JP7272522B2 - Data analysis device, data analysis system, data analysis method and data analysis program - Google Patents

Description

The present disclosure relates to a data analysis device, data analysis system, data analysis method, and data analysis program.

2. Description of the Related Art In recent years, commercial area analysis for formulating sales strategies and store opening strategies has been performed using a large amount of sales of products and services obtained via communication networks, information on customer visit rates, and the like. For example, Patent Literature 1 discloses a system that uses a map application provided through the Internet to enable user-friendly trade area analysis.

JP-A-10-307868

However, in the system described in Patent Document 1, the target area is selected on the map data, and the sales situation at the own store is only shown for each selected target area. It was difficult to conduct a trade area analysis that focuses on the relationship between

Therefore, the present disclosure has been made to solve the above problems, and the purpose is to evaluate the visit ratio for two points (for example, own store and competing stores) set on the map. It is to provide an analysis device, a data analysis system, a data analysis method and a program.

In order to achieve the above object, a data analysis device according to a first aspect of the present disclosure is a device for analyzing a trade area related to two points set on a map, and for each predetermined area on the map, area visit ratio for the two points based on the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points among the customers who are active in A ratio calculation unit, an overall visit ratio calculation unit that calculates an overall visit ratio for two locations based on the number of customers who visited the first location and the number of customers who visited the second location, and for each predetermined area on the map based on the total visit ratio, and an output unit for outputting the evaluation result.

Further, in order to achieve the above object, a data analysis system according to a second aspect of the present disclosure is a system that analyzes a trade area related to two points set on a map, for each predetermined area on the map, Based on the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points among the customers active in the area, the area visit ratio to the two points is calculated. an area visit ratio calculation unit; an overall visit ratio calculation unit that calculates an overall visit ratio for two locations based on the number of customers who visited the first location and the number of customers who visited the second location; An evaluation unit that evaluates the area visit ratio for two points for each area based on the overall visit ratio, and an output unit that outputs the evaluation result.

Further, in order to achieve the above object, a data analysis method according to a third aspect of the present disclosure is a method of analyzing a commercial area related to two points set on a map, for each predetermined area on the map, Based on the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points among the customers active in the area, the area visit ratio to the two points is calculated. calculating a total visit ratio for two points based on the number of customers who visited the first point and the number of customers who visited the second point; evaluating the ratio based on the overall visit ratio; and outputting the evaluated result.

Moreover, in order to achieve the above object, a program according to a fourth aspect of the present disclosure causes a computer to execute the above data analysis method.

According to the present disclosure, it is possible to evaluate the visit ratio for two points set on the map.

1 is a configuration diagram of a data analysis system 1 according to Embodiment 1. FIG. 2 is a functional block diagram showing an example of the functional configuration of the customer terminal 100; FIG. 3 is a block diagram showing an example of a functional configuration of a server device 300; FIG. 3 is a diagram showing an example of customer information 323. FIG. 3 is a diagram showing an example of position information 324. FIG. It is a figure which shows an example of estimation of an address. It is a figure which shows an example of the visit ratio of area|region A1 and A2. It is a figure which shows an example of the visit ratio of area|region A3 and A4. It is a figure which shows an example of an output result. 4 is a flow chart showing the flow of processing of the server device 300. FIG. 3 is a block diagram showing the hardware configuration of the server device 300; FIG. 2 is a configuration diagram of a data analysis system 2 according to Embodiment 2. FIG. 5 is a block diagram showing an example of a functional configuration of a server device 500; FIG. It is a figure which shows an example of an output result. 5 is a flow chart showing the flow of processing of the server apparatus 500. FIG.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all the drawings for explaining the embodiments, common constituent elements are given the same reference numerals, and repeated explanations are omitted. It should be noted that the embodiments described below do not unduly limit the content of the present disclosure described in the claims. Also, not all the components shown in the embodiments are essential components of the present disclosure.

<Embodiment 1>
A data analysis system 1 according to the present embodiment is a system for analyzing commercial areas related to two points set on a map. Trade area analysis is the analysis of, for example, which region customers come to a certain store from, and what kind of products and services sell in a certain region. is performed on

In this embodiment, as two points on the map, for example, own shop (corresponding to the first point.) And competing shops (corresponding to the second point.) are set, and for each predetermined area on the map, the corresponding Evaluate the customer's own store and competitor store visit ratios in the area based on the own store and competitor store visit ratios for all customers. In other words, by comparing the visit ratio (average) of all customers, by evaluating the areas where customers visit competing stores more or less than the average in each area, sales strategies and store opening strategies in relation to competing stores It is a very effective index when considering Rather than simply comparing the ratio of visits between your own store and those of your competitors, by making comparisons based on the overall visit ratio, data analysts will be able to grasp the distinctive areas in relation to competitor stores, which It becomes easy to analyze whether such conditions affect the visit rate of the own store.

FIG. 1 is a configuration diagram of a data analysis system 1 according to this embodiment. The configuration and outline of the data analysis system 1 will be described with reference to FIG.

In FIG. 1, a data analysis system 1 is a system for analyzing two commercial areas set on a map, and includes a plurality of customer terminals 100, an information processing terminal 200, and a server device 300. . The server device 300 is communicably connected to a plurality of customer terminals 100 and information processing terminals 200 via a network NW. The network NW is, for example, a WAN (Wide Area Network). Here, the points set on the map are, for example, shopping centers, supermarkets, shops such as department stores, etc., but are not limited to these, and include sightseeing spots, public facilities such as libraries and hospitals, or transportation such as stations. It may be an engine stop. Note that in the following description, the point targeted for trade area analysis may be referred to as a visited place.

The customer terminal 100 is an information processing terminal used by a customer, and the customer terminal 100 is implemented by, for example, a smart phone, a tablet terminal, or the like. In the present embodiment, a customer is a person who has an information processing terminal installed with a program (for example, a smartphone application) that causes the server device 300 to acquire location information used in the data analysis system 1. However, it is not limited to this, as long as the server device 300 can acquire the location information of the terminal itself, it may be in any form. Customers may include customers who simply drop by without performing any service or transaction.

