JP7461621B2 - Price quote device, price quote system, and price quote method - Google Patents

Description

The present invention relates to a price quote device, a price quote system, and a price quote method, and in particular to a price quote device, system, and method that can estimate the number of items to be sold according to the price of the item and quote a price according to the customer.

The selling price of a product is determined by the balance between supply and demand. Auctions in the market are a typical example of this. However, individual auctions are not held for each product; the selling price of the product is set by the seller, and the product is purchased at that price. In this case, price setting, such as discounts, is also at the convenience of the seller. Apart from this type of one-way sales from supplier to buyer, a system for mutual trading of products over the Internet has been proposed (see Patent Document 1).

In response to this, a mechanism for determining prices according to supply and demand, known as dynamic pricing, has been proposed to make price setting more flexible when selling goods from sellers to buyers. By introducing this mechanism, it becomes easier for both sellers (suppliers) and buyers (demand parties, consumers) to conclude transactions under better conditions.

When a seller decides to implement dynamic pricing, the following concerns may arise: Product prices may fluctuate frequently through dynamic pricing.
From the perspective of regular buyers who frequently purchase products from the same seller, frequent fluctuations in product prices can lead to distrust in the seller. Therefore, even though sellers recognize the benefits of dynamic pricing, they tend to be hesitant to introduce it.

JP 2018-49632 A

The present invention has been made in consideration of the above points, and provides a price display device, a price display system, and a price display method that enable flexible price determination by introducing dynamic pricing, and also take into account the interests of regular buyers, and can display prices that satisfy both sellers and buyers.

That is, the price presentation device of the first aspect is characterized by comprising: a current product information acquisition unit that acquires a product, the selling price of the product, and the number of sales of the product; a high-priced product information acquisition unit that acquires the number of high-priced sales when the product is sold for a predetermined period of time at a high selling price that is higher than the selling price of the product; a low-priced product information acquisition unit that acquires the number of low-priced sales when the product is sold for a predetermined period of time at a low selling price that is lower than the selling price of the product; an estimation unit that estimates the expected number of sales for each price of the product from the selling price and number of sales, the high selling price and the high-priced sales number, and the low selling price and the low-priced sales number for the product; a customer ranking setting unit that ranks customers and sets customer rankings; and a customer-specific price setting unit that sets individual prices for the product for each customer according to the customer rankings.

The second aspect of the price display device is characterized in that there are multiple types of high and low selling prices.

The third aspect of the price display device is characterized in that the product is selected according to the sales volume of the seller of the product.

The fourth aspect of the price display device is characterized in that when ranking customers, customer rankings are set based on the frequency of use by the product seller or the purchase amount.

The fifth aspect of the price display device is characterized by having a notification unit that notifies a customer of the individual price of a product and the same product according to the customer's ranking.

The price display device of the present invention includes a current product information acquisition unit that acquires the product, the selling price of the product, and the number of sales of the product, a high-price product information acquisition unit that acquires the number of high-price sales when the product is sold for a specified period at a high selling price that is higher than the selling price of the product, a low-price product information acquisition unit that acquires the number of low-price sales when the product is sold for a specified period at a low selling price that is lower than the selling price of the product, an estimation unit that estimates the expected number of sales for each price of the product from the selling price and number of sales, the high selling price and the high-price sales number, and the low selling price and the low-price sales number, a customer ranking setting unit that ranks customers and sets customer rankings, and a customer-specific price setting unit that sets individual prices for products for each customer according to customer rankings. This allows for flexible price determination through the introduction of dynamic pricing, and can also take into account the interests of regular purchasers, making it possible to present prices that satisfy both sellers and purchasers.

1 is a schematic diagram showing a configuration of a price presentation device and a price presentation system according to an embodiment; FIG. 13 is a schematic diagram showing the relationship between products and high-price sales volumes. FIG. 13 is a schematic diagram showing the relationship between products and low-priced sales. FIG. 1 is a conceptual diagram showing an estimation model. FIG. 13 is a schematic diagram showing setting of customer rankings. 13 is a display example showing the contents of a notification to a customer. 1 is a flowchart illustrating a price presentation method according to an embodiment.

