Customer reception proposal device

Abstract

By referencing purchase history and the products purchased, we can accurately determine specific customer service methods suitable for the products we want to sell, thereby efficiently increasing sales. [Solution] The customer service suggestion device includes means 130 for generating a machine learning model that outputs a customer service method by inputting a product to be sold and a purchase history, after training it with a dataset of products that have been successfully sold, the purchase history of the product before the sale, and the customer service method used when the product was sold; means 140 for inputting a dataset of a product to be sold and a purchase history into the machine learning model and generating a customer service method; means 160 for calculating sales or the number of sales related to the product on which the generated customer service method was implemented; means 170 for determining whether sales or the number of sales are below a predetermined target value; and means for causing the machine learning model to undergo additional training. If it is determined that the sales are below the target value, means 190 for inputting a dataset of a product to be sold and a purchase history into the machine learning model after additional training and regenerating another customer service method.

Description

Technical Field

[0001] It relates to a technology that proposes a customer service method when selling products.

Background Art

[0002] In the scenario of selling products (including services), depending on the skill of the customer service method, products that should originally sell may not sell, or conversely, products that should be difficult to sell may sell easily.

[0003] Also, in Patent Document 1, a system is proposed that supports providing an appropriate customer service and an easy shopping environment by presenting a customer service method tailored to the customers who come to the store to the store employees and setting the notification destination according to the business content of the store employees.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the above prior art, when a business operator sells a product that a consumer wishes to purchase, there is a problem that a specific countermeasure method for increasing the conclusion probability related to the sale of the product cannot be obtained.

[0006] Therefore, in view of the above problems, an object of the present invention is to provide a customer service proposal device that can accurately obtain a specific customer service method suitable for a product to be sold by referring to the purchased products in the purchase history and can efficiently expand sales.

Means for Solving the Problems

[0007] One form of the customer service suggestion device disclosed is characterized by comprising: a model generation means that generates a machine learning model that outputs a specific customer service method by inputting a product to be sold and the purchase history, after training it with a dataset of products that have been successfully sold, the purchase history of the product before the sale, and the specific customer service method used when the product was sold; a model storage means that stores parameters that define the input and output characteristics of the machine learning model that outputs a specific customer service method in response to the input of the product to be sold and the purchase history; a customer service method generation means that inputs one dataset of the product to be sold and the purchase history to the machine learning model and generates one of the specific customer service methods; a customer service implementation means that performs processing to implement the one specific customer service method at the sales site; a verification information calculation means that calculates the sales of the product on which the one specific customer service method was implemented; a target achievement determination means that determines whether the sales are below a predetermined target value; an additional learning means that causes the machine learning model to perform additional learning; and a customer service method regeneration means that, if it is determined by the target achievement determination means that the sales are below a predetermined target, inputs one dataset of the product to be sold and the purchase history to the machine learning model after the additional learning and regenerates another specific customer service method.

[0008] Furthermore, other forms of the customer service suggestion device disclosed include: a model generation means that generates a machine learning model that outputs a specific customer service method by inputting a product to be sold and the purchase history, after training it with a dataset of products that have been successfully sold, the purchase history of the product before it was sold, and the specific customer service method used when the product was sold; a model storage means that stores parameters that define the input / output characteristics of the machine learning model that outputs a specific customer service method in response to the input of the product to be sold and the purchase history; a customer service method generation means that inputs one dataset of the product to be sold and the purchase history into the machine learning model and generates one specific customer service method; a customer service implementation means that performs processing to implement the one specific customer service method at the sales site; a verification information calculation means that calculates the number of sales for the product on which the one specific customer service method was implemented; a target achievement determination means that determines whether the number of sales is below a predetermined target value; an additional learning means that causes the machine learning model to perform additional learning; and a customer service method regeneration means that, if it is determined by the target achievement determination means that it is below the target, inputs one dataset of the product to be sold and the purchase history into the machine learning model after the additional learning and regenerates another specific customer service method. [Effects of the Invention]

[0009] The customer service suggestion system, which is disclosed here, uses the purchased items in the purchase history as a reference to accurately obtain specific customer service methods suitable for the products to be sold, thereby enabling efficient sales expansion. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram shows an overview of the customer service suggestion device according to this embodiment. [Figure 2] This is a functional block diagram of the customer service suggestion device according to this embodiment. [Figure 3] This figure shows an example of a training dataset according to this embodiment. [Figure 4] This figure shows an example of the hardware configuration of the customer service suggestion device according to this embodiment. [Figure 5]This flowchart shows the processing flow example using the customer service suggestion device according to this embodiment. [Modes for carrying out the invention]

[0011] The embodiments for carrying out the present invention will be described with reference to the drawings. (Operating principle of the customer service suggestion device according to this embodiment)

[0012] The operating principle of the customer service suggestion device (hereinafter simply referred to as "this device") 100 according to this embodiment will be explained using Figures 1 and 2. Figure 1 is a diagram showing the connection relationship between this device 100 and other devices, and Figure 2 is a functional block diagram of this device 100.

