JP6735878B1 - Information processing apparatus, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique for presenting to a user a provision condition capable of making a proposal for further increasing the sales of an accommodation facility. SOLUTION: A calculation unit that calculates a reservation upper limit number of accommodation units for each provision condition pattern in an accommodation unit, a first prediction unit that predicts a predicted value of the number of reservations related to an accommodation unit, and a reservation upper limit for each provision condition pattern. Number, the predicted value of the number of reservations of the accommodation unit, and the unit price of the accommodation unit, the second prediction unit that predicts the sales forecast for each of the provision condition patterns, and the predetermined forecast of the sales forecast for each of the provision condition patterns. An information processing apparatus having an output unit that outputs at least one provision condition pattern satisfying a condition. [Selection diagram]

Description

The present invention relates to an information processing device, an information processing method, and a program.

Currently, there are many services available that allow you to book accommodations online. By using these services, the user can quickly make a reservation for the accommodation facility. For example, Patent Literature 1 discloses a technique that can specify an accommodation facility and reserve the accommodation facility by specifying the number of guests and the number of rooms with a simple operation.

JP, 2011-053756, A

By changing the conditions for providing accommodation rooms in various ways, accommodation facilities are conducting trial and error to increase sales. For example, even in the same room, trial and error is performed so that sales can be increased by changing the minimum number of guests, the rank of accommodation charges, the presence or absence of meals, etc. for each day of the week or season.

However, since the relationship between the provision condition of the accommodation room and the sales is complicated, it is difficult to sufficiently improve the sales by relying on the experience of the manager and making trial and error.

Therefore, it is an object of the present invention to provide a technique of presenting to a user a provision condition capable of making a proposal for further increasing the sales of an accommodation facility.

An information processing apparatus according to an aspect of the present invention includes a calculation unit that calculates a reservation upper limit number of accommodation units for each provision condition pattern in an accommodation unit, and a first prediction unit that predicts a predicted value of the number of reservations related to accommodation units. A second prediction unit that predicts a sales forecast for each provision condition pattern based on the maximum number of reservations for each provision condition pattern, a predicted value for the number of reservations for the accommodation unit, and a unit price for the accommodation unit; and for each provision condition pattern Of the sales forecast described above, an information processing device having an output unit that outputs at least one provision condition pattern that satisfies a predetermined condition.

Advantageous Effects of Invention According to the present invention, it is possible to provide a technique of presenting to a user a provision condition capable of making a proposal for further increasing the sales of an accommodation facility.

It is a figure which shows an example of the accommodation reservation management system which concerns on this embodiment. It is a figure showing an example of hardware constitutions of a sales forecasting device and a terminal. It is a figure which shows the functional block structural example of a sales forecasting apparatus. It is a figure which shows an example of room utilization information, failure room information, and provision condition information. It is a figure which shows an example of reservation prediction information and cancellation prediction information. It is a figure which shows an example of sales forecast information. It is a figure which shows an example of the present provision condition information and change restriction information. It is a figure which shows an example of sales forecast information (after a filter). It is a flow chart which shows an example of a processing procedure when a sales forecasting device creates sales forecasting information. It is a flow chart which shows an example of a processing procedure when a sales forecasting device outputs a offer condition pattern in which sales are improved. It is a figure which shows an example of the screen displayed on a terminal.

Embodiments of the present invention will be described with reference to the accompanying drawings. In addition, in each of the drawings, those denoted by the same reference numerals have the same or similar configurations.

<System configuration>
FIG. 1 is a diagram showing an example of an accommodation reservation management system 1 according to the present embodiment. The accommodation reservation management system 1 includes a sales prediction device 10 (information processing device), a terminal 20, a reservation management device 30, a website management device 50, and a website device 40. The sales forecasting device 10, the terminal 20, the reservation management device 30, the website management device 50, and the website device 40 are connected via a wireless or wired communication network N and can communicate with each other. it can.

The sales forecasting device 10 is a device that forecasts the sales at the accommodation facility and proposes, among the providing conditions set for each accommodation unit, the providing conditions that are estimated to increase the sales. The accommodation unit is a unit of a facility or a place that the accommodation facility provides to the customer in order to accommodate the customer, and includes, for example, one room, one building, one camping site, and the like. Hereinafter, for convenience of explanation, it is assumed that the accommodation unit is one room (hereinafter referred to as “accommodation room”).

The provision condition is a condition set by the accommodation facility in the accommodation room when the accommodation room is provided to the user who makes the accommodation reservation, and includes, for example, the charge rank, the minimum number of guests, the meal type, whether the room type is upgraded, and the sale. Includes channel designation, etc. Room type upgrade means providing the customer with a room of a higher price than the room type reserved by the customer at the discretion of the accommodation facility. Specifically, the act of providing a twin room to a customer who reserved a single room with the single price. In situations where there are many single room reservations and few twin room reservations, booking a single room will improve the overall sales of the accommodation rather than leaving the twin room empty. Will be done. The sales channel is a channel for accepting reservations for accommodation facilities, and is, for example, an accommodation reception site or a travel agency.

The sales forecasting device 10 may be configured by one or a plurality of physical servers, a virtual server that operates on a hypervisor, or a cloud server. May be used.

The terminal 20 is a terminal used by the accommodation facility, and by accessing the sales forecasting device 10, the terminal 20 displays the provision condition estimated to improve the sales most. As the terminal 20, any terminal having a communication function such as a smartphone, a tablet terminal, a mobile phone, a personal computer (PC), a notebook PC, a personal digital assistant (PDA), and a home game machine can be used. ..