The customer terminal 100 continuously acquires location information via a program (for example, an application dedicated to smartphones) installed in the customer terminal 100 . For example, the GPS (Global Positioning System) is used to detect location information indicating the location of the device itself. Then, the customer terminal 100 continuously transmits the detected location information to the server device 300 according to a request from the server device 300, for example.

The information processing terminal 200 is an information processing terminal used by a user who performs trade area analysis, and is realized by, for example, a smart phone, a tablet terminal, a PC (Personal Computer), or the like. The information processing terminal 200 receives the operation of the user who conducts the trade area analysis, for example, starts a browser application program, and sends information indicating the visited place set by the user (point to be the target of the trade area analysis) to the server. Send to device 300 . Note that the number of information processing terminals 200 is not limited to that shown in FIG. 1, and a plurality of terminals may be provided.

Server device 300 is a server device that corresponds to a data analysis device that evaluates the visit ratio for two points set on a map. When the server device 300 acquires the information indicating the visited places from the information processing terminal 200 , the server device 300 calculates the visit ratio for the two points based on the position information received from the customer terminal 100 . Then, for each area on the map, the visit ratio for two points is evaluated, and the evaluation result is transmitted to the information processing terminal 200 . For example, the server device 300 calculates a score indicating how many customers visit which location with respect to the visit ratio of all customers for each area, and outputs the evaluation results in a manner that allows the magnitude of the score to be understood. You may make it output. Specifically, each area on the map may be displayed in different colors for each score, or may be displayed as a numerical value. A method of calculating the score will be described later. In addition, in this specification, the two points set on the map are described as "own shop" and "competing shop".

As described above, by evaluating the visit ratio for two locations using a score, it is possible to ask the user who analyzes the trade area how many customers in which area are compared to the overall visit ratio (average) to competing stores. You can show how often you visit. As a result, the user who conducts the trade area analysis can easily grasp the relevant area as an area requiring analysis such as a sales strategy.

FIG. 2 is a functional block diagram showing an example of the functional configuration of the customer terminal 100. As shown in FIG. A functional configuration of the customer terminal 100 will be described with reference to FIG. Note that the customer terminal 100 of the present embodiment may have a configuration in which some of the constituent elements (each unit) in FIG. 2 are omitted. Further, the information processing terminal 200 of this embodiment may have the same functional configuration as the customer terminal 100, and the description of the functional configuration of the information processing terminal 200 will be omitted.

The customer terminal 100 is, for example, a portable terminal such as a mobile phone including a smart phone, a tablet terminal, a notebook PC, or a PDA (Personal Digital Assistant). The customer terminal 100 also includes a wearable device, which is an information processing terminal in the form of spectacles or a watch. Furthermore, the customer terminal 100 may include various smart devices having information processing functions for acquiring location information. For example, the customer terminal 100 may include a smart vehicle such as an automobile.

2, the customer terminal 100 has a communication section 110, an input section 120, a display section 130, a detection section 140, and a control section 150. As shown in FIG.

The communication unit 110 performs various controls for communicating with the server device 300 via the network NW, and its functions are realized by hardware such as various processors or communication ASICs, programs, and the like. .

The input unit 120 is an input interface that receives various operations from the user. For example, the input unit 120 is implemented by operation keys, buttons, a touch panel, a microphone for voice input, etc. provided on the customer terminal 100 .

The display unit 130 is a display device for displaying various types of information, and is realized by, for example, a liquid crystal display. When a touch panel is adopted for the customer terminal 100, part of the input section 120 and the display section 130 are integrated.

The detection unit 140 detects various types of information regarding the customer terminal 100 . Specifically, the detection unit 140 detects the physical state of the customer terminal 100 . In the example shown in FIG. 2 , the detection section 140 has a position detection section 141 .

The position detector 141 detects the current position of the customer terminal 100 . Specifically, the position detection unit 141 receives radio waves transmitted from the satellite positioning system, and detects position information (for example, latitude and longitude) indicating the current position of the customer terminal 100 based on the received radio waves. Note that the position detection unit 141 may acquire position information using a different method. For example, if the customer terminal 100 has a function equivalent to that of a contactless IC card used at a station ticket gate or a store (or if the customer terminal 100 has a function of reading the history of the contactless IC card). case), the customer terminal 100 records the information that the fare is settled at the station and the location of use. The position detection unit 141 detects this information and acquires it as position information. Further, the position detection unit 141 may detect position information that can be acquired from an access point when the customer terminal 100 communicates with a specific access point. Further, the position detection unit 141 may detect the estimated position of the customer terminal 100 based on the identification information (IP address, etc.) when the customer terminal 100 is connected to the Internet.

Note that the detection unit 140 is not limited to the position detection unit 141 and may include various devices that detect various states of the customer terminal 100 . For example, the detection unit 140 includes a microphone that collects sounds around the customer terminal 100, an illuminance sensor that detects the illuminance around the customer terminal 100, an acceleration sensor that detects physical movement of the customer terminal 100 (or gyro sensor, etc.), a humidity sensor that detects the humidity around the customer terminal 100, a geomagnetic sensor that detects the magnetic field at the location where the customer terminal 100 is located, and the like. Further, the detection unit 140 may detect various information using the function of a sensor. For example, the detection unit 140 may detect the number of steps of the customer using the customer terminal 100 using the function of an acceleration sensor. Further, the detection unit 140 uses the function of an acceleration sensor to detect motion information indicating whether the customer terminal 100 is operating or stationary at regular intervals or at each timing when the customer terminal 100 operates. You may

The control unit 150 is, for example, a controller, and a CPU (Central Processing Unit), MPU (Micro Processing Unit) or the like transfers various programs stored in a storage device inside the customer terminal 100 to a RAM (Random Access Memory). ) as a work area. Also, the control unit 150 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The control unit 150 controls the processing of providing the location information of the customer terminal 100 to the server device 300 . For example, the control unit 150 executes and controls a program (for example, a smartphone application) for implementing the process of providing position information of the customer terminal 100 . The program may be pre-installed in the customer terminal 100, or may be downloaded from the server device 300 (or an external server that provides various programs) and installed in the customer terminal 100 according to the operation of the customer who owns the customer terminal 100. may

The control unit 150 realizes or executes the functions and effects of information processing described below by executing the above-described programs using the RAM as a work area.

