Hotel guest room reservation bargaining and bidding recommendation method based on big data

Abstract

The invention discloses a hotel guest room reservation bargaining and bidding recommendation method based on big data. The method comprises the following steps: determining a future moment and a target guest room of a target hotel; predicting a guest room reservation growth rate of the target hotel at a future moment; calculating the future empty room rate of the target hotel at the future moment according to the current room remaining amount of the target hotel and the room reservation growth rate at the future moment; according to the guest room reservation historical big data of all hotels, calculating a guest room predicted price corresponding to the future room vacancy rate of the target guest room; according to the guest room reservation historical big data of the target hotel, calculating a price interval with the highest target guest room historical order transaction rate; and calculating the room recommendation price of the target room of the target hotel at the future moment according to the room prediction price corresponding to the future room vacancy rate of the target room and the price interval with the highest historical order transaction rate. According to the invention, the accuracy of pricing and price returning of merchants and the satisfaction of users on room prices can be improved, and thus the success rate of hotel room reservation is improved.

Description

Technical Field

[0001] The present invention relates to the field of hotel reservation and big data technology, and in particular to a method for recommending bargaining prices for hotel room reservations based on big data. Background Art

[0002] Online booking of hotel rooms has become an indispensable part of modern travel, providing users with a convenient and efficient accommodation booking experience. Through major online travel booking platforms or hotel official websites, users can easily browse and compare room types, prices, geographical locations and user reviews of different hotels, so as to make more informed choices. During the online booking process, users only need to enter basic information such as destination, check-in date and check-out date, and the system will immediately display a list of hotels that meet their needs. These lists usually include detailed information such as the hotel's star rating, room type (such as single room, double room, suite, etc.), price range, and whether breakfast is included. Some platforms also provide real-time inventory query functions to ensure that users can book their favorite rooms.

[0003] However, there is still a problem with the current online hotel booking method, that is, users can only book hotel rooms with website intermediaries according to the prices displayed on the website or APP, and cannot communicate and negotiate prices directly with one or several hotel merchants, resulting in users only passively accepting standardized services, with little choice and no bargaining. In fact, from the user's perspective, they hope to communicate with multiple hotels and obtain preferential prices through a competitive bargaining model. From the merchant's perspective, when a hotel has vacant rooms, the merchant is willing to establish communication with interested users, and after bargaining, sell the vacant rooms at a preferential price. Because hotel rooms, as a special commodity, have a strong timeliness of value. Once the room is not sold in time, its value will be zero after expiration.

[0004] In response to the above problems, some hotel room bargaining platforms have emerged in the prior art. However, the current hotel room bargaining platforms do not provide merchants with a recommended price. For merchants, they generally set prices and bargain based on the current vacancy rate (i.e., the proportion of vacant rooms). However, when the user's reservation time is relatively late (such as a time one or two months after the user's reservation), the merchant cannot estimate the room demand at a future time, i.e., the future vacancy rate. At this time, the merchant cannot give accurate pricing and bargaining, resulting in poor accuracy and efficiency in bargaining between users and merchants. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a hotel room reservation bargaining bid recommendation method based on big data, by predicting future vacancy rates and analyzing room reservation history big data to generate recommended room prices for specific future times and target hotels, thereby improving the accuracy of merchant pricing and bargaining and user satisfaction with room prices, thereby improving the success rate of hotel room reservations.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: The hotel room reservation bargaining bid recommendation method based on big data includes: S1: Determine the future time when a bid recommendation is required and the target rooms of the target hotel; S2: Get the current remaining number of rooms in the target hotel; S3: Predict the room booking growth rate of the target hotel in the future; S4: Calculate the future vacancy rate of the target hotel at a future time according to the current remaining number of rooms of the target hotel and the room booking growth rate at a future time; S5: Calculate the predicted room price corresponding to the future vacancy rate of the target room based on the historical big data of room reservations of all hotels; S6: Calculate the price range with the highest historical order conversion rate for the target rooms based on the target hotel's room reservation history big data; S7: Calculate the recommended room price of the target room of the target hotel at a future time according to the predicted room price corresponding to the future vacancy rate of the target room and the price range with the highest historical order conversion rate.

[0007] Preferably, in step S3, the room reservation history big data of the target hotel is obtained; and the room reservation growth rate of the target hotel at a future moment is calculated based on the room reservation history big data of the target hotel.

[0008] Preferably, in step S3, the target hotel's room reservation history big data is obtained; the remaining room quantities of the target hotel in the previous several years at the current time period and the future time period are respectively obtained based on the target hotel's room reservation history big data; the room reservation growth rate of the target hotel in the previous several years at the future time period is calculated based on the remaining room quantities in the previous several years at the current time period and the future time period; the average value of the room reservation growth rate of the target hotel in the previous several years at the future time period is used as the room reservation growth rate of the target hotel in the future time period.