The reservation management device 30 is a device that manages reservations at accommodation facilities. The reservation management device 30 collectively manages reservations received via the website device 40, which will be described later, reservations received via a travel agency, a telephone, or the like.

The website device 40 is a device that provides a website that accepts reservations for accommodation facilities online. A plurality of website devices 40 may be included in the accommodation reservation management system 1.

The website management device 50 is a device that manages the provision conditions set in the accommodation device set in the website device 40. The website management device 50 provides a function of simultaneously changing the provision conditions set in the plurality of website devices 40 or collectively changing the provision conditions over a plurality of dates. The website management device 50 is also called a site controller. By operating the website management device 50, the manager of the accommodation facility can efficiently manage the provision conditions set in the accommodation room.

<Hardware configuration>
FIG. 2 is a diagram illustrating a hardware configuration example of the sales forecasting device 10 and the terminal 20. The sales forecasting device 10 and the terminal 20 include a processor 11 such as a CPU (Central Processing Unit) and a GPU (Graphical processing unit), a storage device 12 such as a memory, a HDD (Hard Disk Drive) and/or an SSD (Solid State Drive), It has a communication IF (Interface) 13 that performs wired or wireless communication, an input device 14 that receives an input operation, and an output device 15 that outputs information. The input device 14 is, for example, a keyboard, a touch panel, a mouse and/or a microphone or the like. The output device 15 is, for example, a display and/or a speaker.

<Function block configuration>
FIG. 3 is a diagram showing a functional block configuration example of the sales forecasting apparatus 10. The sales prediction device 10 includes a storage unit 100, an acquisition unit 101, a reception unit 102, a calculation unit 103, a reservation prediction unit 104 (first prediction unit), a cancellation prediction unit 105 (third prediction unit), The sales prediction unit 106 (second prediction unit), the output unit 107, and the DB management unit 108 are included. The storage unit 100 can be realized by using the storage device 12 included in the sales forecasting device 10. Further, the acquisition unit 101, the reception unit 102, the calculation unit 103, the reservation prediction unit 104, the cancellation prediction unit 105, the sales prediction unit 106, the output unit 107, and the DB management unit 108 are sales prediction devices. It can be realized by the processor 11 of 10 executing the program stored in the storage device 12. Further, the program can be stored in a storage medium. The storage medium storing the program may be a computer-readable non-transitory computer readable medium. The non-transitory storage medium is not particularly limited, but may be a storage medium such as a USB memory or a CD-ROM.

The storage unit 100 stores room usage information, failure room information, provision condition information, reservation information, reservation prediction information, cancellation prediction information, sales prediction information, current provision condition information, and change restriction information. , And sales forecast information (after filtering).

The room utilization information is information indicating whether or not each accommodation room in the accommodation facility is available. FIG. 4A shows an example of room usage information. “Date” indicates the date when the accommodation room is used. The "room ID" is an identifier that uniquely identifies the accommodation room. “Room type” indicates the type of accommodation room. In the present embodiment, there may be only one type of accommodation room, or a plurality of types of accommodation rooms (for example, a normal Japanese room and an open-air Japanese room) may be included. In the former case, the "room type" may be omitted (provided condition information, reservation information, reservation forecast information, sales forecast information, current provision condition information, change restriction settings, and sales forecast information, which will be described below. The same applies to (after filtering)).

The "maximum number of guests" indicates the maximum number of people who can stay. The "use flag" indicates whether or not the accommodation room can be used on the date indicated by "date". For example, 1 may be available and 0 may be unavailable. The setting that the accommodation room is unavailable is set when it is desired to prevent the accommodation room from being reserved, such as when the accommodation facility wants to reserve a room for group reservation in advance.

The failure room information is information indicating a period during which the accommodation room cannot be used due to maintenance such as repair. FIG. 4B shows an example of failure room information. The "room ID" is an identifier that uniquely identifies the accommodation room. The “failure start date” and the “failure end date” respectively indicate the start date and the end date of the period in which the accommodation room cannot be used.

The provision condition information is information indicating the provision conditions that can be set in the accommodation room. FIG. 4C shows an example of the provision condition information. “Room type” indicates the type of accommodation room. “Charge rank” indicates a charge rank that can be set. In the example of C in FIG. 4, it is shown that either the weekday fee or the holiday fee can be set for the normal Japanese-style room and the open-air Japanese-style room.

The "minimum number of guests" indicates the minimum number of people who can use the accommodation room. The “meal type” indicates whether or not to provide a meal, the type of meal that can be provided, and the like. In the example of C of FIG. 4, the presence or absence of a meal is illustrated, but the types of meals that can be provided may be set, such as meal types A, B, and C, without meals, with breakfast, The presence or absence of a more detailed meal, such as with dinner or with morning and dinner, may be set. In this case, the reservation information, reservation prediction information, sales prediction information, current provision condition information, and “meal type” present in the sales prediction information (after filtering), which will be described below, similarly provide the types of meals that can be provided, Alternatively, the presence/absence of provision of detailed meals such as a room without meals, breakfast, dinner, or morning and dinner is stored. “UPG (Upgrade)” indicates whether or not to allow the user who made the reservation to upgrade the accommodation room when checking in to the accommodation facility. "Pattern 1" and "Pattern 2" indicate to which type of accommodation room the upgrade is permitted. In FIG. 4C, "Pattern 1" and "Pattern 2" are shown, but only "Pattern 1" may be used. In addition, “Pattern 3” or more may be included in order to enable setting of a plurality of types of accommodation rooms to be upgraded. For example, in the example of C in FIG. 4, it is indicated that the normal Japanese-style room can be upgraded to the open-air Japanese-style room, and the open-air Japanese-style room cannot be upgraded. The “sales channel” indicates a sales channel that allows reception of reservations for accommodation rooms.

