CN113763052A

CN113763052A - Method for determining geographic service range of shop

Info

Publication number: CN113763052A
Application number: CN202111075899.XA
Authority: CN
Inventors: 潘瑾; 赵华; 韦启昕; 严星
Original assignee: Shengdoushi Shanghai Technology Development Co Ltd
Current assignee: Shengdoushi Shanghai Science and Technology Development Co Ltd
Priority date: 2021-09-14
Filing date: 2021-09-14
Publication date: 2021-12-07

Abstract

The application provides a method for determining a geographic service range of a store, which comprises the steps of obtaining information of online users of the store, wherein the information of the online users at least comprises identification data and geographic positions of the online users; acquiring identification data of a store-to-store consumer user of a store; determining a geographic location of the store-to-store consuming user based on the identifying data of the store-to-store consuming user and the information of the online user; and determining a geographic service range of the store based on the geographic location of the consumer-to-store user. The method can accurately determine the geographic service range of the shop, and further determine the user portrait information in the geographic service range for the refined operation of the shop.

Description

Method for determining geographic service range of shop

Technical Field

The present application relates to data processing, and more particularly, to a method for determining a geographic service range of a store based on user information of the store.

Background

In store management and operation in industries such as catering and retail, it is an important aspect to combine digital technology to realize refined operation of store granularity to improve store operation efficiency and accuracy.

However, existing digitizing platforms acquire representation information of users who are typically consumed online (also referred to as online users), and the representation information is typically a global user representation, such that user representation information corresponding to store-to-store consumption scenarios and online consumption scenarios cannot be accurately distinguished. The store offline data with too little information content cannot provide powerful data support for the refined operation of stores, advertisement putting and the like.

Therefore, there is a need for an improvement in the geographic service area of stores, particularly in the offline operational scenario of stores.

Disclosure of Invention

According to an embodiment of the present application, a method for determining a geographic service scope of a store is proposed, one object of which is to solve at least one of the problems set forth above, enabling an accurate identification of the geographic service scope of the store.

According to an aspect of the present application, there is provided a method for determining a geographic service range of a store, comprising: acquiring information of online users of the shop, wherein the information of the online users at least comprises identification data and geographic positions of the online users; acquiring identification data of a store-to-store consumer user of a store; determining a geographic location of the store-to-store consuming user based on the identifying data of the store-to-store consuming user and the information of the online user; and determining a geographic service range of the store based on the geographic location of the consumer-to-store user.

According to another aspect of the application, a computer-readable storage medium is proposed, on which a computer program is stored, the computer program comprising executable instructions which, when executed by a processor, carry out the method as described above.

According to yet another aspect of the application, an electronic device is proposed, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the executable instructions to implement the method as described above.

The method for determining the geographic service range of the shop can accurately determine the geographic service range particularly aiming at offline to-shop consumption scenes, further determine user portrait information in the geographic service range of the shop, clearly define service objects and business conditions of the shop, and provide accurate and powerful data support for refined operation, potential opportunity discovery, project preparation and personalized marketing such as advertisement putting. By combining big data of the online digital operation platform and support of artificial intelligence processing, abundant information resources can be provided for offline shops. This determination of the range of services radiated by the store is particularly suitable for off-line store management and operation, such as in the catering and retail industries.

Drawings

The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

FIG. 1 is a schematic flow chart diagram of a method for determining geographic service areas of a store in accordance with an embodiment of the present invention.

Fig. 2 is a schematic block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. In the drawings, the size of some of the elements may be exaggerated or distorted for clarity. The same reference numerals denote the same or similar structures in the drawings, and thus detailed descriptions thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, etc. In other instances, well-known structures, methods, or operations are not shown or described in detail to avoid obscuring aspects of the application.

In this context, the geographic service scoping scheme for stores is introduced at store-level granularity. The terms "store" and "store" are equally understood to mean an operating unit that provides goods and/or services to users or customers at the physical or geographic location. In the catering industry, for example, goods typically include various foods (which may also be referred to as dishes) provided by a restaurant store and services associated with the foods. The user arrives at the restaurant store for food consumption in a hall, or submits a take-away order in an online manner for consumption at a location outside the store. In the retail industry, a user goes to the store to purchase a product (merchandise) or to purchase and receive a service provided by the store. The scenario of the present application is described herein with respect to a restaurant (e.g., a fast food restaurant) as an example, but those skilled in the art will appreciate that the scenario is merely exemplary and not limiting of the scope of the present application.

