JP6225442B2 - Price determination program, price determination device, price determination method, price calculation support program, price calculation support method, and price calculation support device - Google Patents

Description

  The present invention relates to a price determination program, a price determination device, and a price determination method.

  For example, there are various methods for setting the price of a product. For example, there is a technique for determining whether or not the set price of an exhibition item deviates from the market price in an online auction or the like. In such a technique, it is possible to determine whether or not the set price of the exhibited product deviates from the market price, and set an appropriate price for the product based on the determination result.

  In addition, for example, when setting an estimated price of an automobile, an apparatus using an average value of a market price of an automobile having the same or similar specification as the automobile for which the estimated price is set is known. In such a technique, it is possible to set an appropriate price for an automobile by setting an estimated price based on an average value of market prices of products having the same or similar specifications.

JP 2003-316975 A JP 2003-173406 A

  The price of the product is set using the sales record of the product having the same or similar specification as the product for which the price is set. However, although the price of goods largely depends on external factors such as fluctuations in the price of goods, the actual situation is that no consideration is given to external factors such as fluctuations in the price of goods. As a result, it is difficult to set an appropriate price for a product only when the specification of the product is taken into consideration.

  In one aspect, an object is to provide a price determination program, a price determination device, and a price determination method that can determine an appropriate price of a product that reflects external factors such as fluctuations in the market price of the product.

  In one plan, the computer is caused to execute a process of calculating a first distribution indicating a distribution for each designated period of each product from a storage unit that stores the attribute information, time information, and actual price of each product in association with each other. . Further, the computer is caused to execute a process of calculating a second distribution indicating a distribution of timings at which prices should be determined based on the first distribution. Further, when the attribute information of the target product is detected, the computer is caused to execute processing for specifying a product similar to the attribute information and the actual price of the product from the storage unit. Further, the computer is caused to execute processing for specifying a position occupied in the first distribution related to the actual price of the specific product. Furthermore, the computer is caused to execute processing for determining the price of the target product based on the transition from the first distribution to the second distribution related to the identified position and the actual price of the specific product.

  In the disclosed aspect, it is possible to determine an appropriate price for a product that reflects external factors such as price fluctuations of the product.

FIG. 1 is an explanatory diagram illustrating an example of a price determination apparatus according to an embodiment. FIG. 2 is an explanatory diagram showing an example of the configuration of the sales performance DB. FIG. 3 is an explanatory diagram showing an example of the actual price table. FIG. 4 is an explanatory diagram showing an example of a price range table. FIG. 5 is an explanatory diagram illustrating an example of an increase / decrease rate table. FIG. 6 is an explanatory diagram illustrating an example of each process of the price determination device. FIG. 7 is an explanatory diagram showing an example of a box whisker used for price range distribution. FIG. 8 is a flowchart showing an example of the processing operation of the CPU of the price determination apparatus related to the monthly price range distribution process. FIG. 9 is a flowchart illustrating an example of the processing operation of the CPU of the price determination device related to the price determination process by price range. FIG. 10 is a flowchart illustrating an example of the processing operation of the CPU of the price determination device related to the commodity price determination process. FIG. 11 is an explanatory diagram illustrating an example of a processing operation related to the commodity price determination process. FIG. 12 is an explanatory diagram illustrating an example of an information processing apparatus that executes a price determination program.

  Hereinafter, embodiments of a price determination program, a price determination apparatus, and a price determination method disclosed in the present application will be described in detail based on the drawings. The disclosed technology is not limited by the present embodiment. Moreover, you may combine suitably each Example shown below in the range which does not cause contradiction.

  FIG. 1 is an explanatory diagram illustrating an example of a price determination apparatus according to an embodiment. A price determination device 1 shown in FIG. 1 includes a storage unit 10 and a CPU (Central Processing Unit) 20. The price determination apparatus 1 is an information processing apparatus such as a personal computer or a workstation. The price determination device 1 is connected to the input device 2 and the output device 3. The input device 2 is an input interface such as a keyboard, a mouse, a medium reading device, and a touch panel function, for inputting various information and the like to the price determination device 1. The output device 3 is an output interface that outputs various information from the price determination device 1 to the outside, and includes, for example, a display unit 3A.

  The storage unit 10 stores various types of information, for example, a semiconductor memory device such as a RAM (Random Access Memory) and a flash memory, and a storage device such as a hard disk device and an optical disk device. The storage unit 10 includes a sales performance DB 11, a performance price table 12, a price range table 13, and an increase / decrease rate table 14.