The control unit 150 acquires various information detected by the detection unit 140 as position information by controlling the detection unit 140 . For example, the control unit 150 controls the position detection unit 141 to acquire position information of the customer terminal 100 and time information corresponding to the time when the position information was detected as position information.

Also, the control unit 150 may acquire information about the status of communication performed by the communication unit 110 as the position information. For example, the control unit 150 may acquire mutual communication status between the customer terminal 100 and a predetermined access point. In addition, when the customer terminal 100 has a call function, the control unit 150 may acquire information such as the time of the call, the destination of the call, and the duration of the call. In addition, when the customer terminal 100 has a photographing function, the control unit 150 may acquire information such as the time when the photographing was performed, the position information at which the photographing was performed, and the photographing time. Then, the control unit 150 may acquire the location information of the customer terminal 100 based on these pieces of information.

Also, the control unit 150 may acquire position information periodically (every minute, every three minutes, every five minutes, every hour, etc.), for example. Also, the timing at which the control unit 150 acquires the position information may be set by the server device 300 . Also, the control unit 150 may acquire the position information at the timing when a predetermined event occurs. For example, the control unit 150 acquires position information according to a predetermined event, such as when the above-described non-contact IC card function is used, or when a camera is photographed.

Further, the control unit 150 controls the communication unit 110 to transmit the acquired position information to the server device 300 . For example, under the control of the control unit 150, the communication unit 110 transmits identification information for identifying the customer terminal 100, location information detected by the detection unit 140, and the date and time when the location information was detected to the server device 300. Send to At this time, the control unit 150 may transmit the position information and the like to the server device 300 each time the position information is acquired, or may transmit the position information and the like to the server device 300 at predetermined intervals. For example, the control unit 150 controls the communication unit 110 to periodically (every minute, every three minutes, every five minutes, every hour, etc.) transmit the position information to the server device 300 .

FIG. 3 is a block diagram showing an example of the functional configuration of the server device 300. As shown in FIG. A functional configuration of the server device 300 will be described with reference to FIG.

The server device 300 has a communication section 310 , a storage section 320 and a control section 330 . The server device 300 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from an administrator or the like who uses the server device 300, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. may have.

The communication unit 310 performs various controls for communicating with the customer terminal 100 and the information processing terminal 200 via the network NW. etc.

The storage unit 320 is realized by, for example, a semiconductor memory device such as a RAM or flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 320 has map information 321 , visited place information 322 , customer information 323 and location information 324 .

The map information 321 stores map data. Map data can specify a position, for example, by latitude and longitude. Further, the map data may be such that an area on the map is divided into a mesh and the position is specified by identification information for identifying each divided area.

The visited place information 322 stores information of visited places (points targeted for trade area analysis and corresponding to two points set on the map) transmitted from the information processing terminal 200 . The information on the visited place is, for example, an address, an ID for identifying the visited place, and position information indicating the position of the visited place such as latitude and longitude. Further, in the map data stored in the map information 321, identification information for identifying each area divided into a mesh may be stored as position information. The visited places are, for example, the own store and competing stores.

The customer information 323 stores information about customers. Location information 324 stores customer location information. Here, an example of the customer information 323 and the location information 324 will be described with reference to FIGS. 4 and 5. FIG.

FIG. 4 is a diagram showing an example of the customer information 323. As shown in FIG. As shown in FIG. 4, the customer information 323 has items such as "customer ID" and "address".

"Customer ID" indicates identification information for identifying a customer. In addition, in this embodiment, identification information shall correspond with the reference code used for description. For example, a customer identified by customer ID "C1" indicates customer C1.

"Address" is the customer's home address, which is estimated by the estimation unit 332, which will be described later. It should be noted that information input by the user, such as member information of the store, may be used. Moreover, the customer information is not limited to the address, and may include other information about the customer such as age, place of work, and occupation. In other words, the information about the customer is information indicating the area in which the customer is active. It may be a place (for example, a station, a school, a commercial facility, etc.) that represents a person's lifestyle. In addition, in this embodiment, the area where the customer resides will be described as the area where the customer is active.

In FIG. 4, the address of the customer information is indicated by a concept such as "A01", but in practice it is indicated by specific information. For example, in the map data, the address item is indicated by identification information indicating mesh-divided areas, names of prefectures, names of municipalities, latitudes and longitudes, and the like.

That is, FIG. 4 shows that the address of customer C1 indicated by customer ID "C1" is "A01".

FIG. 5 is a diagram showing an example of the position information 324. As shown in FIG. As shown in FIG. 5, the location information 324 has items such as "terminal ID", "customer ID", "date and time of acquisition", and "location information".

"Terminal ID" indicates identification information for identifying a customer terminal. For example, the terminal identified by the terminal ID "100-1" indicates the customer terminal 100-1.

“Acquisition date and time” indicates the date and time when the location information was acquired by the customer terminal 100 or the server device 300 . Note that FIG. 5 shows an example in which position information is stored every two hours or every day, but in reality, position information is acquired at more detailed intervals (for example, every five minutes). good too. “Position information” indicates the position information of the customer terminal 100 . In FIG. 5, "location information" is represented by a concept such as "AA01", but in reality, it is represented by measured values such as longitude and latitude, location information of access points, location of station ticket gates, and the like. may be Further, it may be identification information indicating a divided area in the map data.

