CN110930180B - Data analysis method, system and computer equipment based on regional member marketing scene - Google Patents
Data analysis method, system and computer equipment based on regional member marketing scene Download PDFInfo
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Abstract
The invention discloses a data analysis method, a system and computer equipment based on a regional member marketing scene, wherein the method comprises the following steps: according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid; constructing a store information database, and corresponding the region geographic position information after gridding to the entity store coding information and the grid coding information; constructing a user information database; and matching address information generated by the online behavior triggered by the user, acquiring entity store information corresponding to the area where the user is located, and attributing the user to an effective marketing area of an offline store through the online behavior of the user. According to the invention, the address information data generated by the online behavior of the user is acquired to locate the offline address information of the user, so that the online user and the offline store are in actual connection in geographic position through the address, and the online user is precisely belonged to the marketable area range of the offline store, thereby realizing the grid management of the sales area.
Description
Technical Field
The invention belongs to the technical field of electronic commerce, and particularly relates to a data analysis method, a system and computer equipment based on a regional member marketing scene.
Background
In the traditional retail industry, physical stores accumulate a large number of consumer groups by absorbing members, and develop various marketing campaigns by periodically pushing merchandise information.
With the development of electronic commerce, the O2O (Online To Offline) mode is mature, and O2O is a business mode combining online transaction of goods or services based on an electronic commerce website with actual experience of goods or services based on a physical store, so that the electronic commerce website becomes a front stage of business of the physical store, and the physical store becomes a back stage of business of the electronic commerce website.
In recent years, online consumption platforms are becoming increasingly large, and companies of the original deep ploughing off-line entity stores are also in the army electric business row. The online rich marketing means enable online member teams to develop continuously. If online members and offline physical store members are fused, merchants can conveniently know the consumed people and potential consumed people around the physical store, and integrated marketing is facilitated.
Patent number 201710944600.7 discloses a transaction data processing method, device and system. In a scene (namely an off-line shopping scene) that a user purchases goods in an entity store, on-line identities of the user on a third party/on-line transaction server are used for generating transaction data for off-line goods, or on-line prices of the off-line goods are used for generating transaction data for the off-line goods, and the transaction data are respectively synchronized to a store terminal and a user terminal, so that an on-line and off-line combined transaction data processing mode is realized, convenience in management, maintenance, operation and the like of on-line advantage off-line transactions can be utilized, the competitiveness of the entity store in an e-commerce environment is improved, and the development of the entity store is promoted. But the patent does not accurately manage the off-line physical store, nor does the patent accurately associate the on-line member with the off-line physical store by address.
Disclosure of Invention
The technical problem solved by the invention is to provide a data analysis method based on a regional member marketing scene, which can accurately correlate the online member with the offline physical store through the position information.
The technical solution for realizing the purpose of the invention is as follows: a data analysis method based on a regional member marketing scene comprises the following steps:
according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid, and two adjacent grids are not overlapped;
constructing a store information database;
constructing a user information database;
and matching address information generated by the online behavior triggered by the user, acquiring the information of the physical store corresponding to the area where the user is located, and attributing the user to an effective marketing area of the physical store through the online behavior of the user.
Preferably, the meshing of the target area specifically includes:
and constructing a radiation area by taking the geographic position of the entity store as the center, wherein the radiation area is a closed polygon, the radiation areas of two adjacent entity stores are not repeated, and the closed polygon areas respectively constructed by all entity stores in the target area form a network together to realize the meshing division of the target area.
Preferably, the store information database includes:
the merchant entity store region geographic position information comprises longitude and latitude data corresponding to a store geographic position central point and longitude and latitude information of a radiation region boundary, and is simultaneously input into an online map tool;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the above information corresponds to each other.
Preferably, the user information database includes:
user address information, including longitude and latitude information corresponding to the address information;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the above information corresponds to each other.
Preferably, the user information database is divided into two types, wherein the first type is a historical database, and comprises address information, entity store coding information and grid coding information generated by historical user behaviors;
the second category is an incremental database that includes address information, physical store code information, and grid code information generated by incremental user actions.