  The sales record DB 11 is a database that manages sales record data that is attribute information of each product. FIG. 2 is an explanatory diagram showing an example of the configuration of the sales performance DB 11. The sales record DB 11 shown in FIG. 2 manages record ID 11A, sales date 11B, vehicle type 11C, record price 11D, year 11E, mileage 11F, repair history 11G, and color 11H in association with each other. doing. The record ID 11A is an ID for identifying the sales result data of the product. The sales date 11B is the date of the sales performance of the product. The vehicle type 11C identifies the type of vehicle that is a product. The actual price 11D is the actual sales price of the product. The model year 11E identifies the model year of the car as a product. The travel distance 11F is a travel distance of a car that is a product. The repair history 11G is a repair history of a car that is a product. The color 11H identifies the color of the car that is the product.

  FIG. 3 is an explanatory diagram showing an example of the actual price table 12. The actual price table 12 shown in FIG. 3 is a table in which actual prices in units of months (t-March, t-February, t-March ...) are classified and registered for each vehicle type. The actual price table 12 shown in FIG. 3 manages sales data on a monthly basis by associating a code 12A, an actual price 12B, a year 12C, a mileage 12D, and a repair history 12E. The code 12A is a code for identifying a record of sales performance data. The actual price table 12 manages the actual sales price of a certain vehicle type in ascending order on a monthly basis.

  FIG. 4 is an explanatory diagram showing an example of the price range table 13. The price range table 13 shown in FIG. 4 manages the actual price for each price range in units of months (t-March, t-February, t-March ...), for example. The price range includes, for example, a maximum price range 13A, a 75% price range 13B, a median price range 13C, a 25% price range 13D, and a minimum price range 13E. When the order of each actual price from the maximum value to the minimum value is converted as a percentage, the maximum value is expressed as 100%, the median value is 50%, and the minimum value is expressed as 0% in descending order of the actual price. For example, if there are 100 actual prices, the maximum value is the first highest price, 75% is the 25th price, the median is the 50th price, and 25% is the 75th actual price. The price and the minimum value correspond to the 100th price.

  The maximum price range 13A is, for example, the maximum value (100%) ≦ X1 <75%, the 75% price range 13B is, for example, 75% ≦ X2 <50%, and the median price range 13C is, for example, 50% = expressed as X3. Further, the price range 13D of 25% is expressed by, for example, 50% <X4 ≦ 25%, and the minimum price range 13E is expressed by, for example, 25% <X5 ≦ 0%.

  The price range table 13 also manages the predicted price for each price range 13A to 13E at the current time t. In addition, the present time t is the timing which estimates the price of the prediction object goods with no sales performance, for example. The CPU 20 can acquire the first price range distribution in units of the actual price for each price range by referring to the price range table 13, and the second price range distribution of the predicted price for each price range at the current time t. Can be obtained. For example, when focusing on the actual price in t-March corresponding to the 75% price range 13B in the price range table 13, the CPU 20 can acquire the actual price “78.75” in t-March.

  FIG. 5 is an explanatory diagram showing an example of the increase / decrease rate table 14. The increase / decrease rate table 14 shown in FIG. 5 manages the increase / decrease rate compared with the actual price of the previous month for every price range 14A (14B, 14C, 14D, 14E) for a certain vehicle type, for example. The CPU 20 can acquire the increase / decrease rate compared with the actual price of the previous month by referring to the increase / decrease rate table 14. For example, when the CPU 20 pays attention to the rate of increase / decrease in t-January of the price range 14D of 25% in the rate-of-change table 14, from the actual price in t-Feb. Increase / decrease rate of “0.56” can be acquired.

  CPU20 reads the program memorize | stored in ROM which is not shown in the memory | storage part 10, and performs various functions as a process based on the read program. The CPU 20 includes a first calculation unit 21, a second calculation unit 22, a specifying unit 23, and a determination unit 24 as execution units for various processes. The 1st calculation part 21 calculates the price range distribution of the performance price of each goods from the sales performance DB11 for the designated period, for example, a monthly unit. The second calculation unit 22 calculates the second price band distribution at the current time t at which the price should be determined based on the first price band distribution on a monthly basis.

  When detecting the search condition for the prediction target product, the specifying unit 23 specifies the product that matches the search condition and the actual price of the product from the actual price table 12. Further, the specifying unit 23 specifies a monthly price range related to the actual price of the specific product. The determination unit 24 calculates the predicted price of the prediction target product based on the price transition from the first price range distribution by month related to the specified price range to the second price range distribution at the current time t and the actual price of the specific product. decide.

  FIG. 6 is an explanatory diagram illustrating an example of each process of the price determination device 1. The CPU 20 of the price determination device 1 extracts the sales record data of the model 001 of the vehicle type A from the sales record DB 11 on a monthly basis. The first calculation unit 21 in the CPU 20 sorts the extracted sales result data of the model 001 of the model A in descending order of the actual price, and registers the monthly sales result data in the actual price table 12 shown in FIG. (Step S1).

