JP2020187525A - Information processing device, information processing method and program - Google Patents

Abstract

To provide a technology that presents, to a user, a providing condition capable of providing a proposal to further improve the sales amount at a lodging facility.SOLUTION: An information providing device includes: a calculating unit that calculates an upper limit number of reservations per lodging for each providing pattern per lodging; a first predicting unit that predicts the predicted value of the number of reservations per lodging; a second predicting unit that predicts a sales prediction for each providing condition pattern on the basis of the upper limit number of reservations for each providing condition pattern, the predicted value of the number of reservations per lodging, and the unit price per lodging; and an outputting unit that outputs at least a providing condition pattern satisfying a predetermined condition among the sales predictions for the respective providing condition patterns.SELECTED DRAWING: Figure 3

Description

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

Currently, there are many services that allow you to book accommodation online. By using these services, the user can quickly make a reservation for accommodation facilities. For example, Patent Document 1 discloses a technique capable of designating an accommodation facility and making a reservation for the accommodation facility in which the number of guests and the number of rooms are specified by a simple operation.

Japanese Unexamined Patent Publication No. 2011-053756

Accommodation facilities are conducting trial and error to increase sales by changing the conditions for providing accommodation rooms in various ways. For example, even in the same room, trial and error is carried out to increase sales 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 and the season.

However, since the relationship between the conditions for providing accommodation rooms and sales is complicated, it is difficult to sufficiently improve sales because only trial and error is performed depending on the experience of the manager.

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

The information processing device according to one aspect of the present invention includes a calculation unit that calculates the maximum number of reservations for the accommodation unit and a first prediction unit that predicts the predicted value of the number of reservations for the accommodation unit for each provision condition pattern in the accommodation unit. , The second prediction unit that predicts the sales forecast for each offer condition pattern based on the maximum number of reservations for each offer condition pattern, the predicted value for the number of reservations for each accommodation unit, and the unit price for each accommodation unit, and for each offer condition pattern The information processing apparatus includes an output unit that outputs at least one provision condition pattern that satisfies a predetermined condition among the sales forecasts of the above.

According to the present invention, it is possible to provide a technique for presenting to a user the provision conditions capable of making a proposal for further improving 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 which shows the hardware configuration example of a sales forecasting apparatus and a terminal. It is a figure which shows the functional block composition example of the sales forecasting apparatus. It is a figure which shows an example of a room use information, a breakdown 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 the 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 the sales forecast information (after filtering). It is a flowchart which shows an example of the processing procedure when the sales forecasting apparatus creates the sales forecast information. It is a flowchart which shows an example of the processing procedure when the sales forecasting apparatus outputs the provision condition pattern which improves the sales. 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 each figure, those having the same reference numerals have the same or similar configurations.

<System configuration>
FIG. 1 is a diagram showing an example of the accommodation reservation management system 1 according to the present embodiment. The accommodation reservation management system 1 includes a sales forecasting 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 to each other 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 predicts sales at an accommodation facility and proposes provision conditions that are estimated to improve sales among the provision conditions set for each accommodation unit. The accommodation unit is a unit of facilities and places provided by the accommodation facility to the customer in order to accommodate the customer, and includes, for example, one room, one building, and one camp site. Hereinafter, for convenience of explanation, the accommodation unit will be described as one room (hereinafter, referred to as “accommodation room”).

The offer condition is a condition set by the accommodation facility in the accommodation room when providing the accommodation room to the user who makes the accommodation reservation. Includes channel designation, etc. A room type upgrade means, at the property's discretion, providing the customer with a room of a room type that is more expensive than the room type booked by the customer. Specifically, it indicates an act of providing a twin room to a customer who has reserved a single room at the single rate. In situations where there are many single room reservations and few twin room reservations, it is better to make a reservation as a single room than to leave the twin room vacant, which will improve the sales of the entire accommodation facility. Will be done. The sales channel is a route 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 or the like, may be configured by using a virtual server operating on a hypervisor, or may be a cloud server. It may be configured using.

