CN105469286A - Real estate user selection method - Google Patents
Real estate user selection method Download PDFInfo
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- CN105469286A CN105469286A CN201610003355.5A CN201610003355A CN105469286A CN 105469286 A CN105469286 A CN 105469286A CN 201610003355 A CN201610003355 A CN 201610003355A CN 105469286 A CN105469286 A CN 105469286A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0263—Targeted advertisements based upon Internet or website rating
Abstract
The invention relates to the field of real estate E-commerce, and particularly relates to a real estate user selection method. The method comprises selection of multi-stage data. Selection of multi-stage data comprises the following specific steps: behavior data of first target users are gathered, each different behavior is assigned and is multiplied by the statistic times sum of each behavior to serve as a standard value; and a target user is selected randomly for behavior statistics, the standard value is compared, next-stage selection is carried out if a certain percentage is met, and the step is cycled until marketing target users can be selected. The method has the beneficial effects that assignment and total statistics can be carried out on different activity behaviors of different target users, comparison with the preset standard values is then carried out, real estate purchasers in different requirement stages can be selected and classified quickly and accurately, a proper marking strategy can be made for the real estate purchasers in a targeted mode, the real estate sales turnover rate is thus improved, whenever the user needs a marketing scheme can be known, proper recommendation is made, the user favorable rate is improved, and the repeated real estate purchasing rate of old customers can be improved.
Description
Technical field
The present invention relates to house property e-commerce field, particularly a kind of house property user screening technique.
Background technology
Traditional business marketing mode has two kinds usually, and one obtains business demand by market study, develops reverse a group or all users carry out promoting service; The second is by research history data, draws potential business demand and target customers, carries out promoting service for a group user.Mode one is the marketing of service-oriented, and market lights a cigarette and can not investigate hundreds of millions of users, and result has certain one-sidedness, and the business developed accordingly is very energy-conservation occurs deviation, also allows enterprise assume responsibility for high cost of marketing; Mode two is user oriented marketing, but traditional operation data is discrete unit, and the retrieval of target customers is very difficult, even if find target customers to be also a more wide in range colony, can not accomplish the marketing towards unique user.
Long-term customer service experience tells us, occurs immediately when client needs most you, and allow client at the indiscriminate valency of pleasantly surprised middle impression service, and when client is unwanted, never go to bother, this is only the tidemark of service.Traditional marketing mode is not because have technological means to accomplish real customer-oriented marketing, so always can not obtain satisfactory effect and easily cause the problems such as customer complaint.
Summary of the invention
Goal of the invention of the present invention is: for above-mentioned technical matters, a kind of house property user method for sieving is provided, can assignment be carried out to the different crawler behaviors of different targeted customers and do total statistics, comparing with the standard value preset, can be quick, sort out the house purchaser of different demand levels accurately, targetedly suitable marketing strategy is carried out to the house purchaser of different demand levels, thus the sale probability of transaction of house property can be improved, and can control user when need promote marketing program, accomplish in good time recommendation, improve the positive rating of user, thus frequent customer can be improved turn one's head home ownership.
Technical solution of the present invention is:
A kind of house property user screening technique, comprises multi-stage data screening, and described multi-stage data screening, comprises following concrete steps:
(1) collect the first object user behavior data meeting setting screening, assignment is carried out in behaviors different for first object user;
(2) according to the assignment of step (1), the number of times of each crawler behavior of each first object client is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of all first object clients is carried out addition statistics total value, be default first standard value, then typing garbled data storehouse;
(3) random selecting first object client, according to the assignment of step (1), is multiplied by the assignment of each crawler behavior to the number of times of its each crawler behavior, can obtain the crawler behavior summation of first object client;
(4) by the crawler behavior summation of first object client, compared with default first standard value in typing garbled data storehouse, when each behavior total value > of first object user presets the 30%-40% of the first standard value, the second targeted customer will be upgraded to;
(5) according to the assignment of step (1), the number of times of each second each crawler behavior of target customer is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of the second all target customers is carried out addition statistics total value, be default second standard value, then typing garbled data storehouse;
(6) the crawler behavior summation of random selecting second target customer, compared with default second standard value in typing garbled data storehouse, as the 30%-40% of each behavior total value > second standard value of the second targeted customer, this second targeted customer is screened, becomes marketing objectives user.