  Furthermore, the first calculation unit 21 registers the actual price in the actual price table 12 on a monthly basis, and then stores the actual price for each price band on the monthly basis in the price band table 13 shown in FIG. 4 as a price band distribution. Registration is performed sequentially (step S2). In addition, CPU20 performs a monthly price range distribution process by step S1 and S2. Further, the second calculation unit 22 in the CPU 20 calculates the increase / decrease rate by comparing the actual price of the designated month with the actual price of the previous month for each price range, and the increase / decrease rate of the designated month is shown in FIG. Registration is sequentially made in the increase / decrease rate table 14.

  Further, the second calculation unit 22 sequentially registers the predicted price in the same price range for the specified month based on the actual price and the predicted price in the previous month for each price range (step S3). For each price range, the second calculation unit 22 calculates the predicted price of the next month using the exponential smoothing method of the predicted price of the previous month + the coefficient α × (actual price of the previous month−predicted price of the previous month). Are registered in the price range table 13. The coefficient α is set to a fixed value of “0.9”, for example, but can be changed as appropriate. The second calculation unit 22 calculates a predicted price indicating a price band distribution at a current time t at which a price is to be determined based on a monthly price band distribution, and the predicted price indicating the price band distribution at a current time t is calculated in a price band table. 13 is registered.

  FIG. 7 is an explanatory diagram showing an example of a box whisker used for price range distribution. In the box whisker 40 shown in FIG. 7, the tip of the upper whiskers has the maximum value (100%), the tip of the lower whiskers has the minimum value (0%), the first quartile has the price of 25%, the second The quartile is the median (50%) price and the third quartile is the 75% price. Further, the box whiskers 40 include a maximum price range X1 (100%) ≦ X1 <75%), a 75% price range X2 (75% ≦ X2 <50%), and a median price range X3 (50% = X3), a price range X4 of 25% (50% <X4 ≦ 25%), and a minimum price range X5 (25% <X5 ≦ 0%). The CPU 20 can display a box-and-whisker 40 in monthly units (t-March, t-February, t-March, t-month) on the display unit 3A. The user can recognize the transition of the actual price and the predicted price for each price range by looking at the box whisker 40 displayed on the screen of the display unit 3A. The price range means the degree of the product. For example, the price range X2 of 75% is “good”, the median price range X3 is “normal”, and the price range X4 of 25% is “bad”. It may be given meaning.

  Next, the operation of the price determination apparatus 1 of the present embodiment will be described. FIG. 8 is a flowchart showing an example of the processing operation of the CPU 20 of the price determination apparatus 1 related to the monthly price range distribution process. The monthly price range distribution process shown in FIG. 8 is a process of calculating and registering the price range distribution related to the sales performance data of the target product on a monthly basis from the sales performance data.

  The first calculation unit 21 in the CPU 20 shown in FIG. 8 designates the target period of the sales performance data of the target product from the sales performance DB 11 (step S11). The target period is, for example, a period from the present to one year ago. The first calculation unit 21 refers to the sales date 11B of the target product and designates the first month within the target period (step S12). The first month in the target period is, for example, March one year ago when the target period is one year and the current month is March.

  The first calculation unit 21 extracts the sales result data of the target product for the specified month from the sales result DB 11 (step S13), and registers the extracted sales result data of the target product for the specified month in the actual price table 12. (Step S14). Further, the first calculation unit 21 classifies the sales result data of the target product in the specified month into a predetermined price range (step S15). The predetermined price ranges are, for example, five types of price ranges of maximum value, 75%, median value, 25%, and minimum value. The first calculation unit 21 sorts the price range by rearranging in descending order of the actual price within the specified month.

  The first calculation unit 21 registers the first price range distribution, which is the result of classifying the price range for the specified month, in the price range table 13 (step S16). The price range classification result is the product price for each price range. The first calculating unit 21 determines whether or not the registration of the price range classification results for all months in the target period is completed after registering the price range classification results for the specified month in the price range table 13. (Step S17). When the registration of the classification result of the price range for all months in the target period is completed (Yes at Step S17), the first calculation unit 21 ends the processing operation illustrated in FIG.

  If the registration of the price range classification results for all months in the target period has not been completed (No at Step S17), the first calculation unit 21 increments the designated month by 1 and designates the next month (Step S18). In order to extract the sales result data of the designated month, the process proceeds to step S13.

  In the monthly price range distribution process shown in FIG. 8, the price range is classified based on the monthly sales performance data for each product, and the price range distribution as a result of the monthly price range is registered in the price range table 13. As a result, the CPU 20 can obtain the monthly price range distribution of the target product with reference to the price range table 13.

  FIG. 9 is a flowchart showing an example of the processing operation of the CPU 20 of the price determination device 1 related to the price determination process by price range. The price determination process for each price range shown in FIG. 9 sequentially registers the actual price of each month for each price range of the product, and based on the rate of change compared with the actual price from the previous month, This is a process of calculating and registering the predicted price of the current price range.

