A hotel revenue management system and method

Abstract

The present invention discloses a hotel revenue management system and method, the hotel revenue management system comprises: a data collection layer, which obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer; an analysis and prediction layer, which analyzes the analysis charts and predicts the future business data of the hotel in different dimensions; an operation optimization layer, which optimizes the hotel operation plan; a budget management module, which scientifically manages and optimizes the hotel operation budget; and a report generation module, which generates various market performance index reports. The present invention can provide a reference price for the hotel according to the market supply and demand relationship and the hotel's daily revenue target according to the algorithm, facilitate the hotel to implement dynamic pricing, improve the price system and pricing level; and can also provide dynamic quotation for the team; reasonably reserve the room quantity, set the upper and lower limits of the price, and bring the maximum benefit to the hotel's operation.

Description

Technical Field

[0001] The present invention relates to the field of hotel operation, and in particular to a hotel revenue management system and method. Background Art

[0002] At present, many hotels are unable to maximize their profits in actual operations and have many strategic problems. The prices of indirect channels are lower than those of direct channels, the price system is rigid, and the prices are not high enough when they should be high and not low enough when they should be low. For example:

[0003] Scenario 1: Customers are lost to indirect channels, such as online travel agencies (OTA), resulting in increased commissions and loss of customer trust. When market demand is high, high-priced customers cannot be received, resulting in loss of room rates and profits; when market demand is low, price-sensitive customers cannot be received, resulting in loss of occupancy rate and income.

[0004] Scenario 2: Guests who stay for more than 2 days cannot book a room; requests that should not be refused are rejected; occupancy rates in the off-season and the shoulder season are not maximized; customer satisfaction is low; and revenue for the entire week or the entire month is not maximized. If the room is sold out too early, when a request with a higher comprehensive profit comes, it can only be rejected; if the room is sold too late, it will be wasted or low-priced business will be accepted. Summary of the invention

[0005] In view of the above-mentioned deficiencies in current hotel operations, the present invention provides a hotel revenue management system and method, which can propose the best operation plan and bring the greatest benefits to the hotel's operation.

[0006] To achieve the above object, the embodiments of the present invention adopt the following technical solutions:

[0007] A hotel revenue management system, comprising:

[0008] The data collection layer obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer;

[0009] The analysis and prediction layer analyzes the analytical charts and predicts the hotel's future operating data in different dimensions;

[0010] The operation optimization layer optimizes the hotel operation plan;

[0011] Budget management module, scientifically manage and optimize the hotel operating budget;

[0012] Report generation module, generates various market performance index reports.

[0013] According to one aspect of the present invention, the data collection layer obtains hotel data from the hotel management system PMS and / or the Internet, and the operating data includes room nights, average room rate and room revenue, and generates different operating data analysis charts in terms of room type, channel and market segment dimensions.

[0014] According to one aspect of the present invention, the analysis and prediction layer has a built-in room nights prediction module and a house price prediction module, and the room nights prediction module includes an algorithm module and an exhibition report module.

[0015] According to one aspect of the present invention, the algorithm module configures corresponding algorithms according to the obtained analysis charts to predict the number of room nights of the hotel in different dimensions in the future; the exhibition report module obtains useful information about events that affect the hotel market demand every day in the city where the hotel is located through data mining, and predicts the demand for room nights brought by the event to the city where the hotel is located; the results of the comprehensive algorithm module and the exhibition report module are used to accurately predict the number of room nights of the hotel in different dimensions in the future.

[0016] According to one aspect of the present invention, the room rate prediction module fits a price elasticity curve based on the relationship between the number of room nights and the average room rate in historical hotel operating data, and then calculates the optimal price as the average room rate prediction result based on the number of room nights predicted by the room night prediction module. The product of the number of room nights and the average room rate is the room revenue prediction result.

[0017] According to one aspect of the present invention, the business optimization layer has a built-in price suggestion module, an overbooking module and a business replacement module; the price suggestion module gives price suggestions based on the business data forecast results, the day's market demand, the on-hand reservation situation and the competitor's pricing.

[0018] According to one aspect of the present invention, the overbooking module automatically makes recommendations on the number of room nights for each room type that are overbooked on a fully booked day, and records and counts the results and benefits of overbooking.

[0019] According to one aspect of the present invention, the business replacement module performs business replacement analysis, calculates the impact of each business on the overall revenue and profit of the hotel, and proposes an acceptable minimum quotation and sales strategy.

[0020] A hotel revenue management method based on a hotel revenue management system, the hotel revenue management method comprising the following steps:

[0021] The hotel management system PMS transmits hotel data to the data collection layer;

[0022] The data collection layer categorizes and organizes the data;

[0023] The analysis and prediction layer predicts and optimizes the classified and organized data;

[0024] Import the optimized data into the database and wait for user calls.