That is, FIG. 5 shows that the customer terminal 100-1 identified by the terminal ID "100-1" is used by the customer C1 identified by the customer ID "C1". Further, as an example of the position information, the position information acquired at “2018/11/1 8:00” indicates that the customer terminal 100-1 is located at the position indicated by the position information “AA01”. Further, as another example of the location information, the location information acquired at “10:00 on November 1, 2018” indicates that the customer terminal 100-1 is located at the location indicated by the location information “AA02”. there is

Returning to FIG. 3, the controller 330 will be described. The control unit 330 is, for example, a controller, and various programs (corresponding to data analysis programs) stored in a storage device inside the server device 300 are executed by the CPU, MPU, or the like using the RAM as a work area. Realized. Also, the control unit 330 is a controller, and is implemented by an integrated circuit such as an ASIC or FPGA, for example.

The control unit 330 includes an acquisition unit 331, an estimation unit 332, an area visit ratio calculation unit 333, a visit rate calculation unit 334, an overall visit ratio calculation unit 335, a score calculation unit 336, and an output unit 337. and implements or executes the information processing functions and actions described below. Note that the internal configuration of the control unit 330 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it performs information processing described later.

The acquisition unit 331 acquires various types of information. For example, the acquisition unit 331 acquires information on the visited place from the information processing terminal 200 and stores it in the visited place information 322 . Further, for example, the acquisition unit 331 identifies the customer terminal 100 based on information when the customer terminal 100 uses a dedicated application or website, and continuously obtains the location information of the customer terminal 100 from the identified customer terminal 100. to get to. Specifically, the acquisition unit 331 acquires various types of information detected or acquired by the customer terminal 100 as position information. The acquisition unit 331 acquires position information from the customer terminal 100 at predetermined time intervals, and stores the acquired position information in the position information 324 of the storage unit 320 .

Note that the acquisition unit 331 does not need to acquire the position information itself used by each processing unit, which will be described later. For example, the acquisition unit 331 may acquire location information of the customer terminal 100 based on information detected from the customer terminal 100 . Specifically, even when the region visit ratio calculation unit 333, which will be described later, uses information indicating the region in which the customer resides when calculating the visit ratio, the acquisition unit 331 receives the customer's number from the customer terminal 100. It is not necessary to acquire the information of the area in which the user resides. That is, the acquiring unit 331 acquires the position information indicated by the longitude and latitude detected by the customer terminal 100, converts it into identification information for identifying divided areas on the map, and converts the converted information into each of the following information. It may be stored in the position information 324 as the position information used by the processing unit.

Further, the acquisition unit 331 may acquire position information together with date and time information including day of the week information. For estimating the customer's home, for example, the relationship with the date and time and the day of the week is useful, such as the position where the customer is located early in the morning on weekdays, the position where the customer is located during the daytime on weekdays, and the position where the customer is located on holidays. Conceivable. For this reason, the acquisition unit 331 may acquire location information together with date and time information including day of the week information to increase the amount of location information to be acquired, so that estimation with higher accuracy may be performed.

Also, the acquisition unit 331 does not necessarily acquire the location information at the same time as the customer terminal 100 acquires the location information. That is, the acquisition unit 331 may acquire the location information stored in the storage unit of the customer terminal 100 when using it for processing instead of acquiring the location information in real time. Thereby, the server apparatus 300 can reduce the processing amount for communication.

Further, the acquisition unit 331 may acquire the status of communication between the customer terminal 100 and another device as the position information. Specifically, the acquisition unit 331 acquires the communication status in which the customer terminal 100 accesses the Internet or the like via an access point. In this case, the acquisition unit 331 acquires information detected from the communication status with the access point as the position information. Specifically, the acquisition unit 331 may determine the installation position of the access point communicating with the customer terminal 100 and acquire the determined installation position of the access point as the position information of the customer terminal 100 . In addition, the acquisition unit 331 acquires the location information of the customer terminal 100 by detecting communication between the device of the station ticket gate and the customer terminal 100, or acquires the location information based on the IP address of the customer terminal 100, as described above. may be obtained. Further, the acquisition unit 331 may acquire the location information through a customer's SNS (Social Networking Service) or the like.

The estimation unit 332 estimates the area where the customer resides. For example, the estimating unit 332 estimates the area where the customer who owns the customer terminal 100 is estimated to be at home, based on the location information of the customer terminal 100 in the early morning or at night on weekdays. Here, an example of estimating the address of customer C1 who owns customer terminal 100-1 will be described with reference to FIG.

FIG. 6 is a diagram showing an example of address estimation. In FIG. 6, a map 60 is divided into a mesh. With the upper left corner of the map 60 as the origin, the range indicated by the coordinates of 1 in the vertical direction and 1 in the horizontal direction is defined as an area A11. Similarly, the range indicated by the coordinates of 2 vertically and 1 horizontally counting from the origin of the map 60 is area A12, and the range indicated by the coordinates of 3 vertically and 1 horizontally counting from the origin of the map 60 is area A13. and

For example, as shown in FIG. 6, the estimation unit 332 maps the predetermined range in which the customer terminal 100-1 exists to the coordinates in light of the position information. In the map 60, in the location information acquired from the customer terminal 100-1, hatching indicates areas where the customer terminal 100-1 is likely to be located (that is, areas with a high probability distribution). Note that the probability distribution may indicate the probability of all the location information acquired regarding the customer, or indicate the probability that the customer is located within a predetermined range when narrowed down to a specific time period or day of the week. can be anything.

In the example shown in FIG. 6, the area A55 is hatched. This indicates that the customer terminal 100-1 has been in the range indicated by the area A55 for a relatively long time. For example, an area A55 indicates that the location information corresponds to a range indicating a distribution value exceeding a predetermined threshold in the probability distribution indicating the probability that the customer terminal 100-1 is located in the range shown on the map 60. FIG.

Further, the estimating unit 332 determines from the location information of the customer terminal 100-1 stored in the location information 324 that relatively many customer terminals 100-1 are located in the range indicated by the area A55 during early mornings, evenings, and holidays on weekdays. Then judge. In this case, the estimation unit 332 estimates that the area indicated by the area A55 is the area where the home address of the customer C1 who owns the customer terminal 100-1 exists, that is, the area where the customer resides.