The configuration of the price quote device 1 of the embodiment is shown as a schematic diagram in FIG. 1. In terms of hardware, the price quote device 1 is composed of a CPU 100, a ROM 210, a RAM 220, and a storage unit 230. Other components include a main memory, an LSI, etc. An output unit 240 and an input unit 250 are connected to the price quote device 1. The price quote device 1 is composed of a processing unit 101 that is realized using various electronic computers (computational resources) such as personal computers (PCs), mainframes, workstations, cloud computing systems, and even tablet terminals and smartphones. In terms of software, it is realized by a price quote program loaded into the main memory, etc.

Furthermore, as shown in FIG. 1, the price quote device 1 can be constructed as a price quote system 1S by connecting it to a terminal 11 of a customer who will purchase the product via a wired or wireless line such as an Internet line 10. Since the processing unit 101 in the price quote system 1S and the processing unit 101 in the price quote device 1 have a common configuration, they will be described below as the price quote device 1.

When each functional unit of the price quote device 1 (price quote system 1S) in FIG. 1 is realized by software, the processing unit 101 of the price quote device 1 is realized by executing the instructions of a program, which is software that realizes each function. The recording medium that stores this program can be a "non-transient tangible medium," such as a CD, DVD, semiconductor memory, or programmable logic circuit. In addition, this program may be supplied to the computer of the price quote device 1 (price quote system 1S) via any transmission medium (such as a communications network or broadcast waves) that can transmit the program.

The memory unit 230 in the processing unit 101 of the price quote device 1 is a known storage device such as an HDD or SSD. In addition, each functional unit that executes various calculations within the price quote device is a computing element such as a CPU 100.

The processing unit 101 of the price presentation device 1 specifically includes a current product information acquisition unit 110, a high-priced product information acquisition unit 120, a low-priced product information acquisition unit 130, an estimation unit 140, a customer ranking setting unit 150, a customer-specific price presentation unit 160, and a notification unit 170. Furthermore, the processing unit 101 of the price presentation device 1 includes an output unit 240, an input unit 250, etc.

The output unit 240 is a known display (liquid crystal display device, organic EL display device, etc.) or speaker, and displays the results of calculations performed by the processing unit 101 of the price quote device 1. In particular, it displays images and the like formed by the notification unit 170. The input unit 250 is a CD or DVD drive, a keyboard, a mouse, etc. It is possible to use a display with a touch panel function for the output unit 240, and it is also possible to use it in combination with the output unit 240. In addition, the processing unit 101 is also provided with an I/O buffer (not shown) for inputting and outputting signals. These are merely examples, and may be combined as appropriate and selected optimally.

The functional units of the price display device 1 of the embodiment will be described with reference to Figs. 2 to 6. First, the current product information acquisition unit 110 acquires and stores the type of product currently being sold by the seller (basically one type), the seller's current selling price of that product (current price), and the number of products sold by the seller in a specified period (actual number of sales or offers). The processing unit memory unit 230 stores the type of product, the current selling price (current price), and the number of products sold by the seller in a specified period (current sales).

The goods sold by the seller refer to general items such as food, miscellaneous goods, and clothing, as well as various types of tickets (airline tickets, theater tickets, etc.), and services such as accommodation, gym fees, and hair styling fees at beauty salons. In this way, the scope of goods is broad and includes services, and there are no limitations. The seller also includes the provider of services. In this document, for the sake of convenience, the terms "goods" and "seller" are used for explanation. Depending on the reality, the terms "goods" and "seller" may be replaced with "services" and "provider." In addition, the specified period is the period during which the seller sells the goods in question, and may be, for example, one to three months in monthly units, one to three weeks in weekly units, or one to seven days in daily units, as appropriate.

In the figure, a customer who purchases a product from a seller has a terminal 11. The terminal 11 is a personal computer (PC), a tablet terminal, a smartphone, etc. Notification content, which will be described later, is displayed on the terminal 11. The processing unit 101 of the price quote device 1 may be provided by the seller of the product, or by another business connected via the Internet line 10. Obviously, there are no restrictions on the number of customers (terminals) or sellers. It can be expanded to the number that can be processed by the processing unit 101.