Preferably, the matching of address information generated by the user triggering on-line behavior is specifically:
performing first matching on the generated address information in a user information database, if consistent address information exists, successfully performing the first matching, and simultaneously acquiring entity store coding information and grid coding information from the user information database;
otherwise, the first matching fails, and then longitude and latitude information corresponding to the address is obtained by calling an online map tool;
performing second matching on the longitude and latitude information in a store information database:
if the longitude and latitude are in the radiation area of the physical store, the second matching is successful, and meanwhile, the physical store coding information and the grid coding information are obtained from a store information database; and inserting the related information as incremental data into an incremental database of the user information database;
otherwise, the second matching fails and a prompt message is sent out.
Preferably, after the first matching and/or the second matching fail, a target area judging step is further included to judge whether address information generated by the online behavior triggered by the user is in the target area.
A data parsing system based on a localized membership marketing scenario, comprising:
the target area meshing module is used for meshing and dividing the target area according to the distribution condition of entity stores of a merchant, each entity store corresponds to one grid, and two adjacent grids are not overlapped;
store information database;
a user information database;
and the information matching module is used for matching address information generated by the online behavior triggering of the user, acquiring entity store information corresponding to the area where the user is located, and attributing the user to an effective marketing area of an offline store through the online behavior of the user.
Compared with the prior art, the invention has the remarkable advantages that: 1) According to the online and offline marketing method, online behaviors (such as access and purchase) of the user are positioned to online address information of the user, text type addresses are converted into numerical longitude and latitude, the effective marketing range of the store in business sense is converted into grid longitude and latitude number group values corresponding to the store in digital sense, online members and offline entity stores are accurately associated through the addresses, so that more accurate grid management can be performed on the entity stores, and subsequent marketing activities can be conveniently carried out; 2) According to the online and offline member fusion method, the online user and the offline store are in digital precise connection, so that the online and offline member fusion purpose is achieved, online and offline integrated marketing can be carried out on members, O2O fusion is further realized, and the online and offline member fusion method is better towards smart retail; 3) According to the invention, the address information data generated by the online behavior of the user is acquired to locate the offline address information of the user, so that the online user and the offline store are in actual connection in geographic position through the address, and the purpose of accurately attributing the online user to the marketable area of the offline store is achieved through the association of the position information data, thereby realizing the grid management of the sales area.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a data parsing method based on a regional member marketing scenario according to the present invention.
FIG. 2 is a flow chart of a method for matching address information generated by user-triggered online behavior in the present invention.
Fig. 3 is a schematic diagram of a data analysis system framework based on a regional member marketing scenario according to the present invention.
Fig. 4 is a schematic diagram of an embodiment of the present invention.
Detailed Description
Referring to fig. 1, the data parsing method based on the regional member marketing scene of the present invention includes the following steps:
according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid, and two adjacent grids are not overlapped;
constructing a store information database;
constructing a user information database;
and matching address information generated by the online behavior triggered by the user, acquiring the information of the physical store corresponding to the area where the user is located, and attributing the user to an effective marketing area of the physical store through the online behavior of the user.
The above-mentioned meshing of the target area specifically includes:
and constructing a radiation area by taking the geographic position of the entity store as the center, wherein the radiation area is a closed polygon, the radiation areas of two adjacent entity stores are not repeated, and the closed polygon areas respectively constructed by all entity stores in the target area form a network together to realize the meshing division of the target area. The radiation area corresponding to the store can be divided according to the geographical position of the store, the situation of surrounding cells and the like, the radiation area is actually a closed polygon, a net is formed after a plurality of polygons are spliced, the radiation area is called as a grid, the grid division avoids superposition, the grids cannot be overlapped, and one grid can only correspond to one store. When the radiation area is constructed, the consumption condition and population distribution condition of the local area can be combined for division, so long as grids formed by all entity stores can be ensured to cover the whole target area. The radiation area can be properly enlarged for stores with strong sales capability, stores with weak sales capability or new stores, and the radiation area can be properly reduced. The target area is an area where the merchant is ready to sell coverage and may be a country, a province, a city, or a county, etc.
The store information database includes:
the merchant entity store region geographic position information comprises longitude and latitude data corresponding to a store geographic position central point and longitude and latitude information of a radiation region boundary, and is simultaneously input into an online map tool; the online map tool is an existing online map and the like, and comprises a Goldmap, a hundred-degree map, a Beidou map and the like.
The physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number; the arrangement method of the store numbers can be determined by the merchant by the self, so long as one entity store can be guaranteed to correspond to one number.
The grid coding information comprises the number of grids, and each grid is provided with a unique grid number.
The geographic position information, the physical store coding information and the grid coding information of the physical store area of the merchant are in one-to-one correspondence, and one piece of information can be matched with the other two pieces of information.
The user information database includes:
user address information, including longitude and latitude information corresponding to the address information;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the user address information, the entity store coding information and the grid coding information are in one-to-one correspondence, and one piece of information can be matched with the other two pieces of information. The user information database is used for accelerating the matching speed and shortening the matching time.
The user information database is divided into two types, wherein one type is a historical database, and comprises address information, entity store coding information and grid coding information generated by historical user behaviors; the historical user behavior refers to address information entered by a previous user when purchasing or browsing merchandise, typically in the form of a province |city |district|street|district|house number. One user may correspond to a plurality of address information.
The second type is an incremental database which comprises address information, entity store coding information and grid coding information generated by incremental user behaviors; including address information generated by new users and also new address information data added by existing users.
And geographic position information, physical store coding information and grid coding information of the merchant physical store area in the store information database are in one-to-one correspondence. Each merchant physical store area geographic location information corresponds to only one physical store code information and also corresponds to only one grid code information.
The matching of address information generated by the user triggering on-line behavior is specifically as follows:
performing first matching on the generated address information in a user information database, if consistent address information exists, successfully performing the first matching, and simultaneously acquiring entity store coding information and grid coding information from the user information database;
otherwise, the first matching fails, and then longitude and latitude information corresponding to the address is obtained by calling an online map tool;
performing second matching on the longitude and latitude information in a store information database:
if the longitude and latitude are in the radiation area of the physical store, the second matching is successful, and meanwhile, the physical store coding information and the grid coding information are obtained from a store information database; and inserting the related information as incremental data into an incremental database of the user information database;
otherwise, the second matching fails and a prompt message is sent out.
In the matching process, first matching is carried out in a user information database, after the first matching fails, an online map tool is called, longitude and latitude information corresponding to the address is obtained, and second matching is carried out in a store information database by utilizing the longitude and latitude information. Therefore, the matching time can be greatly shortened, and the relevant information of the entity store corresponding to the address information generated by the online behavior triggered by the user can be rapidly acquired.
Referring to fig. 2, the latitude and longitude information is matched with the store information database for the second time, specifically:
if the longitude and latitude are in the radiation area of the physical store (the longitude and latitude are in the longitude and latitude range of the boundary of the radiation area, that is, the address is in the radiation range of the physical store), the matching is successful, and meanwhile, the physical store coding information and the grid coding information are acquired from the store information database; because the geographic position information, the physical store coding information and the grid coding information of the physical store area of the merchant in the store information database are in one-to-one correspondence, the unique physical store coding information and the grid coding information can be obtained through the longitude and latitude information, and the related information is inserted into an incremental database of the user information database as incremental data, so that the data in the database is expanded, and the next quick matching is facilitated. If the longitude and latitude are not in the radiation area of the physical store, the second matching fails, and prompt information is sent out.
And after the first matching and/or the second matching fail, a target area judging step is further included for judging whether address information generated by the online behavior triggered by the user is in the target area or not. The target area determination is performed after the first matching failure, or
The second matching is performed after the failure, or after both the first matching and the second matching. In practice, when the newly added address information exceeds the radiation area of the physical store, for example, the target area served by the merchant is within the geographical range of province a, but the newly added address is province C, and exceeds the radiation range of all physical stores of the merchant, at this time, prompt information needs to be sent for background processing.
Preferably, the target area judgment is carried out after the second matching failure, whether the address information generated by the online platform member is in the target area or not is judged, if not, the corresponding information record is empty, and prompt information is sent out; and if the matching step is executed again in the target area, performing a new round of matching.
According to the invention, the online behavior (such as access, purchase and the like) of the user is positioned to the online address information of the user, the text type address is converted into the numerical longitude and latitude, the effective marketing range of the store in business sense is converted into the corresponding grid longitude and latitude number group value of the store in digital sense, and the online member and the offline entity store are accurately associated through the address, so that more accurate grid management can be performed on the entity store, and the subsequent marketing activities can be conveniently carried out.
