CN104820863A - Consumer portrait generation method and device - Google Patents
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- CN104820863A CN104820863A CN201510142155.3A CN201510142155A CN104820863A CN 104820863 A CN104820863 A CN 104820863A CN 201510142155 A CN201510142155 A CN 201510142155A CN 104820863 A CN104820863 A CN 104820863A
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Abstract
The invention relates to the technical field of data statistic analysis, in particular to a consumer portrait generation method and device. The method comprises the following steps: when a mobile terminal held by a consumer is accessed into a wireless network of a market, obtaining the consumer information of the consumer; and inputting the consumer information of the consumer and the preset information of each store in the market into a consumer portrait decision tree model, and obtaining the consumer portrait of the consumer, wherein the consumer portrait decision tree model is a model which is established according to member consumer information in advance and is used for predicting the consumer portrait. The consumer portrait is established through a great quantity of obtained consumer information and the information of the stores in the market, and the consumption habits, the consumption level and the like of the consumer can be precisely obtained so as to select potential consumers of commodities through analysis and comparison with the commodities to carry out targeted marketing promotion on the consumers.
Description
Technical field
The present invention relates to data statistic analysis technical field, be specifically related to a kind of consumer representation generation method and device.
Background technology
Along with the becoming increasingly prosperous of universal, E-commerce market of network technology, increasing consumer behavior can be completed by network selling platform.It is reported, in recent years, the E-commerce market sales volume of China increased rapidly with the speed per year over 20%.Meanwhile, the but atrophy gradually of traditional entity business.Trace it to its cause, traditional sales mode has the inferior position that cannot overcome compared with network selling pattern: price is higher, do shopping to limit by space-time, commodity number and kind cannot meet consumer demand sometimes, the time of shopping and transportation cost higher.But, just highlight and the epoch of fast development in the advantage of network selling, traditional entity sales mode also have network selling more irreplaceable advantages: buy What You See Is What You Get, food and drink consumption entertainment mode, promote the needing of emotion communication.This is also the basic reason that it cannot be replaced completely by ecommerce.
From current present situation, the normally used marketing strategy of electricity business obtains consumption and browsing information by recording consumer on the net, thus the analysis of personal consumption history and Consumer groups's preference commodity are carried out association analysis, for consumer provides personalized Push Service.
And traditional physical stores mostly just relies on questionnaire and consumer record as information source, analyzes the consumer behavior of consumer.Its acquisition of information speed, quantity and comprehensive being all difficult to are equal to network selling.On the other hand, after a large amount of consumer characteristics of acquisition and consumption preferences, by setting up consumer spending portrait, store manager can be made fully to understand consumption feature and the shopping need of consumer, and then formulate rational development strategy, thus market accurately and line reach the standard grade under seamless fusion, utilize more effective marketing methods, for consumer provides personalized Push Service.
But realize this goal, how by a large amount of consumer characteristics of obtaining and consumption preferences and then to set up consumer representation be key wherein, this is also the key problem that prior art exists.
Summary of the invention
By obtaining a large amount of consumer characteristics and consumption preferences and then setting up the defect of consumer representation, a kind of consumer representation generation method and device cannot be the invention provides in existing pick-up unit.
A kind of consumer representation generation method provided by the invention, comprising:
When the wireless network in the mobile terminal access market that consumer holds, obtain the consumer information of this consumer;
By in the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtain the consumer representation of this consumer;
Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
Further, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence.
Further, the step of the consumer information of described this consumer of acquisition, comprising:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
Further, described by before the step in the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, also comprise:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
Further, described consumer representation is the attribute of consumer, comprises Noumenon property and consumption propensity attribute.
On the other hand, present invention also offers a kind of consumer representation generating apparatus, comprising:
Acquisition module, when the mobile terminal for holding consumer accesses the wireless network in market, obtains the consumer information of this consumer;
Generation module, for by the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtains the consumer representation of this consumer;
Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
Further, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence.