Aiming at the scene of line down to store consumption, the effective service range of the store on the physical position, which influences the user, is determined, namely the geographic service range of the store (also called as the invisible store circle of the store), and the user condition reaching the store for consumption in the geographic service range is collected, so that effective suggestions can be further provided for activities such as fine operation of the store, advertisement putting and the like.

The geographic service range of a store is the physical service range or radiation range in which the store provides goods and services, and the range mainly refers to the range of the resident geographic position of a user who is on-line, that is, who arrives at the physical/geographic position of the store for on-site consumption. The trade circle of stores, either an overlay trade circle or an implicit trade circle, referred to in this application, refers to this geographic range of service, as opposed to a trade circle in a city or region (also referred to as a trade circle), which generally refers to a business concentration area that includes a plurality of different stores.

For off-line stores, store operations and managers are more concerned with portrait information of users of store consumers. Therefore, the present disclosure is directed to obtaining user representation information of a store consumption user, wherein the user representation information includes category attributes (e.g., gender, age, occupation, income, etc.) of the user, the number of users corresponding to the category, time (time and duration) and frequency of the store consumption, user preferences for goods and/or services, and the like. The user pictorial information of the store-to-store consumer user exhibits significant correlation with the geographic location of the store, the geographic location of the user himself, and the geographic environment surrounding those geographic locations (e.g., roads, rivers, particular buildings, etc.). For example, a restaurant store near a business district having a plurality of office buildings typically serves office workers, and the pictorial information of the store-to-store consuming users represents characteristics of the office workers, including, for example, that the consumption of working meals is high, typically during lunch breaks, and that the users have a demand for delivery time of food products due to limited rest time. Another situation may be presented for dining establishments near residential areas that typically serve home users, where portrait information features of users for consumption to the store typically include children's meals on inclined dishes, such as dinner versus time and relative insensitivity to delivery of the dishes. In the geographic location surrounding environment, the location distribution of the consumer users to the store may present obvious regional characteristics due to the blockage of an urban expressway, so that the geographic service range of the restaurant is more likely to be limited to the expressway, or the distribution density of the restaurant in the corresponding geographic region is reduced due to the existence of other competitive brand restaurants or siblings of the same brand near the restaurant. Such a scenario as described above may present a completely different situation for online users, with the order (particularly take-away orders) accepted at the online interface of a certain restaurant being less sensitive to geographic location and time.

The operation management and marketing activities of stores have a very high dependency on user profile information of the restaurant store to the store consuming users (the hall consumers). In the refined operation process of stores, dish configuration, personnel configuration, raw material storage, store site selection and the like in the stores are required to be adapted to the requirements and preferences of users who often arrive at the stores for consumption. Determining the representation information of the user first requires determining the user population of interest, i.e., those users who frequently consume to the store. On a probabilistic basis, these to-store consumer users should be distributed in an area within the geographic service range of the store, so determining the geographic service range of the offline store becomes the basis for determining the user portrait information. For stores where online services exist, the store consumers may not always be in store for consumption. For example, the restaurant food user may submit a take order online, or the restaurant food user arrives at the store and uses a mobile terminal, such as a mobile phone, to scan a code to order or to order a food item with a WeChat (the restaurant food user belongs to the online user at the same time). However, the hall diners are users who consume on site in the store, and the geographical distribution information on the map of the hall diners is also relevant to the management and opening of the stores, and for example, the opening and closing of a sibling store under the same brand flag need to consider the situation of internal competition caused by overlapping of geographical service ranges among the stores. Marketing campaigns such as accurate advertising also require further analysis for conversion, penetration, etc. of the catering users within the geographic service range of the store.

Although such stores as restaurant stores providing catering services are more urgent to accurately understand the geographic location distribution and user representation information of these store-to-store consumer users, there is a problem that this is not matched with the information acquisition sources of the existing digital platforms. As introduced above, existing digitizing platforms typically acquire user representation information formed from online user information consumed at the online interface of the store. Because the online interface of a store is typically operated and maintained by the brand or parent company to which the store belongs, the online interface of the store is a unified interface that integrates all subordinate stores, such that online users do not focus on and specify the location of a particular offline store when browsing, accessing, and submitting orders on the online interface. The information of the online user obtained through the online interface is generally not limited to the actual geographic location of the online user, so that the user portrait information generated by the online user information is generally global user portrait information, and the user portrait information generating consumption-related behaviors corresponding to the store arrival consumption scene and the online consumption scene cannot be accurately distinguished.