[0013] As shown in Figure 1, the device 100 is connected to the store terminal 290 via a communication network 300. The communication network 300 may be either wired or wireless. The store terminal 290 is a device that informs the device 100 of the sales status and inventory status of products sold in the store (including a virtual store), and may be, for example, a POS (Point of Sales) system.

[0014] As shown in Figure 2, the device 100 includes a learning data storage means 110, a model storage means 120, a model generation means 130, a customer service method generation means 140, a customer service implementation means 150, a verification information calculation means 160, a goal achievement determination means 170, an additional learning means 180, and a customer service method regeneration means 190. Note that the storage means 110 and 120 do not necessarily have to be provided by the device 100; the device 100 may utilize storage means 110 and 120 provided by an external device.

[0015] The learning data storage means 110 stores a learning data set 240 of the product 210 that has been successfully sold, the purchase history (purchased products) 220 before the sale of the product 210, and the specific customer approach method 230 when selling the product 210. The learning data set 240 is added and updated over time. The specific customer approach method 230 includes sales talks to customers, advertisements displayed on displays (digital signage), coupons, etc., and combinations thereof may also be used.

[0016] Fig. 3 shows an example of the learning data storage means 110. As shown in Fig. 3, the learning data storage means 110 stores, for example, by associating the product 210: instant miso soup, the purchase history 220: a group of products effective for the liver, and the customer approach method 230: It works for hangovers! Also, the learning data storage means 110 stores, for example, by associating the product 210: cosmetics, the purchase history 220: none, and the customer approach method 230: Even famous actresses keep using it! Note that the information stored in the learning data storage means 110 is not limited to these. The model storage means 120 stores parameters that define the input / output characteristics of the machine learning model 280 described later.

[0017] The model generation means 130 generates a machine learning model 280 that outputs a specific customer approach method 270 by subjecting the learning data set 240 to machine learning in the machine learning model 280 and inputting the product 250 to be sold and the purchase history 260 of the target customer. The learning algorithm is not particularly limited.

[0018] The customer approach method generation means 140 inputs a data set of a product 250 with a sales desire and the purchase history 260 of the target customer into the machine learning model 280 to generate a specific customer approach method 270. The specific customer approach method 270 includes sales talks to customers, advertisements displayed on displays (digital signage), coupons, etc., and combinations thereof may also be used.

[0019] The customer service execution means 150 performs a process of causing a specific customer service method 270 to be executed at the sales site of the product 250 to be sold. The customer service execution means 150 causes, for example, the specific customer service method 270 to be displayed on the employee terminal or causes the specific customer service method 270 to be displayed on the in-store display.

[0020] The verification information calculation means 160 calculates the sales amount or the number of sales of the product 250 to be sold. The verification information calculation means 160 obtains information on the sales amount or the number of sales of the product 250 to be sold from the store terminal 290 and performs an operation of aggregating it.

[0021] The target achievement determination means 170 determines whether the sales amount or the number of sales of the product 250 calculated by the verification information calculation means 160 is below a predetermined target value. The target value can be set as appropriate.

[0022] The additional learning means 180 causes the machine learning model 280 to perform additional learning using the additional / updated learning dataset 240. Generally, before and after performing the additional learning, the input / output characteristics of the machine learning model 280 change. Also, when the process by the additional learning means 180 is performed, the information stored in the model storage means 120 is also updated.

[0023] When the customer service method regeneration means 190 is determined by the target achievement determination means 170 to be "below", it inputs a dataset of the product 250 to be sold and the purchase history 260 of the target customer into the machine learning model 280 after the additional learning, and regenerates another specific customer service method 270.

[0024] Based on the above operating principle, the apparatus 100 can accurately obtain a specific customer service method 270 suitable for the product 250 for which sales are desired, referring to the purchased product 260 in the purchase history, and can efficiently expand sales. (Hardware configuration of the customer service proposal apparatus according to the present embodiment)

[0025] An example of the hardware configuration of the device 100 will be explained using Figure 4. Figure 4 is a diagram showing an example of the hardware configuration of the device 100. As shown in Figure 4, the device 100 has a CPU (Central Processing Unit) 510, ROM (Read-Only Memory) 520, RAM (Random Access Memory) 530, auxiliary storage device 540, communication I / F 550, input device 560, display device 570, and storage medium I / F 580.