The reservation information is information indicating a reservation already made. FIG. 4D shows a specific example of the reservation information. The “acquisition date” indicates the date and time when the sales forecasting device 10 acquires the reservation information from the reservation management device 30. The "reservation ID" is an identifier that uniquely identifies a reservation. The "room ID" is an identifier that uniquely identifies the accommodation room. The "room type", "number of guests", and "meal type" store the provision conditions designated by the user at the time of reservation. In addition, the “accommodation date” and the “reservation date” respectively indicate the date when the user stays and the date when the user makes a reservation. The "reservation fee" indicates the fee of the accommodation room reserved by the user.

The reservation prediction information is information indicating the predicted value of the number of reservations regarding the accommodation room. FIG. 5A shows an example of reservation prediction information. “Staying date” indicates the staying date for which the number of reservations is predicted. “Room type” indicates the type of accommodation room. “Charge rank”, “minimum number of guests”, and “meal type” store the provision conditions for providing accommodation rooms. The "predicted number of reservations" indicates the accommodation room of the type shown in "Room type" on the day shown in "Staying date" in "Charge rank", "Minimum number of guests", "Meal type" and "Sales channel". Indicates the expected number of reservations when provided under the provision conditions. For example, in the example of FIG. 5A, on February 1, 2019, a normal Japanese-style room is a weekday charge rank, the minimum number of guests is 1, and there is no meal type. In the case, it indicates that a reservation for 13.5 rooms may be made. Similarly, on February 1, 2019, if the open-air Japanese-style room is a weekday charge rank, the minimum number of guests is two, the meal type is available, and the reservation is accepted by Company A, the number of rooms will be 1.0. It indicates that a reservation may be made.

The cancellation prediction information is information that is included in the reservation information and indicates the probability of canceling a reservation that has already been made. FIG. 5B is a diagram showing an example of cancellation prediction information. The "reservation ID" indicates a reservation included in the reservation information. The “cancellation probability” indicates the probability that the reservation indicated by the reservation ID will be canceled.

The sales forecast information is information indicating a forecasted sales value for each provision condition pattern in the accommodation room. The provision condition pattern indicates a combination of a plurality of provision conditions. The provision condition pattern in the sales forecast information is a provision condition obtained by combining the provision condition patterns for each type of accommodation room (for example, the provision condition pattern for a normal Japanese room and the provision condition pattern for an open-air Japanese room) for all types of accommodation rooms. It may be a pattern. In addition, a provision condition pattern (that is, a pattern in which an accommodation room is upgraded) is included when a reservation is accepted as at least a part of the accommodation room of a predetermined type (for example, an open-air Japanese-style room) as another type of accommodation room (for example, a Japanese-style room). It may be.

FIG. 6 shows an example of sales forecast information. FIG. 6 shows one table in which the upper right side and the lower left side are connected, although they are shown separately in the upper and lower sides for convenience of illustration. The "night of stay" indicates a day on which sales are predicted. “Room type” indicates the type of accommodation room. The "reservation upper limit number" indicates the maximum number of rooms in which the accommodation room of the type shown in "room type" can accept a reservation on the "night of stay". The upper limit number of reservations is the number of rooms calculated by excluding the number of already reserved rooms and the number of rooms that cannot be used due to maintenance such as repair from the number of rooms existing in the accommodation facility. “Charge rank”, “minimum number of guests”, “meal type” and “UPG” indicate provision conditions. The “pattern” of “UPG” corresponds to “pattern 1” and “pattern 2” of the provision condition information. The “number of rooms” of “UPG” indicates how many rooms are to be upgraded. The “predicted reservation number” stores the predicted reservation number corresponding to the provision condition acquired from the reservation prediction information.

The columns “room type” to “sales forecast (individual)” are repeatedly stored in one record for each room type. In the example of FIG. 6, there are one column from "reservation upper limit number" to "sales forecast (individual)" for the normal Japanese-style room and one column from "reservation upper limit number" to "sales forecast (individual)" to the open-air Japanese-style room It is included in the record. If there are other types of accommodation rooms in addition to the normal Japanese-style room and the open-air Japanese-style room, the columns from "Reservation upper limit number" to "Sales forecast (individual)" for the other types of accommodation rooms are also one. It will be added to the record.

“Sales channel” indicates a channel for accepting a reservation for an accommodation room. In the example of FIG. 6, there are two sales channels of Company A and Company B. When the sales channel is “1”, the reservation is accepted through the sales channel, and when the sales channel is “0”, the reservation is accepted through the sales channel. It shows that it is not forbidden. In the example of FIG. 6, one sales channel can be specified for each record, but it may be specified separately for each room type. For example, in one record, a combination such that a normal Japanese room accepts a reservation only by company A and an open-air Japanese room accepts a reservation only by company B may be possible.

“Sales forecast (total)” indicates the total value of sales forecast (individual) in each room type. The “priority” is a value sequentially assigned from 1 in descending order of sales forecast (total). The priorities may be given to the same room type in descending order of sales forecast (individual). The sales forecast information includes, for example, five patterns of sales conditions in an accommodation room of room type A, three patterns of provision conditions in an accommodation room of room type B, and two sales channels on a certain staying day. In this case, the sales forecast information of the accommodation date stores a provision condition pattern of 45 records in total of 3×5×3 (three ways when dealing with only one sales channel and when dealing with both sales channels). Will be.