The terminal 20 is a terminal used by the accommodation facility, and displays the provision conditions that are estimated to improve the sales most by accessing the sales forecasting device 10. 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 mobile information terminal (PDA), and a home game device 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 described later, reservations received via a travel agency, 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 room, which is set in the website device 40. The website management device 50 provides functions such as changing the provision conditions set in the plurality of website devices 40 at the same time, or changing the provision conditions over a plurality of dates at once. The website management device 50 is also called a site controller. The manager or the like of the accommodation facility can efficiently manage the provision conditions set in the accommodation room by operating the website management device 50.

<Hardware configuration>
FIG. 2 is a diagram showing 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, an HDD (Hard Disk Drive) and / or an SSD (Solid State Drive), and the like. It has a communication IF (Interface) 13 that performs wired or wireless communication, an input device 14 that accepts input operations, 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. The output device 15 is, for example, a display and / or a speaker.

<Functional block configuration>
FIG. 3 is a diagram showing an example of a functional block configuration of the sales forecasting device 10. The sales forecasting 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), and the like. It includes a sales forecasting unit 106 (second forecasting unit), an output unit 107, and a DB management unit 108. 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. In addition, the program can be stored in a storage medium. The storage medium in which the program is stored may be a non-transitory computer readable medium. The non-temporary storage medium is not particularly limited, but may be, for example, a storage medium such as a USB memory or a CD-ROM.

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

Room usage information is information indicating whether or not each accommodation room in the accommodation facility is available. A of FIG. 4 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 this embodiment, there may be only one type of accommodation room, or a plurality of types of accommodation rooms (for example, a normal Japanese-style room and an open-air Japanese-style 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 setting, and sales forecast information described below. The same applies to (after filtering).

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

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

The provision condition information is information indicating the provision conditions that can be set in the accommodation room. FIG. 4C shows an example of 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 charge or the holiday charge can be set for the normal Japanese-style room and the open-air Japanese-style room.

"Minimum number of guests" indicates the minimum number of guests who can use the accommodation room. "Meal type" indicates whether or not to provide a meal, the types of meals that can be provided, and the like. In the example of C in FIG. 4, the presence or absence of meals is illustrated, but the types of meals that can be provided may be set, for example, meal types A, B, and C, without meals, with breakfast, and so on. The presence or absence of more detailed meals may be set, such as with dinner or with breakfast and dinner. In this case, the types of meals that can be provided also for the "meal type" that exists in the reservation information, reservation forecast information, sales forecast information, current provision condition information, and sales forecast information (after filtering) described below. Alternatively, detailed meal availability such as staying without meals, with breakfast, with dinner or with breakfast and dinner is stored. "UPG (Upgrade)" indicates whether or not the user who made the reservation is allowed to upgrade the accommodation room when checking in to the accommodation facility. "Pattern 1" and "Pattern 2" indicate which type of accommodation room is allowed to be upgraded. In C of FIG. 4, "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 make it possible to set a plurality of patterns for the type of accommodation room to be upgraded. For example, in the example of C in FIG. 4, it is shown that the Japanese-style room can be upgraded to the open-air Japanese-style room, and the open-air Japanese-style room cannot be upgraded. “Sales channel” indicates a sales channel that allows reservations for accommodation rooms to be accepted.

The reservation information is information indicating a reservation that has already been made. D of FIG. 4 shows a specific example of 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 the 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 specified by the user at the time of booking. Further, the "accommodation date" and the "reservation date" indicate the date on which the user stays and the date on which the reservation is made, respectively. The "reservation fee" indicates the fee for the accommodation room reserved by the user.

The reservation prediction information is information indicating a predicted value of the number of reservations for the accommodation room. A of FIG. 5 shows an example of reservation prediction information. “Accommodation date” indicates the accommodation date for which the number of reservations is predicted. "Room type" indicates the type of accommodation room. The "price rank", "minimum number of guests" and "meal type" store the provision conditions when providing the accommodation room. The "estimated number of reservations" indicates the type of accommodation shown in the "room type" on the date indicated in the "accommodation date" in the "price rank", "minimum number of guests", "meal type" and "sales channel". Indicates the expected number of reservations when provided under the conditions of provision. For example, in the example of A in FIG. 5, on February 1, 2019, it was assumed that the normal Japanese-style room would be accepted for reservation at company A with a weekday rate rank, a minimum number of guests of one, no meal type, and so on. If so, it indicates that 13.5 rooms may be reserved. Similarly, on February 1, 2019, if the open-air Japanese-style room has a weekday rate rank, the minimum number of guests is 2, there is a meal type, and company A accepts reservations, 1.0 room Indicates that a reservation may be made.