  In FIG. 9, the 2nd calculation part 22 in CPU20 designates the object period of the sales performance data of object goods from sales performance DB11 (step S21). The second calculation unit 22 refers to the sales date 11B of the vehicle type with the target product and specifies the first month within the target period (step S22).

  The second calculation unit 22 designates one price range among a plurality of price ranges in the price range distribution (step S23). The plurality of price ranges are, for example, five types of price ranges such as maximum value, 75%, median value, 25%, and minimum value. After designating the price range, the second calculation unit 22 calculates the predicted price for the next month in the same designated price range based on the actual price and the forecast price for the designated month in the designated price range (step S24). The second calculation unit 22 calculates the predicted price of the next month by “predicted price of the previous month + coefficient α × (actual price of the previous month−predicted price of the previous month)”.

  The second calculator 22 registers the predicted price for the next month in the price range table 13 (step S25). For example, when the predicted price of the 75% price range of t-February is calculated, the second calculation unit 22 registers the predicted price in the entry corresponding to the 75% price range in the price range table 13. Is.

  The second calculation unit 22 registers the predicted price of the next month in the specified price range in the price range table 13, and then determines whether there is an actual price of the next month in the specified price range (step S26). If there is an actual price for the next month (Yes at Step S26), the second calculation unit 22 registers the actual price in an entry corresponding to the predicted price for the next month (Step S27). For example, when there is an actual price in the price range of 75% in t-February, the second calculation unit 22 includes the entry in t-February corresponding to the price range 13B of 75% in the price range table 13. Register the actual price.

  Further, the second calculation unit 22 calculates an increase / decrease rate of the actual price in the designated price range from the designated month to the next month based on the designated month in the same designated price range and the actual price in the next month (step S28). The second calculation unit 22 calculates the rate of change between the actual price in the specified month and the actual price in the next month as the rate of change.

  When calculating the increase / decrease rate of the designated price range from the specified month to the next month, the second calculation unit 22 registers the increase / decrease rate of the specified price range in the increase / decrease rate table 14 as the increase / decrease rate of the next month (step S29). For example, when the second calculation unit 22 calculates the increase / decrease rate of the 75% price band 13B from t-February to t-January, the t- Register the rate of change in the January rate change entry.

  Further, the second calculation unit 22 determines whether or not the designation of all months within the target period has been completed (step S30). When the designation of all months within the target period is completed (Yes at Step S30), the second calculation unit 22 determines whether or not there is an undesignated price range at Step S23 (Step S31). If there is an unspecified price range (Yes at Step S31), the second calculation unit 22 proceeds to Step S23 in the drawing. In addition, when there is no unspecified price range (No at Step S31), the second calculation unit 22 ends the processing operation illustrated in FIG.

  When the designation of all months within the target period has not been completed (No at Step S30), the second calculation unit 22 increments the designated month by +1 and designates the next month (Step S32). The second calculation unit 22 shifts to step S24 to calculate the predicted price of the same price range of the next month of the designated month, with the next month as the designated month.

  That is, the second calculation unit 22 registers the monthly actual price and the predicted price in the price band table 13 for each price range (maximum value, 75%, median value, 25%, minimum value) of the product. Furthermore, the 2nd calculation part 22 registers the increase / decrease rate compared with the actual price of the last month in the increase / decrease rate table 14 for every price range of goods.

  If there is no actual price in the specified price range for the next month (No in step S26), the second calculation unit 22 determines the specified month based on the actual price in the specified price range in the specified month and the predicted price in the same specified price range in the next month. The increase / decrease rate of the product price in the designated price range from the next month to the next month is calculated (step S33). The second calculation unit 22 calculates the rate of change between the actual price in the specified month and the predicted price in the next month as the rate of change. The process of step S33 is executed, for example, when there is no actual price for the next month of the current time t without an actual sales record.

  When calculating the increase / decrease rate of the specified price range from the specified month to the next month, the second calculation unit 22 registers the increase / decrease rate of the predicted price for the next month in the specified price range in the increase / decrease rate table 14 (step S34). ). For example, when the increase / decrease rate of the 75% price range 13B from t-Jan to the current time t is calculated, the second calculation unit 22 increases / decreases the current time t of the 75% price range 14B in the increase / decrease rate table 14. Register the rate of increase / decrease in the rate entry.

  That is, the CPU 20 registers the predicted price at the current time t with no sales record in the price range table 13 for each price range (maximum value, 75%, median value, 25%, minimum value) of the product. As a result, the second calculation unit 22 can acquire the actual price and the predicted price for each price range by referring to the price range table 13.