The biggest difference between the to-store consumption scenario and the online consumption scenario is that the to-store consumption users may not use the traffic portal used in the online manner to provide information to the store consumption users and the digitization platform may not be able to accurately acquire representation information of these users. For example, the in-store consumer user can order a meal in the face of the waiter of the store and pay by swiping a card or cash through a POS machine, or the in-store consumer user can reserve a seat of a dining store in advance through a fixed telephone or a mobile telephone and then later go to the store for consumption, and the in-store consumer scene can not collect data related to the user through a digital platform providing an online ordering service, particularly an online takeout interface or platform. Without accurate user portrait information, user features cannot be extracted to assist a store in performing fine operation and accurately delivering advertisements to users. Taking advertisement placement as an example, online advertisements can be pushed to online users through a website scrolling page or online coupon providing platform of online service, offline advertisements can be shown to users who pass by in a certain past time to store consumption and other potential users through a lamp box, a billboard and the like, wherein the online advertisements and the offline advertisements are generally directed to two types of users which are completely different.

Therefore, it is one of the problems to be solved by the solution of the present application to obtain relevant data that can be used to generate user representation information of a user who proceeds to store consumption in an online off-store using a data source of a digital platform providing an online service.

An exemplary method for determining geographic service areas of a store according to an embodiment of the present application is described below in conjunction with FIG. 1.

First, in step S110, information of online users of a store is acquired, wherein the information at least includes identification data and geographic locations of the online users.

The information of the online users can be acquired and collected through a digital platform providing online services. The digitizing platform may include an online interface of a store as described above or an online interface provided by a third party platform (e.g., an online interface of a take-away ordering platform). Generally, whether an online order (typically a primarily take-out order) is from an online interface of the store's own online platform or an online interface from a third party platform, the physical store that is offline needs to be provided with identification data and a geographic location required to deliver goods or services to the online user, where the identification data is used to confirm the identity of the user, and the geographic location represents the location to which the corresponding goods or services in the order will be delivered. The method may also obtain data for determining user information from other approaches when the third party platform may not provide the user information. Thus, the information of the online user needs to include at least identification data of the user and information related to the geographical location.

The online user submits an online order to the store or accesses an online interface of the store/third party platform in an online manner. The online mode may include at least one of: scanning image codes (bar codes, two-dimensional codes and other forms of pattern codes) by an image acquisition unit (such as a camera) of a mobile phone or device, using a mobile program (App, generally provided by the operator of a store) pre-installed on the mobile phone or device, using applets embedded in chat software such as a WeChat and applets provided by a Payment treasure (these may all be referred to as messaging applets), directly accessing online store versions of web pages or pages provided on the Internet, and via a third party software platform that primarily provides online takeaway services. Wherein, when the online user accesses the third party platform online, the shop can extract the information of the online user from the takeaway order information forwarded by the third party platform.

The identification data of the online user is used to confirm the identity of the user and may include any one or a combination of the user's membership account, social software account, payment account and telephone number at the store. The member account may be established by the user in advance at the store site or on an online interface of the store. When a user visits an online interface of a store for the first time, a member account can be established first regardless of whether the user submits an order or not. The social software account corresponds to logging into the online interface of the store from a social software applet such as WeChat or Payment. The scanned image code is determined according to the content pointed by the image code, if the content or the target pointed by the image code is an online interface of a shop in the form of a webpage or a page, the user can log in and identify the user through a member account, and if the content or the target pointed by the image code is a corresponding mobile device App or a social software applet, the user can log in through the corresponding App in the mobile device through a member account or through a social software account. Some stores provide online interfaces or social software support a user's phone number (primarily a mobile phone number) as an account number, where the user's member account number, social software account number, and phone number are linked. In addition to the login account, the identity of the user may also be identified by the payment account number provided by the online user in the last payment step of the setup order process. The payment account may include a bank account (if the bank supports the store to obtain the account or card number information of the savings card or credit card), an electronic payment account (e.g., a payment account for an online payment method such as WeChat payment, Paoyao payment, Unionpay, paypal, etc.), a payment card account requiring a pre-stored amount of money to be bound or purchased separately under a member account of the store, a discount card and a consumption ticket bound to the above account or capable of identifying the user identity although the account is not bound, etc. When an order is submitted via a third-party platform, the corresponding member account, social software account, or payment account may be unavailable, and the phone number of the online user in the take-away order may also be used to identify the user.