The cancellation prediction information is information included in the reservation information indicating the probability that the already entered reservation will be canceled. 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 cancelled.

The sales forecast information is information indicating a sales forecast 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 pattern in which the provision condition pattern for each type of accommodation room (for example, the provision condition pattern for a normal Japanese-style room and the provision condition pattern for an open-air Japanese-style room) are further combined for all types of accommodation rooms. It may be a pattern. It also includes a provision condition pattern (that is, a pattern in which the accommodation room is upgraded) when at least a part of a predetermined type of accommodation room (for example, an open-air Japanese-style room) is accepted as a reservation for another type of accommodation room (for example, a normal Japanese-style room). It may be.

FIG. 6 shows an example of sales forecast information. FIG. 6 is divided into upper and lower parts for convenience of illustration, but shows one table in which the right side of the upper row and the left side of the lower row are connected. "Accommodation date" indicates the date on which sales are predicted. "Room type" indicates the type of accommodation room. The "maximum number of reservations" indicates the maximum number of rooms that the type of accommodation shown in the "room type" can accept reservations on the "accommodation date". The maximum number of reservations is the number of rooms calculated by excluding the number of rooms already reserved and the number of rooms that cannot be used due to maintenance such as repairs from the number of rooms existing in the accommodation facility. "Price 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" in "UPG" indicates how many rooms to upgrade. The "predicted number of reservations" stores the predicted number of reservations corresponding to the provision conditions acquired from the reservation prediction information.

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

"Sales channel" indicates a channel for accepting reservations for accommodation rooms. In the example of FIG. 6, there are two sales channels of company A and company B. If it is "1", the reservation is accepted on that sales channel, and if it is "0", the reservation is accepted on that sales channel. It shows that it will not be broken. 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 the Japanese-style room usually accepts reservations only by company A and the open-air Japanese-style room accepts reservations only by company B may be possible.

"Sales forecast (total)" indicates the total value of sales forecast (individual) for each room type. The "priority" is a value given in order from 1 in descending order of sales forecast (total). The priority may be given to the same room type in descending order of sales forecast (individual). As for the sales forecast information, for example, on a certain accommodation day, there are 5 patterns of combinations of sales conditions in the accommodation room of room type A, 3 patterns of combinations of provision conditions in the accommodation room of room type B, and 2 sales channels. In this case, the sales forecast information for the accommodation day stores a total of 45 record provision condition patterns of 3 × 5 × 3 (three ways of handling only one sales channel and handling both sales channels). Will be.

The current provision condition information is the provision condition currently set by the accommodation facility, and may be, for example, the provision condition of the accommodation room set in the website device 40. FIG. 7A shows an example of the current provision condition information. "Accommodation date" indicates the date on which sales are predicted. "Room type" indicates the type of accommodation room. Values indicating the current provision conditions are stored in the "price rank", "minimum number of guests", "meal type", "UPG", and "sales channel".

The change restriction information is information determined by the manager of the accommodation facility, etc., and indicates whether or not it is possible to change the provision conditions of the current accommodation room. FIG. 7B shows an example of change restriction information. "Room type" indicates the type of accommodation room. Values indicating whether or not changes can be made from the current provision conditions are stored in the "price rank", "minimum number of guests", "meal type", "UPG", and "sales channel". For example, in the example of B in FIG. 7, it is shown that it is permissible to raise the charge rank but not to lower the charge rank for a normal Japanese-style room. If you already have a reservation in a normal Japanese-style room, suddenly lowering the price rank from a certain point may cause unfairness among customers, but by not allowing the price rank to be lowered. , It is possible to prevent such a feeling of unfairness from occurring.

The sales forecast information (after filtering) is the sales forecast information excluding the provision condition pattern that is not allowed in 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 price rank of the normal Japanese-style room, the minimum number of people reserved, and the meal type are weekdays, one person, and meals, respectively, and the provision condition pattern for upgrading two rooms from the normal Japanese-style room to the open-air Japanese-style room. Indicates that is excluded.