The high-priced product information acquisition unit 120 acquires the number of high-priced sales of the same product when the same product is sold over a specified period of time for a high selling price that is higher than the current selling price (current price) of the same product as the product currently being sold by the seller. Furthermore, the high selling price is not limited to one type, but can be multiple types.

Similarly, the low-priced product information acquisition unit 130 acquires the number of low-priced sales when an identical product is sold over a specified period of time for a product that is the same as a product currently being sold by a seller, at a low selling price that is lower than the price at which the seller is currently selling the product (current price). Furthermore, the low selling price is not limited to one type, and there can be multiple types.

In fact, data on the number of high-price sales when a high selling price is set that is higher than the current price is accepted. Furthermore, data on the number of low-price sales when a low selling price is set that is lower than the current price is accepted. This will be explained using the schematic diagrams in Figures 2 and 3.

In Figure 2, a specific product G is being sold by a seller. At this time, the selling price of product G is P, and the number of products sold in a specified period (e.g., one month) is S. Next, when the price P is raised to a first high selling price P1 and the product is sold for a specified period, the number of products sold becomes the high-price sales number S1. Furthermore, when the price P is raised to a second high selling price P2 and the product is sold for a specified period, the number of products sold becomes the high-price sales number S2.

In Figure 3, when the price P of product G is reduced to a second low selling price P3 and sold for a specified period of time, the number of sales is the low-priced sales number S3. Furthermore, when the price P is reduced to a second low selling price P4 and sold for a specified period of time, the number of sales is the price-priced sales number S4. The number of high selling prices set in Figure 2 and the number of low selling prices set in Figure 3 are appropriate, as they depend on the quantity, nature, etc. of the product (service).

The relationship between the current price and the number of sales can be ascertained from the sales record of the seller of the product. However, when changing the price of a product using dynamic pricing or the like, it is difficult to accurately estimate the increase or decrease in the number of sales (sales) that will accompany the price change. Alternatively, the prediction will be very rough. Therefore, the seller of the product actually changes the price setting of the product, and the actual number of sales (sales) at different prices is obtained. This provides training data for the estimation unit 140, which will be described next.

The estimation unit 140 estimates the expected sales volume for each price of product G, which is the same as the product being sold by the seller, from the selling price P and sales volume S of product G, the high selling price P1 (and further P2) and high price sales volume S1 (and further S2) of product G, and the low selling price P3 (and further P4) and low price sales volume S3 (and further S4) of product G. In other words, the estimation unit 140 performs machine learning using the listed price and sales volume data as training data to estimate the expected sales volume according to the price of product G.

Machine learning techniques used for data processing in the estimation unit 140 include support vector machine (SVM), model tree, decision tree, neural network, multiple linear regression, locally weighted regression, and probabilistic search methods.

The conceptual diagram in Figure 4 shows the estimated sales volume and estimation model for each price of product G in the estimation unit 140. In Figure 4(a), the sales price (current price) P of the reference product G and its sales volume S are shown. The high sales price P1 and high price sales volume S1 of the product G, and the low sales price P3 and low price sales volume S3 of the product G are then added to this. From these three pieces of data, for example, the estimated sales volume for each price of the product G is estimated as the straight line shown by the thick line. Therefore, the sales volume for prices other than the current price P, high sales price P1, and low sales price P3 is linearly interpolated based on the regression line.

In FIG. 4(b), the sales price (current price) P of the reference product G and its sales volume S are shown. Then, the high sales prices P1 and P2 and high price sales volumes S1 and S2 of the product G, as well as the low sales prices P3 and P4 and low price sales volumes S3 and S4 of the product G are further added. From these five pieces of data, for example, the expected sales volume for each price of the product G is estimated as the curve shown by the thick line. Therefore, the sales volumes for prices other than the current price P, the high sales prices P1 and P2, and the low sales prices P3 and P4 are complemented based on regression analysis.

As a result, the expected sales volume for each price of the product G is estimated. In particular, in the example of Figure 4(b), if the selling price is raised to the high selling price P2, a sudden drop in sales volume is estimated. Of course, the optimal approximation function is selected through machine learning depending on the relationship (correlation) between the selling price and the sales volume, and the number of plots of the selling price and the sales volume, and the expected sales volume for each price of the product is estimated. Therefore, when dynamic pricing is implemented, the products to be sold and the expected sales volume according to their detailed price settings are clarified. This makes it easier for the product seller to forecast sales.