The current provision condition information is a provision condition currently set by the accommodation facility, and may be a provision condition of the accommodation room set in the website device 40, for example. FIG. 7A shows an example of the current provision condition information. The "night of stay" indicates a day on which sales are predicted. “Room type” indicates the type of accommodation room. The "charge rank", "minimum number of guests", "meal type", "UPG", and "sales channel" store values indicating the current provision conditions.

The change restriction information is information determined by the administrator of the accommodation facility or the like, and indicates whether or not it is possible to change the current provision condition of the accommodation room. FIG. 7B shows an example of the change restriction information. “Room type” indicates the type of accommodation room. The "charge rank", "minimum number of guests", "meal type", "UPG", and "sales channel" store values indicating whether or not the current provision conditions can be changed. For example, in the example of B of FIG. 7, it is shown that, for a normal Japanese-style room, the charge rank can be increased, but the charge rank cannot be decreased. If you already have a reservation in a Japanese-style room, if you suddenly lower the price rank from a certain point, it may cause an unfair feeling among customers, but by not allowing the price rank to decline. It is possible to prevent such an unfair feeling from occurring.

The sales forecast information (after filtering) is the sales forecast information excluding provision condition patterns that are not permitted by the change restriction information. FIG. 8 shows an example of sales forecast information (after filtering). In the example of FIG. 8, on February 1, 2019, the charge rank, the minimum number of reservations, and the meal type of the normal Japanese-style room are weekday, one person, and meal, respectively, and the provision condition pattern for upgrading from the normal Japanese-style room to the open-air Japanese-style room Indicates that it is excluded.

The acquisition unit 101 acquires, from the reservation management device 30, reservation information including the number of confirmed reservations regarding accommodation rooms. Further, the acquired reservation information is stored in the storage unit 100.

The reception unit 102 receives a setting from the administrator of the accommodation facility or the like via the terminal 20 or the like whether or not it is possible to change the current provision condition of the accommodation room. The accepting unit 102 stores the accepted setting content in the change restriction information stored in the storage unit 100.

The calculation unit 103 has a function of calculating the reservation upper limit number of accommodation rooms for each of the provision condition patterns in the accommodation rooms. Further, the calculation unit 103 may calculate the upper limit number of reservations for each provision condition pattern and each type of accommodation room. Further, the calculation unit 103 may calculate the reservation upper limit number based on the number of rooms that can be secured for the accommodation room, the confirmed reservation number, and the reservation number estimated to be canceled.

The reservation prediction unit 104 (first prediction unit) has a function of predicting a predicted value of the number of reservations regarding the accommodation room and storing the predicted value in the storage unit 100 as reservation prediction information. The reservation predicting unit 104 may predict the predicted value by any method, but may make the prediction using, for example, a learned model that predicts the number of future reservations. The learned model uses learning data that inputs past reservation data according to the provision conditions (for example, price rank, minimum number of guests, meal type, sales channel, etc.) and outputs the number of reservations. It can be generated by training the model. The learned model is not limited to this, and may be a model in which the input data also includes the sales status of each area, the presence or absence of an event held around the accommodation facility, the provision conditions of competitors, and the like. Further, the reservation prediction unit 104 may predict the predicted value of the number of reservations for each of the plurality of types of accommodation rooms. The learned model may be generated for each sales channel, or may be generated by learning the sales channel as an input value.

The cancellation prediction unit 105 estimates the probability that the reservation will be canceled for each confirmed reservation included in the reservation information acquired by the acquisition unit 101, and stores it in the storage unit 100 as cancellation prediction information. The cancellation prediction unit 105 may predict the probability of cancellation by any method. For example, when a reservation condition (for example, a reservation date, a stay date, a minimum reservation number, a room type, etc.) is input, The prediction may be performed using a trained model that outputs the probability that the reservation will be canceled.

Further, the cancellation prediction unit 105 estimates a reservation that is estimated to be canceled by comparing the confirmed reservation included in the reservation information acquired by the acquisition unit 101 with the cancellation prediction information. For example, the cancellation prediction unit 105 may consider that the reservation is canceled when the probability of cancellation is equal to or higher than a predetermined probability (for example, 50% or higher).

The sales forecasting unit 106 forecasts a sales forecast for each provision condition pattern based on the upper limit number of reservations for each provision condition pattern, the predicted value of the number of reservations for the accommodation room, and the unit price of the accommodation room. In the present embodiment, the unit price may represent the unit price per room, or may represent the unit price per person in one room. In addition, the sales forecasting unit 106 provides the provision conditions based on the upper limit number of reservations for each provision condition pattern and each type of accommodation room, the predicted value of the number of reservations for each type of accommodation room, and the unit price for each type of accommodation room. The sales forecast for each pattern may be predicted.