[0009] Preferably, in step S5, the predicted room price corresponding to the future vacancy rate of the target room is calculated by the following steps: S501: Acquire historical big data of room reservations of all hotels including target rooms; S502: Filtering out the historical room prices of each hotel in each set vacancy rate range from the historical big data of room reservations of all hotels; S503: Calculating the historical average price of rooms in each vacancy rate range according to the historical prices of rooms of all hotels in each vacancy rate range; S504: Match the historical average price of the guest rooms in the corresponding vacancy rate range according to the future vacancy rate of the target guest room as the predicted price of the guest room corresponding to the future vacancy rate of the target guest room.

[0010] Preferably, in step S6, the price range with the highest target room order conversion rate is calculated by the following steps: S601: Obtaining historical big data of room reservations of a target hotel; S602: Filter out the total historical reservation volume of the target guest rooms and the historical reservation volume of the target guest rooms in each price range from the target hotel's room reservation history big data; S603: Calculate the historical order completion rate of the target guest room in each price range according to the total historical reservation volume of the target guest room and the historical reservation volume in each price range; S604: Select the price range with the highest historical order conversion rate for the target guest room.

[0011] Preferably, in step S7, it is determined whether the predicted room price corresponding to the future vacancy rate of the target room is within the price range with the highest historical order conversion rate: if so, the predicted room price is used as the recommended room price for the target room of the target hotel at the future time; otherwise, the interval price closest to the predicted room price is selected from the price range, and then the average of the interval price and the predicted room price is calculated as the recommended room price for the target room of the target hotel at the future time.

[0012] Compared with the prior art, the hotel room reservation bargaining price recommendation method based on big data in the present invention has the following beneficial effects: The present invention can more accurately predict the future vacancy rate of the hotel through the current remaining number of rooms in the hotel and the predicted booking growth rate in the future, and calculate the predicted room price in combination with the historical big data of room bookings of all hotels. This dynamic pricing strategy combining the prediction of future vacancy rate and the historical big data of room bookings helps the hotel maintain a high utilization rate of the rooms under different market conditions and maximizes the hotel's revenue to a certain extent. At the same time, the recommended room price of the present invention is calculated and generated based on the predicted predicted room price and the price range with the highest historical order transaction rate. By using the price range with the highest historical order transaction rate, the generated recommended price is closer to the expectations and willingness to pay of most users, thereby improving the accuracy of merchant pricing and bargaining and the user's satisfaction with the room price, thereby improving the success rate of hotel room reservations.

[0013] The present invention can quickly generate recommended room prices for specific future times and target hotels by predicting future vacancy rates and analyzing historical big data of room reservations, which not only reduces the time and effort of manual pricing, but also improves the automation of the room reservation process, thereby improving the efficiency of hotel room reservations. At the same time, through big data analysis and prediction, hotels can more accurately grasp the dynamics of the future market and customer needs, thereby formulating more competitive room price strategies, which helps hotels stand out in the fierce market competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which: Figure 1 Logic block diagram of the method for recommending bargaining bids for hotel room reservations based on big data. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0016] The following is a further detailed description through specific implementation methods: Example: This embodiment discloses a method for recommending bargaining prices for hotel room reservations based on big data.

[0017] like Figure 1 As shown in the figure, the hotel room reservation bargaining bid recommendation method based on big data includes: S1: Determine the future time when a bid recommendation is required and the target rooms of the target hotel; In this embodiment, the future time refers to a certain date, such as May 20, 2024. A target guest room refers to a certain type of guest room, such as a king-size bed room, which can be used as a target guest room. Therefore, there are many hotels that include target guest rooms.

[0018] S2: Get the current remaining number of rooms in the target hotel; S3: Predict the room booking growth rate of the target hotel in the future; S4: Calculate the future vacancy rate of the target hotel at a future time according to the current remaining number of rooms of the target hotel and the room booking growth rate at a future time; S5: Calculate the predicted room price corresponding to the future vacancy rate of the target room based on the historical big data of room reservations of all hotels; S6: Calculate the price range with the highest historical order conversion rate for the target rooms based on the target hotel's room reservation history big data; S7: Calculate the recommended room price of the target room of the target hotel at a future time according to the predicted room price corresponding to the future vacancy rate of the target room and the price range with the highest historical order conversion rate.