The customer ranking setting unit 150 ranks customers and sets customer rankings. The explanation so far has been about the process of executing so-called dynamic pricing. That is, for the seller of a product, the contribution of each customer and the amount of contribution are first numerically visualized. Then, the advantage (merit) of regular customers is added to the price resulting from dynamic pricing. This increases the satisfaction of regular customers with the seller.

Figure 5 is a schematic diagram of a customer ranking table 30, which lists the usage frequency "Ta, Tb, Tc, Td, ..." and purchase amount "Ma, Mb, Mc, Md, ..." for each customer "Ca, Cb, Cc, Cd, ..." in the left column. The usage frequency is information for each customer, such as the number of visits in the past month and the most recent visit date. The purchase amount is, for example, the average purchase amount or the accumulated purchase amount during visits in the past month. When ranking customers, priority may be given to either the usage frequency or the purchase amount, or both may be taken into consideration. The usage frequency and purchase amount are set appropriately depending on the type of product.

The right column shows the ranking of each customer (1, 2, 3, 3, ...). For example, the top ranked customer Ca will receive a 10% discount on the price offered through dynamic pricing, the second ranked customer Cb will receive a 7% discount, and the third ranked customer Cc will receive a 4% discount. Of course, this is just one example, and the method of awarding benefits to customers by the product seller is not limited. Other appropriate methods include increasing the bonus points when purchasing a product, adding coupons, etc.

The customer-specific price setting unit 160 sets the price of the product for each customer according to the customer ranking. The customer-specific price setting unit 160 takes into account the customer-specific information of the customer ranking, and sets a favorable price for the specific customer in addition to the price resulting from the implementation of dynamic pricing. In the example of discount mentioned above, if the normal selling price of the product is 2,000 yen and 1,900 yen is offered as a result of the implementation of dynamic pricing, the discount for customer Ca, who is ranked first, is 10%, so an additional 10% is discounted from 1,900 yen, and 1,710 yen is set as the individual price for customer Ca.

The notification unit 170 notifies the customer of the individual price of the product and the same product according to the customer ranking. Even if an individual price is set for each customer, it is necessary to have the target customers actually visit the store. Therefore, the individual price is notified to each target customer. The notification unit 170 aggregates the information on the target product and individual price, and generates, for example, a display 40 of image data as shown in FIG. 6. The display of the output unit 240 then displays the image data 40 on its screen. Furthermore, the processing unit 101 of the price presentation device 1 transmits the image data 40 to the customer's terminal 11. Thus, the customer can be exposed to advantageous information and be motivated to visit the store.

In the display 40 in FIG. 6, "Mr. Ca" is displayed as the customer name 41, and "G" is displayed as the product name 42. Then, "1,710 yen" is displayed as the individual price 43 of the product G for that customer Ca. This is the offered price. The image data 40 is a type of so-called recommendation notification (recommendation information).

The selection of products (including services) as explained so far reflects the intentions of the seller of the product. In this case, best-selling products are targeted for dynamic pricing, which increases their appeal to customers. Target products are selected based on the sales volume of the product seller. Specifically, the sales volume and sales amount for each product (service) over a specified period (such as the most recent month) are tallied and each product is ranked. The top-ranking products are then selected.

The price quote method and price quote program of the embodiment will now be described with reference to the flowchart of FIG. 7. The price quote method of the embodiment is executed by the price quote device 1 (its processing unit 101) (computer) of the embodiment based on the price quote program of the embodiment. The price quote program of the embodiment causes the price quote device 1 (its processing unit 101) (computer) of FIG. 1 to execute a current product information acquisition function, a high priced product information acquisition function, a low priced product information acquisition function, an estimation function, a customer ranking setting function, a customer-specific price setting function, and a notification function. Each of these functions is executed in the order shown. Each function overlaps with the description of the management support device 1 of the embodiment described above, so details will be omitted.

The flowchart in FIG. 7 shows the flow of the price display method of the embodiment, which includes various steps such as a current product information acquisition step (S110), a high-priced product information acquisition step (S120), a low-priced product information acquisition step (S130), an estimation step (S140), a customer ranking setting step (S150), a customer-specific price setting step (S160), and a notification step (S170). In addition, the price display method of the embodiment also includes various steps not shown that are necessary as appropriate, such as storing the calculation results, calling them up, other calculations, input, output, storage, etc.

