CA3159785A1 - Data parsing method based on regionalized membership marketing scene, system and computer equipment - Google Patents
Data parsing method based on regionalized membership marketing scene, system and computer equipmentInfo
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Abstract
Disclosed are a data analysis method and system based on a regionalized membership marketing scene, and a computer device. The method comprises: performing gridding division on a target region according to a distribution condition of physical stores of a merchant, each physical store corresponding to one grid; constructing a store information database, and corresponding the gridded regional geographic position information to physical store code information and grid code information; constructing a user information database; and matching address information generated for a user to trigger an online behavior, obtaining physical store information corresponding to a region where the user is located, and attributing the user to an effective marketing region of offline stores by means of the online behavior of the user. According to the present invention, offline address information of the user is positioned by obtaining address information data generated by the online behavior of the user, so that the online user and the offline store generate an actual relation about a geographic position by means of an address, the online user is accurately attributed to a marketing region range of the offline stores, and gridding management of sales regions is achieved.
Description
DATA PARSING METHOD BASED ON REGIONALIZED MEMBERSHIP
MARKETING SCENE, SYSTEM AND COMPUTER EQUIPMENT
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of e-commerce technology, and more particularly to a data parsing method based on a regionalized membership marketing scene, and corresponding system and computer equipment.
Description of Related Art
MARKETING SCENE, SYSTEM AND COMPUTER EQUIPMENT
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of e-commerce technology, and more particularly to a data parsing method based on a regionalized membership marketing scene, and corresponding system and computer equipment.
Description of Related Art
[0002] In the traditional retail industry, entity stores accumulate great quantities of consumer groups by the mode of recruiting members, and carry out various marketing activities by periodically pushing commodity information.
[0003] With the development of the e-commerce, the 020 (Online to Offline) pattern has become increasingly mature. 020 is a commercial business pattern that combines online transactions based on commodities or services of e-commerce websites with actual experiences based on commodities or services of entity stores, enabling the e-commerce websites to become the forestage of entity store transactions, and enabling the entity stores to become the backstage of e-commerce website transactions.
[0004] Over the recent years, online consumption platforms have been growing increasingly, companies that were originally deep-rooted in offline entity stores are joining in the rank of e-commerce one after the other. Rich and variegated online marketing means have been incessantly broadening the online membership groups. If online members are merged with offline entity store members, merchants will be facilitated to know of the Date recue/date received 2022-05-02 population already consumed around entity stores and potential population to consume, and integrated marketing will be made easier.
[0005] Patent No. 201710944600.7 has made public a transaction data processing method, and device and system thereof. In a scene where a user purchases a commodity at an entity store (namely an offline shopping scene), transaction data is generated for the offline commodity with the online identification of the user on a third-party/online transaction server, or transaction data is generated for the offline commodity with the online price of the offline commodity, and the transaction data is synchronized to the store terminal and the user terminal respectively, whereby is realized an online to offline transaction data processing mode, and it is made possible to utilize the online advantages to bring about conveniences to such aspects as administration, maintenance and manipulation of offline transactions, to enhance competitiveness of entity stores under the e-commerce environment, and to promote development of the entity stores. However, this patent fails to address exact administration of offline entity stores, and also fails to precisely associate online members with offline entity stores through addresses.
SUMMARY OF THE INVENTION
SUMMARY OF THE INVENTION
[0006] The technical problem to be solved by the present invention is to provide a data parsing method based on a regionalized membership marketing scene to realize precise association of online members with offline entity stores through positional information.
[0007] Technical solutions for achieving the objective of the present invention are as follows.
There is provided a data parsing method based on a regionalized membership marketing scene, and the method comprises the following steps:
There is provided a data parsing method based on a regionalized membership marketing scene, and the method comprises the following steps:
[0008] so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
Date recue/date received 2022-05-02
Date recue/date received 2022-05-02
[0009] constructing a store information database;
[0010] constructing a user information database; and
[0011] matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
[0012] Preferably, partitioning a target region into grids specifically includes:
[0013] constructing radiant regions with a geographical location where the entity stores locate as a center, wherein the radiant regions assume a closed polygon, the radiant regions of two adjacent entity stores are not repetitive to each other, closed polygonal regions respectively constructed by all the entity stores in the target region together constitute a net, and partitioning of the target region into grids is realized.
