Information processing device

Abstract

To allow specific products to be efficiently sold.SOLUTION: An information processing device includes a control unit, a communication unit, and a storage unit. The control unit performs operations including accepting designation of a target vehicle, classifying customer data into one or more clusters through unsupervised learning based on the genre of store used by each customer in the past and the date and time of store use, identifying a cluster among the one or more clusters that has a high ownership rate of the target vehicle, and outputting information regarding stores that are frequently used by users belonging to the identified cluster.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an information processing device. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a technique for outputting an action (such as sending a discount coupon) to a target customer to purchase a target product based on the customer's past purchased products and frequency of store visits (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2022 / 064688 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned background art, there is room for improvement in terms of estimating potential customers for a specific product (such as a vehicle).

[0005] In view of the above circumstances, an object of the present disclosure is to enable efficient sales of specific products by using machine learning based on unsupervised learning. [Means for solving the problem]

[0006] An information processing device according to an embodiment of the present disclosure includes: An information processing device including a control unit, a communication unit, and a storage unit, the control unit comprising: Accepting a designation of a target vehicle; classifying customer data into one or more clusters by unsupervised learning based on the genre of store used by each customer in the past and the date and time of use of the store; Identifying a cluster having a high ownership rate of the target vehicle among the one or more clusters; outputting information about stores that are frequently used by users belonging to the identified cluster; The operation includes: Effect of the Invention

[0007] According to one embodiment of the present disclosure, efficient sales of specific products are possible. [Brief description of the drawings]

[0008] [Figure 1] FIG. 13 is a diagram showing a tenant list. [Diagram 2] FIG. 11 is a flowchart illustrating an information processing method performed by the information processing device. [Diagram 3] FIG. 1 is a diagram showing a self-organizing map. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The information processing system S of this embodiment includes an information processing device 1 and a user terminal that can communicate with each other via a network NW. The network NW includes, for example, a mobile communication network, the Internet, or a fixed communication network. The process executed by the information processing device 1 may be executed by multiple information processing devices 1 that are distributed.

[0010] The information processing device 1 may be a server that supports the provision of services by a business operator. The information processing device 1 may be installed, for example, in a facility dedicated to the business operator or in a shared facility including a data center.

[0011] The information processing device 1 includes a control unit 11, a communication unit 12, and a storage unit 13. The components of the information processing device 1 are communicably connected to each other via, for example, a dedicated line.

[0012] The control unit 11 includes, for example, one or more general-purpose processors including a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The control unit 11 may include one or more dedicated processors specialized for specific processing. Instead of including a processor, the control unit 11 may include one or more dedicated circuits. The dedicated circuits may be, for example, a FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The control unit 11 may include an ECU (Electronic Control Unit). The control unit 11 transmits and receives any information via the communication unit 12.

[0013] The communication unit 12 includes a communication module compatible with one or more wired or wireless LAN (Local Area Network) standards for connecting to a network. The communication unit 12 may include a module compatible with one or more mobile communication standards including LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation). The communication unit 12 may include a communication module compatible with one or more short-range communication standards or specifications including Bluetooth (registered trademark), AirDrop (registered trademark), IrDA, ZigBee (registered trademark), Felica (registered trademark), or RFID. The communication unit 12 transmits and receives any information via the network.

[0014] The memory unit 13 includes, but is not limited to, for example, a semiconductor memory, a magnetic memory, an optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a RAM or a ROM. The RAM is, for example, an SRAM or a DRAM. The ROM is, for example, an EEPROM. The memory unit 13 may function as, for example, a main memory device, an auxiliary memory device, or a cache memory. The memory unit 13 may store information resulting from analysis or processing by the control unit 11. The memory unit 13 may store various information related to the operation or control of the information processing device 1, etc. The memory unit 13 may store system programs, application programs, embedded software, etc. The memory unit 13 may be provided outside the information processing device 1 and accessed by the information processing device 1.

[0015] The information processing method executed by the information processing system S of this embodiment will be described in detail below. A flowchart of the information processing method is shown in FIG.

[0016] In A of FIG. 2, the control unit 11, at an arbitrary time point in S11, acquires from the memory unit 13 a purchase history indicating past store use by one or more customers, and executes a narrowing-down process according to a specified aggregation period (e.g., one year). The purchase history of one or more customers may be, for example, a purchase history at a specific commercial facility. In S12, the control unit 11 executes a narrowing-down process based on a specified degree of use. The execution result may include, for example, the following information in association with a membership code (e.g., 001, 002) assigned to each customer: ·sex ·age Date of use · Day of use ·Usage time · Stores used (e.g. tenants) Store genre ·Used amount In S13, the control unit 11 executes a process of removing unnecessary data (i.e., noise) and missing data to obtain a post-cleansing purchase history. For example, usage data of an information center may be removed. The control unit 11 stores the post-cleansing purchase history in the storage unit 13.

