program

Abstract

Improve the techniques used for making proposals to customers. [Solution] The program causes the information processing device 10 to perform the following operations: train a learning model corresponding to each store using machine learning, which takes store information of the vehicle dealership and customer information of customers at the dealership as input and outputs suggestion information of vehicles to be suggested to customers from among the multiple vehicles handled by the dealership; and input customer information of target customers, which are one or more customers that meet predetermined conditions from among the multiple customers at the dealership, into the learning model, thereby obtaining the suggestion information output from the learning model.

Description

Technical Field

[0001] This disclosure relates to a program.

Background Art

[0002] Conventionally, technologies related to proposals to customers are known. For example, Patent Document 1 discloses a sales proposal system for supporting conversations in sales activities.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When a conventional sales proposal system is applied to vehicle sales, the situations (such as handled vehicles and options) may vary from dealership to dealership. It may be difficult to make appropriate proposals according to the situations of each dealership with a uniform system.

[0005] In view of such circumstances, an object of the present disclosure is to improve the technology related to proposals to customers.

Means for Solving the Problems

[0006] A program according to an embodiment of the present disclosure uses, as input, store information of a vehicle dealership and customer information of a customer at the dealership, and trains, by machine learning, a learning model corresponding to each store that outputs proposal information of a vehicle to be proposed to the customer among a plurality of vehicles handled by the dealership, and obtains the proposal information output from the learning model by inputting the customer information of a target customer, which is one or more customers satisfying a predetermined condition and specified from among a plurality of customers at the dealership, into the learning model. A program that causes an information processing device to perform an action that includes [a specific action]. [Effects of the Invention]

[0007] According to one embodiment of this disclosure, the technology for making proposals to customers is improved. [Brief explanation of the drawing]

[0008] [Figure 1] This block diagram shows a schematic configuration of a system according to one embodiment of the present disclosure. [Figure 2] This is a flowchart showing the operation of an information processing device according to one embodiment of this disclosure. [Modes for carrying out the invention]

[0009] Embodiments of this disclosure are described below. In this specification, “customer at a retail store” refers to a customer who has previously used a retail store.

[0010] (Summary of the embodiment) Referring to Figure 1, an overview of System 1 according to one embodiment of this disclosure will be described. System 1 comprises an information processing device 10 and a terminal device 20. The information processing device 10 and the terminal device 20 are connected to each other via a network 30 such as the Internet and a mobile communication network.

[0011] The information processing device 10 includes one or more computers capable of communicating with each other, such as a server device. The information processing device 10 stores the learning model.

[0012] The terminal device 20 is one or more computers, such as a PC (Personal Computer), smartphone, or tablet. The terminal device 20 is used, for example, by staff at a vehicle dealership.

[0013] First, an overview of this embodiment will be described, and details will be described later. The program according to this embodiment causes the information processing device 10 to perform the following operations: take store information of the vehicle dealership and customer information of customers at the dealership as input, and output suggestion information of vehicles to be suggested to customers from among the multiple vehicles handled by the dealership, and input customer information of target customers, which are one or more customers that meet predetermined conditions and are identified from among the multiple customers at the dealership, into the learning model, thereby obtaining the suggestion information output from the learning model.

[0014] According to this embodiment, suggestion information is generated by a learning model trained using store information unique to each retailer as input. This makes it possible to generate appropriate suggestion information according to the circumstances of each retailer.

[0015] (Configuration of the information processing device 10) As shown in Figure 1, the information processing device 10 comprises a control unit 100, a storage unit 102, and a communication unit 104.

[0016] The control unit 100 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The processor is a general-purpose processor such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit), or a dedicated processor specialized for a specific process, but is not limited to these. The programmable circuit is an FPGA (Field-Programmable Gate Array), but is not limited to this. The dedicated circuit is an ASIC (Application Specific Integrated Circuit), but is not limited to this. The control unit 100 performs various processes related to the operation of the information processing device 10 and controls each part of the information processing device 10.