The technology ultimate principle of the present invention program is: by carrying out assignment to the different crawler behaviors of all first object users, all second targeted customers, and carries out all total value of statistics respectively, as the first standard value and the second standard value; Random selecting first object user is according to relevant assignment and statistical method, the different crawler behaviors of first object user are carried out statistics summation, compare with the certain percentage of the first standard value again, when reaching the certain percentage of default standard value, first object user will upgrade to the second targeted customer, after in like manner the different crawler behaviors of the second targeted customer being carried out statistics summation, compare with the certain percentage of the second standard value, when reaching the certain percentage of default standard value, the second targeted customer will upgrade to marketing objectives user.Such technical scheme compared with prior art, by after behavior assignment to behavioral statistics as the standard value preset, random targeted customer is carried out multistage screening statistics and compares with multistage standard value, obtain final marketing objectives user; The application is high by the accuracy rate of the marketing objectives user that the method screens, and is marketed by such targeted customer, improves the success ratio of marketing; And the targeted customer meeting each stage can be filtered out, targetedly the targeted customer of each different phase can be carried out different process, until can be marketing objectives user by first object user screening.
In the present invention, as further restriction, all first object user behaviors of described satisfied setting screening comprise user interactions behavior and member registration behavior.
In the present invention, described interacting activity behavior comprises network activity behavior; Described network activity behavior comprises visual crawler behavior, the behavior of product information scheduled event, browses webpage time crawler behavior and voice service crawler behavior.
In the present invention, as further, the behavior of described product information scheduled event comprises the behavior of product Collecting, purchase by group registration activities behavior and room crawler behavior is seen in registration.
In the present invention, as further, described voice service crawler behavior comprises the behavior of voice call interactive event, the behavior of online customer service interactive event and the behavior of phone interactive event.
In the present invention, do not upgrade in step (4) and become the first object user of the second targeted customer, step (3) will be returned and proceed crawler behavior statistics, until upgrade to the second targeted customer.
In the present invention, in step (6), be not filtered into the second targeted customer into marketing objectives user, step (5) will be returned and proceed crawler behavior statistics, until become marketing objectives user.
Beneficial effect of the present invention is:
1. house property user screening technique of the present invention, compared with prior art, by after behavior assignment to behavioral statistics as the standard value preset, random targeted customer is carried out multistage screening statistics and compares with multistage standard value, obtain final marketing objectives user; The application is high by the accuracy rate of the marketing objectives user that the method screens, and is marketed by such targeted customer, improves the success ratio of marketing, also can accomplish in good time recommendation, improves the positive rating of user, thus can improve frequent customer and turn one's head home ownership; And the targeted customer meeting each stage can be filtered out, targetedly the targeted customer of each different phase can be carried out different process, until can be marketing objectives user by first object user screening.
2. by method of the present invention, purposive is preset as targeted customer by satisfactory client, until filter out marketing objectives user through of the present invention point of screening technique, marketing strategy is targetedly carried out to the marketing objectives user filtered out, the cycle of marketing can be shortened, previous step continuation tracking statistics is returned to not becoming marketing objectives user person, until become marketing objectives user, the time repeating draw samples can be saved, improve conversion ratio, effective reduction marketing and handling cost, increase economic efficiency.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in example of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment 1:
A kind of house property user screening technique, comprise multi-stage data screening, concrete steps are as follows:
(1) collect all first object user behavior datas meeting setting screening, different assignment is carried out in behaviors different for all first object users;
(2) according to the assignment of step (1), the number of times of each crawler behavior of each first object client is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of all first object clients is carried out addition statistics total value, be default first standard value, then typing garbled data storehouse;
(3) random selecting first object client, according to the assignment of step (1), is multiplied by the assignment of each crawler behavior to the number of times of its each crawler behavior, can obtain the crawler behavior summation of first object client;
(4) by the crawler behavior summation of first object client, compared with default first standard value in typing garbled data storehouse, when each behavior total value > of first object user presets the 30%-40% of the first standard value, the second targeted customer will be upgraded to; ;
(5) according to the assignment of step (1), the number of times of each second each crawler behavior of target customer is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of the second all target customers is carried out addition statistics total value, be default second standard value;
(6) the crawler behavior summation of random selecting second target customer, compared with default second standard value in typing garbled data storehouse, as the 30%-40% of each behavior total value > second standard value of the second targeted customer, this second targeted customer is screened, becomes marketing objectives user.