The output unit 107 has a function of outputting at least one provision condition pattern satisfying a predetermined condition in the sales forecast for each provision condition pattern. Further, the output unit 107, as a supply condition pattern satisfying a predetermined condition, has a higher sales than a sales prediction corresponding to a preset supply condition (currently set supply condition) among sales forecasts for each supply condition pattern. You may make it output the provision condition pattern with big prediction. Further, the output unit 107 corresponds to a preset provision condition from the sales forecast for each provision condition pattern after excluding the provision condition pattern designated by the user as the provision condition pattern satisfying a predetermined condition. You may make it output the provision condition pattern whose sales forecast is larger than sales forecast. The output unit 107 may output the provision condition pattern with the largest sales forecast as the provision condition pattern satisfying the predetermined condition. In addition, the output unit 107 determines whether or not to accept the reservation as the provision condition pattern, that is, the charge rank of the accommodation room, the minimum number of guests, the meal type, and at least a part of the accommodation room of the predetermined type as the accommodation room of another type (that is, , Whether or not room type upgrade is allowed), and designation of a sales channel may be output.

The DB management unit 108 performs operations such as creation of various information (database) stored in the storage unit 100, data update, addition and deletion.

<Processing procedure>
Next, a series of processing procedures until the sales prediction device 10 calculates the sales prediction value for each provision condition pattern in the accommodation room and presents the sales prediction value to the accommodation facility will be specifically described.

FIG. 9 is a flowchart showing an example of a processing procedure when the sales forecasting device 10 creates sales forecasting information. Note that the sales forecasting device 10 calculates the sales forecasting information after a predetermined period from the current date, but the predetermined period is arbitrary. For example, the predetermined period may be two months or half a year. In the following description, it is assumed that the predetermined period is 2 months.

Before the processing procedure illustrated in FIG. 9 is performed, the reservation prediction unit 104 predicts the predicted value of the number of reservations regarding the accommodation room for each staying date, each room type, and each pattern of provision conditions to make a reservation prediction. It is stored in the storage unit 100 as information. Further, the cancellation prediction unit 105 predicts the cancellation probability for each reservation ID recorded in the reservation information and stores it in the cancellation prediction information.

In step S100, the calculation unit 103 calculates the number of vacant rooms for each room type (the actual number of rooms existing in the accommodation facility) for each stay day (two months after the next day) based on the room usage information and the failure room information. Until every day). For example, it is assumed that the room utilization information and the failure room information store the data shown in A of FIG. 4 and B shown in FIG. 4, respectively. First, the calculation unit 103 counts the number of rooms for which the "usage flag" is available ("1") from the room usage information for each date and for each room type, and thus the room for each room type. The number is calculated for each stay day. Subsequently, the calculation unit 103 extracts, from the failed room information, the accommodation room that cannot be used due to the failure in the period from the next day to two months later. Then, the calculation unit 103 calculates the number of vacant rooms for each room type by subtracting the number of accommodation rooms that cannot be used from the calculated number of rooms for each room type.

In step S101, the cancellation prediction unit 105 predicts the actual number of reservations after cancellation is considered for each staying day, based on the reservation information and the cancellation prediction information. For example, it is assumed that the reservation information and the cancellation prediction information store the data shown in D of FIG. 4 and B of FIG. 5, respectively. For the reservations with reservation IDs 001, 002, and 003, the cancellation probabilities are 20%, 40%, and 60%, respectively. For example, if it is considered that a reservation with a cancellation probability of 50% or more is to be canceled, the cancellation prediction unit 105 regards the reservation with the reservation ID 003 as being canceled and excludes it from the actual number of reservations.

In step S102, the calculation unit 103 subtracts the actual number of reservations after the cancellation is considered, which is predicted in step S101, from the number of vacant rooms for each room type and each staying day calculated in step S100. Calculate the maximum number of reservations for each and every staying day.

In step S103, the DB management unit 108 creates a possible provision condition pattern based on the provision condition information, and stores the created pattern in the sales forecast information. For example, in the example of C in FIG. 4, for a normal Japanese room, the charge rank is two patterns of weekdays and holidays, the minimum number of guests is two patterns of one or two people, and the meal type is no meal or with meals. There are two patterns. Assuming that the maximum number of reservations for the open-air Japanese-style room (the number calculated in step S102) is 2, there are three upgrade patterns: no upgrade, one room upgrade, and two room upgrades. become. Therefore, for a normal Japanese room, there are 2×2×2×3=24 providing condition patterns.

Next, for the open-air Japanese-style room, the charge ranks are two patterns of weekdays and holidays, the minimum number of guests is two patterns of one or two, and the meal type is one pattern with meals. There is no upgrade. Therefore, there are 2×2×2×1=8 provision condition patterns for the open-air Japanese-style room.

Next, the DB management unit 108 creates a provision condition pattern for the normal Japanese-style room and the open-air Japanese-style room as a whole. There are 2×2×2×3=24 ways of providing conditions patterns for a normal Japanese room, and 2×2×2×1=8 ways of providing conditions patterns for an open-air Japanese room. Further, there are three patterns in which the sales channel is limited to only company A, only company B is used, and both companies A and B are used. Therefore, as a whole, there are 24×8×3=576 types of provision condition patterns.

Subsequently, the DB management unit 108 repeats the creation of the combination pattern under the provision conditions for the entire normal Japanese-style room and the open-air Japanese-style room for each staying day of two months (assuming 60 days), thereby forecasting the sales. Create information. That is, in the sales forecast information, 576 records×60 days=34,560 records are generated. In this state, in the sales forecast information, data is filled in the columns of “accommodation date”, “room type”, “rate rank”, “minimum reservation number”, “meal type” and “UPG”. ..