[0026] The CPU 510 is a device that executes programs stored in the ROM 520. It processes data loaded into the RAM 530 according to program instructions and controls the entire device 100. The ROM 520 stores the programs and data that the CPU 510 will execute. When the CPU 510 executes a program stored in the ROM 520, the RAM 530 loads the programs and data to be executed and temporarily holds the calculation data during the calculation.

[0027] The auxiliary storage device 540 is a device that stores the basic software, such as the OS (Operating System) and the application program according to this embodiment, along with related data. For example, it may include a learning data storage means 110 and a model storage means 120. The auxiliary storage device 540 is, for example, an HDD (Hard Disk Drive) or flash memory.

[0028] The communication interface 550 is an interface for exchanging data with other devices (such as POS systems) 290 that provide communication functions, by connecting to a communication network 300 such as a wired or wireless LAN (Local Area Network) or the Internet.

[0029] The input device 560 is a device for inputting data into the main device 100, such as a keyboard. The display device (output device) 570 is a device consisting of an LCD (Liquid Crystal Display) or the like, and functions as a user interface for the user to use the functions of the main device 100 and to make various settings. The storage medium I / F 580 is an interface for sending and receiving data with storage media 590 such as CD-ROMs, DVD-ROMs, and USB memory.

[0030] Each of the means of this device 100 may be realized by the CPU 510 executing a program corresponding to each means stored in the ROM 520 or auxiliary storage device 540. Alternatively, each of the means of this device 100 may be realized by the processing related to each means being implemented as hardware. Furthermore, the program according to the present invention may be read from an external server device via a communication I / F 550, or read from a storage medium 590 via a storage medium I / F 580, and the device 100 may execute the program. (Example of processing by the customer service suggestion device according to this embodiment) An example of processing performed by the device 100 will be explained using Figure 5. Figure 5 is a flowchart showing the flow of processing performed by the device 100.

[0031] In S10, the model generation means 130 trains the machine learning model 280 using the training dataset 240, and generates a machine learning model 280 that outputs specific customer service methods 270 by inputting the desired products for sale 250 and the purchase history (products purchased) of the target customer 260. The learning algorithm of the machine learning model 280 is not particularly limited. Furthermore, the parameters that define the input / output characteristics of the machine learning model 280 generated by the processing in S10 are stored in the model storage means 120.

[0032] In S20, the customer service method generation means 140 inputs a dataset of a desired product 250 and the purchase history 260 of the target customer into the machine learning model 280, and causes it to generate a specific customer service method 270. The specific customer service method 270 includes sales talk to the customer, advertisements displayed on a display (digital signage), coupons, etc., and may also be a combination of these.

[0033] Then, in S20, the customer service implementation means 150 performs a process to implement the specific customer service method 270 at the sales site. For example, the customer service implementation means 150 displays the specific customer service method 270 on an employee terminal or on an in-store display.

[0034] Furthermore, after processing in S20, the additional learning means 180 performs additional learning on the machine learning model 280 using the additional and updated training dataset 240. Generally, the input / output characteristics of the machine learning model 280 change before and after the additional learning. Also, when processing is performed by the additional learning means 180, the information stored in the model storage means 120 is updated.

[0035] In S30, the verification information calculation means 160 calculates the sales or number of units sold for the product 250 that the store wants to sell. The verification information calculation means 160 obtains and aggregates information regarding the sales or number of units sold for the product 250 that the store wants to sell from the store terminal 290.

[0036] Then, in S30, the target achievement determination means 170 determines whether the sales or number of products sold for the product 250 calculated in S30 is below a predetermined target value. The target value can be set as appropriate.

[0037] If it is determined in S30 that the result is "below," in S40 the customer service method regeneration means 190 inputs a dataset of one product to be sold 250 and the purchase history 260 of the target customer into the machine learning model 280 after additional training, and regenerates other specific customer service methods 270.

[0038] By performing the above-described process, the device 100 can accurately obtain specific customer service methods 270 suitable for the desired product 250 by referring to the purchased products 260 in the purchase history, thereby efficiently expanding sales.

[0039] Although embodiments of the present invention have been described in detail above, the present invention is not limited to these specific embodiments, and various modifications and changes are possible within the scope of the gist of the present invention as described in the claims. [Explanation of Symbols]

[0040] 100 Customer service suggestion device 110 Learning data storage means 120 Model Storage Methods 130 Model generation means 140 Customer service method generation means 150 Means of providing customer service 160 Verification Information Calculation Method 170 Goal achievement determination means 180 Additional learning methods 190 Customer service method reproduction means 210 Successful Sales Products 220 Purchase history of products sold before the sale (products purchased in that purchase) 230 Specific customer service methods when selling products 240 training datasets 250 items I want to sell 260 Purchase history of the target customer (and the products purchased) 270 Specific customer service methods generated by machine learning models 280 Machine Learning Models 290 store terminals (POS systems) 300 Communication Networks 510 CPU 520 ROM 530 RAM 540 Auxiliary storage 550 Communication Interfaces 560 Input Device 570 Output device 580 Storage Media Interface 590 Storage medium