For example, the estimating unit 332 receives settings for performing such estimation in advance, and executes the above-described estimation processing by comparing the received settings with the position information. Specifically, the estimation unit 332 estimates that the address of the customer C1 exists in the range when the probability distribution of the predetermined range exceeds a predetermined threshold. As an example, the estimating unit 332 uses the acquired location information of the customer terminal 100-1 (for example, the acquired location information of the customer C1 narrowed down to the time period from 6:00 to 8:00 in a period of one week). ), if the probability distribution indicating the probability that the customer C1 is located in the predetermined range exceeds "0.5 (50%)", it is estimated that the address of the customer C1 exists in the range.

The estimating unit 332 stores the estimated area where the customer lives as described above in the customer information 323 of the storage unit 320 as the customer's address. In the above description, the customer's address was given as an example of the area where the customer is active, which is estimated by the estimation unit 332. Similarly, the customer's place of work, etc. can also be estimated. That is, the estimating unit 332 can calculate a customer's frequent appearance pattern based on the probability that the customer is located within a predetermined range, and can estimate the location representing the customer's lifestyle.

As described above, in this embodiment, the area where the customer lives is described as the area where the customer is active.

Returning to FIG. 3, the area visit ratio calculation unit 333 calculates the visit ratio of customers to the two points transmitted from the information processing terminal 200 for each predetermined area on the map. Here, the region visit ratio calculation unit 333 calculates the region visit ratio based on the visit rate calculated by the visit rate calculation unit 334, which will be described later.

For each predetermined area on the map, the visit rate calculation unit 334 estimates the visit rate of customers who have visited the own store among the customers who are active in the area, Calculate the second visit rate that estimates the rate.

A method of calculating the area visit ratio in a predetermined area on the map will be specifically described below. First, the visit rate calculation unit 334 extracts customers residing in a predetermined area of the map data based on the customer's address stored in the customer information 323. and competitors). Note that the predetermined area may be a mesh-like divided area or an administrative division, and can be freely set by the user who conducts the trade area analysis.

That is, the visit rate for each customer's visit location (own store and competing stores) in an area Ai (i = 1 to n: n is a natural number) obtained by dividing the target area for trade area analysis into predetermined areas is as follows. calculated to

(Visit rate of own store of customers in area Ai (equivalent to first visit rate))=(Number of customers who visited own store and reside in area Ai)/(Number of customers residing in area Ai) ... formula (1)

(Rate of customers in area Ai visiting competitive stores (equivalent to second visit rate))=(Number of customers living in area Ai who visited competing stores)/(Number of customers living in area Ai) (2)

Here, whether or not a certain customer has visited the visited place is determined from the acquired location information of the customer. For example, based on the position information 324, the visit rate calculation unit 334 determines whether the position indicated by the position information of the customer is included in the area where the visited place exists. At this time, based on the date and time when the position information was detected, if the customer's position is included in the area where the visited place exists for a predetermined period of time (for example, 30 minutes or longer), the customer visits the visited place. It can be judged that In addition, if the ratio of the number of location information data included in the area where the visited location exists to the total location information data acquired for a customer is greater than or equal to a predetermined threshold, it is determined that the customer has visited the location. You may

Next, the area visit ratio calculator 333 calculates the area visit ratio for the two points. Specifically, the area visit ratio calculation unit 333 calculates area visits to the own store and competing stores by the customers based on the customers residing in the predetermined area of the map data extracted by the visit rate calculation unit 334 as described above. The ratio is calculated as follows.

(Visit ratio of customers in area Ai to own store and competitor store)=(Number of customers who have visited own store and reside in area Ai)/(Number of customers who have visited competitor store and reside in area Ai) . . . Formula (3)

Here, the region visit ratio calculation unit 333 calculates the confidence interval (smaller end point is referred to as the "lower confidence limit", and the larger end point is referred to as the "upper confidence limit".

(Lower limit of region visit ratio)=(Lower confidence limit of visit rate of own store)/(Upper confidence limit of visit rate of competitor store) Equation (4)

(Upper limit of area visit ratio) = (Upper limit of confidence in visit rate of own store)/(Lower limit of confidence in visit rate of competing stores) Equation (5)

The reason why the upper limit and lower limit of the area visit ratio are calculated based on the confidence interval of the predetermined reliability of the visit rate is as follows. That is, in trade area analysis, since it is not realistic to conduct a survey of visit ratios for all residents in a certain area, sample surveys are generally conducted. However, since the visit ratio varies depending on which residents are extracted as samples, the value of the customer visit ratio in a certain area calculated by the area visit ratio calculation unit 333 as described above (that is, the formula (3)) alone , it is imprecise to conclude that it is the rate of visits of all populations in an area. Therefore, in this embodiment, the visit ratio is calculated based on the visit rate having a certain width (confidence interval). That is, the visit ratio is calculated assuming that "the visit ratio exists in that section with a certain degree of reliability (for example, 95%)". Thus, by calculating the visit ratio based on the visit rate given the accuracy, it is possible to accurately evaluate the calculation result.

In this embodiment, the Wilson score method is used as a method for obtaining a confidence interval. adjustment Wald method), Jeffeys method, etc. can be used. Since these are well-known techniques, detailed description thereof is omitted.

The overall visit ratio calculation unit 335 calculates the total number of customers for the two points from the number of customers who have visited the own store and the number of customers who have visited the competing stores, which are obtained based on the customer information 323 and the location information 324 stored in the storage unit 320 . Calculate the visit ratio. Specifically, it is calculated as follows.

(Ratio of total visits of customers' own store to competing stores) = (Number of customers who visited own store) / (Number of customers who visited competing stores) … Formula (6)

The score calculation unit 336 corresponds to one function of the evaluation unit that evaluates the area visit ratio for two points in each predetermined area on the map based on the overall visit ratio. That is, the score calculation unit 336 calculates, for each predetermined area, a competition score indicating which of the customers who are active in the area visits the own store or the competing stores more often than the overall visit ratio. Specifically, the score calculation unit 336 calculates the competition score as follows using the upper/lower limits of the region visit ratio and the total visit ratio calculated by the above formulas (4) to (6).