The technology ultimate principle of the present invention program is: by carrying out assignment to the different crawler behaviors of all first object users, all second targeted customers, and carries out all total value of statistics respectively, as the first standard value and the second standard value; Random selecting first object user is according to relevant assignment and statistical method, the different crawler behaviors of first object user are carried out statistics summation, compare with the certain percentage of the first standard value again, when reaching the certain percentage of default standard value, first object user will upgrade to the second targeted customer, after in like manner the different crawler behaviors of the second targeted customer being carried out statistics summation, compare with the certain percentage of the second standard value, when reaching the certain percentage of default standard value, the second targeted customer will upgrade to marketing objectives user.Such technical scheme compared with prior art, by after behavior assignment to behavioral statistics as the standard value preset, random targeted customer is carried out multistage screening statistics and compares with multistage standard value, obtain final marketing objectives user; The application is high by the accuracy rate of the marketing objectives user that the method screens, and is marketed by such targeted customer, improves the success ratio of marketing; And the targeted customer meeting each stage can be filtered out, targetedly the targeted customer of each different phase can be carried out different process, until can be marketing objectives user by first object user screening.
In the present invention, as further restriction, all first object user behaviors of described satisfied setting screening comprise user interactions behavior and member registration behavior.
In the present invention, described interacting activity behavior comprises network activity behavior; Described network activity behavior comprises visual crawler behavior, the behavior of product information scheduled event, browses webpage time crawler behavior and voice service crawler behavior.
In the present invention, as further, the behavior of described product information scheduled event comprises the behavior of product Collecting, purchase by group registration activities behavior and room crawler behavior is seen in registration.
In the present invention, as further, described voice service crawler behavior comprises the behavior of voice call interactive event, the behavior of online customer service interactive event and the behavior of phone interactive event.
In the present invention, as further, the different rows of first object client be between assignment identical/not identical; The different rows of the second target customer be between assignment identical/not identical.
Embodiment 2:
The different crawler behaviors of first object user are carried out assignment, online customer service mutual-action behavior=1, purchase by group registration=2, browse webpage time crawler behavior=3, member registration behavior=4, phone interactive event behavior=5 ... access time, section was that all each crawler behaviors of first object user on July 12 ,-2015 years on the 8th July in 2015 are added up, and was preset as the first standard value;
The different crawler behavior of the second targeted customer is identical from the different crawler behavior assignment of first object user, access time, section was that all each crawler behaviors of second targeted customer on July 12 ,-2015 years on the 8th July in 2015 are added up, and was preset as the second standard value;
Random selecting targeted customer, the crawler behavior different to it is added up:
Xn representative of consumer behavior assignment, certain score value is given in each behavior;
The online houses selling office of X1 representative access, purchasing by group of this building of registering, score value is X1;
The freephone consulting of certain building is dialed in X2 representative, and score value is X2;
Under X3 represents line, certain circuit of registration registration sees that room group is movable, and score value is X3;
Summation is Y=bX1+cX2+dX3+ ... + nXn
B, c, d ..., the n respectively different crawler behavior of representative of consumer number of times;
Randomly draw a first object user and carry out statistics screening, table specific as follows:
Table 1 obtains the statistical form of marketing objectives user
Carry out estate sales to marketing objectives user, if market successfully, then terminate estate sales, if it is unsuccessful to market, then by the information labeling of this marketing objectives user, adjustment marketing strategy, until market successfully.
