Offline store site selection method, electronic equipment and computer readable storage medium

Abstract

The invention discloses an offline store site selection method, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring user original data of an online order of an online platform; performing decryption processing on the private information of the original data of the user, and performing encryption processing on a delivery address of the original data of the user to generate a corresponding address code; based on the re-purchase rate, the brand approval degree, the consumption amount, the user activeness and the purchased commodity characteristics, high-net-value users are screened through intersection operation; based on the address codes corresponding to the high-net-value users, obtaining a plurality of distribution areas of the high-net-value users through statistical analysis; calculating scores of the plurality of distribution areas based on the distribution areas and the pedestrian flow, the traffic convenience and the number of competitors in the distribution areas; and determining an offline store site based on the scores of the plurality of distribution areas. According to the invention, the problem that the existing method for determining the offline store site selection by using the online order user data is not comprehensive and accurate is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an offline store site selection method, an electronic device and a computer-readable storage medium. Background Art

[0002] In today's digital business environment, online businesses have accumulated a huge amount of order data, which contains a lot of information closely related to offline store location selection. However, there are many deficiencies in the use of online order user data for offline store location selection. On the one hand, there is a lack of a perfect mechanism for protecting user private information, which can easily lead to privacy leakage problems; on the other hand, when screening high-net-worth users who are more instructive for site selection decisions, most of them only consider a limited number of dimensions, which is not comprehensive and accurate enough, and it is difficult to fully tap the truly valuable potential consumer groups and their regions. Therefore, there is an urgent need for an innovative and comprehensive algorithm to solve these problems and achieve efficient, accurate, safe and reliable offline store location analysis. Summary of the invention

[0003] In order to overcome the defects of the prior art, an offline store location selection method, an electronic device and a computer-readable storage medium are provided to solve the problem that the existing method of determining offline store locations using online order user data is not comprehensive and accurate enough.

[0004] To achieve the above purpose, a method for selecting an offline store location is provided, comprising the following steps:

[0005] Obtaining original user data of online orders from online platforms;

[0006] Decrypting the private information of the user's original data, and encrypting the delivery address of the user's original data to generate a corresponding address code;

[0007] Based on repurchase rate, brand recognition, consumption amount, user activity and purchased product characteristics, high net worth users are screened through intersection operations;

[0008] Based on the address codes corresponding to the high net worth users, obtaining multiple distribution areas of the high net worth users through statistical analysis;

[0009] Based on the distribution areas and the flow of people, traffic convenience, and number of competitors within the distribution areas, scores of the multiple distribution areas are calculated;

[0010] Based on the scores of the multiple distribution areas, the offline store location is determined.

[0011] Furthermore, the private information includes user name and contact information.

[0012] Furthermore, a preset encryption algorithm is used to encrypt the delivery address of the user's original data to generate a corresponding address code.

[0013] Furthermore, the brand recognition includes the user evaluation emotion dimension, product rating dimension, and brand activity participation dimension.

[0014] Furthermore, the consumption amount includes the average customer unit price and the cumulative consumption amount.

[0015] Furthermore, the user activity includes the frequency of the user's login to the online platform and the number of interactions with the online platform.

[0016] Furthermore, the purchased product characteristics include the purchase ratio of high-end products, the purchase rate of new products and the customer life cycle value.

[0017] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is used to control the processor to operate to execute an offline store location selection method.

[0018] The present invention provides a computer-readable storage medium, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is used to control the processor to operate to execute an offline store location selection method.

[0019] The beneficial effect of the present invention is that the offline store site selection method of the present invention effectively avoids the risk of leakage of user privacy information while fully utilizing the value of online order data through strict private information decryption and address information encryption conversion, and complies with laws, regulations and business ethics requirements.

[0020] The offline store site selection method of the present invention comprehensively and deeply screens high-net-worth users from multiple dimensions, overcoming the limitations of traditional single or small-dimension screening, and more accurately locks in potential consumer groups with high brand loyalty, strong spending power and high spending potential, making offline store site selection more targeted and scientific.