The current product information acquisition function acquires the product, the selling price of the product, and the number of products sold (S110; current product information acquisition step). The current product information acquisition function is executed by the current product information acquisition unit 110 of the price presentation device 1 (computer) in FIG. 1.

The high-priced product information acquisition function acquires the high-priced sales volume when the same product is sold for a predetermined period of time at a high selling price that is higher than the selling price of the same product (S120; high-priced product information acquisition step). The high-priced product information acquisition function is executed by the high-priced product information acquisition unit 120.

The low-priced product information acquisition function acquires the low-priced sales volume when the same product is sold for a predetermined period of time at a low selling price that is lower than the selling price of the same product (S130; low-priced product information acquisition step). The low-priced product information acquisition function is executed by the low-priced product information acquisition unit 130.

The estimation function estimates the expected sales volume for each price of the product from the sales price and sales volume, the high sales price and the high price sales volume, and the low sales price and the low price sales volume (S140; estimation step). The estimation function is executed by the estimation unit 140.

The customer ranking setting function ranks customers and sets customer rankings (S150; customer ranking setting step). The customer ranking setting function is executed by the customer ranking setting unit 150.

The customer-specific price setting function sets the price of a product for each customer according to the customer's ranking (S160; customer-specific price setting step). The customer-specific price setting function is executed by the customer-specific price setting unit 160.

The notification function notifies the customer of the product and the individual price of the same product according to the customer ranking (S170; notification step). The notification function is executed by the notification unit 170.

The price quote program can be implemented using, for example, scripting languages such as ActionScript, JavaScript (registered trademark), Python, and Ruby, or compiler languages such as C, C++, C#, Objective-C, Swift, and Java (registered trademark).

1 Price quote device 1S Price quote system 10 Internet line 11 Customer terminal 30 Customer ranking list 40 Classification item 41 Revenue 42 Expenses 43 Invested capital turnover rate 100 CPU
REFERENCE SIGNS LIST 101 Processing section 110 Current product information acquisition section 120 High price product information acquisition section 130 Low price product information acquisition section 140 Estimation section 150 Customer ranking setting section 160 Customer-specific price presentation section 170 Notification section 210 ROM
220 RAM
230 Storage section 240 Output section 250 Input section G Product P Selling price of product S Sales quantity of product P1, P2 High selling price S1, S2 High price sales quantity P3, P4 Low selling price S3, S4 Low price sales quantity

Claims (5)