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

The reception unit 102 receives a setting from the manager or the like of the accommodation facility whether or not it is possible to change the provision conditions of the current accommodation room via the terminal 20 or the like. The reception unit 102 stores the received setting contents in the change restriction information stored in the storage unit 100.

The calculation unit 103 has a function of calculating the maximum number of reservations for the accommodation room for each provision condition pattern in the accommodation room. Further, the calculation unit 103 may calculate the maximum number of reservations for each provision condition pattern and for each type of accommodation room. Further, the calculation unit 103 may calculate the maximum number of reservations based on the number of rooms that can be secured for the accommodation room, the number of confirmed reservations, and the number of reservations that are 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 for the accommodation room and storing it in the storage unit 100 as reservation prediction information. The reservation prediction unit 104 may predict the predicted value by any method. For example, the reservation prediction unit 104 may use a learned model for predicting the number of reservations in the future. The trained model uses learning data that inputs past reservation data according to provision conditions (for example, price rank, minimum number of guests, meal type, sales channel, etc.) and accommodation date, and outputs the number of reservations. It can be generated by training the model. The trained model is not limited to this, and may be a model that includes the sales status for each area, the presence or absence of events held around the accommodation facility, the provision conditions of competitors, and the like in the input data. 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 trained model may be generated for each sales channel, or may be generated by training 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, reservation date, accommodation date, minimum number of reservations, room type, etc.) is input, the cancellation prediction unit 105 may predict the probability. You may make a prediction using a trained model that outputs the probability that the reservation will be canceled.

Further, the cancellation prediction unit 105 estimates the reservation 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 the sales forecast for each provision condition pattern based on the maximum 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 addition, in this embodiment, the unit price may represent the unit price per room, or may represent the unit price per person in one room. Further, the sales forecasting unit 106 provides provision conditions based on the maximum number of reservations for each type of accommodation 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 that satisfies a predetermined condition among the sales forecasts for each provision condition pattern. Further, the output unit 107 sells more than the sales forecast corresponding to the preset provision condition (currently set provision condition) among the sales forecast for each provision condition pattern as the provision condition pattern satisfying the predetermined condition. The provision condition pattern with a large prediction may be output. 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 specified by the user as the provision condition pattern satisfying the predetermined condition. It is also possible to output a provision condition pattern in which the sales forecast is larger than the sales forecast. Further, the output unit 107 may output the provision condition pattern having the largest sales forecast as the provision condition pattern satisfying a predetermined condition. Further, as a provision condition pattern, the output unit 107 accepts a reservation for the price 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 to allow room type upgrades), and the specification of the sales channel, at least one of which may be output.

The DB management unit 108 performs operations such as creating, updating, adding, and deleting various types of information (database) stored in the storage unit 100.

<Processing procedure>
Subsequently, a series of processing procedures until the sales forecasting device 10 calculates the sales forecasting value for each provision condition pattern in the accommodation room and presents the sales forecasting 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 forecast information. The sales forecasting device 10 calculates the sales forecast information from the current date until a predetermined period later, 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 two months.

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

In step S100, the calculation unit 103 determines the number of vacant rooms for each room type (actual number of rooms existing in the accommodation facility) for each accommodation day (two months after the next day) based on the room usage information and the failed room information. Calculate every day). For example, it is assumed that the data shown in A of FIG. 4 and B of FIG. 4 are stored in the room usage information and the failed room information, respectively. First, the calculation unit 103 counts the number of rooms for which the "usage flag" is available ("1") from the room usage information by date and by room type, so that the room for each room type The number is calculated for each staying date. Subsequently, the calculation unit 103 extracts from the failed room information the accommodation rooms that cannot be used due to the failure in the period from the next day to two months later. Subsequently, the calculation unit 103 calculates the number of vacant rooms for each room type for each accommodation day by subtracting the number of unusable accommodation rooms 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 consideration of cancellation for each accommodation date based on the reservation information and the cancellation prediction information. For example, it is assumed that the data shown in D of FIG. 4 and B of FIG. 5 are stored in the reservation information and the cancellation prediction information, respectively. For reservations with reservation IDs 001, 002 and 003, the cancellation probabilities are 20%, 40% and 60%, respectively. For example, assuming that a reservation having a cancellation probability of 50% or more is considered to be canceled, the cancellation prediction unit 105 considers that the reservation with the reservation ID 003 is canceled and excludes it from the actual number of reservations.