[0021] The offline store site selection method of the present invention comprehensively considers multiple factors such as the regional distribution of high-net-worth users and the surrounding business environment, and uses a scientific spatial analysis algorithm to provide a comprehensive, objective and accurate decision-making basis for offline store site selection, which helps enterprises to rationally allocate resources, reduce store opening risks, and improve economic benefits and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0023] Figure 1Schematic diagram of the process of offline store location selection method according to an embodiment of the present invention.

[0024] Figure 2 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0025] Figure 3 4 is a system architecture diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0027] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] Reference Figure 1 As shown, the present invention provides an offline store location selection method, comprising the following steps:

[0029] S1. Obtain the original user data of online orders on the online platform.

[0030] User original data includes but is not limited to private information and delivery address. Private information includes user name and contact information.

[0031] Specifically, the original data of online order users on the online platform is collected, including user name, contact information, delivery address, purchase item details, etc.

[0032] The collection methods include manual entry or direct connection with a third-party online platform system. The original data of online order users on the online platform is collected to form an order database for data collection.

[0033] S2. Decrypt the private information of the user's original data, and encrypt the delivery address of the user's original data to generate a corresponding address code.

[0034] For private information, data desensitization technology is used to process it. For example, for user names, the surname is retained and the first name is replaced with a specific character (such as "*"); for contact information, the middle digits are hidden. This operation can ensure that subsequent data analysis is not substantially affected and effectively prevent the leakage of user private information.

[0035] As a preferred implementation, a preset encryption algorithm is used to encrypt the delivery address of the user's original data to generate a corresponding address code.

[0036] In this embodiment, the user's delivery address information is extracted and split according to the administrative division levels such as province, city, district (county), and street. With the help of professional geocoding databases and algorithm tools, the information of each level is mapped to the corresponding longitude and latitude coordinate range, and then the longitude and latitude coordinate range is encrypted by a preset encryption algorithm (such as weighted sum of coordinate values ​​and then taking the remainder) to generate a unique corresponding Gcode. This allows the address information to be converted into a code form that can be used for analysis while avoiding the leakage of the specific address, realizing the organic combination of privacy protection and data availability.

[0037] Preferably, the default encryption algorithm is the Geohash algorithm.

[0038] S3. Based on repurchase rate, brand recognition, consumption amount, user activity and purchased product characteristics, high net worth users are screened through intersection operations.

[0039] In this embodiment, the steps of calculating the repurchase rate and screening high net worth users include:

[0040] Determine the time range for calculating the repurchase rate (such as the past year), and extract the order record data of each user during this period from the online order database, including fields such as user unique identification, order generation time, and product information.

[0041] By grouping and counting the number of purchases of each user, the repurchase rate is calculated according to the formula:

[0042] Repurchase rate = (number of orders with purchase frequency greater than 1 / total number of orders) × 100%, calculate the repurchase rate of each user.

[0043] Then, according to the actual business situation, a reasonable repurchase rate threshold (for example, 30%) is set to screen out user groups whose repurchase rate reaches or exceeds the threshold. Such users have a continuous demand for the product and initially possess the characteristics of high-net-worth users.

[0044] In this embodiment, brand recognition includes the user evaluation emotion dimension, the product rating dimension, and the brand activity participation dimension.

[0045] Emotional dimension of user reviews: Natural language processing technology is used to extract keywords from user reviews, analyze emotional tendencies, and calculate brand recognition scores for each user based on product ratings and user participation in brand activities. Users with scores of 60 or above are screened out. Assume that 8,000 people remain after this screening step.

[0046] Product rating dimension: Filter out users with a rating of 4 or above based on the product rating, and take the intersection with the results of other dimensions to further narrow the scope.

[0047] Brand activity participation dimension: filter based on the user's participation records in brand activities and the corresponding scores, and comprehensively consider the screening results of each dimension.

[0048] In this embodiment, the consumption amount includes the average customer unit price and the cumulative consumption amount.