Referring to fig. 3, a data parsing system based on a regional member marketing scenario includes:
the target area meshing module is used for meshing and dividing the target area according to the distribution condition of entity stores of a merchant, each entity store corresponds to one grid, and two adjacent grids are not overlapped; the meshing division of the target area specifically comprises:
and constructing a radiation area by taking the geographic position of the entity store as the center, wherein the radiation area is a closed polygon, the radiation areas of two adjacent entity stores are not repeated, and the closed polygon areas respectively constructed by all entity stores in the target area form a network together to realize the meshing division of the target area.
A store information database comprising:
the merchant entity store region geographic position information comprises longitude and latitude data corresponding to a store geographic position central point and longitude and latitude information of a radiation region boundary, and is simultaneously input into an online map tool;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises the number of grids, and each grid is provided with a unique grid number.
A user information database comprising:
user address information, including longitude and latitude information corresponding to the address information;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the above information corresponds to each other.
The user information database is divided into two types, wherein the first type is a historical database which comprises address information generated by historical user behaviors, corresponding physical store regional geographic position information, physical store coding information and grid coding information;
the second type is an incremental database, which includes address information generated by incremental user actions, geographic location information of a corresponding physical store area, physical store code information and grid code information.
And the information matching module is used for matching address information generated by the online behavior triggering of the user, acquiring entity store information corresponding to the area where the user is located, and attributing the user to an effective marketing area of an offline store through the online behavior of the user.
The matching of address information generated by the user triggering on-line behavior is specifically as follows:
performing first matching on the generated address information in a user information database, if consistent address information exists, successfully performing the first matching, and simultaneously acquiring entity store coding information and grid coding information from the user information database;
otherwise, the first matching fails, and then longitude and latitude information corresponding to the address is obtained by calling an online map tool;
performing second matching on the longitude and latitude information in a store information database:
if the longitude and latitude are in the radiation area of the physical store, the second matching is successful, and meanwhile, the physical store coding information and the grid coding information are obtained from a store information database; and inserting the related information as incremental data into an incremental database of the user information database;
otherwise, the second matching fails and a prompt message is sent out.
The system also comprises a target area judging module, a target area judging module and a target area judging module, wherein the target area judging module is used for judging whether address information generated by an online platform member is in a target area or not, and if not, prompting information is sent out; and if the information is in the target area, calling an information matching module to perform new-round matching.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid, and two adjacent grids are not overlapped;
constructing a store information database;
constructing a user information database;
and matching address information generated by the online behavior triggered by the user, acquiring the information of the physical store corresponding to the area where the user is located, and attributing the user to an effective marketing area of the physical store through the online behavior of the user.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid, and two adjacent grids are not overlapped;
constructing a store information database;
constructing a user information database;
and matching address information generated by the online behavior triggered by the user, acquiring the information of the physical store corresponding to the area where the user is located, and attributing the user to an effective marketing area of the physical store through the online behavior of the user.
According to the online and offline integrated marketing system, an online user and an offline entity store are in digital precise connection, so that the purpose of online and offline member integration is achieved, online and offline integrated marketing can be carried out on members, O2O integration is further achieved, and the online and offline integrated marketing system is better oriented to smart retail.
The invention is described in further detail below in connection with specific steps.
A data analysis method based on a regional member marketing scene comprises the following steps:
step 1, store data preparation, namely recording store codes of entities of a merchant and longitude and latitude data corresponding to geographical position center points of the entities into a store information database, and recording an online map tool;
and 2, dividing the grid of the target area, namely dividing the radiation area corresponding to the store according to the geographic position of the entity store, the situation of surrounding cells and the like, wherein the radiation area is actually a closed polygon, and the plurality of polygons are spliced to form a net, so that the radiation area is called as a grid, the grid division is prevented from overlapping, the grids cannot be overlapped, and one grid can only correspond to one store.