Subsequently, the DB management unit 108 stores the reservation upper limit number for each room type and each staying day calculated in step S102 in the “reservation upper limit number” of the sales forecast information. At this time, the DB management unit 108 adjusts the number of rooms to be stored in the “reservation upper limit number” based on the number of upgrades. For example, it is assumed that the reservation upper limit numbers on February 1, 2019 calculated in step S102 are 10 and 2 for the normal Japanese room and the open-air Japanese room, respectively. In this case, the DB management unit 108 stores 10 rooms and 2 rooms in the "reservation upper limit number" of the normal Japanese room and the open-air Japanese room, respectively, without upgrade. When upgrading one room, 11 rooms and 1 room are stored in the "reservation upper limit number" of the normal Japanese room and the open-air Japanese room, respectively. When two rooms are upgraded, 12 rooms and 0 rooms are stored in the "reservation upper limit number" of the normal Japanese room and the open-air Japanese room, respectively.

In step S104, the DB management unit 108 stores the “predicted reservation number” stored in the reservation prediction information in the “reservation predicted number” of the sales prediction information created in step S103. More specifically, the DB management unit 108 reads one record from the sales forecast information, and the provision condition pattern in the read record (that is, “room type”, “rate rank”, “minimum reservation number”, and “meal type”). “)” is searched from the reservation prediction information, and the “reservation prediction number” of the hit record is stored in the “reservation prediction number” of the sales prediction information.

For example, among the provision conditions in the record in the first row of FIG. 6, the provision conditions for the normal Japanese-style room are that the charge rank is weekday, the minimum reservation number is 1, the meal type is none, and the sales channel is Company A. And Company B. In the reservation prediction information, the room is usually a Japanese room, the price rank is weekday, the minimum reservation number is 1, the meal type is none, and the sales channel is Company A. There are 13.5 rooms. In addition, in a Japanese-style room, the charge rank is weekday, the minimum reservation number is 1, the meal type is none, and the sales channel is Company B. There are no rooms. Therefore, the DB management unit 108 stores 13.5+11.0=24.5 in the “reserved predicted number” corresponding to the normal room in the sales prediction information. Similarly, the provision conditions for the open-air Japanese-style room among the provision conditions in the record in the first row of FIG. 6 are that the price rank is weekday, the minimum reservation number is 1, the meal type is available, and the sales channel is A. Company and company B. In the reservation prediction information, it is an open-air Japanese-style room, the charge rank is weekday, the minimum reservation number is 1, the meal type is available, and the sales channel is Company A. , 1.0 room. In the open-air Japanese-style room, the charge rank is weekday, the minimum reservation number is 1, the meal type is available, and the sales channel is Company B. The “predicted reservation number” is 0. There are 7 rooms. Therefore, the DB management unit 108 stores 1.7 in the “predicted number of reservations” corresponding to the outdoor room in the sales forecast information. The DB management unit 108 repeats the processing described above for all the records of the sales forecast information, thereby performing the processing of storing the “reserved predicted number” in the sales forecast information.

In step S105, the calculation unit 103 calculates the sales forecast for each type of accommodation room for each record of the sales forecast information, and stores the sales forecast in the “sales forecast (individual)” of the sales forecast information. Further, the calculation unit 103 calculates the total value of “sales forecast (individual)” for each type of accommodation room and stores it in “sales forecast (total)”. Specifically, the calculation unit 103 calculates the “sales forecast (individual)” based on the “reservation upper limit number” and the “reservation number forecast” of each record of the sales forecast information. When the “reservation upper limit number” is less than or equal to the “reservation number prediction”, that is, when the predicted reservation number cannot be accommodated, the calculation unit 103 multiplies the unit price of one room by the “reservation upper limit number”. The calculated number is stored in “sales forecast (individual)”. On the other hand, when the “reservation upper limit number” exceeds the “reservation number prediction”, that is, when the predicted number of reservations can be accommodated, the calculation unit 103 determines the unit price of one room and the “reservation number prediction”. The multiplied number is stored in “sales forecast (individual)”.

Here, assuming that the unit price of a Japanese-style room is 10,000 yen per person (without meals) or 13,000 yen per person (with meals), the unit price of an open-air Japanese-style room is 30,000 yen per person. A procedure for calculating the sales forecast for the first record of the sales forecast information shown in FIG. 6 when it is assumed that (with meals) is described. First, since the maximum number of reservations in a normal Japanese room is 10, and the prediction of the number of reservations is 24.5, reservations can only be received for 10 rooms at the maximum. The minimum reservation number is one. Therefore, the calculation unit 103 stores 10 rooms×10,000 yen×1 person=100,000 yen in the “sales forecast (individual)” of the normal room. Next, since the maximum number of reservations in the open-air Japanese-style room is 2 and the predicted number of reservations is 1.7, the maximum number of reservations that can be received is one. The minimum reservation number is one. Therefore, the calculation unit 103 stores 1 room×30,000 yen×1 person=30,000 yen in the “sales forecast (individual)” of the outdoor room.

In actual reservations, not all reservations are necessarily the minimum reservation number. For example, when the maximum number of guests is 4 and the minimum number of reservations is 1, it is usual that there are customers who make reservations with 2 people and customers who make reservations with 3 people. Therefore, the calculation unit 103 calculates the ratio of the reserved number of people based on the past reservation information, and calculates the sales forecast (individual) based on the calculated ratio of the reserved number of people and the unit price according to the reserved number of people. Good. For example, according to the past reservation information, the ratios of reservation by one person and reservation by two people are 50% and 50%, respectively, and the unit price when making a reservation by one person and the case when making a reservation by two people It is assumed that the unit price of each is 10,000 yen and 15,000 yen, respectively. In this case, the calculation unit 103 may store 5 rooms x 10,000 yen + 5 rooms x 15,000 yen = 125,000 yen in the "sales forecast (individual)" of the normal room.