[0014] Preferably, the store information database includes:
[0015] merchant entity store region geographical location information, which includes longitude and latitude data to which a store geographical location center point corresponds and longitude and latitude information of radiant region boundaries, and which is recorded in an online mapping tool at the same time;
[0016] entity store coding information, including numbers of the entity stores, each entity store having a unique entity store number; and
[0017] grid coding information, including numbers of the grids, each grid having a unique grid number;
[0018] wherein the above pieces of information correspond to one another.
[0019] Preferably, the user information database includes:
[0020] user address information, including longitude and latitude information to which the address information corresponds;
[0021] entity store coding information, including numbers of the entity stores, each entity store Date recue/date received 2022-05-02 having a unique entity store number; and
[0022] grid coding information, including numbers of the grids, each grid having a unique grid number; wherein
[0023] the above pieces of information correspond to one another.
[0024] Preferably, the user information database is classified into two types, wherein one type is a historical database that includes address information generated by historical user behaviors, entity store coding information and grid coding information;
[0025] the other type is an incremental database that includes address information generated by incremental user behaviors, entity store coding information and grid coding information.
[0026] Preferably, matching address information generated by a user-triggered online behavior specifically includes:
[0027] performing first matching on the generated address information in the user information database, wherein the first matching succeeds if there is consistent address information, and simultaneously obtaining the entity store coding information and the grid coding information from the user information database;
[0028] otherwise, the first matching fails, thereafter obtaining the longitude and latitude information to which the address information corresponds by invoking the online mapping tool;
[0029] performing secondary matching on the longitude and latitude information in the store information database; wherein
[0030] the secondary matching succeeds if the longitude and latitude are within the radiant region of the entity store, simultaneously obtaining the entity store coding information and the grid coding information from the store information database, and inserting relevant information as incremental data into the incremental database of the user information database;
[0031] otherwise, the secondary matching fails, sending out reminder information.
Date recue/date received 2022-05-02
Date recue/date received 2022-05-02
[0032] Preferably, a target region judging step is further included to judge whether the address information generated by a user-triggered online behavior is in the target region after the first matching and/or the secondary matching have/has failed.
[0033] There is provided a data parsing system based on a regionalized membership marketing scene, and the system comprises:
[0034] a target region grid-partitioning module, for so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
[0035] a store information database;
[0036] a user information database; and
[0037] an information matching module, for matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of offline stores through the online behavior of the user.
[0038] In comparison with prior-art technology, the present invention achieves the following apparent advantages: 1) the present invention locates offline address information of the user through online behaviors of the user (such as access, purchase, etc.), converts the text-type address to numerical value-type longitude and latitude, converts the store valid marketing range in the sense of businesses to grid longitude and latitude array values to which stores correspond in the sense of digits, and precisely associate online members with offline entity stores through addresses, whereby is made possible to more exactly administer entity stores by grids, and to facilitate the execution of subsequent marketing activities; 2) the present invention digitally establishes precise relation between online users and offline stores, and achieves the objective of merging online and offline members, whereby is made possible to carry out integrated online and offline marketing on the members, to further realize 020 fusion, and to better adapt to smart retail;
3) the present invention locates offline address information of the user by obtaining address information Date recue/date received 2022-05-02 data generated by online behaviors of the user, so as to actually associate the online user with offline stores through the address in terms of geographical location, to achieve the objective of exactly assigning the online user within a range of marketable regions of the offline stores through the association of positional information data, whereby is realized gridded administration of sales regions.
3) the present invention locates offline address information of the user by obtaining address information Date recue/date received 2022-05-02 data generated by online behaviors of the user, so as to actually associate the online user with offline stores through the address in terms of geographical location, to achieve the objective of exactly assigning the online user within a range of marketable regions of the offline stores through the association of positional information data, whereby is realized gridded administration of sales regions.