[0049] Average customer spending: Calculate the average customer spending of each user in the past year, filter out users with an average customer spending of 500 yuan or more, and combine them with the previous screening results for analysis.

[0050] Cumulative consumption amount: Statistics are collected on the user’s cumulative consumption amount since the first purchase, and users with cumulative consumption exceeding RMB 10,000 are selected to also participate in the comprehensive screening.

[0051] In this embodiment, user activity includes the frequency of the user's login to the online platform and the number of interactions with the online platform.

[0052] Login frequency: Count the number of times users log into the platform each month, filter out users who log in more than 15 times per month, and combine other screening criteria to determine whether to include them in the high-net-worth user group.

[0053] Number of interactions with online platforms: Count the number of interactions of users on the platform each month, filter out users who interact more than 50 times per month, and further improve the screening scope.

[0054] In this embodiment, the purchased commodity characteristics include the purchase ratio of high-end products, the purchase rate of new products, and the customer life cycle value.

[0055] Proportion of high-end product purchases: Analyze the proportion of high-end products purchased by users to the total number of products purchased, screen out users whose purchases of high-end products account for more than half, and take this into consideration in the overall screening.

[0056] New product purchase rate: Calculate the user's new product purchase rate and screen out users with a new product purchase rate of 30% or above to participate in the final screening.

[0057] Customer Lifetime Value (CLV) screening: Calculate the CLV of each user by building a model, screen out the top 20% of users by CLV, and intersect it with the screening results of other dimensions.

[0058] Comprehensive screening: After screening and intersection operations in the above dimensions, the number of high-net-worth user groups is finally determined.

[0059] S4. Based on the address codes corresponding to the high net worth users, multiple distribution areas of the high net worth users are obtained through statistical analysis.

[0060] S5. Calculate and obtain scores for multiple distribution areas based on the distribution areas and the flow of people within the distribution areas, traffic convenience, and the number of competitors.

[0061] S6. Determine the location of offline stores based on the scores of multiple distribution areas.

[0062] Steps S4 to S6 are site selection analysis. Specifically, the regional distribution statistics of the Gcodes corresponding to the selected high-net-worth users can be analyzed, and the density of high-net-worth users in different regions can be displayed by drawing regional heat maps and other visualization methods. At the same time, combined with the surrounding business environment factors (such as traffic, competitor distribution, traffic convenience, etc.), spatial analysis algorithms (such as kernel density estimation, buffer zone analysis, etc.) are used to comprehensively evaluate the potential of opening offline stores in each region, and finally determine the preferred location area for offline stores.

[0063] The offline store location selection method of the present invention effectively avoids the risk of leakage of user privacy information through strict private information decryption and address information encryption conversion, while making full use of the value of online order data, and complies with laws, regulations and business ethics requirements.

[0064] The offline store site selection method of the present invention comprehensively and deeply screens high-net-worth users from multiple dimensions, overcoming the limitations of traditional single or small-dimension screening, and more accurately locks in potential consumer groups with high brand loyalty, strong spending power and high spending potential, making offline store site selection more targeted and scientific.

[0065] The offline store site selection method of the present invention comprehensively considers multiple factors such as the regional distribution of high-net-worth users and the surrounding business environment, and uses a scientific spatial analysis algorithm to provide a comprehensive, objective and accurate decision-making basis for offline store site selection, which helps enterprises to rationally allocate resources, reduce store opening risks, and improve economic benefits and market competitiveness.

[0066] In order to further illustrate the offline store location selection method of the present invention in detail, the following embodiments are given for illustration.

[0067] Example 1

[0068] The offline store location selection method of the present invention comprises the following steps:

[0069] S10. Obtaining original user data of online orders on the online platform.

[0070] Assuming that an e-commerce platform has online order user data for the past three years, relevant data tables containing basic user information, order details (including product information, purchase time, delivery address, etc.) and user reviews are extracted from the database as the basic data source for subsequent algorithm implementation.