In step S102, the calculation unit 103 subtracts the actual number of reservations after consideration of cancellation predicted in step S101 from the number of vacant rooms for each room type and each accommodation date calculated in step S100, so that the room type Calculate the maximum number of reservations for each and each 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-style room, the price rank is two patterns of weekdays and holidays, the minimum number of guests is two patterns of one or two people, and the meal types are no meal and with meal. There are two patterns. Assuming that the maximum number of open-air Japanese-style rooms reserved (the number calculated in step S102) is two rooms, there are three upgrade patterns: no upgrade, only one room upgrade, and two room upgrades. become. Therefore, for a normal Japanese-style room, there are 2 × 2 × 2 × 3 = 24 different provision condition patterns.

Next, for the open-air Japanese-style room, the price 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 one pattern with meals. Also, there is no upgrade. Therefore, for the open-air Japanese-style room, there are 2 × 2 × 2 × 1 = 8 different provision condition patterns.

Next, the DB management unit 108 creates a provision condition pattern for the entire Japanese-style room and the open-air Japanese-style room. For a normal Japanese-style room, there are 2 × 2 × 2 × 3 = 24 different provision condition patterns, and for an open-air Japanese-style room, there are 2 × 2 × 2 × 1 = 8 different provision condition patterns. In addition, there are three patterns in which the sales channel is only company A, only company B, and both company A and company B. Therefore, as a whole, there are 24 × 8 × 3 = 576 different provision condition patterns.

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

Subsequently, the DB management unit 108 stores the maximum number of reservations for each room type and each accommodation date calculated in step S102 in the "maximum number of reservations" of the sales forecast information. At this time, the DB management unit 108 adjusts the number of rooms to be stored in the "upper limit number of reservations" based on the number of upgrades. For example, suppose that the maximum number of reservations on February 1, 2019 calculated in step S102 is 10 rooms and 2 rooms, respectively, for a normal Japanese-style room and an open-air Japanese-style room. In this case, the DB management unit 108 stores 10 rooms and 2 rooms in the "upper limit number of reservations" of the normal Japanese-style room and the open-air Japanese-style room, respectively, without upgrading. When upgrading one room, 11 rooms and 1 room are stored in the "maximum number of reservations" for the normal Japanese-style room and the open-air Japanese-style room, respectively. When upgrading 2 rooms, 12 rooms and 0 rooms are stored in the "maximum number of reservations" for the normal Japanese-style room and the open-air Japanese-style room, respectively.

In step S104, the DB management unit 108 stores the "forecast reservation number" stored in the reservation forecast information in the "reservation forecast number" of the sales forecast 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 (that is, "room type", "price rank", "minimum number of reservations", and "meal type" in the read record. The records that match ")) are searched from the reservation forecast information, and the "reservation forecast number" of the hit record is stored in the "reservation forecast number" of the sales forecast information.