[0039] The present invention will be described in greater detailed below in conjunction with accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Fig. 1 is an overall flowchart illustrating the data parsing method based on regionalized membership marketing scene in the present invention;
[0041] Fig. 2 is a flowchart illustrating the step of matching address information generated by a user-triggered online behavior in the present invention;
[0042] Fig. 3 is a view schematically illustrating the framework of the data parsing system based on regionalized membership marketing scene in the present invention; and
[0043] Fig. 4 is a view illustrating embodiments of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
DETAILED DESCRIPTION OF THE INVENTION
[0044] In conjunction with Fig. 1, the data parsing method based on regionalized membership marketing scene of the present invention comprises the following steps:
[0045] so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
[0046] constructing a store information database;
Date recue/date received 2022-05-02
Date recue/date received 2022-05-02
[0047] constructing a user information database; and
[0048] matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
[0049] Partitioning a target region into grids specifically includes:
[0050] constructing radiant regions centering on a geographical location where the entity stores locate, wherein each of the radiant regions assume a closed polygon, the radiant regions of two adjacent entity stores are non-overlapping with each other, closed polygonal regions respectively constructed by all the entity stores in the target region together constitute a net, thereby partitioning of the target region into grids is realized. It is specifically possible to base on the circumstances of geographical location where the stores locate and the surrounding communities to partition the radiant regions to which the stores correspond, the radiant region is actually a closed polygon, plural polygons are joined to form a net, such radiant regions are referred to as grids, overlapping should be avoided in the partitioning into grids, the various grids should not overlap one another and one grid can only correspond to one store. When the radiant regions are constructed, partitioning can be made in conjunction with the consumption circumstance of the local regions and the population distribution circumstance, as long as it can be ensured that the grids formed by all the entity stores can cover the entire target region. With respect to stores with stronger sales capabilities, the radiant regions can be adequately enlarged, with respect to stores with weaker sales capabilities or with respect to new stores, the radiant regions can be adaptively shrunk. The target region is a region which the merchant prepares to cover by sales, and can be a country, a province, a city or a county, etc.
[0051] The store information database includes:
[0052] merchant entity store region geographical location information, which includes longitude and latitude data to which a store geographical location center point corresponds and Date recue/date received 2022-05-02 longitude and latitude information of radiant region boundaries, and which is recorded in an online mapping tool at the same time; the online mapping tool is such an existent online map as Amap map, Baidu map, Beidou map, etc.;
[0053] entity store coding information, including numbers of the entity stores, each entity store having a unique entity store number; the arranging method of store numbers can be determined by the merchant itself, as long as it is ensured that one entity store corresponds to one number; and
[0054] grid coding information, including numbers of the grids, each grid having a unique grid number.
[0055] The merchant entity store region geographical location information, the entity store coding information and the grid coding information correspond to one another on a one-to-one basis, and the other two pieces of information can be matched out through one piece of information therefrom.
[0056] The user information database includes:
[0057] user address information, including longitude and latitude information to which the address information corresponds;
[0058] entity store coding information, including numbers of the entity stores, each entity store having a unique entity store number; and
[0059] grid coding information, including numbers of the grids, each grid having a unique grid number;
[0060] the user address information, the entity store coding information and the grid coding information correspond to one another on a one-by-one basis, and the other two pieces of information can be matched out through one piece of information therefrom. The user information database functions to accelerate matching speed and shorten matching time.
[0061] The user information database is classified into two types, one type is a historical database that includes address information generated by historical user behaviors, entity store Date recue/date received 2022-05-02 coding information and grid coding information. The historical user behaviors indicate address information previously input by the user while purchasing commodities or browsing commodities, and the format thereof is usually province I city I
district I street I community I house number. One user can correspond to plural pieces of address information.
district I street I community I house number. One user can correspond to plural pieces of address information.
[0062] The other type is an incremental database that includes address information generated by incremental user behaviors, entity store coding information and grid coding information, including address information generated by new users, and also including new address information data added by existent users.
[0063] The merchant entity store region geographical location information, the entity store coding information and the grid coding information in the store information database have one-to-one correspondence relations. Each piece of merchant entity store region geographical location information only corresponds to one piece of entity store coding information, and also only corresponds to one piece of grid coding information at the same time.
[0064] Matching address information generated by a user-triggered online behavior specifically includes:
[0065] performing first matching on the generated address information in the user information database, wherein the first matching succeeds if there is consistent address information, and simultaneously obtaining the entity store coding information and the grid coding information from the user information database;
[0066] otherwise, the first matching fails, thereafter obtaining the longitude and latitude information to which the address corresponds by invoking the online mapping tool;
[0067] performing secondary matching on the longitude and latitude information in the store information database; wherein
[0068] the secondary matching succeeds if the longitude and latitude are within the radiant region Date recue/date received 2022-05-02 of the entity store, simultaneously obtaining the entity store coding information and the grid coding information from the store information database, and inserting relevant information as incremental data into the incremental database of the user information database;
[0069] otherwise, the secondary matching fails, sending out reminder information.