When grid division is carried out, a corresponding store is selected or input, an online map tool locates the geographic position of the store according to the longitude and latitude of the store, polygons are drawn around the store according to the sales coverage area, and then drawing results are submitted. The system locates the longitude and latitude values of each endpoint of the polygon, and records the corresponding store code, longitude and latitude value array data and grid code (which can be automatically generated by the system according to a certain code specification) into the store information database.
And step 3, address data preparation, namely, storing longitude and latitude values corresponding to the existing address in advance in a user information database in order to avoid the conditions of delay, blocking and the like of the external platform in the process of calling the same address for multiple times and acquiring the online map tool, and avoiding the conditions of possible return delay, blocking and the like of the external platform in the process of calling in real time.
(1) Preparation of stock data: the address information of the platform member is acquired, the format is generally the address number of the section |city |district|street|district|house number, an online map tool is called, longitude and latitude corresponding to each address are acquired, and the corresponding relation between the address and the longitude and latitude is stored in a historical database for later matching of address data generated by user behaviors.
(2) Preparation of incremental data: if the user behavior generates a new address, the maintained address data is obtained in real time, and an online map tool is called in real time to obtain the longitude and latitude corresponding to the address, and the longitude and latitude are used as incremental data to be inserted into an incremental database.
And 4, matching longitude and latitude of the user behavior address, and after the user triggers the online behavior to generate address information (for example, the user is located at the address after the user access behavior is authorized, the user receiving address is acquired after the user purchase behavior is generated, and the like), matching the address stored in the user information database according to the user behavior address to obtain longitude and latitude values corresponding to the user behavior address, entity store code information and grid code information. If the related information is not matched, step 5 is executed.
And step 5, after obtaining the longitude and latitude values corresponding to the user behavior addresses, performing secondary matching on the longitude and latitude values in a store information database according to a related tool component (such as an IsPtInPoly static method of JAVA) to obtain grid codes corresponding to corresponding arrays.
Since one grid code corresponds to one store code, the corresponding store code can be remapped by the grid code. Therefore, the user is belonged to an effective marketing area of the off-line store through the on-line behavior of the user, and the aims of fusion and fine grid management of the on-line and off-line members are really achieved.
The present invention will be described in further detail with reference to examples.
Examples
The embodiment is exemplified by purchasing behavior generated by a user, and specifically includes the following steps in combination with fig. 4:
s1, a store code corresponding to a store A is A001, a longitude and latitude corresponding to a store center position is (32.1,118.4), a store code corresponding to a store B is A002, a longitude and latitude corresponding to a store center position is (32.5,118.7), a corresponding relation table is established, and a mapping relation between store codes and longitudes and latitudes is recorded, wherein the table is shown in table 1.
TABLE 1
| Store code | Store latitude | Store longitude |
| A001 | 32.1 | 118.4 |
| A002 | 32.5 | 118.7 |
S3, according to the actual sales area of the store, a grid is drawn around the store through an on-line map tool, longitude and latitude of each endpoint of the grid form a longitude and latitude array, and the corresponding relation is stored as a table 2.
TABLE 2
| Store code | Grid latitude | Grid longitude | Grid numbering |
| A001 | 31.8 | 116.4 | W001 |
| A001 | 32.6 | 116.1 | W001 |
| A001 | 32.5 | 120.7 | W001 |
| A001 | 31.3 | 119.4 | W001 |
S4, an A address is arranged in a common distribution address information table of a user, the corresponding longitude and latitude is 32.2,118.6, the corresponding longitude and latitude falls in the grid range of a store A, and the mapping relation between the A address and the store A001 is recorded in a data table and is recorded as table 3.
TABLE 3 Table 3
| Address of | Address latitude | Address longitude | Address correspondence grid | Address-corresponding store |
| A | 32.2 | 118.6 | W001 | A001 |
S5, two orders of a certain user are generated, one order is an order with an A address, the other order is an order with a B address, and the B address is not shown in the table 3. And matching the two order addresses with the table 3, wherein the order of the A address is matched with the A001 store, the order of the B address cannot be matched with the A001 store, transmitting the B address to an online map tool, acquiring the longitude and latitude corresponding to the B address, analyzing that the longitude and latitude fall within the grid range of the B store, inserting the mapping relation between the B address and the A002 store into the table 3, and coding the A002 store on the order of the B address, so that the processing of the detail data table is completed.