[0017] In B of FIG. 2, in S21, the control unit 11 acquires the post-cleansing purchase history and attribute information from the memory unit 13, and executes a combining process on a customer-by-customer basis. The attribute information may be, for example, customer information registered when registering a point card issued by a commercial facility. Next, in S22, the control unit 11 executes a calculation process of the usage ratio to acquire customer characteristics. The granularity of the customer characteristics can be set arbitrarily. The customer characteristics may include the following information in association with a membership code: ·sex ·age Usage rate by time period Usage ratios distinguished between weekdays and holidays Usage ratio by store genre The control unit 11 stores the customer characteristics in the memory unit 13 .

[0018] In C of FIG. 2, the control unit 11 acquires customer features from the memory unit 13 in S31 and then performs a weighting process on the customer features. In S32, the control unit 11 performs unsupervised learning on the customer features to classify the customer data into one or more clusters. In S33, the control unit 11 performs two-dimensional projection on the customer features to acquire customer classification map data. The customer classification map is also called a self-organizing map. An example of a self-organizing map is shown in FIG. 3. The control unit 11 stores the customer classification map data in the memory unit 13. The customer classification map data may include information on a corresponding cluster (e.g., cluster 1, cluster 2, etc.) in association with a membership code.

[0019] In D of FIG. 2, the control unit 11 acquires customer classification map data and owned vehicle model information for each customer from the storage unit 13 in S41, and executes a combining process on a customer basis. The owned vehicle model information may include a corresponding cluster (e.g., cluster 1) and information on the owned vehicle model (e.g., vehicle A) in association with the membership code. Next, in S42, the control unit 11 executes a calculation process for the ownership rate of the specified target vehicle. In S43, the control unit 11 acquires the results of the calculation process and the target vehicle model information from the storage unit 13, and then executes a narrowing process to acquire the ownership rate of the target vehicle for each cluster. The control unit 11 stores the ownership rate in the storage unit 13.

[0020] In E of FIG. 2, the control unit 11 acquires the ownership rate of the target vehicle for each cluster from the memory unit 13 in S51, and calculates the store utilization rate for each cluster. That is, the touch points between the cluster and the store are taken into consideration. In S52, the control unit 11 identifies clusters in which the ownership rate of the target vehicle is higher than a predetermined value by sorting the utilization rates in descending order. The control unit 11 may acquire information about stores whose utilization rate is higher than a predetermined value. For example, the control unit 11 may execute at least one of the following processes. A list of stores with a usage rate higher than a predetermined value for each cluster is obtained (see FIG. 1). The store list may include, for example, the usage rate (e.g., 1st place) and the name of the store to be used (e.g., Store A) in association with the cluster. Within each cluster, the number of customers is calculated for each store that the customer uses, and a ranking is assigned based on the number of customers (see Figure 1). For each store, calculate the ratio of people using each cluster, and assign cluster ratio rankings in descending order of the ratio of people using each cluster. The overall rank is calculated by adding up the number of users rank and the cluster ratio rank, and the numbers are sorted from lowest to highest (see Figure 1). The control unit 11 may transmit an advertisement for the specified target vehicle to any user terminal.

[0021] As described above, according to this embodiment, the control unit 11 of the information processing device 1 receives a designation of a target vehicle, classifies customer data into one or more clusters by unsupervised learning based on the genre of store used by each customer in the past and the date and time of store use, identifies a cluster having a high ownership rate of the target vehicle from among the one or more clusters, and outputs information about stores that are frequently used by users belonging to the identified cluster. With this configuration, the information processing device 1 can perform customer classification that is more in line with the customer's lifestyle, for example, by classifying not only the store genre used by the customer but also taking into account a time concept. Furthermore, the information processing device 1 can derive the positional relationship of the customer from the latest purchase history by using a customer classification model created based on this embodiment. Furthermore, the information processing device 1 can estimate the store that is frequently used by the potential customer of the target vehicle, and can enable efficient business by advertising the store. Furthermore, the information processing device 1 can classify users with similar temporal behavior patterns into the same cluster by customer classification that also takes into account the date and time of store use, so that marketing through advertising can be performed to "people with similar thinking or behavior patterns" other than the user who owns the target vehicle.

[0022] Although the present disclosure will be described based on various drawings and examples, it should be noted that those skilled in the art may make various modifications and alterations based on the present disclosure. Other modifications are possible within the scope of the present disclosure. For example, the functions included in each means or step can be rearranged so as not to cause logical inconsistencies, and multiple means or steps can be combined into one or divided.

[0023] For example, in the above embodiment, a program for executing all or part of the functions or processes of the information processing device 1 can be recorded in a computer-readable recording medium. The computer-readable recording medium includes a non-transitory computer-readable medium, such as a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. The program is distributed by, for example, selling, transferring, or lending a portable recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. The program may also be distributed by storing the program in the storage of an arbitrary server and transmitting the program from the arbitrary server to another computer. The program may also be provided as a program product. The present disclosure can also be realized as a program executable by a processor. [Explanation of symbols]

[0024] S Information Processing System

Claims

[Claim 1] An information processing device including a control unit, a communication unit, and a storage unit, the control unit comprising: Accepting a designation of a target vehicle; classifying customer data into one or more clusters by unsupervised learning based on the genre of store used by each customer in the past and the date and time of use of the store; Identifying a cluster having a high ownership rate of the target vehicle among the one or more clusters; outputting information about stores that are frequently used by users belonging to the identified cluster; An information processing device that performs operations including the steps of:

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