Further, described acquisition module is further used for:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
Further, described device also comprises sets up module, for:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
Further, described consumer representation is the attribute of consumer, comprises Noumenon property and consumption propensity attribute.
A kind of consumer representation generation method provided by the invention and device, consumer representation is set up by the information of retail shop in a large amount of consumer information of obtaining and market, consumer spending custom and the level of consumption etc. can be drawn accurately, and then carry out promotion targetedly through contrasting with some commercial analysis the potential consumer therefrom selecting these commodity.
Accompanying drawing explanation
Can understanding the features and advantages of the present invention clearly by reference to accompanying drawing, accompanying drawing is schematic and should not be construed as and carry out any restriction to the present invention, in the accompanying drawings:
Fig. 1 is the schematic flow sheet of consumer representation generation method in one embodiment of the invention;
Fig. 2 is the structural representation of consumer representation generating apparatus in one embodiment of the invention.
Embodiment
Now in conjunction with the accompanying drawings and embodiments technical solution of the present invention is further elaborated.
Fig. 1 shows the schematic flow sheet of consumer representation generation method in the present embodiment, and as shown in Figure 1, a kind of consumer representation generation method that the present embodiment provides, comprising:
S1, when the wireless network in the mobile terminal access market that consumer holds, obtains the consumer information of this consumer.Wherein, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence; Described retail shop information spinner will comprise: retail shop's type of merchandise, brand class and sales situation etc.
S2, by the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtains the consumer representation of this consumer; Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
Described consumer representation specifically refers to the attribute of consumer, comprises Noumenon property and consumption propensity attribute.Wherein, Noumenon property mainly comprises: sex, age, income level, the level of consumption, occupation type etc.; Consumption propensity attribute mainly comprises: brand preference, taste of diet, special consumer behavior etc.
Further, obtain the step of the consumer information of this consumer in described S1, comprising:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
The method obtaining consumer information with tradition is compared: mobile terminal (mobile phone, panel computer etc.) has become the necessity of most people, and it not in release data, records position and other relevant informations of possessor all the time.It can be used as data source, can obtain while greatly reducing cost magnanimity that classic method cannot obtain, in real time, on a large scale, the individual and community information of dynamic, objective and accurate consumer, make personal marketing become possibility.
Because every platform mobile terminal has a unique MAC Address, can using the unique identifier of this MAC Address as consumer.As long as consumer enters WIFI areal coverage, his/her positional information all will be acquired.By real-time like this, omnibearing acquisition of information, one can be had comprehensively to understand to the consumer behavior of consumer.
Customer site's information and temporal information are determined in communication handshake agreement between the mobile terminal held by the WiMAX WIFI equipment in market and consumer and the retail shop border in market, can understand the purchase intention of consumer in all directions.Its sex (corresponding with retail shop type), age (the consumer groups age main with retail shop is corresponding) and the information of income (brand of retail shop) can be inferred according to retail shop's type of patronizing of consumer, by its trip (looking for WIFI to obtain from airport or train) information, the information such as the professional category of consumer can be judged from the side.
And then personal attribute's (comprising age, sex, income, professional category etc.) of derivation consumer, for precision marketing.WIFI equipment can Real-time Obtaining customer site information, according to the behavior pattern of its historical activity information and similar consumer, by backstage computing, for consumer provides real-time service, also real-time operation result can be supplied to supvr, for the activities such as marketing and service provide Data support.
Further, described S2, by before the step in the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, also comprises:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
Wherein, member's consumer information comprises: sex, age, occupation type, income level and consumption information etc.; The positional information of member consumer in market and temporal information comprise: the visiting frequency, mean residence time, stop T.T., patronize retail shop's number, patronize retail shop's type number, patronize number of times maximum food and drink retail shop type, patronize number of times maximum clothes retail shop type, patronize the maximum brand styles of number of times etc.