The geographic location of the online user may be determined by order information submitted by the user or the access location of the user. When a user browses an online interface of a shop in an online mode or submits an order, the current geographic position of the online user can be determined through the access address of the user. The access address may be the location of the user's mobile device, which may be provided by a wireless network to which the user's mobile device has access or a location unit of the mobile device, or the location of the user's fixed terminal to access the internet, which may be provided by a network provider to access the internet. According to different location technologies, the access address can be accurate to a block, a building, or even an area in meters. Thus, the current location of the online user can be taken as the geographic location of the online user even if the user merely browses the online interface without submitting an order. When the order submitted by the online user on the online interface of the shop is a takeout order directly submitted to the shop or the online user submits the takeout order on the online interface of the third-party platform, the delivery address in the takeout order can be determined as the geographic position of the online user.

Typically, the online user is required to first log in on the online interface of the store before browsing and submitting an order, so that the identification data of the online user can be obtained in association with the geographic location prior to or at the same time as browsing and/or selecting a product and/or service provided by the store. For orders submitted by the third party platform (mainly take-out orders), the identification data and the geographic location can be obtained in a correlated manner after the orders are received.

The information of the online users can be obtained in real time by the online digitizing platform and submitted to the stores in real time or periodically, or stored in a database within the digitizing platform or provided separately therefrom for access by the stores when needed.

In step S120, identification data of the store-to-store consumer user of the store is acquired.

According to embodiments of the present application, there is a general need to distinguish between online users and store-to-store consumption users. There are fewer ways to obtain identifying data of a store consuming user to determine the identity of the user than for an online user. Generally, a consumer user at a store submits an order mainly by queuing to order at the front of a counter or kiosk and scanning a code using a mobile phone and device or ordering using an App or applet within the store. When a counter or a self-service terminal orders, the user informs a salesperson or pays after selecting a selected dish on a user interface of the self-service terminal. The counter supports cash payment, POS machine card swiping, electronic payment (such as online payment methods including WeChat payment, Paoyao payment, Unionpay payment, paypal and the like) and payment card/discount card/consumption ticket payment and the like. The POS machine card swiping is equivalent to bank card payment, so except for cash payment, all other payment modes of counter payment are similar to the mode of order payment of an online user, and a payment account number of a shop consumption user can be obtained or a member account number, a social software account number and the like of the user can be further obtained to serve as identification data of the user, so that the identity of the user can be identified. In general, the ratio of all payments to store consumption is very low (for example, 10% or less) in the case of cash payment, and therefore, the specific case where the identification data of the part of the store-to-store consumer cannot be tracked due to cash payment has little influence on the acquisition effect of the identification data and the geographical position of the store-to-store consumer as a whole. When the in-store consuming user uses a mobile phone and equipment to scan codes or uses an App or an applet to order in a store, which is equivalent to the operation that the user submits an order online with the identity of the online user at the moment, the store can also obtain at least one of a member account number, a social software account number, a payment account number and a telephone number of the user as the identification data of the in-store consuming user by accessing the data collected by the online digital platform and marking the data as the in-store consuming user. Unlike the online user submitting an online order mentioned above, this part of the store-to-store consumer users consume in a cannibalistic manner within the store without providing the store with a take-out address. It follows that identification data to store consuming users is available through their payment information. In addition, for the customers who come to the store and use mobile phones and equipment to scan codes or use apps or applets to order dishes, the account numbers of the customers who come to the store, social software account numbers and telephone numbers and the like can be acquired by setting that the customers must log in the account numbers before the customers visit the online interface of the store to further browse and select corresponding dishes. When the self-service terminal orders a meal, the user can also be set to log in the member account first to obtain preferential mode to assist in obtaining the member account data as the identification data.

It should be noted that even if the user takes food away after submitting an order for the store consumption and does not eat the food in the restaurant store, the user is considered to have the store consumption because the food/service of the order does not have a process of takeaway delivery. When the user of the store-to-store consumer reserves a seat, room, and/or dish of the store by telephone or the like in advance, the telephone number may be used as the identification data of the user.