(competition score) =
Logarithm of (lower limit of visit ratio of area Ai)/(overall visit ratio) (if lower limit of visit ratio of area Ai>overall visit ratio) Expression (7)
Logarithm of (upper limit of visit ratio of area Ai)/(overall visit ratio) (if upper limit of visit ratio of area Ai<overall visit ratio) Expression (8)
0 ... (if none of the above)

The competition score will be further described with reference to FIGS. 7 and 8. FIG.

FIG. 7 is a diagram showing an example of visit ratios of areas A1 and A2. In FIG. 7, areas A1 and A2 are, for example, one of the areas on the map divided into meshes as shown in FIG. Ratios are shown.

Here, the visit ratio 71 has a lower limit of the visit ratio exceeding the total visit ratio, and corresponds to the case of the above formula (7). This means that there are more customers visiting the own store compared to the overall visit ratio, and by calculating the competition score, it is possible to determine how many customers visit the store compared to the overall visit ratio. can be shown. The visit ratio 72 has an upper limit lower than the overall visit ratio, and corresponds to the above formula (8). This means that the number of customers visiting the own store is smaller than the overall visit ratio. can be shown.

FIG. 8 is a diagram showing an example of visit ratios of areas A3 and A4. In FIG. 8, areas A3 and A4 are, for example, one of the areas on the map divided into meshes as shown in FIG. Ratios are shown.

Here, in both visit ratios 81 and 82, the lower limit of the visit ratio does not exceed the overall visit ratio, and the upper limit of the visit ratio does not fall below the overall visit ratio. In such a case, it is determined that there is no significant difference between the region visit ratio and the overall visit ratio, and the competition score is calculated as 0 (zero).

As described above, the score calculation unit 336 can indicate the area visit ratio for each predetermined area using a numerical value that indicates the number of visitors to the own store or competing stores based on the overall visit ratio.

Returning to FIG. 3, the output unit 337 outputs the competition score calculated as described above. For example, the output unit 337 superimposes the competition score on the map data of the map information 321 stored in the storage unit 320 with a color (such as a gradation) associated with each numerical value, and performs information processing via the communication unit 310. Send to the terminal 200 . The user of the information processing terminal 200 can easily grasp where the area particularly requiring analysis is based on the competition score superimposed on the transmitted map data.

FIG. 9 is a diagram showing an example of output results. The map 60 in FIG. 9 is the same as the diagram described in FIG. 6, and the own shop exists in the area A25 and the competing shops exist in the area A52. The competition score calculated by the score calculation unit 336 is color-coded according to the numerical value as indicated by gradations 91 to 95 and evaluated. The output unit 337 displays the evaluation result superimposed on the map 60 . In the example of FIG. 9, the closer to the gradation 91, the more visits to the own store compared to the overall visit ratio, and the closer to the 95th gradation, the more visits to competing stores.

In FIG. 9, for example, in areas A36, 46 and 47, it can be seen that customers in these areas often visit competing stores. Based on this evaluation result, the data analyst can notice that there are many visits to competing stores in areas relatively close to the own store, and that analysis of the cause and some kind of countermeasure are necessary. In this example, the competition score is displayed by color-coding the area according to the value, but the output of the competition score is not limited to this example. You may do so.

In this way, by presenting the competitive score, it is possible to clarify the areas that require countermeasures that are advantageous in terms of sales strategy to competing stores.

FIG. 10 is a flow chart showing the processing flow of the server device 300. As shown in FIG. The flow of processing in the server device 300 will be described with reference to FIG. Note that the processing illustrated in FIG. 10 is an example of the processing of the server device 300 . As the processing of the server device 300, in addition to this, the processing of each functional configuration of the server device 300 described above may be combined.

In step S<b>101 , the acquisition unit 331 determines whether the communication unit 310 has received location information from the customer terminal 100 . If it is determined that position information has not been received (N in step S101), the process returns to step S101.

On the other hand, if it is determined that position information has been received (Y in step S101), the acquisition unit 331 acquires position information from the communication unit 310 and stores it in the position information 324 of the storage unit 320 in step S102.

In step S<b>103 , the acquisition unit 331 determines whether the communication unit 310 has received from the information processing terminal 200 information on two visited locations (for example, own store and competitor store) that are targets of trade area analysis. If it is determined that the information on the visited place has not been received (N in step S103), the process returns to step S101.

On the other hand, if it is determined that the information on the visited place has been received (Y in step S103), in step S104, the visit rate calculation unit 334 calculates, for each predetermined region on the map, Calculate the customer visit rate. Note that the estimating unit 332 may estimate the area where the customer resides when calculating the visit rate, or estimate the area where the customer resides in advance based on the position information acquired during a predetermined period of time. may be stored in In addition, based on the information about the customer (the area where the customer is active) stored in the customer information 323, such as the customer who works in the area instead of the customer who lives in the area, the customer who often stays on holidays, etc. You can also calculate the visit rate by attribute.

In step S105, the region visit ratio calculation unit 333 calculates the visit ratio for each region based on the visit rate calculated by the visit rate calculation unit 334 in step S104. Specifically, the region visit ratio calculator 333 obtains the lower limit and upper limit of the region visit ratio based on a confidence interval of a predetermined reliability (for example, 95%) of the visit rate.

In step S106, the total visit ratio calculation unit 335 calculates the following from the number of customers who have visited the own store and the number of customers who have visited competing stores, which are obtained based on the customer information 323 and the location information 324 stored in the storage unit 320. Calculate the overall visit ratio for the two sites.

In step S107, the score calculation unit 336 calculates, for each predetermined area, a competition score indicating whether the customers who are active in the area visit the own store or the competing stores more often than the overall visit ratio. do. Specifically, the score calculation unit 336 calculates the competition score using the upper/lower limit of the region visit ratio and the overall visit ratio calculated in step S105.