[0071] S20: Decrypt the private information of the user's original data, and encrypt the delivery address of the user's original data to generate a corresponding address code.

[0072] According to the decryption method described above, the user private information in the extracted data is processed. For example, the user name "Zhang San" is changed to "Zhang*" after processing; the contact information "13812345678" is changed to "138****5678", completing the decryption of the private information of the entire data set to ensure that subsequent operations will not involve privacy leakage issues.

[0073] For the user's delivery address information, taking "No. XX, XX Street, Chaoyang District, Beijing" as an example, first split out the hierarchical information such as "Beijing" and "Chaoyang District", and then use the geocoding tool to obtain the corresponding approximate longitude and latitude coordinate range (assuming the longitude range is [116.3°, 116.5°], and the latitude range is [39.9°, 40.1°]), and then use the set encryption algorithm (such as multiplying the longitude and latitude values ​​by different weights and then adding and rounding them) to generate the corresponding Gcode, such as "G0012", and replace the original detailed address with this Gcode and record it in the dataset.

[0074] S30. Based on repurchase rate, brand recognition, consumption amount, user activity and purchased product characteristics, high net worth users are screened through intersection operations.

[0075] Repurchase rate calculation and screening: Count the number of orders each user has placed in the past year and the number of orders with purchase times greater than 1, calculate the repurchase rate of each user according to the repurchase rate calculation formula, and screen out user groups with a repurchase rate of 30% or above. Assume that the number of users obtained in the initial screening is 10,000.

[0076] Brand recognition measurement and screening:

[0077] Emotional dimension of user reviews: Natural language processing technology is used to extract keywords from user reviews, analyze emotional tendencies, and calculate brand recognition scores for each user based on product ratings and user participation in brand activities. Users with scores of 60 or above are screened out. Assume that 8,000 people remain after this screening step.

[0078] Product rating dimension: Filter out users with a rating of 4 or above based on the product rating, and take the intersection with the results of other dimensions to further narrow the scope.

[0079] Brand activity participation dimension: filter based on the user's participation records in brand activities and the corresponding scores, and comprehensively consider the screening results of each dimension.

[0080] Filter by factors related to consumption amount:

[0081] Average customer spending: Calculate the average customer spending of each user in the past year, filter out users with an average customer spending of 500 yuan or more, and combine them with the previous screening results for analysis.

[0082] Cumulative consumption amount: Statistics are collected on the user’s cumulative consumption amount since the first purchase, and users with cumulative consumption exceeding RMB 10,000 are selected to also participate in the comprehensive screening.

[0083] User activity related factors screening:

[0084] Login frequency: Count the number of times users log into the platform each month, filter out users who log in more than 15 times per month, and combine other screening criteria to determine whether to include them in the high-net-worth user group.

[0085] Interaction with the online platform: Count the number of interactions of users on the platform each month, filter out users who interact more than 50 times per month, and further improve the screening range.

[0086] Filtering of factors related to purchasing product characteristics:

[0087] Proportion of high-end product purchases: Analyze the proportion of high-end products purchased by users to the total number of products purchased, screen out users whose purchases of high-end products account for more than half, and take this into consideration in the overall screening.

[0088] New product purchase rate: Calculate the user's new product purchase rate and screen out users with a new product purchase rate of 30% or above to participate in the final screening.

[0089] Customer Lifetime Value (CLV) screening: Calculate the CLV of each user by building a model, screen out the top 20% of users by CLV, and intersect it with the screening results of other dimensions.

[0090] Comprehensive screening: After screening and intersection operations in the above dimensions, the high-net-worth user group was finally determined to be 6,000 people.

[0091] S40. Based on the address codes corresponding to the high net worth users, obtain multiple distribution areas of the high net worth users through statistical analysis.

[0092] S50. Calculate and obtain scores of the plurality of distribution areas based on the distribution areas and the flow of people, traffic convenience, and number of competitors within the distribution areas.