[0070] During the matching process, first matching is firstly performed in the user information database, the online mapping tool is invoked only after the first matching has failed to obtain longitude and latitude information to which the address corresponds, and the longitude and latitude information is used to perform secondary matching in the store information database. Matching time can thus be greatly shortened, and entity store related information to which the address information generated by the user-triggered online behavior corresponds can be quickly obtained.
[0071] With reference to Fig. 2, performing secondary matching on the longitude and latitude information in the store information database is specifically as follows:
[0072] if the longitude and latitude are within the radiant region of the entity store (the longitude and latitude are within the longitude and latitude range of the radiant region boundaries, in other words, the address is located within the radiant range of the entity store), the matching succeeds, the entity store coding information and the grid coding information are simultaneously obtained from the store information database; since the merchant entity store region geographical location information, the entity store coding information and the grid coding information in the store information database have one-to-one correspondence relations, through the aforementioned longitude and latitude information can be obtained the one unique piece of entity store coding information and the one unique piece of grid coding information, and the relevant information is taken as incremental data to be inserted into the incremental database of the user information database, to enlarge data in the database, and to facilitate the next quick matching. If the longitude and latitude are not within the radiant region of the entity store, the secondary Date recue/date received 2022-05-02 matching fails, and reminder information is sent out.
[0073] A target region judging step is further included to judge whether the address information generated by a user-triggered online behavior is in the target region after the first matching and/or the secondary matching have/has failed.
[0074] Target region judgement is performed after the first matching has failed, or after the secondary matching has failed, or after the first matching and the secondary matching have both failed. In practice, when newly added address information exceeds the radiant region of the entity store, for instance, the target region serviced by a merchant is within the geographical range of Province A, but a newly added address is Province C, the range radiated by all the entity stores of the merchant is exceeded, it is required at this time to send out reminder information for processing by the backstage.
[0075] Preferably, target region judgment is performed after the secondary matching has failed, to judge whether the address information generated by an online platform member is in the target region, if not, corresponding information record is null, and reminder information is sent out; if yes, the matching step is executed again, and a new round of matching is performed.
[0076] The present invention locates offline address information of the user through online behaviors of the user (such as access, purchase, etc.), converts the text-type address to numerical value-type longitude and latitude, converts the store valid marketing range in the sense of businesses to grid longitude and latitude array values to which stores correspond in the sense of digits, and precisely associate online members with offline entity stores through addresses, whereby is made possible to more exactly administer entity stores by grids, and to facilitate the execution of subsequent marketing activities.
[0077] With reference to Fig. 3, there is provided a data parsing system based on a regionalized Date recue/date received 2022-05-02 membership marketing scene, and the system comprises:
[0078] A target region grid-partitioning module that is employed for so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
partitioning a target region into grids specifically includes:
partitioning a target region into grids specifically includes:
[0079] constructing radiant regions centering on a geographical location where the entity stores locate, wherein the radiant regions assume a closed polygon, the radiant regions of two adjacent entity stores are non-overlapping with each other, closed polygonal regions respectively constructed by all the entity stores in the target region together constitute a net, and partitioning of the target region into grids is realized.
[0080] A store information database that includes:
[0081] merchant entity store region geographical location information, which includes longitude and latitude data to which a store geographical location center point corresponds and longitude and latitude information of radiant region boundaries, and which is recorded in an online mapping tool at the same time;
[0082] entity store coding information, including numbers of the entity stores, each entity store having a unique entity store number; and
[0083] grid coding information, including numbers of the grids, each grid having a unique grid number.
[0084] A user information database that includes:
[0085] user address information, including longitude and latitude information to which the address information corresponds;
[0086] entity store coding information, including numbers of the entity stores, each entity store having a unique entity store number; and
[0087] grid coding information, including numbers of the grids, each grid having a unique grid number; wherein Date recue/date received 2022-05-02
[0088] the above pieces of information correspond to one another.
[0089] The user information database is classified into two types, one type is a historical database that includes address information generated by historical user behaviors, corresponding entity store region geographical location information, entity store coding information and grid coding information;
[0090] the other type is an incremental database that includes address information generated by incremental user behaviors, corresponding entity store region geographical location information, entity store coding information and grid coding information.