Further, when calculating the “sales forecast (individual)”, the amount obtained by subtracting the commission for each sales channel may be calculated. If there are multiple sales channels, the commission may be subtracted assuming that the same number of reservations are made in each sales channel, or it is assumed that reservations will be made according to the ratio of the estimated number of reservations. Then, the commission may be subtracted. For example, if Company A's fee is 10% and Company B's fee is 20%, and if the fee is not excluded and sales of 100,000 yen are expected for a normal Japanese-style room, then 50,000 x 10% + 50,000 x 20% = 15 85,000 yen excluding 5,000 yen may be stored in the sales forecast (individual). Similarly, if you can expect sales of 30,000 yen for the open-air room without removing the fee, you can sell 25,500 yen for the open-air room excluding 15,000 × 10% + 15,000 × 20% = 4500 yen. You may make it store in prediction (individually).

Subsequently, the calculation unit 103 stores 100,000 yen + 30,000 yen = 130,000 yen in the "sales forecast (total)". The calculation unit 103 repeats the process described above for all the records of the sales forecast information, thereby performing the process of storing “sales forecast (total)” in the sales forecast information.

Note that the sales forecasting device 10 may repeat the processing procedure described with reference to FIG. 9 every predetermined period (for example, daily batch processing in the nighttime zone). Since the reservation status changes daily, it is possible to provide the accommodation facility with a sales forecast based on the latest data.

FIG. 10 is a flowchart showing an example of a processing procedure when the sales forecasting device 10 outputs a provision condition pattern for improving sales.

In step S200, the output unit 107 excludes the record of the provision condition pattern that the accommodation facility does not desire from the sales forecast information from the “current provision condition information” and the “change restriction information”. For example, it is assumed that the “current provision condition information” and the “change restriction information” store the data shown in A of FIG. 7 and B of FIG. 7, respectively.

First, on February 1, 2019, the current Japanese-style room offer conditions are that the price rank is weekday, the minimum number of people to book is one, there is no meal type, and the maximum number of rooms that can be upgraded is one. Is. Similarly, on February 1, 2019, the current provision conditions for the outdoor Japanese-style room are that the price rank is weekday, the minimum reservation number is 1, the meal type is available, and the number of rooms that can be upgraded is zero. is there.

Next, according to the change restriction setting, it is permissible for the normal Japanese room to raise the charge rank above the current level (that is, to make it a holiday charge), change the minimum number of guests, and change the meal type. However, the number of rooms that can be upgraded is not changed (that is, one room remains). Similarly, according to the change restriction setting, the open-air Japanese-style room does not change the price rank (that is, remains the weekday rate), does not change the minimum number of guests (that is, remains one person), and the meal type It is set not to change (that is, have meals) and not to upgrade (that is, the same as the current situation).

Therefore, the output unit 107 excludes the record of February 1, 2019 for the normal Japanese-style room, which has upgraded two or more open-air Japanese-style rooms, and the open-air Japanese-style room has a charge rank other than weekdays. , Exclude records where the minimum reservation number is 2 or more and the meal type is not available. The output unit 107 stores, in the storage unit 100, the sales forecast information after the records of the provision condition patterns not desired by the accommodation facility are excluded as the sales forecast information (after filtering).

In step S201, the output unit 107 outputs a record whose sales forecast is lower than the sales forecast corresponding to the currently set provision condition pattern (that is, the provision condition pattern in which sales decrease) to the sales forecast information (after filtering). ) Is excluded.

In step S202, the output unit 107 outputs the provision condition pattern having the largest “sales forecast (total)” to the terminal 20 for each staying date from the records stored in the sales forecast information (after filtering). ..

FIG. 11 shows an example of a screen displayed on the terminal 20. The example of FIG. 11 shows that the sales forecast for February is referred to at least before February 1, for example, during January. On the left side of FIG. 11, how much the sales increase for each staying day is displayed in a calendar format. On the right side of FIG. 11, the provision condition pattern in which it is estimated that the sales are most improved on the accommodation day selected on the left side screen is displayed. In the example of FIG. 11, it is displayed that if the charge rank is changed on weekdays and the minimum number of guests is changed to 1 for all types of accommodation rooms, the sales are estimated to increase by about 100,000 yen.

When the “Compare with other plan” button is pressed, the screen transitions to a screen displaying the sales forecasts of a plurality of provision condition patterns side by side. The screen displays a list of sales forecasts of the current provision condition patterns and sales forecasts of one or more provision condition patterns designated by the user. By referring to the screen, the user can simulate the change in sales when the provision conditions are changed.

Note that, on the left side of FIG. 11, a frame for displaying a date may be displayed in a plurality of stages in different colors according to the amount of increase in sales and the degree of improvement in sales. For example, the amount of money on the day of the highest sales is set as the maximum value, and the maximum value is equally divided into four levels, so that the date from 0 to 1/4 x maximum value is white, and 1/4 x maximum value ~ Yellow for dates up to 2/4 x maximum, light red for dates up to 2/4 x maximum to 3/4 x maximum, red for dates up to 3/4 x maximum to maximum It may be displayed.

Heretofore, a series of processing procedures has been described in which the sales prediction device 10 calculates the sales prediction value for each provision condition pattern in the accommodation room and presents the sales prediction value to the accommodation facility.