It is important to distinguish whether the collected user identification data is from an online user or from an offline to store consumer user. Whether the identification data of the user collected by the store comes from the store consumer user can be judged by the source of the identification data. Since both the counter and the kiosk are located within the store, the identification data of the user ordering food through the counter and the kiosk may be judged to be from the store-arriving consumer user. For a store-to consumer user who scans a code using a mobile phone and device in a store and orders a meal using an App or applet, it can be determined that these orders are from the store-to consumer user by, for example, determining that no take-away address exists in the orders that they submit online, first selecting the name and location of the store that he is in before logging in, browsing, and selecting dishes, and the obtained identification data is considered as identification data of the store-to consumer user. According to the embodiment of the application, when an online user submits an online order (such as a take-out order) but not to consume a store, a delivery address of the online order needs to be input in advance before at least one of operations of logging in an account, browsing or selecting goods/services in an online interface and submitting the order and/or paying the order, and a name and a position of a store where the online order is located need to be determined in advance before at least one of the operations when the store consuming user submits the order in an online mode. That is, the on-line order submission by the on-store consuming user is initially entered at the store, and the on-line user is initially entered at the shipping address.

Next, in step S130, the geographical location of the store consuming user is determined by matching the identification data of the store consuming user and the online user.

Typically, a consumer user is rarely actively providing their own geographic location by the store. In this context, the geographic location of the to-store consuming user actually refers to the resident geographic location of the to-store consuming user. Resident geographic location refers to a geographic location where a user often resides or resides at periodic time periods or for long periods of time, such as the same period of time each day. Resident geographic locations include, but are not limited to, home addresses, work unit locations, parking lots, shopping malls/restaurants/gyms/amusement parks/museums/tourist attractions frequently visited on weekdays or holidays, and the like. An end-to-store consumer user may have multiple resident geographic locations at the same time. Generally, some of the users who have consumed in the store submit online orders (takeaway orders) from an online interface or a third-party platform which is not in the store, and some online users never go to a certain store for consumption. However, the number of to-store consuming users who never consume online to the store has a relatively low percentage of all to-store consuming users, and the information of only those users who have consumed at the store in the identity of the online user has no effect on determining the geographic service scope of the store.

The method and the system for the online user to the store consumption user mainly aim at extracting the relation between the online user and the store consumption user in the specific situation that the online user and the store consumption user are partially overlapped, and mining and acquiring the geographic position of the store consumption user. The matching between the online user and the to-store consuming user is accomplished by the identification data of the users, and the information having the same identification data is considered to belong to the same user, so when the identification data of the online user matches the identification data of the to-store consuming user, the data of the two users is considered to be the data of the same user to determine the geographical location of the online user as the geographical location of the to-store consuming user. The matching operation uses the identification data of the same data type, for example, one or more items of a member account, a social software account, a payment account and a telephone number are respectively matched, and whether certain data items are the same or not is judged. When there is a unique correspondence between these data items, it is possible to constitute a data group by a plurality of data items belonging to the same user to determine whether there is data partially overlapping between a data item and a data group or between two data groups.

If the on-line consuming user also performs the operation of browsing or submitting an order on line as the on-line user, the geographic location of the users who submit the order on line and consume on site at the store should cover the corresponding geographic location (resident geographic location) when the users arrive at the store for consumption, i.e. in this case, the resident geographic location data is a subset of the online acquired geographic location data. By acquiring the user information of the users who submit orders in an online mode and consume at the store site, corresponding geographic position data can be extracted from the user information acquired by the online digital platform, so that the geographic service range of the store and the portrait information of the users in the range can be determined. Since the number of customers who have submitted online orders from off-store online interfaces or third party platforms is much smaller than the number of customers who have both submitted orders online and consumed on-site at the store, the geographical location of the latter can be used to represent the geographical locations of all of the off-store customers, thereby determining the geographic service scope of the store.

The matching of the identification data may be performed at the store or at a data center of a parent company of the brand to which the store belongs, in which case the information of the online user needs to be obtained from an online digital platform or a separate database as mentioned above. The matching operation may also be performed at a server or processing center, such as a database provided on a network or cloud platform, or a server or processing center associated with the database, and then the matching result is transmitted to the corresponding store or data center above.