Embodiment 3:
The different crawler behaviors of first object user are carried out assignment, such as product information scheduled event behavior=1, purchase by group registration=2, product Collecting behavior=3, member registration behavior=4, phone interactive event behavior=5 ... access time, section was that all each crawler behaviors of first object user on June 22 ,-2015 years on the 17th June in 2015 are added up, and was preset as the first standard value;
The different crawler behavior of the second targeted customer is identical from the different crawler behavior assignment of first object user, access time, section was that all each crawler behaviors of second targeted customer on June 22 ,-2015 years on the 17th June in 2015 are added up, and was preset as the second standard value;
Random selecting targeted customer, the crawler behavior different to it is added up:
Xn representative of consumer behavior assignment, certain score value is given in each behavior;
The online houses selling office of X1 representative access, purchasing by group of this building of registering, score value is X1;
The freephone consulting of certain building is dialed in X2 representative, and score value is X2;
Under X3 represents line, certain circuit of registration registration sees that room group is movable, and score value is X3;
Summation is Y=aX1+bX2+cX3+ ... + nXn
A, b, c ..., the n respectively different crawler behavior of representative of consumer number of times;
Randomly draw a first object user and carry out statistics screening, table specific as follows:
Table 2 obtains the statistical form of the second targeted customer
For the second targeted customer be not filtered into as marketing objectives user, will return in first object subscriber data and proceed crawler behavior statistics, until become marketing objectives user.
Embodiment 4:
The different crawler behaviors of first object user are carried out assignment, such as product information scheduled event behavior=1, purchase by group registration=2, visual crawler behavior=4, member registration behavior=4, phone interactive event behavior=5 ... access time, section was that all each crawler behaviors of first object user on August 2 ,-2015 years on the 28th July in 2015 are added up, and was preset as the first standard value;
The different crawler behavior of the second targeted customer is identical from the different crawler behavior assignment of first object user, access time, section was that all each crawler behaviors of second targeted customer on August 2 ,-2015 years on the 28th July in 2015 are added up, and was preset as the second standard value;
Random selecting targeted customer, the crawler behavior different to it is added up:
Xn representative of consumer behavior assignment, certain score value is given in each behavior;
The online houses selling office of X1 representative access, purchasing by group of this building of registering, score value is X1;
The freephone consulting of certain building is dialed in X2 representative, and score value is X2;
Under X3 represents line, certain circuit of registration registration sees that room group is movable, and score value is X3;
Summation is Y=aX1+bX2+cX3+ ... + nXn
A, b, c ..., the n respectively different crawler behavior of representative of consumer number of times;
Randomly draw a first object user and carry out statistics screening, table specific as follows:
Table 3 is unscreened the statistical form electing the second targeted customer as
Becoming the first object user of the second targeted customer for not upgrading, carrying out crawler behavior statistics by continuing to return in first object subscriber data, until upgrade to the second targeted customer.
Claims (8)
1. a house property user screening technique, comprises multi-stage data screening, it is characterized in that: described multi-stage data screening, comprises following concrete steps:
(1) collect the first object user behavior data meeting setting screening, assignment is carried out in behaviors different for first object user;
(2) according to the assignment of step (1), the number of times of each crawler behavior of each first object client is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of all first object clients is carried out addition statistics total value, be default first standard value, then typing garbled data storehouse;
(3) random selecting first object client, according to the assignment of step (1), is multiplied by the assignment of each crawler behavior to the number of times of its each crawler behavior, can obtain the crawler behavior summation of first object client;
(4) by the crawler behavior summation of first object client, compared with default first standard value in typing garbled data storehouse, when each behavior total value > of first object user presets the 30%-40% of the first standard value, the second targeted customer will be upgraded to;
(5) according to the assignment of step (1), the number of times of each second each crawler behavior of target customer is multiplied by the assignment of each crawler behavior, schedule to last with a couple of days again, the crawler behavior of the second all target customers is carried out addition statistics total value, be default second standard value, then typing garbled data storehouse;
(6) the crawler behavior summation of random selecting second target customer, compared with default second standard value in typing garbled data storehouse, as the 30%-40% of each behavior total value > second standard value of the second targeted customer, this second targeted customer is screened, becomes marketing objectives user.
2. a kind of house property user screening technique according to claim 1, is characterized in that: all first object user behaviors of described satisfied setting screening comprise user interactions behavior and member registration behavior.
3. a kind of house property user screening technique according to claim 1, is characterized in that: described interacting activity behavior comprises network activity behavior; Described network activity behavior comprises visual crawler behavior, the behavior of product information scheduled event, browses webpage time crawler behavior and voice service crawler behavior.
4. a kind of house property user screening technique according to claim 3, is characterized in that: the behavior of described product information scheduled event comprises the behavior of product Collecting, purchase by group registration activities behavior and room crawler behavior is seen in registration.