For example, among the provision conditions in the record on the first line of FIG. 6, the provision conditions for a Japanese-style room are that the price rank is weekdays, the minimum number of reservations is 1, there is no meal type, and the sales channel is company A. And company B. In the reservation forecast information, the "estimated number of reservations" in the offer condition pattern is usually Japanese-style room, the price rank is weekdays, the minimum number of reservations is 1, there is no meal type, and the sales channel is company A. There are 13.5 rooms. In addition, the "estimated number of reservations" in the provision condition pattern, which is usually a Japanese-style room, the price rank is weekdays, the minimum number of reservations is 1, there is no meal type, and the sales channel is company B, is 11. There are 0 rooms. Therefore, the DB management unit 108 stores 13.5 + 11.0 = 24.5 in the "reservation forecast number" corresponding to the normal room of the sales forecast information. Similarly, among the provision conditions in the record on the first line of FIG. 6, the provision conditions of the open-air Japanese-style room are that the price rank is weekdays, the minimum number of reservations is 1, there is a meal type, and the sales channel is A. Company and Company B. In the reservation forecast information, the "estimated number of reservations" in the offer condition pattern is an open-air Japanese-style room, the price rank is weekdays, the minimum number of reservations is 1, there is a meal type, and the sales channel is company A. , 1.0 room. In addition, it is an open-air Japanese-style room, the price rank is weekdays, the minimum number of reservations is 1, there is a meal type, and the sales channel is company B. The "estimated number of reservations" in the provision condition pattern is 0. There are 7 rooms. Therefore, the DB management unit 108 stores 1.7 in the "reservation forecast number" corresponding to the open-air room of the sales forecast information. The DB management unit 108 repeats the process described above for all the records of the sales forecast information to store the "reservation forecast 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 it in the "sales forecast (individual)" of the sales forecast information. In addition, the calculation unit 103 calculates the total value of the "sales forecast (individual)" for each type of accommodation room and stores it in the "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 "upper limit of reservations" is less than or equal to the "prediction of the number of reservations", that is, when all the predicted number of reservations cannot be accommodated, the calculation unit 103 multiplies the unit price of one room by the "upper limit of reservations". The number is stored in "Sales Forecast (Individual)". On the other hand, when the "upper limit number of reservations" exceeds the "prediction of the number of reservations", that is, when all the predicted number of reservations can be accommodated, the calculation unit 103 sets the unit price of one room and the "prediction of the number of reservations". The multiplied number is stored in "Sales Forecast (Individual)".

Here, assuming that the unit price of one room in a Japanese-style room is 10,000 yen per person (without meals) or 13,000 yen per person (with meals), the unit price of one room in an open-air Japanese-style room is 30,000 yen per person. The procedure for calculating the sales forecast for the first record of the sales forecast information shown in FIG. 6 will be described on the assumption that (with meals). First, since the maximum number of reservations in a Japanese-style room is usually 10 and the number of reservations is estimated to be 24.5, reservations can only be made for 10 rooms at the maximum. The minimum number of reservations is one. Therefore, the calculation unit 103 stores 10 rooms x 10,000 yen x 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 two and the number of reservations is estimated to be 1.7, reservations can only be made for one room at the maximum. The minimum number of reservations is one. Therefore, the calculation unit 103 stores 1 room x 30,000 yen x 1 person = 30,000 yen in the "sales forecast (individual)" of the open-air room.

In actual reservations, not all reservations are necessarily the minimum number of reservations. For example, when the maximum number of guests is four and the minimum number of reservations is one, there are usually customers who make reservations with two people and customers who make reservations with three people. Therefore, the calculation unit 103 calculates the ratio of the number of reservations based on the past reservation information, and calculates the sales forecast (individual) based on the calculated ratio of the number of reservations and the unit price according to the number of reservations. May be good. For example, according to the past reservation information, the ratios of the reservation by one person and the reservation by two people are 50% and 50%, respectively, and the unit price when making a reservation by one person and the case where the reservation is made by two people. It is assumed that the unit prices of are 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 deducted on the assumption that the same number of reservations will be made in each sales channel, or it is assumed that reservations will be made according to the ratio of the predicted number of reservations. And the fee may be deducted. For example, if the commission of company A is 10%, the commission of company B is 20%, and if you can expect sales of 100,000 yen for a normal Japanese-style room without excluding the commission, 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 the sales of the open-air Japanese-style room can be expected to be 30,000 yen without excluding the commission, the sales of the open-air Japanese-style room will be 25,500 yen excluding 15,000 x 10% + 15,000 x 20% = 4500 yen. It may be stored in the prediction (individual).

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 to store the "sales forecast (total)" in the sales forecast information.

The sales forecasting device 10 may repeat the processing procedure described with reference to FIG. 9 at predetermined periods (for example, batch processing in the nighttime every day). Since the reservation status changes daily, it will be possible to provide accommodations with sales forecasts 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 data shown in A of FIG. 7 and B of FIG. 7 are stored in the "current provision condition information" and the "change restriction information", respectively.