The online buyers and the offline buyers corresponding to the A001 store can be obtained in the later stage according to the clustering or summarizing of the orders, and the relevant attributes and preferences of the buyers can be checked.
The invention can convert text type address into numeric type longitude and latitude by locating the online behavior (such as access, purchase, etc.) of the user to the offline address information of the user. The store effective marketing range in business sense is converted into grid longitude and latitude number group values corresponding to the store in digital sense, so that the purposes of fine grid management and online and offline member integration marketing are achieved by analyzing accurate geographic information data.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of the invention should be assessed as that of the appended claims.
Claims (7)
1. The data analysis method based on the regional member marketing scene is characterized by comprising the following steps of:
s1: according to the distribution condition of the merchant physical stores, meshing and dividing the target area, wherein each physical store corresponds to one grid, and two adjacent grids are not overlapped;
s2: constructing a store information database;
s3: constructing a user information database, wherein the user information database comprises user address information, entity store coding information and grid coding information;
user address information, including longitude and latitude information corresponding to the address information;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the information corresponds to each other;
the user information database is divided into two types, wherein the first type is a historical database which comprises address information generated by historical user behaviors, corresponding entity store coding information and grid coding information;
the second type is an incremental database which comprises address information generated by incremental user behaviors, corresponding entity store coding information and grid coding information;
s4: address information generated by triggering online behaviors of a user is matched, entity store information corresponding to an area where the user is located is obtained, and the user is attributed to an effective marketing area of the entity store through the online behaviors of the user, specifically:
performing first matching on the generated address information in a user information database, if consistent address information exists, successfully performing the first matching, and simultaneously acquiring entity store coding information and grid coding information from the user information database;
otherwise, the first matching fails, and then longitude and latitude information corresponding to the address information is obtained by calling an online map tool;
performing second matching on the longitude and latitude information in a store information database:
if the longitude and latitude are in the radiation area of the physical store, the second matching is successful, and meanwhile, the physical store coding information and the grid coding information are obtained from a store information database; and inserting the related information as incremental data into an incremental database of the user information database;
otherwise, the second matching fails and a prompt message is sent out.
2. The data parsing method based on regional member marketing scenes of claim 1, wherein the meshing of the target area comprises:
and constructing a radiation area by taking the geographic position of the entity store as the center, wherein the radiation area is a closed polygon, the radiation areas of two adjacent entity stores are not repeated, and the closed polygon areas respectively constructed by all entity stores in the target area form a network together to realize the meshing division of the target area.
3. The data parsing method based on the regional membership marketing scene according to claim 1, wherein the store information database comprises:
the merchant entity store region geographic position information comprises longitude and latitude data corresponding to a store geographic position central point and longitude and latitude information of a radiation region boundary, and is simultaneously input into an online map tool;
the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number;
the grid coding information comprises grid numbers, and each grid is provided with a unique grid number;
the above information corresponds to each other.
4. The data analysis method based on the regional membership marketing scene according to claim 1, further comprising a target area judgment step of judging whether address information generated by the online behavior triggered by the user is in a target area after the first and/or second matching fails.