[0093] S60: Determine offline store location based on the scores of the multiple distribution areas.

[0094] For steps S40 to S60, the regional distribution statistics of the Gcodes corresponding to the 6,000 high-net-worth users selected in step S30 are performed, and a regional heat map is drawn. It is found that the density of high-net-worth users is high, such as Gcodes concentrated in the "G0010-G0020" interval area. Further combined with the field survey of the area (investigating the surrounding traffic flow, such as obtaining it through transportation hub data and business district traffic statistics; competitor distribution, querying the situation of similar stores in the surrounding area; transportation convenience, analyzing bus and subway stations, etc.), the spatial analysis algorithm is used for comprehensive evaluation to generate the initial site selection score for each area.

[0095] Suppose three address coordinates are manually input, namely coordinate A (longitude [X1°], latitude [Y1°]), coordinate B (longitude [X2°], latitude [Y2°]), and coordinate C (longitude [X3°], latitude [Y3°]).

[0096] Data matching and integration: Match these three coordinates with the analyzed related data to obtain the number of high-net-worth users and traffic flow around each coordinate. For example, the number of high-net-worth users within a certain range around coordinate A is 200, with a large traffic flow, 3 competitors, and good transportation convenience; the number of high-net-worth users around coordinate B is 150, with average traffic flow, 2 competitors, and medium transportation convenience; the number of high-net-worth users around coordinate C is 180, with a large traffic flow, 4 competitors, and good transportation convenience.

[0097] Quantitative evaluation of rules: Quantitative scoring is performed according to the set weights (30% for the number of high-net-worth users, 30% for the flow of people, 20% for the distribution of competitors, and 20% for the convenience of transportation). Assume that the scoring is as follows based on the actual situation:

[0098] Coordinate A: The score for the number of high-net-worth users (based on comparison with other coordinates, etc., with a maximum score of 30 points) is 25 points, the score for traffic flow is 25 points, the score for competitor distribution (the fewer competitors, the higher the score, with a maximum score of 20 points) is 15 points, and the score for transportation convenience is 18 points. The weighted comprehensive evaluation score is: 25+25+15+18=83 points.

[0099] Coordinate B: The number of high-net-worth users scores 20 points, the flow of people scores 20.6 points, the distribution of competitors scores 18 points, and the transportation convenience scores 15.2 points. The weighted comprehensive evaluation score is: 20+20.6+18+15.2=73.8 points.

[0100] Coordinate C: The number of high-net-worth users scores 22 points, the flow of people scores 24.9 points, the distribution of competitors scores 14 points, and the transportation convenience scores 18 points. The weighted comprehensive evaluation score is: 22+24.9+14+18.6=79.5 points.

[0101] Sorting and optimization: Sorting the three coordinates in descending order according to the comprehensive evaluation scores, we can get coordinate A (83 points) > coordinate C (79.5 points) > coordinate B (73.8 points), so coordinate A is the final recommended offline store location.

[0102] The above is the details of the offline store location selection method of the present invention based on the regional distribution of online order users. The offline store location selection method of the present invention provides an efficient, comprehensive, accurate and privacy-oriented solution for offline store location selection through innovative data processing and analysis processes, and has flexible manual adjustment.

[0103] The present invention provides an electronic device 1, comprising a memory 11 and a processor 12, wherein the memory stores a computer program, and the computer program is used to control the processor to operate so as to execute the offline store location selection method disclosed in any of the aforementioned embodiments.

[0104] The electronic device may be a server, a smart phone, a portable computer, a desktop computer, a tablet computer, etc., which is not limited here.

[0105] The electronic device may include but is not limited to a processor, a memory, an interface device, a communication device, a display device, an input device, a speaker, a microphone, and the like.

[0106] Among them, the processor can be a central processing unit CPU, a graphics processing unit GPU, a microprocessor MCU, etc., which is used to execute computer programs / instructions. The computer programs / instructions can be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc.

[0107] The memory includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), a nonvolatile memory such as a hard disk, etc. The interface device includes, for example, a USB interface, a serial interface, a parallel interface, etc.