[0091] An information matching module that is employed for matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of offline stores through the online behavior of the user.
[0092] Matching address information generated by a user-triggered online behavior specifically includes:
[0093] performing first matching on the generated address information in the user information database, wherein the first matching succeeds if there is consistent address information, and simultaneously obtaining the entity store coding information and the grid coding information from the user information database;
[0094] otherwise, the first matching fails, thereafter obtaining the longitude and latitude information to which the address corresponds by invoking the online mapping tool;
[0095] performing secondary matching on the longitude and latitude information in the store information database; wherein
[0096] the secondary matching succeeds if the longitude and latitude are within the radiant region of the entity store, simultaneously obtaining the entity store coding information and the grid coding information from the store information database, and inserting relevant information as incremental data into the incremental database of the user information Date recue/date received 2022-05-02 database;
[0097] otherwise, the secondary matching fails, sending out reminder information.
[0098] The system further comprises a target region judging module for judging whether address information generated by an online platform member is in the target region, if not, sending out reminder information, if yes, invoking the information matching module to perform anew round of matching.
[0099] There is provided a computer equipment that comprises a memory, a processor and a computer program stored on the memory and operable on the processor, and the following steps are realized when the processor executes the computer program:
[0100] so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
[0101] constructing a store information database;
[0102] constructing a user information database; and
[0103] matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
[0104] There is provided a computer-readable storage medium storing a computer program thereon, and the following steps are realized when the computer program is executed by a processor:
[0105] so partitioning a target region into grids according to a distribution of entity stores of merchants that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
[0106] constructing a store information database;
[0107] constructing a user information database; and Date recue/date received 2022-05-02
[0108] matching address information generated by a user-triggered online behavior, obtaining entity store information to which a region where the user resides corresponds, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
[0109] The present invention digitally establishes precise relation between online users and offline entity stores, and achieves the objective of merging online and offline members, whereby is made possible to carry out integrated online and offline marketing on the members, to further realize 020 fusion, and to better adapt to smart retail.
[0110] The present invention is described in greater detailed below in conjunction with specific steps.
[0111] A data parsing method based on a regionalized membership marketing scene comprises the following steps:
[0112] Step 1 ¨ store data preparation, in which longitude and latitude data to which various entity store numbers of the merchant and various entity store geographical location center points correspond is recorded in the store information database, and input in the online mapping tool;
[0113] Step 2 ¨ target region grid-partition, in which radiant regions to which the stores correspond are partitioned according to circumstances of the geographical location where the stores locate and the surrounding communities, the radiant region is actually a closed polygon, plural polygons are joined like a net, so the radiant regions are referred to as grids, overlapping should be avoided in the partitioning into grids, the various grids should not overlap one another and one grid can only correspond to one store;
[0114] during grid-partitioning, a corresponding store is firstly selected or input, the geographical location of the store is located by the online mapping tool according to longitude and latitude of the store, a polygon is drawn around the store according to the sales coverage region, the drawing result is thereafter submitted. The system locates the Date recue/date received 2022-05-02 longitude and latitude values of the various endpoints of the polygon, and records the corresponding store number, longitude and latitude value array data, and grid number (these can be automatically generated by the system according to a certain coding specification) in the store information database.
[0115] Step 3 ¨ address data preparation, in which, in order to prevent the same address from being repetitively obtained by invoking the online mapping tool, and to prevent the possible circumstances of delays and stutters returning from external platforms during the process of real-time invoking, longitude and latitude values to which existent addresses correspond are disposed in advance in the user information database.
[0116] 1 Stock data preparation: address information of platform members is obtained, the format thereof is usually province I city I district I street I community I house number, the online mapping tool is invoked to obtain longitudes and latitudes to which the various addresses correspond, and correspondence relations between the addresses and the longitudes and latitudes are disposed in the historical database for standby subsequent matching with address data generated by user behaviors.
[0117] 2Incremental data preparation: if a newly added address is generated by user behaviors, address data after maintenance is obtained in real time, the online mapping tool is invoked in real time to obtain the longitude and latitude to which the address corresponds, and the same is inserted as incremental data into the incremental database.