5. a kind of house property user screening technique according to claim 3, is characterized in that: described voice service crawler behavior comprises the behavior of voice call interactive event, the behavior of online customer service interactive event and the behavior of phone interactive event.
6. a kind of house property user screening technique according to claim 1, it is characterized in that: not upgrading in step (4) becomes the first object user of the second targeted customer, step (3) will be returned and proceed crawler behavior statistics, until upgrade to the second targeted customer.
7. a kind of house property user screening technique according to claim 1, it is characterized in that: in step (6), be not filtered into the second targeted customer into marketing objectives user, step (5) will be returned and proceed crawler behavior statistics, until become marketing objectives user.
8. a kind of house property user screening technique according to claim 1, is characterized in that: the different rows of first object client be between assignment identical/not identical; The different rows of the second target customer be between assignment identical/not identical.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557953A (en) * | 2016-11-11 | 2017-04-05 | 无锡雅座在线科技发展有限公司 | Information processing method and device |
| CN106997559A (en) * | 2017-04-13 | 2017-08-01 | 安徽省沃瑞网络科技有限公司 | One kind house-purchase customer transaction data management system |
| CN108537573A (en) * | 2018-03-15 | 2018-09-14 | 链家网（北京）科技有限公司 | Traveller's acquisition methods and system in a kind of real estate's sales |
| CN109191245A (en) * | 2018-08-22 | 2019-01-11 | 山东儒房融科网络科技股份有限公司 | A kind of house property electric business platform |
| WO2019085345A1 (en) * | 2017-11-03 | 2019-05-09 | 平安科技（深圳）有限公司 | Customer sampling-based test marketing method, electronic device, and computer-readable storage medium |
| CN112613886A (en) * | 2020-12-18 | 2021-04-06 | 深圳市思为软件技术有限公司 | WeChat client management method based on enterprise WeChat and related equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800006A (en) * | 2012-07-23 | 2012-11-28 | 姚明东 | Real-time goods recommendation method based on customer shopping intention exploration |
| CN103077172A (en) * | 2011-10-26 | 2013-05-01 | 腾讯科技（深圳）有限公司 | Method and device for mining cheating user |
| CN104090888A (en) * | 2013-12-10 | 2014-10-08 | 深圳市腾讯计算机系统有限公司 | Method and device for analyzing user behavior data |
| CN104765730A (en) * | 2014-01-02 | 2015-07-08 | 株式会社理光 | Method and device for recommending interested people |
-
2016
- 2016-01-04 CN CN201610003355.5A patent/CN105469286A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103077172A (en) * | 2011-10-26 | 2013-05-01 | 腾讯科技（深圳）有限公司 | Method and device for mining cheating user |
| CN102800006A (en) * | 2012-07-23 | 2012-11-28 | 姚明东 | Real-time goods recommendation method based on customer shopping intention exploration |
| CN104090888A (en) * | 2013-12-10 | 2014-10-08 | 深圳市腾讯计算机系统有限公司 | Method and device for analyzing user behavior data |
| CN104765730A (en) * | 2014-01-02 | 2015-07-08 | 株式会社理光 | Method and device for recommending interested people |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557953A (en) * | 2016-11-11 | 2017-04-05 | 无锡雅座在线科技发展有限公司 | Information processing method and device |
| CN106997559A (en) * | 2017-04-13 | 2017-08-01 | 安徽省沃瑞网络科技有限公司 | One kind house-purchase customer transaction data management system |
| WO2019085345A1 (en) * | 2017-11-03 | 2019-05-09 | 平安科技（深圳）有限公司 | Customer sampling-based test marketing method, electronic device, and computer-readable storage medium |
| CN108537573A (en) * | 2018-03-15 | 2018-09-14 | 链家网（北京）科技有限公司 | Traveller's acquisition methods and system in a kind of real estate's sales |
| CN109191245A (en) * | 2018-08-22 | 2019-01-11 | 山东儒房融科网络科技股份有限公司 | A kind of house property electric business platform |
| CN112613886A (en) * | 2020-12-18 | 2021-04-06 | 深圳市思为软件技术有限公司 | WeChat client management method based on enterprise WeChat and related equipment |
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Application publication date: 20160406 |