  Further, the second calculation unit 22 registers, in each increase / decrease rate table 14, the increase / decrease rate compared to the actual price of the previous month of the predicted price at the current time t without actual sales for each price range of the product. As a result, the second calculation unit 22 can acquire the monthly increase / decrease rate for each price range with reference to the increase / decrease rate table 14.

  The CPU 20 of the process shown in FIG. 9 calculates the predicted price at the current time t when there is no sales performance of this month for each price range of the product “predicted price of the previous month + coefficient α × (actual price of the previous month−predicted price of the previous month)”. The predicted price for each price range is registered in the price range table 13. As a result, the CPU 20 can obtain the predicted price for each price range at the current time t with no sales record with reference to the price range table 13.

  The CPU 20 registers the monthly actual price and predicted price in the price band table 13 for each price range (maximum value, 75%, median value, 25%, minimum value) of the product. As a result, the CPU 20 can acquire the monthly actual price and predicted price for each price range of a certain product with reference to the price range table 13.

  Further, the CPU 20 registers the rate of increase / decrease in the increase / decrease rate table 14 with respect to the previous month's actual price of the predicted price at the current time t with no sales record. As a result, the CPU 20 can refer to the increase / decrease rate table 14 and acquire the increase / decrease rate for each price range at the current time t when there is no sales record.

  FIG. 10 is a flowchart illustrating an example of the processing operation of the CPU 20 of the price determination device 1 related to the commodity price determination process, and FIG. 11 is an explanatory diagram illustrating an example of the processing operation of the commodity price determination process. The product price determination process illustrated in FIG. 10 is a process of determining the predicted price of the target product based on the predicted price and the rate of increase / decrease for each price range at the current time t.

  In FIG. 10, the specifying unit 23 in the CPU 20 determines whether or not the search condition for the prediction target product has been input (step S41). The forecast target product is a product for which a product price is predicted. The search conditions are attribute information of the product to be predicted, for example, various conditions such as vehicle type, sales date, actual price, year, mileage, repair history, and color. When the search condition for the prediction target product is input (Yes at Step S41), the specifying unit 23 searches the actual price table 12 for sales result data of product information similar to the search condition (Step S42).

  Based on the search result, the specifying unit 23 determines whether there is sales result data of product information similar to the search condition in the actual price table 12 (step S43). As shown in FIG. 11, the specifying unit 23 is a product similar to the search conditions of the vehicle type “A”, the year “2011”, the travel distance “5000”, the repair history “0”, and the color “2”. Assume that the sales price data of E26 for t-Jan are retrieved from the actual price table 12. The specifying unit 23 determines that the search condition is similar to the search condition when four search conditions are matched among the five search conditions of the vehicle type, year, mileage, repair history, and color. However, the setting of the similar condition can be changed as appropriate. In addition, when the search condition of the important item is matched by changing the weight for each item of the search condition, the CPU 20 may determine that the condition is similar even if the number of condition items matching the search condition is less than four. .

  When there is sales result data of product information similar to the search condition (Yes at Step S43), the specifying unit 23 specifies the actual price corresponding to the sales result data from the actual price table 12 (Step S44). Note that when the sales performance data of the entry code E26 is searched from the actual price table 12, the specifying unit 23 specifies the actual price “30” corresponding to the sales performance data of E26.

  After specifying the actual price corresponding to the sales performance data, the specifying unit 23 specifies the price range of the same month corresponding to the actual price in the price range table 13 (step S45). When specifying the actual price “30” corresponding to the sales result data of E26, the specifying unit 23 specifies the price range “25%” of the actual price “30” from the price range table 13 in t-January. . Furthermore, after specifying the price range of the same month corresponding to the actual price, the specifying unit 23 specifies the rate of increase / decrease of the specific price range from the same month specified from the rate of change table 14 to the present (step S46). The specifying unit 23 specifies the price range of “25%” corresponding to the actual price “30” in t-Jan, and then determines the current time t from t-January of the price range “25%” to the current t. The increase / decrease rate of “0.88” is specified.

  Furthermore, the determination unit 24 in the CPU 20 calculates the predicted price of the prediction target product based on the specified increase / decrease rate and the actual price corresponding to the sales result data as the search result (step S47). The determination unit 24 calculates the predicted price of the prediction target product by multiplying the actual price corresponding to the sales result data as the search result by the rate of increase / decrease. For example, when the search result is sales result data of E26, the determination unit 24 calculates the predicted price “26.4” of the prediction target product with the actual price “30” × the increase / decrease rate “0.88”.

  The determination unit 24 determines the predicted price of the calculated prediction target product, displays it on the display unit 3A (step S48), and ends the processing operation shown in FIG. As a result, the user looks at the display content of the display unit 3A, and for the product at the current time t, for which there is no sales performance for this month, as the predicted price reflecting the market fluctuation of the rate of increase / decrease, Recognize the price.