[0017] The storage unit 102 includes one or more memories. Each memory included in the storage unit 102 may function as, for example, a main memory device, an auxiliary memory device, or a cache memory. The storage unit 102 stores any information used for the operation of the information processing apparatus 10. For example, the storage unit 102 may store, for example, a system program, an application program, and embedded software. In the present embodiment, the storage unit 102 stores a learning model corresponding to each store. The storage unit 102 may store any information related to vehicle sales. The information stored in the storage unit 102 may be updated based on information acquired from the network 30 via the communication unit 104, for example.

[0018] The communication unit 104 includes at least one communication interface connected to the network 30. The communication interface corresponds to, for example, a mobile communication standard such as 4G (4th generation) or 5G (5th generation), or a wired LAN (Local Area Network) communication standard or a wireless LAN communication standard, but is not limited thereto and may correspond to any communication standard.

[0019] (Configuration of the terminal device 20) As shown in FIG. 1, the terminal device 20 includes a control unit 200, an input unit 202, a display unit 204, and a communication unit 206.

[0020] The control unit 200 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The processor is, for example, a general-purpose processor such as a CPU or a GPU, or a dedicated processor specialized for a specific process, but is not limited thereto. The programmable circuit is, for example, an FPGA, but is not limited thereto. The dedicated circuit is, for example, an ASIC, but is not limited thereto. The control unit 200 executes various processes related to the operation of the terminal device 20 and controls each part of the terminal device 20.

[0021] The input unit 202 includes one or more input interfaces. The input unit 202 receives an operation for inputting information used for the operation of the terminal device 20. The input interface may be, for example, a physical key, a capacitive key, a pointing device, a touch screen provided integrally with the display of the display unit 204, or a microphone that receives voice input. Instead of being provided in the terminal device 20, the input unit 202 may be connected to the terminal device 20 as an external input device. As a connection method, any method such as USB (Universal Serial Bus), HDMI (Registered Trademark) (High-Definition Multimedia Interface), or Bluetooth (Registered Trademark) can be used.

[0022] The display unit 204 includes one or more display interfaces. The display interface is, for example, a display that displays information as an image. The display is, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display. The display unit 204 displays the information obtained by the operation of the terminal device 20. Instead of being provided in the terminal device 20, the display unit 204 may be connected to the terminal device 20 as an external display device. As a connection method, any method such as USB, HDMI (Registered Trademark), or Bluetooth (Registered Trademark) can be used.

[0023] The communication unit 206 includes at least one communication interface for connecting to the network 30. The communication interface corresponds to, for example, a mobile communication standard such as 4G or 5G, or a wired LAN communication standard or a wireless LAN communication standard, but is not limited thereto and may correspond to any communication standard.

[0024] (Operation flow of the information processing device 10) Referring to FIG. 2, the operation of the information processing device 10 according to the present embodiment will be described. Hereinafter, the communication between the terminal device 20 and the information processing device 10 is performed via the communication units 104, 206 and the network 30.

[0025] S101: The control unit 100 of the information processing device 10 takes store information of the vehicle dealership and customer information of the dealership as input, and outputs suggestion information of vehicles to be suggested to the customer from among the multiple vehicles handled by the dealership. It trains a learning model corresponding to each store using machine learning. In other words, a different learning model is generated for each store within the information processing device 10.

[0026] Store information may include information indicating one or more of the vehicles handled by the dealership, vehicle equipment options, and pricing plans. Store information may further include information indicating the delivery date and inventory status of each vehicle handled by the dealership. Machine learning using the delivery date and inventory status of each vehicle as input will enable suggestions that take into account the dealership's circumstances, such as prioritizing vehicles with shorter delivery times and vehicles with surplus inventory. Information on vehicles handled by the dealership may include one or more of the following: vehicle type, grade, specifications, engine displacement, fuel efficiency, drive system (front-wheel drive, rear-wheel drive, etc.), and residual value.