[0019] The present invention can more accurately predict the future vacancy rate of the hotel through the current remaining number of rooms in the hotel and the predicted booking growth rate in the future, and calculate the predicted room price in combination with the historical big data of room bookings of all hotels. This dynamic pricing strategy combining the prediction of future vacancy rate and the historical big data of room bookings helps the hotel maintain a high utilization rate of the rooms under different market conditions and maximizes the hotel's revenue to a certain extent. At the same time, the recommended room price of the present invention is calculated and generated based on the predicted predicted room price and the price range with the highest historical order transaction rate. By using the price range with the highest historical order transaction rate, the generated recommended price is closer to the expectations and willingness to pay of most users, thereby improving the accuracy of merchant pricing and bargaining and the user's satisfaction with the room price, thereby improving the success rate of hotel room reservations.

[0020] The present invention can quickly generate recommended room prices for specific future times and target hotels by predicting future vacancy rates and analyzing historical big data of room reservations, which not only reduces the time and effort of manual pricing, but also improves the automation of the room reservation process, thereby improving the efficiency of hotel room reservations. At the same time, through big data analysis and prediction, hotels can more accurately grasp the dynamics of the future market and customer needs, thereby formulating more competitive room price strategies, which helps hotels stand out in the fierce market competition.

[0021] During the specific implementation process, the target hotel's room reservation history big data is obtained; and the target hotel's room reservation growth rate in the future is calculated based on the target hotel's room reservation history big data.

[0022] Specifically, obtain the target hotel's room reservation history big data; obtain the target hotel's room remaining quantity in the current period and the future period in previous years according to the target hotel's room reservation history big data; calculate the target hotel's room reservation growth rate in previous years and the future period according to the room remaining quantity in the current period and the future period in previous years; take the average of the target hotel's room reservation growth rates in previous years and the future period as the target hotel's room reservation growth rate in the future.

[0023] In this embodiment, the previous several years are defined as the previous three years. Take the current time as August 22, 2025 and the booking time as August 25, 2025 as an example for explanation. xx Hotel: The remaining number of rooms on August 22, 2024 (i.e., the same period as the current time) is 50, and the remaining number of rooms on August 25, 2024 (i.e., the same period as the booking time) is 30. The remaining number of rooms on August 22, 2023 (i.e., the same period as the current time) is 45, and the remaining number of rooms on August 25, 2023 (i.e., the same period as the booking time) is 25. The remaining number of rooms on August 22, 2022 (i.e., the same period as the current time) is 56, and the remaining number of rooms on August 25, 2022 (i.e., the same period as the booking time) is 32. Therefore, for Hotel xx: the room booking growth rate on August 25, 2024 (i.e. the same period of booking time) is 40%, the room booking growth rate on August 25, 2023 (i.e. the same period of booking time) is 44.4%, and the room booking growth rate on August 25, 2022 (i.e. the same period of booking time) is 42.8%.

[0024] Finally, the room booking growth rate of xx hotel on August 25, 2025 (i.e. booking time) is (40%+44%+43%) / 3=42.4%.

[0025] The present invention calculates data at future times through data from the same period of previous years in the room reservation history big data. Since the hotel room reservation situation as a whole shows periodic changes, that is, the changes in hotel room reservations at the same period of each year are similar, the present invention makes full use of the room reservation history big data to predict the hotel's room reservation growth rate at future times, and then calculates the future vacancy rate and room prediction price based on the room reservation growth rate, so that the generated recommended price is closer to the expectations and payment willingness of most users, thereby improving the accuracy of merchant pricing and bargaining and user satisfaction with room prices, thereby improving the success rate of hotel room reservations.

[0026] In the specific implementation process, the predicted room price corresponding to the future vacancy rate of the target room is calculated through the following steps: S501: Acquire historical big data of room reservations of all hotels including target rooms; In this embodiment, all hotels include hotels that are within five kilometers from the target hotel and include the target guest rooms.

[0027] S502: Filtering out the historical room prices of each hotel in each set vacancy rate range from the historical big data of room reservations of all hotels; In this embodiment, every 5% interval is set as a vacancy rate interval for screening. For example, the vacancy rate interval may include 1%-5%, 6%-10%, 11%-15%... and so on.

[0028] S503: Calculating the historical average price of rooms in each vacancy rate range according to the historical prices of rooms of all hotels in each vacancy rate range; S504: Match the historical average price of the guest rooms in the corresponding vacancy rate range according to the future vacancy rate of the target guest room as the predicted price of the guest room corresponding to the future vacancy rate of the target guest room.

[0029] In this embodiment, if the future vacancy rate of the target guest room is 13%, 13% is matched to the vacancy rate range of 11%-15%, and the historical average price of the guest room corresponding to 11%-15% is used as the predicted price of the guest room.