After the geographic location data of the store consuming user is obtained, the geographic locations of the store consuming user and the store consuming user may be further filtered in optional step S131. For example, when some user goes on a business to a city and arrives at a certain shop in the city for shop-to-shop consumption or submits an online order on the online interface of the shop, the shop matches the user with online user information obtained in the online digitizing platform to determine that the user belongs to the shop-to-shop consuming user of the shop, and takes the delivery address of the online order as a new geographic location (resident geographic location) of the user. However, the user's previously resident geographic location is not in the city in which the store is located, so that such a geographic location of the store-to-store consumer user has no positive effect on determining the geographic service range of the store and is more considered to be noisy data. These particular users may be deleted from the group of users to the store consuming user and/or the new geographic location may be deleted from the user and the set of geographic locations (resident geographic locations) of the store by defining more parameter conditions on the geographic location, such as the address of the order submitted at the store (delivery address) being different from the user's previous geographic location (resident geographic location), or further refined to the new geographic location being different from the user's home address, work unit address, etc. specific resident geographic location. This particular situation may be further distinguished by a comparison of the resident geographic location with the newly determined geographic location of the store-to-store consumer user at the time, frequency, and other items of information acquired, such as where the resident geographic location should be multiple times, for a long period of time. Through screening, the user who arrives at the store and the geographical position of the user who obviously belongs to the noise data can be respectively eliminated from respective data groups, and the accuracy of the geographical service range of the store is improved.

Next, in step S140, the geographic service range of the store is determined based on the geographic location of the consumer-to-store user.

According to the embodiment of the application, the geographic position data of the shop consumption user can be projected onto the map, and the geographic distribution map of the resident geographic position of the shop consumption user can be obtained. The geographical profile may take the form of a thermodynamic diagram. For example, for a restaurant store, the thermodynamic diagram is a geographic distribution graph formed by resident geographic locations representing the eating users as location nodes, each eating user may correspond to multiple location nodes (representing eating users having multiple resident geographic locations). The thermodynamic diagram includes a projection onto a map such that each location node incorporates distance information (relative to the geographic location of the store). With the store as a reference location, the distance and orientation of the location nodes to the reference location correspond to the distance and orientation of the resident geographic location relative to the store of interest (characterizing the radiation range of the store), and the distance and orientation between the location nodes correspond to the distance and orientation between the respective resident geographic locations on the map. By means of thermodynamic diagrams, the geographical distribution relationship between the resident geographical position of the store consumer user and the store and between the resident geographical positions of the store consumer user can be accurately presented.

On the basis of the thermodynamic diagram data, a graph boundary algorithm can be further used to determine the boundary of the geographic service range of the shop. The graph boundary algorithm may include an AlphaShape graph algorithm or other method that may determine the boundary or contour of a distribution range based on thermodynamic diagram input. Additional parameters such as distance and probability can also be introduced in the graph boundary algorithm to control the operation of the algorithm. The graph boundary algorithm may also be implemented using an artificial intelligence AI model, either a deep learning model or a neural network model, in conjunction with big data techniques.

By determining the geographic service range of a store, data range constraints on user distribution, user consumption levels corresponding to particular areas, geographic distribution of penetration, etc. may be provided for accurately obtaining business conditions within the geographic service area of the store.

According to an embodiment of the present application, the method may further include an optional step S150, in which an operating policy of the store and/or a marketing policy for the store-to-store consumer is determined based on the user representation characteristics of the store-to-store consumer corresponding to the geographic location within the geographic service range of the store. These user representation characteristics may include user preferences for goods (dishes) and services offered by the store, time and frequency of consumption, changes in consumption levels and trends in consumption, and the like. The business strategy and the marketing strategy can be made not only for the consumption users to the store within the geographic service range, but also can be extended to all the users within the geographic service range. The strategy can be further refined into different operation strategies and marketing strategies determined according to the store-to-store consumption users in different areas within the geographic service range, for example, different strategies can be formulated according to streets, communities, office buildings and the like, the analysis data of the distance, permeability and consumption level of the users around the radiation area of the store are mined, and more accurate offline advertisement putting, offline community visiting and specific online activity marketing strategies are formulated.

The business and marketing strategies of the store are not only related to geographic location, but should also be time dependent. The scheme of the embodiment of the present application may acquire update data regarding at least one of information (including identification data and a geographical location) of an online user and identification data to a store consumer over time to repeatedly perform at least one of steps S110 to S150, thereby updating the geographical location to the store consumer, updating a geographical service area of a store, and updating a store business strategy and a marketing strategy.