In step S108, the output unit 337 outputs the competition score calculated in step S107. For example, the output unit 337 superimposes the competition score on the map data of the map information 321 stored in the storage unit 320 with a color (such as a gradation) associated with each numerical value, and performs information processing via the communication unit 310. Send to the terminal 200 . With the above, the server device 300 ends the processing.

(Hardware configuration diagram)
FIG. 11 is a block diagram showing the hardware configuration of the server device 300. As shown in FIG. Server device 300 is implemented in computer 401 . The computer 401 includes a CPU 402 , a main memory device 403 , an auxiliary memory device 404 and an interface 405 .

The operation of each component of server device 300 is stored in auxiliary storage device 404 in the form of a program. The CPU 402 reads out the program from the auxiliary storage device 404, develops it in the main storage device 403, and executes the above processing according to the program. Also, the CPU 402 secures a storage area in the main storage device 403 according to the program. Specifically, the program is a data analysis program that causes the computer 401 to analyze two commercial areas set on the map.

Note that the auxiliary storage device 404 is an example of a non-temporary tangible medium. Other examples of non-transitory tangible media include magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. that are connected via the interface 405 . Also, when this program is distributed to the computer 401 via a network, the computer 401 receiving the distribution may develop the program in the main storage device 403 and execute the processing.

Also, the program may be for realizing part of the functions described above. Further, the program may be a so-called difference file (difference program) that implements the above-described functions in combination with another program already stored in the auxiliary storage device 404 . It should be noted that the hardware configuration shown in FIG. 11 may be configured similarly to the customer terminal 100 and the information processing terminal 200 . The operation of each component in these devices is realized by the CPU according to the program stored in the auxiliary storage device, as in the server device 300 described above.

(Explanation of effect)
As described above, the data analysis system according to the present embodiment sets, for example, the own store and a competing store as two points on the map, and for each predetermined area on the map, the customer's own store in that area. and the ratio of visits to competing stores is evaluated by a competition score calculated based on the visit ratios of all customers to their own store and competing stores. In other words, by comparing the visit ratio (average) of all customers, by evaluating the areas where customers visit competing stores more or less than the average in each area, sales strategies and store opening strategies in relation to competing stores It is a very effective index when considering Rather than simply comparing the ratio of visits between your own store and those of your competitors, by making comparisons based on the overall visit ratio, data analysts will be able to grasp the distinctive areas in relation to competitor stores, which It becomes easy to analyze whether such conditions affect the visit rate of the own store. In addition, it is possible to specify such an area and advertise effectively.

<Embodiment 2>
The data analysis system 1 according to the first embodiment uses the competition score to evaluate which of the two points set on the map is visited by more customers than the overall visit ratio. On the other hand, the data analysis system 2 according to the second embodiment identifies, with respect to the overall visit ratio, a tie area that indicates an area in which both points are visited at the same degree with respect to the two points set on the map. The point is different from the first embodiment.

FIG. 12 is a configuration diagram of the data analysis system 2 according to this embodiment. The data analysis system 2 includes a server device 500 in place of the server device 300 included in the data analysis system 1 according to the first embodiment, but since other points are the same as the data analysis system 1, the description is omitted. .

FIG. 13 is a block diagram showing an example of the functional configuration of the server device 500. As shown in FIG. The server device 500 includes components common to those of the server device 300 except that a control unit 530 is provided instead of the control unit 330 of the server device 300 according to the first embodiment. In addition, the same code|symbol is attached|subjected to the component which is common in the server apparatus 300, and the repetition description is abbreviate|omitted.

The control unit 530 of the server device 500 is, for example, a controller, and various programs (corresponding to data analysis programs) stored in the storage device inside the server device 500 are executed by the CPU, MPU, etc., using the RAM as a work area. It is realized by being Also, the control unit 530 is a controller, and is implemented by an integrated circuit such as an ASIC or FPGA, for example.

The control unit 530 includes an acquisition unit 331, an estimation unit 332, an area visit ratio calculation unit 333, a visit rate calculation unit 334, an overall visit ratio calculation unit 335, an identification unit 536, and an output unit 537. . The control unit 530 differs from the control unit 330 of the server device 300 according to the first embodiment in that it has a specifying unit 536 and an output unit 537 .

The identification unit 536 corresponds to one function of the evaluation unit that evaluates the area visit ratio based on the overall visit ratio. A region that falls within a predetermined range of visit ratios is identified as a tie region.

Specifically, of the area visit ratios in areas Ai (i = 1 to n: n is a natural number) obtained by dividing the target area for trade area analysis into predetermined areas,

(Overall visit ratio×1/α)<(Lower and upper limit of area visit ratio of area Ai)<(Overall visit ratio×α) (α is any natural number equal to or greater than 2)
area Ai that satisfies is specified as a tie area.

That is, the tie area is an area in which the customers who are active in the area visit the own store and competing stores at the same rate as the overall visit ratio, among the predetermined areas on the map.

The output unit 537 outputs the draw area specified by the specifying unit 536 as an evaluation result. For example, the output unit 537 superimposes the area specified as the draw area on the map data of the map information 321 stored in the storage unit 320 and transmits the result to the information processing terminal 200 via the communication unit 310 . The user of the information processing terminal 200 can easily grasp which area particularly requires analysis from the tie area superimposed and displayed on the transmitted map data.

FIG. 14 is a diagram illustrating an example of an output result; The map 60 in FIG. 14 is the same as the diagram described in FIG. 6, and the own shop exists in the area A25 and the competing shops exist in the area A52. The output unit 537 displays the draw area specified by the specifying unit 536 so as to be superimposed on the map 60 as the evaluation result. In the example of FIG. 14, areas A23, 24, 65 and 75 are hatched as draw areas. Based on the results of this evaluation, the data analyst can easily grasp areas in which sales strategies require measures to beat competing stores.