  Further, as shown in FIG. 11, the determination unit 24 displays the box whiskers 41 including the sales performance data E26 of products similar to the prediction target products and the box whiskers 42 related to the current predicted price E31 of the products. The screen is displayed on 3A. As a result, the user can identify the price transition of the product from the month to the current time with reference to the predicted price of the prediction target product by looking at the box whiskers 41 and 42 displayed on the screen.

  Furthermore, the identification part 23 complete | finishes the processing operation shown in FIG. 10, when the search condition of prediction object goods is not input (step S41 negative). Further, when there is no sales record data of product information similar to the search condition (No at Step S43), the determination unit 24 displays a screen indicating “not applicable” on the display unit 3A (Step S49), and ends the processing operation illustrated in FIG. To do.

  The CPU 20 of the process shown in FIG. 10 specifies the actual price, the price range of the sales month of the actual sales, when the actual sales price of the commodity similar to the search condition for the prediction target commodity is in the actual price table 12, An increase / decrease rate from the sales month in the specified price range to the current time t is specified. Further, the CPU 20 predicts the product price of the prediction target product based on the actual price of the product similar to the search condition, the sales month of the actual price, and the rate of change, and displays the predicted price on the display unit 3A. As a result, the user looks at the display content of the display unit 3A and sets the appropriate price for the forecast target product as the forecast price that reflects the market fluctuation of the rate of increase / decrease for the product at the current time t that has not been sold this month. Can be recognized.

  The specifying unit 23 specifies the actual price of “40” when the actual price of the similar product corresponds to the sales actual data of E26 in t-February. Furthermore, since the actual price of “40” is in the price range of 25%, the specifying unit 23 sets the increase / decrease rate “0.56” in the next month t−1 corresponding to the price range of 25%. Specify (see FIG. 5). Further, the specifying unit 23 specifies the current increase / decrease rate “0.88” corresponding to the price range of 25% (see FIG. 5). That is, the specifying unit 23 performs the actual price “40” of E26 in t-February similar to the prediction target product, the increase / decrease rate “0.56” of the 25% price range in t-January, and the 25% price at the current t The band change rate “0.88” is specified. Then, the determination unit 24 calculates the predicted price “19.71” of the prediction target product with 40 × 0.56 × 0.88.

  In the CPU 20 of the embodiment, the past sales performance with the upper and lower variations for each product is regarded as a macro, the actual price of the product is classified into a plurality of price ranges, and the actual price of the product is priced in time series for each price range. Registered in Table 13. As a result, the CPU 20 can acquire a trend of price transition for each price range of the product.

  Further, the CPU 20 calculates the rate of increase / decrease for each price range at the present time t, when there is no sales record, using the trend of price transition for each price range for each product, and calculates the monthly rate of increase / decrease of the product price for each price range. Registered in the increase / decrease rate table 14. As a result, the CPU 20 can acquire an increase / decrease rate that is a price transition for each price range of the product.

  The CPU 20 allocates the past actual price of the same or similar product of the prediction target product, and the predicted price of the prediction target product based on the rate of change from the simultaneous point corresponding to the price range of the actual price to the present time and the allocated actual price To decide. Then, the CPU 20 displays the predicted price of the determined prediction target product on the screen of the display unit 3A. As a result, even at the current time t when there is no sales record, it is possible to present an appropriate price that reflects fluctuations in the commodity price. In addition, it is possible to present an appropriate price that reflects fluctuations in commodity prices without depending on judgments of experienced marketers with rich market experience.

  Moreover, since the price of goods is influenced by both internal and external factors, both must be considered. For example, whether or not a purchaser thinks that a price set for one product is appropriate is greatly influenced by a plurality of attributes of the product, that is, internal factors. For example, in the case of a used car, if the year is new and the mileage is short, there is a high possibility that a high price will be received as a reasonable price.

  On the other hand, the price set for a product is greatly influenced by external factors such as the economy at that time. For example, when the economy is booming, it is more likely that the same product will be accepted as a reasonable price than when the economy is bad.

  There will be a change in the market price of the entire price range between the past and the present, but if the product has the same attributes as the product that was heretic in the price range distribution that was scarce at a certain time in the past, There is a rarity at the present time, so it is heresy in the price range distribution.

  Therefore, in the above-described embodiment, the change trend of the entire price range of the product can be obtained by calculating the price range distribution of the current predicted price from the price range distribution of the actual price per month. Further, in the above embodiment, when calculating the predicted price of the prediction target product, by specifying the price range of the product similar to the prediction target product, the tendency of the prediction target product in the entire product can be specified by the price range. . In other words, in the above-described embodiment, the predicted price reflecting external factors such as the commodity market price can be determined for the prediction target product in consideration of the change tendency of the entire price range of the product and the tendency shown in the entire product of the prediction target product. .