First of all, the current offer conditions for regular Japanese-style rooms on February 1, 2019 are that the price rank is weekdays, the minimum number of reservations is 1, there is no meal type, and the maximum number of rooms that can be upgraded is 1. Is. Similarly, the current offer conditions for the open-air Japanese-style room on February 1, 2019 are that the price rank is weekdays, the minimum number of reservations is 1, there is a meal type, and the number of rooms that can be upgraded is zero. is there.

Next, according to the change restriction setting, it is usually permissible for Japanese-style rooms to raise the price rank from the current level (that is, to make it a holiday price), change the minimum number of guests, and change the meal type. However, it is set that the number of rooms that can be upgraded will not be changed (that is, one room will remain). Similarly, according to the change restriction setting, the open-air Japanese-style room does not change the price rank (that is, the price remains on weekdays), the minimum number of guests does not change (that is, it remains alone), and the meal type is It is set not to change (that is, with meals) and not to upgrade (that is, as it is now).

Therefore, for the record of February 1, 2019, the output unit 107 excludes the record that upgrades two or more open-air Japanese-style rooms for the normal Japanese-style room, and the charge rank for the open-air Japanese-style room is other than weekdays. , Exclude records other than those with a minimum reservation number of 2 or more and a meal type. The output unit 107 stores the sales forecast information after excluding the records of the provision condition pattern desired by the accommodation facility in the storage unit 100 as the sales forecast information (after filtering).

In step S201, the output unit 107 displays the 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 the sales decrease) with the sales forecast information (after filtering). ) Exclude.

In step S202, the output unit 107 outputs to the terminal 20 the provision condition pattern having the largest "sales forecast (total)" for each accommodation 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 offer condition pattern that is estimated to improve the sales most on the accommodation date selected on the left screen is displayed. In the example of FIG. 11, it is displayed that if the rate rank is changed on weekdays and the minimum number of guests is changed to one for all types of accommodation rooms, it is estimated that sales will increase by about 100,000 yen.

In addition, when the "Compare with other plans" button is pressed, the screen transitions to a screen for displaying the sales forecasts of a plurality of provision condition patterns side by side. On the screen, the sales forecast of the current provision condition pattern and the sales forecast of one or more provision condition patterns specified by the user are displayed in a list. By referring to the screen, the user can simulate the change in sales when the provision conditions are changed.

On the left side of FIG. 11, the frame for displaying the date may be displayed in a plurality of stages according to the degree of improvement in sales, in different colors according to the amount of money for which sales are improved. For example, by setting the amount of money on the day when sales are most improved as the maximum value and dividing the maximum value into four equal parts, white for dates from 0 to 1/4 x maximum value, 1/4 x maximum value ~ 2/4 x date up to maximum value is yellow, 2/4 x maximum value ~ 3/4 x date up to maximum value is light red, 3/4 x date up to maximum value ~ maximum value is red It may be displayed.

As described above, the sales forecasting device 10 has described a series of processing procedures until the sales forecasting value is calculated for each provision condition pattern in the accommodation room and the sales forecasting value is presented to the accommodation facility.

<Summary>
According to the embodiment described above, the sales forecast value is calculated for each provision condition pattern in the accommodation room, and the provision condition pattern for improving sales is presented from the forecast value. This makes it possible to provide a technique for presenting to the user the provision conditions that can make a proposal for further improving the sales of the accommodation facility.

In addition, the sales forecasting device 10 calculates sales forecast values for all provision condition patterns in advance, creates 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 in advance only for the provision condition pattern desired by the accommodation facility and the sales forecast information is created, the accommodation facility predicts the sales forecast by changing the provision condition pattern that the accommodation facility does not desire. If the number of unmatched combination patterns increases, it is necessary to recreate the sales forecast information. However, in the present embodiment, since the sales forecast value is calculated in advance for all the provision condition patterns, even if the accommodation facility changes the undesired provision condition pattern, the sales are improved. It becomes possible to present the condition pattern quickly. Further, since it is not necessary to recreate the sales forecast information, it is possible to reduce the processing load of the sales forecast device 10.