[0118] Step 4 ¨ user behavior address longitude and latitude matching, in which, after address information has been generated by a user-triggered online behavior (for instance, an address where the user resides will be located after a user accessing behavior has been authorized, and a receiving address of the user will be obtained after a user purchasing behavior has occurred), addresses disposed in the user information database are matched according to the address of the user behavior, to obtain longitude and latitude values to which the address of the user behavior corresponds, and the entity store coding information and grid coding information. If no relevant information is matched out, step is executed.
[0119] Step 5 ¨ after the longitude and latitude values to which the address of the user behavior Date recue/date received 2022-05-02 corresponds have been obtained, the longitude and latitude values are secondarily matched in the store information database according to relevant tool assembly (such as the IsPtInPoly static method of JAVA) to obtain a grid number to which the corresponding array corresponds.
[0120] Since one grid number corresponds to one store number, the corresponding store number can be remapped via the grid number. Accordingly, the user is assigned to the valid marketing region of offline stores through the online behavior of the user, whereby are authentically achieved the objectives of merging online and offline members and refined and gridded administration.
[0121] The present invention is described in greater detail below in conjunction with an embodiment.
[0122] Embodiment
[0123] A purchasing behavior generated by a user is taken for example in this embodiment, with reference to Fig. 4, the following steps are specifically included:
[0124] 51. The store number to which store A corresponds is A001, the longitude and latitude to which the store central position corresponds are (32.1, 118.4), the store number to which store B corresponds is A002, the longitude and latitude to which the store central position corresponds are (32.5, 118.7), a sheet of correspondence relation table is established to record mapping relations between the store numbers and the longitudes and latitudes, and this table is marked as Table 1.
[0125] Table 1
[0126]
Store Number Store Latitude Store Longitude A001 32.1 118.4 A002 32.5 118.7 Date recue/date received 2022-05-02
Store Number Store Latitude Store Longitude A001 32.1 118.4 A002 32.5 118.7 Date recue/date received 2022-05-02
[0127] S3. Grids are drawn around store A via the online mapping tool according to the actual sales region of the store, longitudes and latitudes of the various endpoints of the grids constitute a longitude and latitude array, and the correspondence relations are stored as Table 2.
[0128] Table 2
[0129]
Store Number Grid Latitude Gird Longitude Grid Number A001 31.8 116.4 W001 A001 32.6 116.1 W001 A001 32.5 120.7 W001 A001 31.3 119.4 W001
Store Number Grid Latitude Gird Longitude Grid Number A001 31.8 116.4 W001 A001 32.6 116.1 W001 A001 32.5 120.7 W001 A001 31.3 119.4 W001
[0130] S4. There is address A in an information sheet of frequently used delivery addresses of a certain user, the corresponding longitude and latitude are (32.2, 118.6), these fall within the grid range of store A, the mapping relation between address A and store A001 is recorded in a datasheet, which is marked as Table 3.
[0131] Table 3
[0132]
Address Address Address Grid to which Store to which Latitude Longitude Address Address Corresponds Corresponds A 32.2 118.6 W001 A001
Address Address Address Grid to which Store to which Latitude Longitude Address Address Corresponds Corresponds A 32.2 118.6 W001 A001
[0133] S5. A certain user generates two orders, one is an order of address A, another is an order of address B, and address B is not in Table 3. The two order addresses are then matched with Table 3, the order of address A is matched with store A001, the order of address B
cannot be matched, address B is then transmitted to the online mapping tool to obtain the longitude and latitude to which address B corresponds, it is parsed that the longitude and Date recue/date received 2022-05-02 latitude fall within the grid range of store B, the mapping relation between address B and store A002 is then inserted into Table 3 and the store number A002 is marked on the order of address B, processing of the detailed datasheet is thus completed.
cannot be matched, address B is then transmitted to the online mapping tool to obtain the longitude and latitude to which address B corresponds, it is parsed that the longitude and Date recue/date received 2022-05-02 latitude fall within the grid range of store B, the mapping relation between address B and store A002 is then inserted into Table 3 and the store number A002 is marked on the order of address B, processing of the detailed datasheet is thus completed.
[0134] The number of online buyers and offline buyers, etc., to which store A001 corresponds can be subsequently obtained by clustering or summarizing the various orders, and relevant attributes and preferences of these buyers can be checked.