5. A data parsing system based on a regional membership marketing scenario, comprising:
the target area meshing dividing module is used for meshing dividing the target area, each entity store corresponds to one grid, and two adjacent grids are not overlapped;
store information database;
the user information database comprises user address information, entity store coding information and grid coding information; user address information, including longitude and latitude information corresponding to the address information; the physical store coding information comprises the numbers of physical stores, and each physical store is provided with a unique physical store number; the grid coding information comprises grid numbers, and each grid is provided with a unique grid number; the information corresponds to each other; the user information database is divided into two types, wherein the first type is a historical database which comprises address information generated by historical user behaviors, corresponding entity store coding information and grid coding information; the second type is an incremental database which comprises address information generated by incremental user behaviors, corresponding entity store coding information and grid coding information;
the address information matching module is used for matching address information generated by triggering online behaviors of a user, acquiring entity store information corresponding to an area where the user is located, and attributing the user to an effective marketing area of the entity store through the online behaviors of the user, and specifically comprises the following steps: performing first matching on the generated address information in a user information database, if consistent address information exists, successfully performing the first matching, and simultaneously acquiring entity store coding information and grid coding information from the user information database; otherwise, the first matching fails, and then longitude and latitude information corresponding to the address information is obtained by calling an online map tool; performing second matching on the longitude and latitude information in a store information database: if the longitude and latitude are in the radiation area of the physical store, the second matching is successful, and meanwhile, the physical store coding information and the grid coding information are obtained from a store information database; and inserting the related information as incremental data into an incremental database of the user information database; otherwise, the second matching fails and a prompt message is sent out.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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| CN201911066148.4A CN110930180B (en) | 2019-11-04 | 2019-11-04 | Data analysis method, system and computer equipment based on regional member marketing scene |
| CA3159785A CA3159785A1 (en) | 2019-11-04 | 2020-07-29 | Data parsing method based on regionalized membership marketing scene, system and computer equipment |
| PCT/CN2020/105640 WO2021088434A1 (en) | 2019-11-04 | 2020-07-29 | Data analysis method and system based on regionalized membership marketing scene, and computer device |
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| CN113781132A (en) * | 2020-06-15 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Online shopping guide method and device |
| CN112347214B (en) * | 2020-11-06 | 2023-07-18 | 平安科技（深圳）有限公司 | Target area dividing method and device, electronic equipment and storage medium |
| CN112907275A (en) * | 2021-01-21 | 2021-06-04 | 长沙市到家悠享网络科技有限公司 | Business district fence configuration method, service information distribution method, equipment and medium |
| CN113763052A (en) * | 2021-09-14 | 2021-12-07 | 胜斗士(上海)科技技术发展有限公司 | Method for determining geographic service range of shop |
| CN114648372A (en) * | 2022-05-23 | 2022-06-21 | 浙江口碑网络技术有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN116188066B (en) * | 2023-03-10 | 2023-08-15 | 广州南方学院 | Intelligent distribution method and system for store clients based on geographic position |
| CN116757737B (en) * | 2023-08-10 | 2023-12-19 | 山景智能(北京)科技有限公司 | Marketing method and device based on address information |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127659A (en) * | 2016-08-26 | 2016-11-16 | 南威软件股份有限公司 | A kind of community grid management system |
| CN107070961A (en) * | 2016-09-30 | 2017-08-18 | 阿里巴巴集团控股有限公司 | Hot spot region based on geographic position data determines method and device |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104156843A (en) * | 2014-08-08 | 2014-11-19 | 上海新跃物流企业管理有限公司 | Logistics distribution management system and logistics distribution management method |
| CN106960294A (en) * | 2016-01-08 | 2017-07-18 | 北京京东尚科信息技术有限公司 | A kind of intelligent order method for sorting and device |
| CN105654366A (en) * | 2016-02-05 | 2016-06-08 | 朱亮红 | Commodity transaction method and server |
| CN111782941B (en) * | 2016-05-11 | 2023-12-12 | 创新先进技术有限公司 | Information recommendation method, device and server |
| CN106296100A (en) * | 2016-08-19 | 2017-01-04 | 河南易之信网络科技有限公司 | The management method of a kind of cross-border logistics business, Apparatus and system |
| CN106447461A (en) * | 2016-10-18 | 2017-02-22 | 成都花娃网络科技有限公司 | Network flower ordering method |
| CN108364143A (en) * | 2017-01-26 | 2018-08-03 | 北京京东尚科信息技术有限公司 | Allocator and delivery system |
| CN107704586B (en) * | 2017-10-09 | 2020-02-07 | 陈包容 | User portrait method, device and system based on user activity address |
| JP6884315B2 (en) * | 2018-06-04 | 2021-06-09 | 鍵和田 芳光 | Logistics support methods, systems and programs |
| CN110930180B (en) * | 2019-11-04 | 2023-07-28 | 苏宁云计算有限公司 | Data analysis method, system and computer equipment based on regional member marketing scene |
-
2019
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127659A (en) * | 2016-08-26 | 2016-11-16 | 南威软件股份有限公司 | A kind of community grid management system |
| CN107070961A (en) * | 2016-09-30 | 2017-08-18 | 阿里巴巴集团控股有限公司 | Hot spot region based on geographic position data determines method and device |
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