First coupling between member's consumer information and the locator data of member consumer (namely matching the locator data of member consumer in market according to date of member's consumer spending and time by mobile phone numbering, Handshake Protocol) is carried out; Then will there is member's consumer data of locating information and information thereof as training data, determine conditional attribute (shop classification and sequence, patronize the frequency, mean residence time, residence time overall length, patronize the period) and decision attribute (i.e. the feature of member, comprises the features such as sex, income, consumption); Finally by optimization study and training, and prune decision tree branch, complete the decision-tree model of consumer representation inferred from attributes.
Consumer's Noumenon property is divided into class variable (sex, occupation type) and continuous variable's (income level, level of consumption).In the present embodiment, need continuous variable to carry out discretize for building Decision-Tree Classifier Model, can by income level according to its probability distribution mode Discrete be low, in, higher, high four grades or carry out more careful division as required.The levels of consumption etc. also do similar division, thus complete the discretize of continuous variable.
For the consumption propensity attribute of consumer, then can add up its visiting frequency respectively by the classification of retail shop, and get the maximum classification of the frequency or label (label such as luxury goods, Sichuan cuisine restaurant as defined to retail shop) the consumption propensity property value as consumer.
For example, market for the wrist-watch more expensive to certain price.Market A wishes the consumer group finding potential this wrist-watch of purchase.For such task, by the locator data of consumer being patronized to market, consumer characteristics is drawn a portrait, attribute according to this wrist-watch excavates potential high value consumer group from consumer representation database, thus carries out precision marketing for it, improves commercial profit.
Concrete flow process: first obtained the positional information and temporal information that are linked into consumer corresponding to the mobile device of this wireless network by Wireless Communication Equipment, and in conjunction with market Nei Ge retail shop information (such as the information such as retail shop's type of merchandise, brand class), by long-time analysis, from the access and residence time information of consumer each retail shop market, each consumer is precisely drawn a portrait; Secondly, determine the attribute of this wrist-watch according to the purchaser record (can obtain from member's consumer information) of the member consumer before this wrist-watch or the marketing positioning of this wrist-watch producer to it, the such as attribute of this wrist-watch determined comprises young consumer, income is higher, the level of consumption is higher, follow the fashion and to attributes such as luxury goods are interesting; Finally, the attribute according to this wrist-watch determined filters out the potential consumer matched with this wrist-watch attribute from the representation data storehouse of consumer, and carries out the marketing methods such as advertisement pushing for the consumer filtered out.
A kind of consumer representation generation method that the present embodiment provides, consumer representation is set up by the information of retail shop in a large amount of consumer information of obtaining and market, consumer spending custom and the level of consumption etc. can be drawn accurately, and then carry out promotion targetedly through contrasting with some commercial analysis the potential consumer therefrom selecting these commodity.
On the other hand, as shown in Figure 2, the present embodiment additionally provides a kind of consumer representation generating apparatus, comprising:
Acquisition module 101, when the mobile terminal for holding consumer accesses the wireless network in market, obtains the consumer information of this consumer;
Generation module 102, for by the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtains the consumer representation of this consumer;
Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
Further, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence.
Further, described acquisition module 101 is further used for:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
Further, described device also comprises sets up module, for:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
Further, described consumer representation is the attribute of consumer, comprises Noumenon property and consumption propensity attribute.
A kind of consumer representation generating apparatus that the present embodiment provides, consumer representation is set up by the information of retail shop in a large amount of consumer information of obtaining and market, consumer spending custom and the level of consumption etc. can be drawn accurately, and then carry out promotion targetedly through contrasting with some commercial analysis the potential consumer therefrom selecting these commodity.
Although describe embodiments of the present invention by reference to the accompanying drawings, but those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, such amendment and modification all fall into by within claims limited range.
Claims (10)
1. a consumer representation generation method, is characterized in that, described method comprises:
When the wireless network in the mobile terminal access market that consumer holds, obtain the consumer information of this consumer;
By in the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtain the consumer representation of this consumer;
Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
2. method according to claim 1, is characterized in that, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence.