Claims

1. A model generation means that generates a machine learning model that outputs the specific customer service method by training it with a dataset of products that have been successfully sold, the purchase history of the product before the sale, and the specific customer service method used when the product was sold, and by inputting the product to be sold and the purchase history. A model storage means for storing parameters that define the input / output characteristics of a machine learning model that outputs the specific customer service method in response to the input of the product to be sold and the purchase history, A customer service method generation means that inputs a dataset of the product to be sold and purchase history into the machine learning model and generates a specific customer service method, A customer service implementation means that performs the process of implementing the aforementioned specific customer service method at the sales site, A verification information calculation means for calculating sales related to products on which the above-mentioned specific customer service method was implemented, A target achievement determination means for determining whether the sales are below a predetermined target value, An additional learning means for causing the aforementioned machine learning model to undergo additional training, A customer service proposal device characterized by having a customer service method regeneration means that, if the target achievement determination means determines that the target has fallen short, inputs a dataset of the product to be sold and purchase history into the machine learning model after additional training and regenerates other specific customer service methods.

2. A model generation means that generates a machine learning model that outputs the specific customer service method by training it with a dataset of products that have been successfully sold, the purchase history of the product before the sale, and the specific customer service method used when the product was sold, and by inputting the product to be sold and the purchase history. A model storage means for storing parameters that define the input / output characteristics of a machine learning model that outputs the specific customer service method in response to the input of the product to be sold and the purchase history, A customer service method generation means that inputs a dataset of the product to be sold and purchase history into the machine learning model and generates a specific customer service method, A customer service implementation means that performs the process of implementing the aforementioned specific customer service method at the sales site, A verification information calculation means for calculating the number of sales of products on which the above-mentioned specific customer service method was implemented, A target achievement determination means for determining whether the sales figures fall below a predetermined target value, An additional learning means for causing the aforementioned machine learning model to undergo additional training, A customer service proposal device characterized by having a customer service method regeneration means that, if the target achievement determination means determines that the target has fallen short, inputs a dataset of the product to be sold and purchase history into the machine learning model after additional training and regenerates other specific customer service methods.

3. A computer equipped with a model storage means that stores parameters defining the input / output characteristics of a machine learning model that outputs the specific customer service method by inputting a product to be sold and the purchase history, after training the model with a dataset of successfully sold products, purchase history prior to the sale of the product, and specific customer service methods used when the product was sold, The model generation means trains on a dataset of products that have been successfully sold, purchase history prior to the sale of the product, and specific customer service methods used when the product was sold, and generates a machine learning model that outputs the specific customer service methods when the product to be sold is input. The customer service method generation means includes the step of inputting a dataset of the product to be sold and purchase history into the machine learning model and causing it to generate a specific customer service method, The means of providing customer service includes the step of performing a process to implement the aforementioned specific customer service method at the sales site, The verification information calculation means includes the step of calculating sales for products on which the above-mentioned specific customer service method was implemented, The goal achievement determination means includes the step of determining whether the sales are below a predetermined target value, The additional learning means includes the step of causing the machine learning model to undergo additional learning, A customer service suggestion method that includes the step of, if the customer service method regeneration means is determined to be below the target achievement determination means, inputting a dataset of the product to be sold and purchase history into the machine learning model after additional training, and regenerating another specific customer service method.

4. A computer equipped with a model storage means that stores parameters defining the input / output characteristics of a machine learning model that outputs the specific customer service method by inputting a product to be sold and the purchase history, after training the model with a dataset of successfully sold products, purchase history prior to the sale of the product, and specific customer service methods used when the product was sold, The model generation means trains on a dataset of products that have been successfully sold, purchase history prior to the sale of the product, and specific customer service methods used when the product was sold, and generates a machine learning model that outputs the specific customer service methods when the product to be sold is input. The customer service method generation means includes the step of inputting a dataset of the product to be sold and purchase history into the machine learning model and causing it to generate a specific customer service method, The means of providing customer service includes the step of performing a process to implement the aforementioned specific customer service method at the sales site, The verification information calculation means includes the step of calculating the number of sales for products on which the above-mentioned specific customer service method was implemented, The goal achievement determination means includes the step of determining whether the number of sales is below a predetermined target value, The additional learning means includes the step of causing the machine learning model to undergo additional learning, A customer service suggestion method that includes the step of, if the customer service method regeneration means is determined to be below the target achievement determination means, inputting a dataset of the product to be sold and purchase history into the machine learning model after additional training, and regenerating another specific customer service method.

5. A product package suggestion program for causing a computer to perform the method described in claim 3 or 4.

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