The embodiments described above are for facilitating the understanding of the present invention, and are not for limiting and interpreting the present invention. The flowchart, sequence, each element included in the embodiment, its arrangement, material, condition, shape, size, and the like described in the embodiment are not limited to those exemplified, and can be appropriately changed. In addition, 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 ... Web site management device, 100 ... Storage unit, 101 ... Acquisition unit, 102 ... Reception unit, 103 ... Calculation unit, 104 ... Reservation prediction unit, 105 ... Cancellation prediction unit, 106 ... Sales forecast unit, 107 ... Output unit, 108 ... DB management unit

Claims (9)

A calculation unit that calculates the maximum number of reservations for the accommodation unit for each provision condition pattern in the accommodation unit,
The first prediction unit that predicts the predicted value of the number of reservations for the accommodation unit,
A second prediction unit that predicts sales forecasts for each offer condition pattern based on the maximum number of reservations for each offer condition pattern, the predicted value for the number of reservations for the accommodation unit, and the unit price for the accommodation unit.
Of the sales forecasts for each provision condition pattern, an output unit that outputs at least one provision condition pattern that satisfies a predetermined condition, and
Information processing device with. The output unit outputs, as a provision condition pattern satisfying the predetermined condition, a provision condition pattern in which the sales forecast is larger than the sales forecast corresponding to the preset provision condition among the sales forecasts for each provision condition pattern.
The information processing device according to claim 1. The accommodation unit includes a plurality of types of accommodation units.
The provision condition pattern is a provision condition pattern that further combines the provision condition patterns for each type of accommodation unit, and is a provision condition pattern when at least a part of the accommodation unit of a predetermined type is accepted as an accommodation unit of another type. Is included,
The calculation unit calculates the maximum number of reservations for each provision condition pattern and for each type of accommodation unit.
The first prediction unit predicts the predicted value of the number of reservations for each of the plurality of types of accommodation units.
The second prediction unit includes a maximum number of reservations for each calculated provision condition pattern and each type of accommodation unit, a predicted value of the number of reservations for each of the predicted plurality of types of accommodation units, and a type of accommodation unit. Forecast sales forecast for each offer condition pattern based on the unit price for each
The information processing device according to claim 1 or 2. The output unit is a sales forecast corresponding to a preset provision condition from the sales forecast for each provision condition pattern after excluding the provision condition pattern specified by the user as the provision condition pattern satisfying the predetermined condition. Outputs a provision condition pattern with a larger sales forecast than the forecast,
The information processing device according to any one of claims 1 to 3. An acquisition unit that acquires reservation information including the number of confirmed reservations related to the accommodation unit, and
It has a third prediction unit that estimates the number of reservations that are estimated to be canceled among the confirmed number of reservations included in the acquired reservation information.
The calculation unit calculates the maximum number of reservations based on the number of rooms that can be secured for the accommodation unit, the number of confirmed reservations, and the number of reservations estimated to be canceled.
The information processing device according to any one of claims 1 to 4. The output unit outputs the provision condition pattern having the largest sales forecast as the provision condition pattern satisfying the predetermined condition.
The information processing device according to any one of claims 1 to 5. As a provision condition pattern, the output unit determines whether or not to accept reservations for the rate rank of the accommodation unit, the minimum number of guests, the meal type, 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 specifications,
The information processing device according to any one of claims 1 to 6. A step in which the information processing device calculates the maximum number of reservations for the accommodation unit for each provision condition pattern in the accommodation unit.
The step in which the information processing device predicts the predicted value of the number of reservations for the accommodation unit,
A step in which the information processing device predicts the sales forecast for each provision condition pattern based on the maximum number of reservations for each provision condition pattern, the predicted value of the number of reservations for the accommodation unit, and the unit price for the accommodation unit.
A step in which the information processing device outputs at least one provision condition pattern that satisfies a predetermined condition among the sales forecasts for each provision condition pattern.
Information processing methods including. On the computer
A step of calculating the maximum number of reservations for each accommodation unit and a step for calculating the maximum number of reservations for each accommodation unit.
The step of predicting the predicted value of the number of reservations for the accommodation unit, and
A step of predicting sales forecast for each offer condition pattern based on the maximum number of reservations for each offer condition pattern, the predicted value of the number of reservations for the accommodation unit, and the unit price of the accommodation unit.
Of the sales forecasts for each provision condition pattern, a step to output at least one provision condition pattern that satisfies a predetermined condition, and
A program to execute.

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