<Summary>
According to the embodiment described above, the sales forecast value is calculated for each of the provision condition patterns in the accommodation room, and the provision condition pattern in which the sales are improved is presented from among them. As a result, it is possible to provide a technique of presenting to the user a provision condition capable of making a proposal for further increasing the sales of the accommodation facility.

In addition, the sales forecasting device 10 calculates sales forecast values for all provision condition patterns in advance to create sales forecast information, and excludes provision condition patterns that the accommodation facility does not desire from the created sales forecast information. did. If the sales forecast value is calculated only for the provision condition pattern desired by the accommodation facility in advance and the sales forecast information is created, the accommodation facility predicts the sales forecast by changing the unwanted provision condition pattern. If the number of combination patterns that have not been increased increases, it is necessary to recreate the sales forecast information. However, in the present embodiment, the sales forecast value is calculated in advance for all of the provision condition patterns. Therefore, even if the accommodation facility changes an undesired provision condition pattern, the provision of improving the sales It becomes possible to promptly present the condition pattern. Further, since it is not necessary to recreate the sales forecast information, the processing load of the sales forecast device 10 can be reduced.

The embodiments described above are for facilitating the understanding of the present invention, and are not for limiting the interpretation of the present invention. The flowcharts and sequences described in the embodiments, the elements included in the embodiments, and their arrangement, materials, conditions, shapes, sizes, and the like are not limited to those illustrated, and can be changed as appropriate. Further, the configurations shown in different embodiments can be partially replaced or combined.

1...Accommodation reservation management system, 10...Sales forecasting device, 11...Processor, 12...Storage device, 13...Communication IF, 14...Input device, 15...Output device, 20...Terminal, 30...Reservation management device, 40...Web Site device, 50... Website management device, 100... Storage unit, 101... Acquisition unit, 102... Reception unit, 103... Calculation unit, 104... Reservation prediction unit, 105... Cancellation prediction unit, 106... Sales prediction unit, 107... Output unit, 108... DB management unit

Claims (7)

A calculation unit that calculates the maximum number of reservations for the accommodation unit for each type of accommodation unit,
A first prediction unit that predicts a predicted value of the number of reservations for each type of accommodation unit;
A plurality of charge ranks applicable to the accommodation unit based on the upper limit number of reservations for each accommodation unit type, a predicted value of the number of reservations for each accommodation unit type, and a unit price for each accommodation unit type And a second prediction unit that predicts a sales forecast for each provision condition pattern generated by combining a plurality of meal types and a plurality of minimum number of guests,
A sales forecast for each of the provision condition patterns , at least one provision having the largest sales forecast selected from the sales forecast for each of the provision condition patterns after excluding the provision condition pattern that the user does not want to provide An output unit that outputs a condition pattern,
Information processing device having a. The output unit outputs a provision condition pattern in which sales forecast is larger than sales forecast corresponding to preset provision conditions among sales forecasts predicted for each provision condition pattern,
The information processing apparatus according to claim 1. The provision condition pattern is a provision condition pattern in which the provision condition patterns for each type of accommodation unit are further combined, and the provision condition for accepting a reservation with at least a part of the accommodation unit of a predetermined type as an accommodation unit of another type. Pattern included,
The information processing apparatus according to claim 1. An acquisition unit for acquiring reservation information including the number of confirmed reservations regarding the accommodation unit,
A third prediction unit that estimates the number of reservations that are estimated to be canceled out of the number of confirmed reservations included in the acquired reservation information,
The calculation unit calculates the reservation upper limit number based on the number of rooms that can be secured for the accommodation unit, the confirmed reservation number, and the reservation number estimated to be canceled,
The information processing device according to claim 1. The output unit, as the provision condition pattern, the charge rank of the accommodation unit, the minimum number of guests, the meal type, whether or not to accept the reservation as at least a part of the accommodation unit of the predetermined type as the accommodation unit of another type, and the sale. Output at least one of the channel designations,
The information processing apparatus according to claim 1. An information processing device, for each type of accommodation unit, a step of calculating a reservation upper limit number of the accommodation unit,
An information processing device, a step of predicting a predicted value of the number of reservations for each type of the accommodation unit,
A plurality of information processing devices applicable to the accommodation unit based on the upper limit number of reservations for each accommodation unit, a predicted value of the number of reservations for each accommodation unit type, and a unit price for each accommodation unit type. A step of predicting a sales forecast for each of the provision condition patterns generated by a combination of the charge rank, multiple meal types, and multiple minimum number of guests,
The information processing device is a sales forecast for each of the provision condition patterns , and is selected from among the sales forecasts for each of the provision condition patterns after the provision condition patterns that the user does not want to provide are excluded , at least the sales forecast is A step of outputting the largest one provision condition pattern,
Information processing method including. On the computer,
Calculating a reservation upper limit number of the accommodation unit for each accommodation unit type,
Predicting a predicted value of the number of reservations for each type of accommodation unit,
Based on the maximum number of reservations for each accommodation unit, the predicted value of the number of reservations for each type of accommodation unit, and the unit price for each type of accommodation unit, a plurality of charge ranks and a plurality of charge ranks applicable to the accommodation unit A step of predicting a sales forecast for each of the provision condition patterns generated by a combination of the meal type and a plurality of minimum number of guests,
A sales forecast for each of the provision condition patterns , at least one provision having the largest sales forecast selected from the sales forecast for each of the provision condition patterns after excluding the provision condition pattern that the user does not want to provide A step of outputting a condition pattern,
A program to execute.

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