[0108] The communication device can, for example, perform wired communication using optical fiber or cable, or perform wireless communication, which can specifically include WiFi communication, Bluetooth communication, 2G / 3G / 4G / 5G communication, etc.

[0109] The display device is, for example, a liquid crystal display screen, a touch display screen, etc.

[0110] The input device may include, for example, a touch screen, a keyboard, a somatosensory input, etc.

[0111] The speaker is used to output audio signals, and the microphone is used to collect audio signals.

[0112] In this embodiment, the memory of the electronic device is used to store a computer program / instruction, which is used to control the processor to operate to implement the offline store location selection method according to the embodiment of the present disclosure. The technician can design the computer program / instruction according to the scheme disclosed in the present disclosure. How the computer program / instruction controls the processor to operate is well known in the art, so it will not be described in detail here. The electronic device can be installed with an intelligent operating system (such as Windows, Linux, Android, IOS, etc.) and application software.

[0113] Figure 2 and Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. The electronic device includes a processor and a memory, the memory stores computer instructions, and when the computer instructions are executed by the processor, the offline store location selection method disclosed in any of the above embodiments is implemented.

[0114] In this embodiment, a computer-readable storage medium is provided, including a memory and a processor, the memory stores a computer program, and the computer program is used to control the processor to operate to execute the offline store location selection method disclosed in any of the aforementioned embodiments.

[0115] Each embodiment in the present disclosure is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and equipment embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0116] The above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The embodiments of the present disclosure may be systems, methods and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the embodiments of the present disclosure.

[0118] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to: an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination of the above. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0119] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0120] The computer program instructions for performing the operation of the embodiments of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, Python, and JavaScript, and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, by utilizing state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the embodiments of the present disclosure.

[0121] Various aspects of the embodiments of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0122] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0123] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0124] Those skilled in the art should understand that, although multiple devices of an electronic device are shown in the figure, the electronic device of the embodiment of the present disclosure may only involve some of the devices, for example, only a processor and a memory.

[0125] For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in this application to form a technical solution.

[0126] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. An offline store location selection method, characterized in that: The following steps are involved: Obtaining original user data of online orders from online platforms; Decrypting the private information of the user's original data, and encrypting the delivery address of the user's original data to generate a corresponding address code; Based on repurchase rate, brand recognition, consumption amount, user activity and purchased product characteristics, high net worth users are screened through intersection operations; Based on the address codes corresponding to the high net worth users, obtaining multiple distribution areas of the high net worth users through statistical analysis; Based on the distribution areas and the flow of people, traffic convenience, and number of competitors within the distribution areas, scores of the multiple distribution areas are calculated; Based on the scores of the multiple distribution areas, the offline store location is determined.

2. The offline store location selection method according to claim 1, characterized in that: The private information includes user name and contact information.

3. The offline store location selection method according to claim 1, characterized in that: The delivery address of the user's original data is encrypted using a preset encryption algorithm to generate a corresponding address code.

4. The offline store location selection method according to claim 1, characterized in that: The brand recognition includes the user evaluation emotion dimension, the product rating dimension, and the brand activity participation dimension.

5. The offline store location selection method according to claim 1, characterized in that: The consumption amount includes the average customer unit price and the cumulative consumption amount.

6. The offline store location selection method according to claim 1, characterized in that: The user activity includes the frequency of the user's login to the online platform and the number of interactions with the online platform.

7. The offline store location selection method according to claim 1, characterized in that: The purchase commodity characteristics include the purchase ratio of high-end products, the purchase rate of new products and the customer life cycle value.

8. An electronic device, characterized in that: It comprises a memory and a processor, the memory stores a computer program, and the computer program is used to control the processor to operate so as to execute the offline store location selection method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: It comprises a memory and a processor, the memory stores a computer program, and the computer program is used to control the processor to operate so as to execute the offline store location selection method according to any one of claims 1 to 7.

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