In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium, on which a computer program is stored, the program comprising executable instructions which, when executed by a processor for example, may implement the steps of the method for determining a geographic service scope of a store as described in any of the above embodiments. In some possible implementations, the various aspects of the present application may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present application described in the method for determining a geographic service range of a store, when said program product is run on said terminal device.

A program product for implementing the above method according to an embodiment of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In an exemplary embodiment of the present application, there is also provided an electronic device that may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the method for determining geographic service areas of stores of any of the above embodiments via execution of the executable instructions.

As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 200 according to this embodiment of the present application is described below with reference to fig. 2. The electronic device 200 shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in FIG. 2, electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.

Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present application described in the present specification for a method for determining a geographic service range of a store. For example, the processing unit 210 may perform the steps as shown in fig. 1.

The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.

The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 230 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the method for determining the geographic service range of a store according to the embodiment of the present application.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

Claims

1. A method for determining a geographic service scope of a store, comprising:

acquiring information of online users of the shops, wherein the information of the online users at least comprises identification data and geographic positions of the online users;

acquiring identification data of a store-to-store consumer of the store;

determining a geographic location of the store-to-store consuming user based on the identifying data of the store-to-store consuming user and the information of the online user; and

determining a geographic service range of the store based on the geographic location of the to-store consumer user.

2. The method of claim 1, wherein the online users comprise users who submit orders to the stores and/or visit the stores online.

3. The method of claim 2, wherein the geographic location of the online user comprises at least one of:

a geographic location at which the online user is located when an order is submitted to the store or the store is visited;

and when the order is a take-away order, the delivery address of the take-away order.

4. The method of claim 2, wherein the online manner comprises accessing an online interface of the store by at least one of scanning image code, a program on a mobile device, a messaging applet, a web page, and third party platform software.

5. The method of claim 1, wherein the identification data includes at least one of a member account number, a social software account number, a payment account number, and a phone number of the user.

6. The method of any of claims 1-3, wherein obtaining information of online users of the store further comprises: the identification data and geographic location of the online user is obtained while or before the online user browses and/or selects products and/or services offered by the store.

7. The method of claim 1, wherein obtaining identification data of a store-to-store consumer user of the store further comprises: acquiring the identification data of the store-to-store consumption user based on the payment information of the store-to-store consumption user.

8. The method of claim 1, wherein obtaining identification data of a store-to-store consumer user of the store further comprises: acquiring identification data of the store-to-store consumer user based on an order submitted to the store by the store-to-store consumer user in an online manner.

9. The method of claim 1, wherein determining the geographic location of the store-to-store consuming user based on the identifying data of the store-to-store consuming user and the information of the online user further comprises: when the identification data of the online user matches the identification data of the store-to-store consuming user, determining the geographic location of the online user as the matched geographic location of the store-to-store consuming user.

10. The method of claim 9, wherein the determined geographic location of the store-to-store consumer user is filtered.

11. The method of claim 9, wherein the store-to-store consumption users are filtered based on their geographic locations.

12. The method of claim 1, wherein determining the geographic service scope of the store based on the geographic location of the to-store consumer user further comprises:

projecting the geographic location of the store consuming user onto a map to determine the geographic profile of the store consuming user;

determining a boundary of the geographic service area of the store based on the geographic profile.

13. The method of claim 12, wherein the geographic profile is a thermodynamic diagram, and wherein the boundaries of the geographic service area are determined by a graph boundary algorithm.

14. The method of claim 13, wherein the graph boundary algorithm is implemented using a deep learning model or a neural network model.

15. The method of claim 1, further comprising updating the geographic service scope of the store based on the identification data of the store-to-store consumer user and the update data of the information of the online user.

16. The method of claim 1, further comprising:

and determining the business strategy of the shop and/or determining the marketing strategy aiming at the store-to-store consumer based on the characteristics of the store-to-store consumer corresponding to the geographic position within the geographic service range.

17. The method of claim 16, further comprising:

determining different marketing strategies for the to-store consumer users in different areas in the geographic service area.

18. The method of any one of claims 1 to 17, wherein the store is a restaurant store.

19. A computer-readable storage medium, on which a computer program is stored, the computer program comprising executable instructions that, when executed by a processor, carry out the method according to any one of claims 1 to 18.

20. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to execute the executable instructions to implement the method of any of claims 1 to 18.