  In the above-described embodiment, the price determination device 1 that determines the appropriate price of a used car is illustrated. However, products whose price range fluctuates depending on market prices such as seasons and economic trends, for example, buying and selling houses, renting houses, It can also be applied to electricity sales.

  Moreover, in the said Example, although the vehicle type, the year type, the mileage, the repair history, the color, etc. were illustrated as attribute information of goods, for example, it is not limited to these, It can change suitably. In the above-described embodiment, the sales result data is managed in the actual price table 12 in units of months as a predetermined period, but the predetermined period can be appropriately changed.

  In the above embodiment, the price range of each product is classified based on the actual sales price of the product.For example, the addition / subtraction according to the content such as the mileage and the repair history is added to or subtracted from the actual sales price, The price range of the product may be classified based on the addition / subtraction result.

  In the above embodiment, the distribution of the price range of the product is illustrated in order to grasp the trend of the entire product in predicting the product price. However, the distribution is not limited to the price range, and the trend of the entire product is grasped. Items that can be used may be used.

  In addition, each component of each part illustrated does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each part is not limited to the one shown in the figure, and all or a part thereof may be functionally or physically distributed / integrated in arbitrary units according to various loads and usage conditions. Can be configured.

  Furthermore, various processing functions performed in each device are performed on a CPU (Central Processing Unit) (or a microcomputer such as an MPU (Micro Processing Unit), MCU (Micro Controller Unit), etc.) in whole or in part. You may make it perform. Various processing functions may be executed entirely or arbitrarily on a program that is analyzed and executed by a CPU (or a microcomputer such as an MPU or MCU) or hardware based on wired logic. Needless to say.

  By the way, the various processes described in the present embodiment can be realized by executing a program prepared in advance by the information processing apparatus. Therefore, in the following, an example of an information processing apparatus that executes a program having the same function as in the above embodiment will be described. FIG. 12 is an explanatory diagram illustrating an example of the information processing apparatus 100 that executes the price determination program.

  In FIG. 12, the information processing apparatus 100 that executes the price determination program includes a ROM 110, a RAM 120, and a CPU 130.

  The ROM 110 stores in advance a price determination program that exhibits the same function as in the above embodiment. Note that the price determination program may be recorded not on the ROM 110 but on a recording medium readable by a drive (not shown). Further, as the recording medium, for example, a portable recording medium such as a CD-ROM, a DVD disk, a USB memory, an SD card, or a semiconductor memory such as a flash memory may be used. The price determination program includes a first calculation program 110A, a second calculation program 110B, a specific program 110C, and a determination program 110D. Note that the programs 110A to 110D may be integrated or distributed as appropriate.

  The RAM 120 stores attribute information, time information, and actual price of each product in association with each other.

  Then, the CPU 130 reads these programs 110A to 110D from the ROM 110, and executes each of the read programs. The CPU 130 functions as the first calculation process 130A, the second calculation process 130B, the specific process 130C, and the determination process 130D with respect to each program 110A to 110D.

  CPU130 calculates the 1st distribution which shows distribution for every designated period of each goods from RAM120 which matched and memorized attribute information, time information, and a track record price of each goods. Furthermore, the CPU 130 calculates a second distribution indicating a distribution of timings at which prices should be determined based on the first distribution. Further, when detecting the attribute information of the target product, the CPU 130 specifies a product similar to the attribute information and the actual price of the product from the RAM 120. Furthermore, CPU130 specifies the position which occupies in the 1st distribution regarding the performance price of the said specific goods. Further, the CPU 130 determines the price of the target product based on the transition from the first distribution to the second distribution related to the specified position and the actual price of the specific product. As a result, it is possible to determine an appropriate price for a product that reflects external factors such as price fluctuations of the product.

DESCRIPTION OF SYMBOLS 1 Price determination apparatus 3A Display part 10 Memory | storage part 11 Sales performance DB
12 actual price table 13 price range table 14 increase / decrease rate table 21 first calculation unit 22 second calculation unit 23 identification unit 24 determination unit

Claims (9)

