainment, or other, at this time, the management system will query the same type of consumption location and consumption content in the database, and continue to determine the more detailed consumption items as in step S711, for example, the food may be Chinese food, western food or snacks, purchase What is the product or purchase item, if it is entertainment, it is a movie, a tour or a concert. After the system analyzes the consumption data to the consumption characteristics described above, as in step S713, the personalized personal consumption information may be correspondingly provided.

If the parking information is used together, the process of generating the personalized personal consumption information can be referred to FIG. 8. In step S801, the management system can obtain the consumer parking information from the parking management system, and can determine that the consumer actually stops in the vicinity of a certain consumption place according to the location information generated by the consumer portable device. In step S803, the management system can Continuously obtaining consumer consumption data, such as location, amount, consumption item, etc., in step S805, the management system can combine parking information with consumption data.

Then, in step S807, the above information becomes the basis for the system to provide the advertisement owner to broadcast the personalized consumption information, and forms the reference data for providing the marketing of the store. Therefore, the advertisement owner (such as the store owner) can provide the place when the consumer stops and moves nearby. Sexual consumption information.

For example, when the management system knows that the consumer is parking near a store or business district, it can be based on time (if it is noon) and third-party information, such as weather (if it is a hot day), at this time, it will work. The advertising campaign strategy set by the advertising owner to issue personalized consumer information, such as a lunch promotion message, will attract consumers to the restaurant for this lunch promotion. When the consumer has lunch, cooperate with the advertising owner's marketing advertising strategy, and know from the consumer description file that the consumer or his peers have the demand for consumption of ice products, the consumer information will be released to the consumer. Consumers go to consumption.

In summary, according to the method for generating personalized personal consumption information and the management system embodiment proposed by the disclosure, the system obtains a huge amount of data formed by the consumer's personal consumption journey and obtaining a large amount of consumption data, which can be obtained after data analysis. The consumption trend of individuals or the masses, consistent with deep learning, can get consumer description files and judge the most efficient personalized consumption information for each consumer.

The above are only the preferred embodiments of the present invention, and all changes and modifications made to the scope of the present invention should be within the scope of the present invention.

Claims (10)

 A method for generating personalized consumption information includes: a management system obtaining consumption data from a portable device carried by a consumer, the consumption data including a location information, a time information, and consumption content, forming a personal consumption in the management system Journey; wherein, the plurality of personal consumption journeys establish a database of human consumption journeys, the personal consumption journey database records a consumer description file corresponding to each consumer; and obtains consumption data and/or non-consumption of the plurality of consumers The data generated by the behavior establishes a huge amount of database; analyzes the consumption data, performs classification, and obtains a consumption type, including analyzing the interaction behavior of various consumers in facing various personalized consumption information, establishing a deep learning database; establishing the consumption Descriptive file, or according to the deep learning result in the deep learning database, the personal consumption journey database and the huge database are fed back to correct the consumer description file corresponding to the consumer according to the consumption data; The consumer profile determines the consumer feature, according to the consumer The fee feature generates a personalized consumer message.    The method for generating personalized consumption information according to claim 1, wherein the huge amount of data is used to record consumption data of a plurality of groups formed by the plurality of consumers obtained from the personal consumption journey database, and further includes consumption Data generated by other non-consumer behaviors; the data generated by the consumption data and non-consumption behaviors are analyzed by a data to obtain an overall trend, including a consumer trend in a business district, a store's consumption analysis, and a regional consumer activity. And/or group consumption trends.    The method for generating personalized consumption information according to claim 2, wherein the management system is coupled to a parking management system for obtaining parking information of the consumer, so that each of the consumption data further includes a parking time, a parking position, and a Time to leave the parking space.    A management system comprising: a computer system, receiving consumption data transmitted by a plurality of consumer portable devices through a network, generating a personalized consumption information for each consumer; and a person purchasing journey database for recording the plurality of bits The consumption data formed by the consumer's consumption process, each consumption data corresponds to each consumer's personal consumption journey, and each consumption data includes at least one consumption time and one consumption location; a huge amount of database is used to record the consumption from the individual The consumption data of the plurality of groups formed by the plurality of consumers obtained by the journey database further includes data generated by other non-consumer behaviors of the consumers; the data generated by the consumption data and the non-consumption behavior are analyzed by a data. Overall trends, including consumer trends in a business district, consumer analysis in a store, consumer activity in a region, and/or group consumption trends; a deep learning database to record consumption from this huge database Data, and/or data generated by non-consumer behavior, establish a consumer profile for each consumer, and Learning to obtain the consumer's reaction to the personalized consumption information, to modify the personal consumption journey database and the data obtained by the data analysis in the huge database; wherein the step of generating the personalized consumption information by the computer system includes Obtaining consumption data from the portable device carried by each consumer, including a location information, a time information, and a consumption content, forming a personal consumption journey in the management system; analyzing the consumption data, obtaining a consumption type; establishing the consumer description File, or according to the deep learning result in the deep learning database, the personal consumption journey database and the huge database are fed back to correct the consumer description file according to the consumption data at any time; and judging from the consumer description file The consumer feature generates the personalized consumption information according to the consumer characteristic.    The management system of claim 4, wherein the management system is coupled to a parking management system for obtaining parking information of the consumer, so that each of the consumption data further includes a parking time, a parking location, and a time of leaving the parking space. .    The management system of claim 4, wherein the response of the personalized consumption information comprises: when each consumer receives the personalized consumption information, delete the personalized consumption information and watch the personalized consumption information immediately or after a short time. Delete after a long period of time, or after reading the personalized consumption information, link to further information.    The management system of claim 6, wherein the consumer profile is modified according to the response of the personalized consumption information, the consumer profile recording any combination of the following materials, including a consumer image, a consumer attribute, A consumer background, a person's description, a need or goal, an emotion or attitude, and a behavioral description.    The management system of claim 4, wherein the management system is implemented by a computer system, the software module of the computer system comprises: a consumption data collection module, configured to collect the plurality of consumer-side portable devices Consumption data; a third-party data collection module for obtaining data generated by non-consumer behavior other than the consumption data obtained by the plurality of consumers through the portable device; and a data analysis module for analyzing the consumer Consumption data of one or more consumptions, used to obtain the path, location, time and consumption status of each consumption, to establish a database of the personal consumption journey; analyze the consumption data of the plurality of consumers, and establish a judgment to determine the overall or The huge database of group consumption trends; and the in-depth analysis of consumption data with complex structures through a machine learning module to build the deep learning database.    The management system of claim 8, further comprising: an advertisement requirement analysis module, which provides a policy for the advertisement owner to set a line advertisement advertisement.    The management system of claim 8, wherein the third-party data collection module is connected to a social media to obtain activity data of each consumer in the social media; and the parking management system is linked to obtain comparison consumption. Parking information; or an external servo system to obtain the consumer's spending.