3. method according to claim 2, is characterized in that, the step of the consumer information of described this consumer of acquisition, comprising:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
4. method according to claim 1, is characterized in that, described by before the step in the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, also comprises:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
5. the method according to any one of Claims 1-4, is characterized in that, described consumer representation is the attribute of consumer, comprises Noumenon property and consumption propensity attribute.
6. a consumer representation generating apparatus, is characterized in that, described device comprises:
Acquisition module, when the mobile terminal for holding consumer accesses the wireless network in market, obtains the consumer information of this consumer;
Generation module, for by the consumer information of described consumer and default market Nei Ge retail shop information input consumer representation decision-tree model, obtains the consumer representation of this consumer;
Wherein, described consumer representation decision-tree model is in advance according to the model for predicting consumer representation that member's consumer information is set up.
7. device according to claim 6, is characterized in that, described consumer information comprises: the positional information of consumer in market and the temporal information of correspondence.
8. device according to claim 7, is characterized in that, described acquisition module is further used for:
After the wireless network in mobile terminal access market, the Wireless Communication Equipment in market obtains the positional information of this mobile terminal in market by Handshake Protocol;
The positional information of the consumer holding this mobile terminal is determined according to the retail shop border in the positional information of described mobile terminal and default market;
When customer site changes, obtain the temporal information of consumer; The temporal information of described consumer comprises consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting.
9. device according to claim 6, is characterized in that, described device also comprises sets up module, for:
The mobile terminal that described member consumer is corresponding is determined according to member's consumer information;
Be linked into the wireless network in market at the mobile terminal that described member consumer is corresponding after, the Wireless Communication Equipment in market obtains the positional information of mobile terminal corresponding to described member consumer in market by Handshake Protocol;
The positional information of this member consumer is determined according to the retail shop border of presetting in the positional information of mobile terminal corresponding to described member consumer and market;
This member consumer's temporal information is obtained when described member customer site changes; Described member consumer's temporal information comprises member consumer and arrives time, the stay time of a certain retail shop and the frequency of visiting;
Consumer representation decision-tree model is set up according to the positional information of described member consumer in market and temporal information, described member's consumer information and default market Nei Ge retail shop information.
10. the device according to any one of claim 6 to 9, is characterized in that, described consumer representation is the attribute of consumer, comprises Noumenon property and consumption propensity attribute.
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Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787756A (en) * | 2016-02-19 | 2016-07-20 | 龚齐伟 | Human behavior analysis apparatus, method and system |
| CN105787071A (en) * | 2016-03-02 | 2016-07-20 | 浪潮通信信息系统有限公司 | Method for achieving mobile phone user behavior portraits based on informationized labels |
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | Cardholder consumption figure generation method and device |