FIG. 15 is a flow chart showing the processing flow of the server device 500 . The flow of processing in server device 500 will be described with reference to FIG. Note that the processing illustrated in FIG. 15 is an example of the processing of the server device 500 . As the processing of the server device 500, in addition to this, the processing of each functional configuration of the server device 500 described above may be combined. Also, since steps S101 to S106 of the processing of the server device 500 are common to the processing of the server device 300, description thereof will be omitted.

In step S207, the identification unit 536 corresponds to one function of the evaluation unit that evaluates the area visit ratio based on the overall visit ratio. A region whose lower limit falls within a predetermined range of the total visit ratio is identified as a draw region.

Next, in step S208, the output unit 537 outputs the draw area specified in step S207 as an evaluation result. For example, the output unit 537 superimposes the area specified as the draw area on the map data of the map information 321 stored in the storage unit 320 and transmits the result to the information processing terminal 200 via the communication unit 310 . With the above, the server device 500 ends the processing.

(Explanation of effect)
As described above, the data analysis system according to the present embodiment identifies, with respect to the overall visit ratio, a tie area indicating an area in which both points are visited at the same rate for two points set on the map. do. Rather than simply comparing the visit ratios of one's own store and those of competing stores, it judges whether it is at the same level based on the overall visit ratio. can be grasped, and it becomes possible to effectively carry out sales promotion measures such as advertisements.

This embodiment can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are intended to be included in the invention described in the claims and their equivalents as well as included in the scope and gist of the invention.

1 data analysis system, 100 customer terminal, 200 information processing terminal, 110 communication unit, 120 input unit, 130 display unit, 140 detection unit, 141 position detection unit, 150 control unit, 300,500 server device, 310 communication unit, 320 Storage unit 321 Map information 322 Visited place information 323 Customer information 324 Location information 330, 530 Control unit 331 Acquisition unit 332 Estimation unit 333 Area visit ratio calculation unit 334 Visit rate calculation unit 335 Overall visit Ratio calculation unit 336 Score calculation unit 536 Identification unit 337, 537 Output unit 401 Computer 402 CPU 403 Main storage device 404 Auxiliary storage device 405 Interface

Claims (11)

A data analysis device that analyzes a trade area about two points set on a map,
an acquisition unit that acquires customer location information;
an estimation unit that estimates an area on the map where the customer resides;
For each predetermined area on the map, among the customers who are estimated to live in the area, the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points an area visit ratio calculation unit that calculates an area visit ratio for the two points based on the number of customers;
an overall visit ratio calculation unit that calculates an overall visit ratio for the two locations based on the total number of customers who have visited the first location and the total number of customers who have visited the second location;
an evaluation unit that evaluates an area visit ratio for the two points for each predetermined area on the map based on the overall visit ratio;
an output unit that outputs the evaluated result;
A data analysis device comprising A first visit rate for estimating a visit rate of customers who visit the first point and a visit rate of customers who visit the second point among customers active in the area for each predetermined area on the map. further comprising a visit rate calculation unit that calculates a second visit rate for estimating
The area visit ratio calculation unit calculates an area visit ratio for the two points based on a confidence interval of a predetermined reliability of the first visit rate and the second visit rate calculated by the visit rate calculation unit. The data analysis device according to claim 1. The area visit ratio calculation unit, for each of the predetermined areas,
calculating the upper limit of the region visit ratio based on the upper limit of the confidence interval of the first visit rate of the region and the lower limit of the confidence interval of the second visit rate of the region;
3. The data analysis apparatus according to claim 2, wherein the lower limit of said region visit ratio is calculated based on the lower limit of said confidence interval of said first visit rate and the upper limit of said confidence interval of said second visit rate of said region. . Based on the area visit ratio and the overall visit ratio, the evaluation unit determines, for each of the predetermined areas , how many customers, among customers who are estimated to live in the area, are at the first location or 4. The data analysis device according to any one of claims 1 to 3, further comprising a score calculation unit that calculates a competition score indicating whether the second location is visited . 5. The data analysis according to claim 4, wherein the score calculation unit calculates the competition score based on the upper limit or lower limit of the area visit ratio for the two points and the overall visit ratio for each of the predetermined areas. Device. Based on the area visit ratio and the overall visit ratio, the evaluation unit determines that, among the customers who are estimated to live in the predetermined area, a similar number of customers are located at the first point or the 6. The data analysis device according to any one of claims 1 to 5, further comprising a specifying unit for specifying a tie region indicating a region in which the second point is visited . 3. The specifying unit specifies, for the predetermined area, an area in which an upper limit and a lower limit of the area visit ratio for the two points of the area are within a predetermined range of the overall visit ratio, as the draw area. 7. The data analysis device according to 6. further comprising a storage unit for storing map information,
The data analysis device according to any one of claims 1 to 7, wherein the output unit outputs the evaluation result superimposed on the map information stored in the storage unit. A data analysis system for analyzing trade areas related to two points set on a map,
an acquisition unit that acquires customer location information;
an estimation unit that estimates an area on the map where the customer resides;
For each predetermined area on the map, among the customers who are estimated to live in the area, the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points an area visit ratio calculation unit that calculates an area visit ratio for the two points based on the number of customers;
an overall visit ratio calculation unit that calculates an overall visit ratio for the two locations based on the total number of customers who have visited the first location and the total number of customers who have visited the second location;
an evaluation unit that evaluates an area visit ratio for the two points for each predetermined area on the map based on the overall visit ratio;
an output unit that outputs the evaluated result;
data analysis system. A data analysis method for analyzing trade areas related to two points set on a map,
obtaining customer location information;
estimating the area on the map where the customer resides;
For each predetermined area on the map, among the customers who are estimated to live in the area, the number of customers who visited the first point out of the two points and the number of customers who visited the second point out of the two points calculating an area visit ratio for the two locations based on the number of customers;
calculating an overall visit ratio for the two locations based on the total number of customers who visited the first location and the total number of customers who visited the second location;
evaluating an area visit ratio for the two points for each predetermined area on the map based on the overall visit ratio;
a step of outputting the evaluated result;
A data analysis method comprising: A data analysis program for causing a computer to execute the data analysis method according to claim 10.

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