[0135] The present invention locates offline address information of the user through online behaviors of the user (such as access, purchase, etc.), converts the text-type address to numerical value-type longitude and latitude, and converts the store valid marketing range in the sense of businesses to grid longitude and latitude array values to which stores correspond in the sense of digits, so as to achieve the objectives of refined and gridded administration and integrated marketing with respect to online and offline members by parsing the exact geographical information data.
[0136] As comprehensible to persons ordinarily skilled in the art, the entire or partial flows in the methods according to the aforementioned embodiments can be completed via a computer program instructing relevant hardware, the computer program can be stored in a nonvolatile computer-readable storage medium, and the computer program can include the flows as embodied in the aforementioned various methods when executed. Any reference to the memory, storage, database or other media used in the various embodiments provided by the present application can all include nonvolatile and/or volatile memory/memories. The nonvolatile memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM) or a flash memory. The volatile memory can include a random access memory (RAM) or an external cache memory. To serve as explanation rather than restriction, the RAM is obtainable in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM
Date recue/date received 2022-05-02 (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM
(RDRAM), etc.
Date recue/date received 2022-05-02 (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM
(RDRAM), etc.
[0137] Technical features of the aforementioned embodiments are randomly combinable, while all possible combinations of the technical features in the aforementioned embodiments are not exhausted for the sake of brevity, but all these should be considered to fall within the scope recorded in the description as long as such combinations of the technical features are not mutually contradictory.
[0138] The foregoing embodiments are merely directed to several modes of execution of the present application, and their descriptions are relatively specific and detailed, but they should not be hence misunderstood as restrictions to the inventive patent scope. As should be pointed out, persons with ordinary skill in the art may further make various modifications and improvements without departing from the conception of the present application, and all these should pertain to the protection scope of the present application.
Accordingly, the patent protection scope of the present application shall be based on the attached Claims.
Date recue/date received 2022-05-02
Accordingly, the patent protection scope of the present application shall be based on the attached Claims.
Date recue/date received 2022-05-02
Claims (10)
1. A data parsing method based on a regionalized membership marketing scene, characterized in comprising the following steps:
partitioning a target region into grids, according to a distribution of entity stores of merchants, in the way that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
constructing a store information database;
constructing a user information database; and matching address information generated by a user-triggered online behavior, obtaining entity store information corresponding to a region where the user resides, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
partitioning a target region into grids, according to a distribution of entity stores of merchants, in the way that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
constructing a store information database;
constructing a user information database; and matching address information generated by a user-triggered online behavior, obtaining entity store information corresponding to a region where the user resides, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
2. The data parsing method based on a regionalized membership marketing scene according to Claim 1, characterized in that the step of partitioning a target region into grids specifically includes:
constructing radiant regions centering on a geographical location where the entity stores locate, wherein each of the radiant regions assume a closed polygon, the radiant regions of two adjacent entity stores are non-overlapping with each other, closed polygonal regions respectively constructed by all the entity stores in the target region together constitute a net, thereby partitioning of the target region into grids is realized.
constructing radiant regions centering on a geographical location where the entity stores locate, wherein each of the radiant regions assume a closed polygon, the radiant regions of two adjacent entity stores are non-overlapping with each other, closed polygonal regions respectively constructed by all the entity stores in the target region together constitute a net, thereby partitioning of the target region into grids is realized.
3. The data parsing method based on a regionalized membership marketing scene according to Claim 1, characterized in that the store information database includes:
merchant entity store region geographical location information, which includes longitude and latitude data to which a store geographical location center point corresponds and longitude and latitude information of radiant region boundaries, wherein the merchant entity store region geographical location information is recorded in an online mapping tool at the same time;
entity store coding information, including numbers of the entity stores, wherein each entity store has a unique entity store number; and grid coding information, including numbers of the grids, wherein each grid has a unique grid number;
wherein the above pieces of information correspond to one another.
merchant entity store region geographical location information, which includes longitude and latitude data to which a store geographical location center point corresponds and longitude and latitude information of radiant region boundaries, wherein the merchant entity store region geographical location information is recorded in an online mapping tool at the same time;
entity store coding information, including numbers of the entity stores, wherein each entity store has a unique entity store number; and grid coding information, including numbers of the grids, wherein each grid has a unique grid number;
wherein the above pieces of information correspond to one another.