| CN106547925A (en) * | 2016-12-13 | 2017-03-29 | 竹间智能科技（上海）有限公司 | Adjustment conversational system responds the method and device of personality |
| CN106779074A (en) * | 2017-01-22 | 2017-05-31 | 腾云天宇科技（北京）有限公司 | Market brand combination prediction method and prediction server |
| CN107800782A (en) * | 2017-10-16 | 2018-03-13 | 重庆智道云科技有限公司 | User intent data capture method, apparatus and system based on Internet of Things |
| CN108734501A (en) * | 2017-04-19 | 2018-11-02 | 嘉兴高恒信息科技有限公司 | A kind of mobile position platform |
| CN108734502A (en) * | 2017-04-19 | 2018-11-02 | 嘉兴高恒信息科技有限公司 | A kind of data statistical approach and system based on user location |
| CN108764958A (en) * | 2018-04-11 | 2018-11-06 | 口碑(上海)信息技术有限公司 | Recommendation method and device based on user characteristics label |
| CN108985835A (en) * | 2018-07-09 | 2018-12-11 | 上海小蚁科技有限公司 | Target group determines method and device, storage medium, terminal |
| CN109146539A (en) * | 2018-06-28 | 2019-01-04 | 深圳市彬讯科技有限公司 | The update method and device of user's portrait |
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| CN109308637A (en) * | 2018-09-12 | 2019-02-05 | 浪潮软件集团有限公司 | Entity consumption scene oriented marketing method, device and system |
| CN109409936A (en) * | 2018-09-28 | 2019-03-01 | 深圳壹账通智能科技有限公司 | Customer consumption portrait generation method, device, equipment and readable storage medium storing program for executing |
| CN109509023A (en) * | 2018-10-26 | 2019-03-22 | 温州博盈科技有限公司 | A kind of big data intelligent marketing system |
| CN109767264A (en) * | 2018-12-20 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Product data method for pushing, device, computer equipment and storage medium |
| CN110645984A (en) * | 2019-09-26 | 2020-01-03 | 上海掌门科技有限公司 | Route updating method for shopping mall, electronic device and computer readable medium |
| CN110675179A (en) * | 2018-07-03 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Marketing information processing method and device, electronic equipment and readable storage medium |
| CN111062555A (en) * | 2019-08-30 | 2020-04-24 | 京东城市(北京)数字科技有限公司 | City portrait construction method and device, medium and electronic equipment |
| CN113783709A (en) * | 2021-08-31 | 2021-12-10 | 深圳市易平方网络科技有限公司 | Conference system-based participant monitoring and processing method and device and intelligent terminal |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN115587853A (en) * | 2022-10-24 | 2023-01-10 | 广州易海创腾信息科技有限公司 | Business portrait analysis method and system combining electronic commerce and big data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040225616A1 (en) * | 2003-05-09 | 2004-11-11 | Arnold Gordon K. | Method, system and computer program product for third-party verification of anonymous e-marketplace transactions using digital signatures |
| CN104008184A (en) * | 2014-06-10 | 2014-08-27 | 百度在线网络技术（北京）有限公司 | Method and device for pushing information |
| CN104182881A (en) * | 2014-07-22 | 2014-12-03 | 诚迈科技（南京）股份有限公司 | Supermarket intelligent shopping guide system based on WIFI indoor positioning and positioning method |
| CN104254851A (en) * | 2012-03-17 | 2014-12-31 | 海智网聚网络技术（北京）有限公司 | Method and system for recommending content to a user |
| CN104346746A (en) * | 2014-09-27 | 2015-02-11 | 深圳市无微不至数字技术有限公司 | Consumption dynamic line plan system and method for offline business district |
-
2015
- 2015-03-27 CN CN201510142155.3A patent/CN104820863A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040225616A1 (en) * | 2003-05-09 | 2004-11-11 | Arnold Gordon K. | Method, system and computer program product for third-party verification of anonymous e-marketplace transactions using digital signatures |
| CN104254851A (en) * | 2012-03-17 | 2014-12-31 | 海智网聚网络技术（北京）有限公司 | Method and system for recommending content to a user |
| CN104008184A (en) * | 2014-06-10 | 2014-08-27 | 百度在线网络技术（北京）有限公司 | Method and device for pushing information |
| CN104182881A (en) * | 2014-07-22 | 2014-12-03 | 诚迈科技（南京）股份有限公司 | Supermarket intelligent shopping guide system based on WIFI indoor positioning and positioning method |