a current product information acquisition unit that acquires, from the seller of the product through an input unit, a specific product currently being sold by a seller, a current selling price of the product, and a sales volume of the product over a first predetermined period of time;
a high-priced product information acquisition unit that acquires, via the input unit, from the seller of the product, a high-priced sales volume when the product is sold for a second predetermined period different from the first predetermined period at a high selling price that is higher than the current selling price of the product;
a low-priced product information acquisition unit that acquires, via the input unit, from the seller of the product, a low-priced sales volume when the product is sold at a low selling price that is lower than the current selling price of the product for a third predetermined period that is different from the first predetermined period and the second predetermined period;
an estimation unit that estimates an expected sales volume for each price of the product through dynamic pricing that executes machine learning using, as training data, the current selling price of the product, the high selling price of the product, the low selling price of the product, the number of sales of the product at the current selling price, the number of sales of the product at the high price, and the number of sales of the product at the low price;
a customer ranking setting unit for setting customer rankings among customers by ranking the customers based on an average purchase amount or an accumulated purchase amount of the products purchased by the customers during a most recent predetermined period of visit to the store of the seller;
a customer-specific price setting unit that sets an individual price according to the customer ranking for each customer , which is different from the sales price corresponding to the expected sales volume of the product, by applying a discount rate preset according to the customer ranking to the sales price corresponding to the expected sales volume of the product estimated by the estimation unit through dynamic pricing for the product;
A price display device comprising: The price display device according to claim 1, further comprising a notification unit that notifies the customer terminal of the product and the individual price for the product. a current product information acquisition unit that acquires, from the seller of the product through an input unit, a specific product currently being sold by a seller, a current selling price of the product, and a sales volume of the product over a first predetermined period of time;
a high-priced product information acquisition unit that acquires, via the input unit, from the seller of the product, a high-priced sales volume when the product is sold for a second predetermined period different from the first predetermined period at a high selling price that is higher than the current selling price of the product;
a low-priced product information acquisition unit that acquires, via the input unit, from the seller of the product, a low-priced sales volume when the product is sold at a low selling price that is lower than the current selling price of the product for a third predetermined period that is different from the first predetermined period and the second predetermined period;
an estimation unit that estimates an expected sales volume for each price of the product through dynamic pricing that executes machine learning using, as training data, the current selling price of the product, the high selling price of the product, the low selling price of the product, the number of sales of the product at the current selling price, the number of sales of the product at the high price, and the number of sales of the product at the low price;
a customer ranking setting unit for setting customer rankings among customers by ranking the customers based on an average purchase amount or an accumulated purchase amount of the products purchased by the customers during a most recent predetermined period of visit to the store of the seller;
a customer-specific price setting unit that sets an individual price according to the customer ranking for each customer , which is different from the sales price corresponding to the expected sales volume of the product, by applying a discount rate preset according to the customer ranking to the sales price corresponding to the expected sales volume of the product estimated by the estimation unit through dynamic pricing for the product;
A price display system comprising: The computer
a current product information acquisition step of acquiring information on a specific product currently being sold by a seller, a current selling price of the product, and a sales volume of the product over a first predetermined period from the seller of the product through an input unit;
a high-priced product information acquisition step of acquiring, via the input unit, from the seller of the product, a high-priced sales volume when the product is sold for a second predetermined period different from the first predetermined period at a high selling price higher than the current selling price of the product;
a low-priced product information acquisition step of acquiring, via the input unit, from the seller of the product, a low-priced sales volume when the product is sold at a low selling price that is lower than the current selling price of the product for a third predetermined period different from the first predetermined period and the second predetermined period;
an estimation step of estimating an expected sales volume for each price of the product through dynamic pricing in which machine learning is performed using as training data the current selling price of the product, the high selling price of the product, the low selling price of the product, the number of sales of the product at the current selling price, the number of sales of the product at the high price, and the number of sales of the product at the low price;
a customer ranking setting step of ranking the customers based on an average purchase amount or an accumulated purchase amount of the products purchased by the customers during a most recent predetermined period of visit to the store of the seller, and setting a customer ranking among the customers;
a customer-specific price setting step of setting an individual price according to the customer rank for each customer, which is different from the sales price corresponding to the expected sales volume of the product, by applying a discount rate preset according to the customer rank from the sales price corresponding to the expected sales volume of the product estimated in the estimation step through dynamic pricing;
A price display method comprising: On the computer,
a current product information acquisition function for acquiring information on a specific product currently being sold by a seller, a current selling price of the product, and a sales volume of the product over a first predetermined period from the seller of the product through an input unit;
a high-priced product information acquisition function for acquiring, via an input unit, from a seller of the product, a high-priced sales volume when the product is sold for a second predetermined period different from the first predetermined period at a high selling price higher than the current selling price of the product;
a low-priced product information acquisition function for acquiring, via an input unit, from a seller of the product, a low-priced sales volume when the product is sold at a low selling price that is lower than the current selling price of the product for a third predetermined period different from the first predetermined period and the second predetermined period;
an estimation function for estimating an expected sales volume for each price of the product through dynamic pricing in which machine learning is performed using, as training data, the current selling price of the product, the high selling price of the product, the low selling price of the product, the number of sales of the product at the current selling price, the number of sales of the product at the high price, and the number of sales of the product at the low price;
a customer ranking setting function for setting a customer ranking among customers by ranking the customers based on an average purchase amount or an accumulated purchase amount of the products of the customers in a recent predetermined period of time at the store of the seller;
a customer-specific price setting function for setting an individual price according to the customer rank for each customer , which is different from the sales price corresponding to the expected sales volume of the product, by applying a discount rate preset according to the customer rank from the sales price corresponding to the expected sales volume of the product estimated by the estimation function through dynamic pricing;
A price display program characterized by realizing the above.

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