On the computer,
From the storage unit that stores the attribute information, time information, and actual price of each product in association with each other, calculate a first distribution indicating a distribution for each price range for each designated period of each product,
The distribution of the predicted price for each price range at the timing for determining the price of the target product based on the actual price for each price range closest to the timing for determining the price of the target product in the first distribution. Calculate the distribution of the two,
When the attribute information of the target product is detected, the product similar to the attribute information and the actual price of the product are specified from the storage unit,
Identify the price range in the first distribution related to the actual price of the specific product,
Based on the rate of change obtained by price transition from the first distribution to the second distribution in the specified price range and the actual price of the specific product in the specified price range, the price of the target product is A price determination program characterized by causing a process to be determined to be executed. The first box whisker indicating the price range in the first distribution related to the actual price of the specific product and the second box whisker indicating the second distribution are displayed on the screen. The price determination program according to claim 1. As a process for calculating the first distribution,
Calculate the first price range distribution, which is the first distribution, showing the price range distribution of the actual price for each specified period of each product,
As a process of calculating the second distribution,
Based on the first price range distribution, calculate a second price range distribution that is the second distribution,
As a process for specifying the price range in the first distribution,
Identify the price range related to the actual price of the specific product in the first price range distribution,
As a process of determining the price of the target product,
Based on the increase / decrease rate obtained by price transition from the first price range distribution to the second price range distribution in the specified price range, and the actual price of the specific product in the specified price range, The price determination program according to claim 1 or 2, wherein a process for determining a price of the target product is executed. As a process of calculating the second price range distribution,
Of the first price range distribution, the distribution of the predicted price for each price range at the timing for determining the price of the target product based on the actual price for each price range closest to the timing for determining the price of the target product The price determination program according to claim 3, wherein a process for calculating the second price range distribution indicating is executed. A storage unit that stores attribute information, time information, and actual price of each product in association with each other;
A first calculation unit for calculating a first distribution indicating a distribution for each price range in each designated period of each product from the storage unit;
The distribution of the predicted price for each price range at the timing for determining the price of the target product based on the actual price for each price range closest to the timing for determining the price of the target product in the first distribution. A second calculation unit for calculating the distribution of the two,
When the attribute information of the target product is detected, the product similar to the attribute information and the actual price of the product are specified from the storage unit, and the price range in the first distribution related to the actual price of the specific product is specified Specific part to do,
Based on the rate of change obtained by price transition from the first distribution to the second distribution in the specified price range and the actual price of the specific product in the specified price range, the price of the target product is A price determination device comprising: a determination unit for determining. Information processing device
From the storage unit that stores the attribute information, time information, and actual price of each product in association with each other, calculate a first distribution indicating a distribution for each price range for each designated period of each product,
The distribution of the predicted price for each price range at the timing for determining the price of the target product based on the actual price for each price range closest to the timing for determining the price of the target product in the first distribution. Calculate the distribution of the two,
When the attribute information of the target product is detected, the product similar to the attribute information and the actual price of the product are specified from the storage unit,
Identify the price range in the first distribution related to the actual price of the specific product,
Based on the rate of change obtained by price transition from the first distribution to the second distribution in the specified price range and the actual price of the specific product in the specified price range, the price of the target product is A method for determining a price, characterized by executing a process for determining. Accept registrations for specific product types,
Identify a product similar to the specific product based on the registered product type,
Based on the transition of the sales performance price of the identified similar product and the rate of increase / decrease of the price range for the specified period stored in the storage unit storing the sales performance price of the product, the price related to the specific product is calculated,
Display the calculated price on the display ,
For each price range, the rate of increase / decrease at the time of designation is calculated based on the price transition between the actual sales price at the time of designation and the most recent actual sales price at the time of designation. When there is no actual price, the predicted price at the time of designation is calculated from the latest actual sales price, and the increase / decrease rate at the time of designation is calculated based on the price transition between the latest actual sales price and the predicted price at the time of designation.
A price calculation support program for causing a computer to execute processing. Accept registrations for specific product types,
Identify a product similar to the specific product based on the registered product type,
Based on the transition of the sales performance price of the identified similar product and the rate of increase / decrease of the price range for the specified period stored in the storage unit storing the sales performance price of the product, the price related to the specific product is calculated,
Display the calculated price on the display ,
For each price range, the rate of increase / decrease at the time of designation is calculated based on the price transition between the actual sales price at the time of designation and the most recent actual sales price at the time of designation. When there is no actual price, the predicted price at the time of designation is calculated from the latest actual sales price, and the increase / decrease rate at the time of designation is calculated based on the price transition between the latest actual sales price and the predicted price at the time of designation.
A price calculation support method, wherein a computer executes processing. A receiving means for accepting registration of a specific product type;
A specifying means for specifying a product similar to the specific product based on the type of the registered product;
First, the price for the specific product is calculated based on the transition of the sales performance price of the specified similar product and the rate of increase / decrease of the price range for the specified period stored in the storage unit storing the actual sales price of the product. 1 calculating means;
Display means for displaying the calculated price on a display unit;
For each price range, the rate of increase / decrease at the time of designation is calculated based on the price transition between the actual sales price at the time of designation and the most recent actual sales price at the time of designation. When there is no actual price, the forecast price at the time of designation is calculated from the latest actual sales price, and the rate of increase / decrease at the time of designation is calculated based on the price transition between the latest actual sales price and the forecast price at the time of designation. A price calculation support apparatus comprising: 2 calculation means .

Images