| CN104346746A (en) * | 2014-09-27 | 2015-02-11 | 深圳市无微不至数字技术有限公司 | Consumption dynamic line plan system and method for offline business district |
Non-Patent Citations (2)
| Title |
|---|
| 许可: "《体验互联网新思维》", 31 January 2015 * |
| 郑双 等: "大数据时代下移动通信行业微营销的研究", 《广西通信技术》 * |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | Cardholder consumption figure generation method and device |
| CN105787756A (en) * | 2016-02-19 | 2016-07-20 | 龚齐伟 | Human behavior analysis apparatus, method and system |
| CN105787071A (en) * | 2016-03-02 | 2016-07-20 | 浪潮通信信息系统有限公司 | Method for achieving mobile phone user behavior portraits based on informationized labels |
| CN106547925A (en) * | 2016-12-13 | 2017-03-29 | 竹间智能科技（上海）有限公司 | Adjustment conversational system responds the method and device of personality |
| CN106779074A (en) * | 2017-01-22 | 2017-05-31 | 腾云天宇科技（北京）有限公司 | Market brand combination prediction method and prediction server |
| CN108734501A (en) * | 2017-04-19 | 2018-11-02 | 嘉兴高恒信息科技有限公司 | A kind of mobile position platform |
| CN108734502A (en) * | 2017-04-19 | 2018-11-02 | 嘉兴高恒信息科技有限公司 | A kind of data statistical approach and system based on user location |
| CN108734502B (en) * | 2017-04-19 | 2023-06-20 | 嘉兴高恒信息科技有限公司 | Data statistics method and system based on user position |
| CN107800782A (en) * | 2017-10-16 | 2018-03-13 | 重庆智道云科技有限公司 | User intent data capture method, apparatus and system based on Internet of Things |
| CN108764958A (en) * | 2018-04-11 | 2018-11-06 | 口碑(上海)信息技术有限公司 | Recommendation method and device based on user characteristics label |
| CN109146539A (en) * | 2018-06-28 | 2019-01-04 | 深圳市彬讯科技有限公司 | The update method and device of user's portrait |
| CN110675179A (en) * | 2018-07-03 | 2020-01-10 | 百度在线网络技术（北京）有限公司 | Marketing information processing method and device, electronic equipment and readable storage medium |
| CN108985835A (en) * | 2018-07-09 | 2018-12-11 | 上海小蚁科技有限公司 | Target group determines method and device, storage medium, terminal |
| CN109191176A (en) * | 2018-07-31 | 2019-01-11 | 国政通科技有限公司 | A kind of method and device for precisely hitting high-end food and drink potential user |
| CN109308637A (en) * | 2018-09-12 | 2019-02-05 | 浪潮软件集团有限公司 | Entity consumption scene oriented marketing method, device and system |
| CN109409936A (en) * | 2018-09-28 | 2019-03-01 | 深圳壹账通智能科技有限公司 | Customer consumption portrait generation method, device, equipment and readable storage medium storing program for executing |
| CN109509023A (en) * | 2018-10-26 | 2019-03-22 | 温州博盈科技有限公司 | A kind of big data intelligent marketing system |
| CN109767264A (en) * | 2018-12-20 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Product data method for pushing, device, computer equipment and storage medium |
| CN111062555A (en) * | 2019-08-30 | 2020-04-24 | 京东城市(北京)数字科技有限公司 | City portrait construction method and device, medium and electronic equipment |
| CN111062555B (en) * | 2019-08-30 | 2023-09-26 | 北京京东智能城市大数据研究院 | City portrait construction method, device, medium and electronic equipment |
| CN110645984A (en) * | 2019-09-26 | 2020-01-03 | 上海掌门科技有限公司 | Route updating method for shopping mall, electronic device and computer readable medium |
| CN110645984B (en) * | 2019-09-26 | 2023-10-10 | 上海掌门科技有限公司 | Route updating method for mall, electronic device, and computer-readable medium |
| CN113783709A (en) * | 2021-08-31 | 2021-12-10 | 深圳市易平方网络科技有限公司 | Conference system-based participant monitoring and processing method and device and intelligent terminal |
| CN113783709B (en) * | 2021-08-31 | 2024-03-19 | 重庆市易平方科技有限公司 | Conference participant monitoring and processing method and device based on conference system and intelligent terminal |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN115587853A (en) * | 2022-10-24 | 2023-01-10 | 广州易海创腾信息科技有限公司 | Business portrait analysis method and system combining electronic commerce and big data |
| CN115587853B (en) * | 2022-10-24 | 2024-04-05 | 广州易海创腾信息科技有限公司 | Business portrayal analysis method and system combining electronic commerce and big data |
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