[0027] Customer information includes primary and secondary information. Primary information includes current vehicle information, which shows information about the vehicle currently owned by the customer, and contract information at the time of purchase of the current vehicle. Current vehicle information may include one or more of the following: make, model, grade, equipment, mileage, license plate number, initial registration date, next inspection deadline, next inspection scheduled date, residual value rate, and trade-in price. Contract information may include one or more of the following: payment method for the current vehicle or past vehicles previously owned by the customer (residual value loan, installment payments, car lease, car subscription, lump sum payment, etc.), down payment, monthly loan payment amount, loan bonus amount, remaining balance, number of loan installments, insurance company, monthly or annual insurance premium, insurance class, name, address, and telephone number. A "residual value loan" means a payment method in which a predetermined guaranteed trade-in price is set aside as the residual value from the purchase price of the vehicle selected by the customer, and the remaining amount is paid in installments over a fixed contract period. Secondary information includes the customer's personal information. Personal information may include one or more of the following: budget, number of family members, family structure, age of children, current status of children (birth, school enrollment, club activities, etc.), presence of pets, hobbies, customer status, plans for relocation or moving, dissatisfaction with or desires regarding the current vehicle (regarding cargo capacity, maneuverability, horsepower, fuel efficiency, equipment, loan amount, effort required to own the vehicle, maintenance costs, etc.), intended use of the vehicle (commuting, leisure, etc.), and purchasing trends. Personal information may also be a report compiled in natural language, for example, from information previously obtained from customers by staff. Among the customer information input into the learning model, several features associated with secondary information (budget, lifestyle, dissatisfaction with the current vehicle, etc.) are extracted, for example, by natural language processing and used to train the learning model. The extracted features may be pre-set.

[0028] The proposed information may include one or more of the following: information about the proposed vehicle (vehicle type, engine type, grade, performance, etc.), fees for each payment method (monthly fees, etc.), proposal type (vehicle size upgrade, etc.), information extracted from secondary information ("The customer recently had a child," etc.), and recommendation statements generated by recommendation statement generation ("The customer's family has grown and the amount of luggage has increased, so the spacious XX vehicle is ideal," etc.). The recommendation statements may include sentences that take into account the customer's lifestyle and provide reasons. The recommendation statements may also include sentences that explain the benefits of lifestyle changes that would occur if the customer switched to the proposed vehicle (e.g., lower costs, being able to travel with the whole family, etc.). For example, the suggested information might include statements such as: "Monthly payment: If you trade in your current vehicle for 600,000 yen, your monthly payment will not change. Fuel efficiency: You use your vehicle for commuting and drive an average of 2,000 km per month, so this HEV (Hybrid Electric Vehicle) will save you XX million yen per year in fuel costs, making it economical. Maintenance: By switching now, you will avoid having to pay for tire replacement and vehicle inspections that may occur in a year, thus reducing your vehicle maintenance costs."

[0029] In this embodiment, when training the learning model, the control unit 100 trains the learning model to be a learning model specific to each store by using supervised learning, where if the learning model outputs vehicle information for a vehicle that has been sold to a customer related to the customer information input to the learning model, it is considered correct, and if the learning model outputs vehicle information for a vehicle that has not been sold, it is considered incorrect. By adopting past sales performance as training data, it is possible to suggest vehicles that have a high probability of being sold. The control unit 100 may also train the learning model using unsupervised learning.

[0030] S102: The control unit 100 identifies one or more customers who meet predetermined conditions as target customers from among multiple customers at the sales store.

[0031] The control unit 100 acquires information indicating predetermined conditions entered into the input unit 202 of the terminal device 20 via the communication unit 104. The control unit 100 identifies one or more customers who meet the entered predetermined conditions as target customers. The predetermined conditions may include at least one of the following: the customer has made a reservation to visit the dealership, the customer's remaining period until the expiration date of their vehicle inspection is less than a threshold, and the customer's vehicle registration date is within a predetermined period. This improves the accessibility and convenience of information for staff, for example, by identifying only customers who will require immediate attention as target customers.

[0032] S103: The control unit 100 inputs customer information and store information for each target customer into a learning model trained for the store, thereby acquiring suggestion information output from the learning model.

[0033] The control unit 100 acquires customer information and store information for each target customer input into the input unit 202 of the terminal device 20 via the communication unit 104. The control unit 100 inputs the acquired customer information and store information into the learning model and acquires the suggestion information output from the learning model. When generating the suggestion information, the learning model may perform the following in this order: suggestion pattern selection (scale up, scale down, etc.), body type selection (sedan, minivan, etc.), engine type selection (HEV, diesel, etc.), vehicle type selection, payment plan selection (lump sum payment, installment payment, etc.), amount calculation, generation of recommendation reasons for the suggested vehicles, and recommendation text generation.