[0030] In the specific implementation process, the price range with the highest order conversion rate for the target guest rooms is calculated through the following steps: S601: Obtaining historical big data of room reservations of a target hotel; S602: Filter out the total historical reservation volume of the target guest rooms and the historical reservation volume of the target guest rooms in each price range from the target hotel's room reservation history big data; In this embodiment, 50 yuan is a price range, and the price ranges may include 100-149 yuan, 150-199 yuan, 200-249 yuan, 250-299 yuan, etc. For example, the target room has a historical booking volume of 50 orders at 100-149 yuan, 80 orders at 150-199 yuan, 65 orders at 200-249 yuan, and 25 orders at 250-299 yuan.

[0031] S603: Calculate the historical order completion rate of the target guest room in each price range according to the total historical reservation volume of the target guest room and the historical reservation volume in each price range; S604: Select the price range with the highest historical order conversion rate for the target guest room.

[0032] During the specific implementation process, it is determined whether the predicted room price corresponding to the future vacancy rate of the target room is within the price range with the highest historical order conversion rate: if so, the predicted room price is used as the recommended room price for the target room of the target hotel at the future time; otherwise, the interval price closest to the predicted room price is selected from the price range, and then the average of the interval price and the predicted room price is calculated as the recommended room price for the target room of the target hotel at the future time.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for recommending bargaining prices for hotel room reservations based on big data, characterized in that: include: S1: Determine the future time when a bid recommendation is required and the target rooms of the target hotel; S2: Get the current remaining number of rooms in the target hotel; S3: Predict the room booking growth rate of the target hotel in the future; S4: Calculate the future vacancy rate of the target hotel at a future time according to the current remaining number of rooms of the target hotel and the room booking growth rate at a future time; S5: Calculate the predicted room price corresponding to the future vacancy rate of the target room based on the historical big data of room reservations of all hotels; S6: Calculate the price range with the highest historical order conversion rate for the target rooms based on the target hotel's room reservation history big data; S7: Calculate the recommended room price of the target room of the target hotel at a future time according to the predicted room price corresponding to the future vacancy rate of the target room and the price range with the highest historical order conversion rate.

2. The method for recommending bargaining prices for hotel room reservations based on big data as claimed in claim 1, characterized in that: In step S3, the room reservation history big data of the target hotel is obtained; and the room reservation growth rate of the target hotel at a future moment is calculated based on the room reservation history big data of the target hotel.

3. The method for recommending bargaining prices for hotel room reservations based on big data as claimed in claim 2, characterized in that: In step S3, the target hotel's room reservation history big data is obtained; the remaining room quantity of the target hotel in the previous several years at the current time and the same period in the future is obtained according to the target hotel's room reservation history big data; the room reservation growth rate of the target hotel in the previous several years at the same period in the future is calculated according to the remaining room quantity in the previous several years at the current time and the same period in the future; the average value of the room reservation growth rate of the target hotel in the previous several years at the same period in the future is used as the room reservation growth rate of the target hotel in the future.

4. The method for recommending bargaining prices for hotel room reservations based on big data as claimed in claim 1, characterized in that: In step S5, the predicted room price corresponding to the future vacancy rate of the target room is calculated by the following steps: S501: Acquire historical big data of room reservations of all hotels including target rooms; S502: Filtering out the historical room prices of each hotel in each set vacancy rate range from the historical big data of room reservations of all hotels; S503: Calculating the historical average price of rooms in each vacancy rate range according to the historical prices of rooms of all hotels in each vacancy rate range; S504: Match the historical average price of the guest rooms in the corresponding vacancy rate range according to the future vacancy rate of the target guest room as the predicted guest room price corresponding to the future vacancy rate of the target guest room.

5. The method for recommending bargaining prices for hotel room reservations based on big data as claimed in claim 1, characterized in that: In step S6, the price range with the highest order conversion rate for the target guest rooms is calculated by the following steps: S601: Obtaining historical big data of room reservations of a target hotel; S602: Filter out the total historical reservation volume of the target guest rooms and the historical reservation volume of the target guest rooms in each price range from the target hotel's room reservation history big data; S603: Calculate the historical order completion rate of the target guest room in each price range according to the total historical reservation volume of the target guest room and the historical reservation volume in each price range; S604: Select the price range with the highest historical order conversion rate for the target guest room.

6. The method for recommending bargaining prices for hotel room reservations based on big data as claimed in claim 1, characterized in that: In step S7, it is determined whether the predicted room price corresponding to the future vacancy rate of the target room is within the price range with the highest historical order conversion rate: if so, the predicted room price is used as the recommended room price for the target room of the target hotel at the future time; otherwise, the interval price closest to the predicted room price is selected from the price range, and then the average of the interval price and the predicted room price is calculated as the recommended room price for the target room of the target hotel at the future time.

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