4. The data parsing method based on a regionalized membership marketing scene according to Claim 1, characterized in that the user information database includes:
user address information, including longitude and latitude information to which the address information corresponds;
entity store coding information, including numbers of the entity stores, wherein each entity store has a unique entity store number; and grid coding information, including numbers of the grids, wherein each grid has a unique grid number;
wherein the above pieces of information correspond to one another.
user address information, including longitude and latitude information to which the address information corresponds;
entity store coding information, including numbers of the entity stores, wherein each entity store has a unique entity store number; and grid coding information, including numbers of the grids, wherein each grid has a unique grid number;
wherein the above pieces of information correspond to one another.
5. The data parsing method based on a regionalized membership marketing scene according to Claim 4, characterized in that the user information database is classified into two types, wherein one type is a historical database that includes address information generated by historical user behaviors, and corresponding entity store coding information and grid coding information;
and wherein the other type is an incremental database that includes address information generated by incremental user behaviors, and corresponding entity store coding information and grid coding information.
and wherein the other type is an incremental database that includes address information generated by incremental user behaviors, and corresponding entity store coding information and grid coding information.
6. The data parsing method based on a regionalized membership marketing scene according to Claim 5, characterized in that the step of matching address information generated by a user-triggered online behavior specifically includes:
performing first matching on the generated address information in the user information database, wherein the first matching succeeds if there is consistent address information, and simultaneously obtaining the entity store coding information and the grid coding information from the user information database;
otherwise the first matching fails, thereafter obtaining the longitude and latitude information, to which the address information corresponds, by invoking the online mapping tool;
performing secondary matching on the longitude and latitude information in the store information database; wherein the secondary matching succeeds if the longitude and latitude are within the radiant region of the entity store, simultaneously obtaining the entity store coding information and the grid coding information from the store information database, and inserting relevant information as incremental data into the incremental database of the user information database;
otherwise the secondary matching fails, thereafter sending out reminder information.
performing first matching on the generated address information in the user information database, wherein the first matching succeeds if there is consistent address information, and simultaneously obtaining the entity store coding information and the grid coding information from the user information database;
otherwise the first matching fails, thereafter obtaining the longitude and latitude information, to which the address information corresponds, by invoking the online mapping tool;
performing secondary matching on the longitude and latitude information in the store information database; wherein the secondary matching succeeds if the longitude and latitude are within the radiant region of the entity store, simultaneously obtaining the entity store coding information and the grid coding information from the store information database, and inserting relevant information as incremental data into the incremental database of the user information database;
otherwise the secondary matching fails, thereafter sending out reminder information.
7. The data parsing method based on a regionalized membership marketing scene according to Claim 6, characterized in further comprising a target region judging step to judge whether the address information generated by a user-triggered online behavior is in the target region, after the first matching and/or the secondary matching have/has failed.
8. A data parsing system based on a regionalized membership marketing scene, characterized in comprising:
a target region grid-partitioning module, for partitioning a target region into grids, according to a distribution of entity stores of merchants, in the way that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
a store information database;
a user information database; and an information matching module, for matching address information generated by a user-triggered online behavior, obtaining entity store information corresponding to a region where the user resides, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
a target region grid-partitioning module, for partitioning a target region into grids, according to a distribution of entity stores of merchants, in the way that each entity store corresponds to a grid, and there is no overlapping between two adjacent grids;
a store information database;
a user information database; and an information matching module, for matching address information generated by a user-triggered online behavior, obtaining entity store information corresponding to a region where the user resides, and assigning the user to a valid marketing region of entity stores through the online behavior of the user.
9. A computer equipment, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, characterized in that steps of the method according to anyone of Claims 1 to 7 are realized when the processor executes the computer program.
10. A computer-readable storage medium, storing a computer program thereon, characterized in that steps of the method according to anyone of Claims 1 to 7 are realized when the computer program is executed by a processor.
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| CN201911066148.4 | 2019-11-04 | ||
| 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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| 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 |
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| 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 |
| CN114579612B (en) * | 2020-12-01 | 2024-09-20 | 湖南微步信息科技有限责任公司 | Addressing method, addressing device, electronic equipment and storage medium |
| CN112907275B (en) * | 2021-01-21 | 2024-09-06 | 长沙市到家悠享网络科技有限公司 | Business circle fence configuration method, service information distribution equipment and medium |
| CN113763052B (en) * | 2021-09-14 | 2024-08-13 | 胜斗士(上海)科技技术发展有限公司 | Method for determining geographic service range of store |
| 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 |
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| 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 |
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