[0034] S104: The control unit 100 displays proposal information for each target customer on the display unit 204 of the terminal device 20.

[0035] S105: The control unit 100 causes the display unit 204 to display one or more user interfaces on the screen where the proposed information is displayed.

[0036] The user interface includes, for example, buttons that accept input to cause the terminal device 20 to perform a predetermined action. The predetermined action may include generating vehicle quotation information related to the proposed information, printing the proposal, and sending the proposal to the target customer. Here, the proposal is a document for the customer created based on the proposed information.

[0037] S106: The control unit 100 causes the terminal device 20 to perform at least one of predetermined actions in response to input to one or more user interfaces.

[0038] Since actions can be performed directly from the screen displaying the proposed information, there is no need to switch to another screen, improving usability.

[0039] While this disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art may make various modifications and alterations based on this disclosure. Therefore, it should be noted that these modifications and alterations are within the scope of this disclosure. For example, the functions, etc., included in each component or step can be rearranged in a logically consistent manner, and multiple components or steps can be combined into one or divided into two.

[0040] For example, in the embodiment described above, it is also possible to have an embodiment in which the configuration and operation of the information processing device 10 and / or terminal device 20 are distributed among multiple computers that can communicate with each other. Furthermore, in the embodiment described above, the terminal device 20 may be equipped with a storage unit for storing the learning model described above, and the operation of the information processing device 10 may be performed by the control unit 200 of the terminal device 20.

[0041] The control unit 100 may further select one or more staff members from among the sales store's staff. The predetermined conditions may also include conditions indicating that each of the selected staff members is responsible for customer service, and conditions indicating that customer service is scheduled within a predetermined period. Since only customers who are handled by the selected staff and who, for example, require customer service in the near future are identified as customers for which proposal information will be created, it becomes easier for the store manager or supervisor to check and manage staff schedules. [Explanation of Symbols]

[0042] 1 System, 10 Information Processing Device, 100 Control Unit, 102 Storage Unit, 104 Communication Unit, 20 Terminal Device, 200 Control Unit, 202 Input Unit, 204 Display Unit, 206 Communication Unit, 30 Network

Claims

1. The process involves training a learning model corresponding to each dealership using machine learning, with the dealership's store information and customer information of the dealership as inputs, and the dealership's output being information on vehicles to be proposed to the customer from among the multiple vehicles it handles, and, By inputting customer information of target customers, which are one or more customers that meet predetermined conditions and are identified from among multiple customers at the aforementioned sales outlet, into the learning model, the proposed information output from the learning model is obtained. A program that causes an information processing device to perform an action that includes [a specific action].

2. The program according to claim 1, A program in which the aforementioned predetermined conditions include a condition indicating at least one of the following: that the customer has made a reservation to visit the dealership; that the remaining period until the expiration date of the vehicle inspection is less than a threshold; and that a predetermined period has elapsed since the initial registration date of the vehicle.

3. The program according to claim 2, The aforementioned operation further includes selecting one or more staff members from among the multiple staff members of the store, The program further includes a condition indicating that the selected one or more staff members are responsible for customer service for that customer, and a condition indicating that customer service is scheduled for that customer within a specified period.

4. The program according to claim 1, The program includes training the learning model by supervised learning, where the training is defined as determining whether it is correct if the learning model outputs vehicle information for a vehicle that has been sold to a customer related to the customer information input to the learning model, and incorrect if the learning model outputs vehicle information for a vehicle that has not been sold.

5. The program according to claim 1, The aforementioned operation is, To display the aforementioned proposal information for each of the aforementioned target customers on the display unit of the terminal device at the vehicle dealership. The display unit will display one or more user interfaces on the screen on which the proposed information is displayed, and In response to input to the one or more user interfaces, the terminal device is instructed to perform at least one of the following: generating estimate information for the vehicle related to the proposed information, printing a proposal document which is a customer-facing document created based on the proposed information, and sending the proposal document to the target customer. A program that further includes the following.

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