[0025] According to one aspect of the present invention, the hotel data is transmitted to the data collection layer through an interface, and the hotel data includes code property data and hotel operation data.

[0026] Advantages of the implementation of the present invention: A hotel revenue management system described in the present invention includes: a data collection layer, which obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer; an analysis and prediction layer, which analyzes the analysis charts and predicts the hotel's future business data in different dimensions; an operation optimization layer, which optimizes the hotel's business plan; a budget management module, which scientifically manages and optimizes the hotel's business budget; and a report generation module, which generates various market performance index reports. According to the algorithm, a reference price is provided for the hotel based on the market supply and demand relationship and the hotel's daily revenue target, which facilitates the hotel to implement dynamic pricing; improves the price system and pricing level; the system's prediction algorithm provides the hotel with room occupancy rate, number of hotel guests, number of check-in and check-out rooms and time periods, number of diners, per-guest consumption, etc., and accurately schedules shifts, prepares materials and arranges work on this basis; so that the hotel can increase its hotel revenue by 5-7% and its profit by more than 50% without increasing or increasing its investment, and its market share is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of a hotel revenue management system according to the present invention;

[0029] Figure 2 A schematic diagram of a hotel revenue management method according to the present invention;

[0030] Figure 3 It is a framework diagram of the hotel revenue management method described in the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, a hotel revenue management system includes:

[0033] The data collection layer obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer;

[0034] The analysis and prediction layer analyzes the analytical charts and predicts the hotel's future operating data in different dimensions;

[0035] The operation optimization layer optimizes the hotel operation plan;

[0036] Budget management module, scientifically manage and optimize the hotel operating budget;

[0037] Report generation module, generates various market performance index reports.

[0038] In actual applications, the data collection layer obtains hotel data from the hotel management system PMS and / or the Internet. The operating data includes room nights, average room rate and room revenue, and generates different operating data analysis charts based on room type, channel and market segment dimensions.

[0039] In practical applications, the hotel's historical data is obtained from the hotel management system PMS, and information about competitors is obtained from the Internet.

[0040] In actual application, hotel rooms are classified into room type, channel and market segment, and corresponding business data analysis charts are formed based on these three classifications.

[0041] In practical applications, market segments may include crew, membership, conference, discount, etc.

[0042] In actual applications, the analysis and prediction layer has built-in room nights prediction module and house price prediction module. The room nights prediction module includes an algorithm module and an exhibition report module.

[0043] In actual applications, the algorithm module configures the corresponding algorithm based on the obtained analysis charts to predict the hotel's future room nights in different dimensions; the exhibition report module obtains useful information about events that affect hotel market demand every day in the city where the hotel is located through data mining, and predicts the demand for room nights brought by the event to the city where the hotel is located; the results of the comprehensive algorithm module and the exhibition report module are used to accurately predict the hotel's future room nights in different dimensions.

[0044] In practical applications, the hotel data obtained includes reservation progress. The algorithm module configures the GDBT algorithm based on the reservation progress and analysis charts to predict the future number of hotel room nights.

[0045] In practical applications, the GBDT algorithm uses an additive model (i.e., a linear combination of basis functions) to continuously reduce the residuals generated during the training process to achieve data classification or regression.

[0046] In actual application, the algorithm module will also calculate the maximum protection capacity per day, that is, the maximum number of room nights, for different room types, channels and market segments.

[0047] In actual applications, events include exhibitions, cultural and sports events, holiday events, weather reports, etc. By automatically obtaining useful information about the above events, we can solve the problems of high manual collection costs, incomplete data, and untimely data. This can provide powerful assistance for hotels in finding business, pricing, promotion, predicting demand, and formulating sales strategies.

[0048] In actual application, based on the number of room nights predicted by the algorithm module, the results of the exhibition report module are combined to accurately predict the number of room nights in the future in terms of room type, channel and market segment, so as to facilitate the hotel to reasonably allocate resources in the off-season and peak season. The peak season requires structural optimization; off-season requires early promotion; package sales; price differentiation; and the development of new market segment customers.

[0049] In actual applications, the room rate prediction module fits the price elasticity curve based on the relationship between the number of nights and the average room rate in historical hotel operating data, and then calculates the optimal price as the average room rate prediction result based on the number of room nights predicted by the room night prediction module. The product of the number of room nights and the average room rate is the room revenue prediction result. In addition, the room rate prediction module reduces the granularity of the hotel's future room nights in three dimensions: room type, channel, and market segment, and calculates the optimal average room rate that maximizes revenue and market share based on the number of room nights, maximum protection capacity, and price elasticity curve.

[0050] In actual applications, the business optimization layer has built-in price suggestion modules, overbooking modules and business replacement modules; the price suggestion module gives price suggestions based on the forecast results of business data, the market demand of the day, the reservation situation on hand and the pricing of competitors. For example, the price suggestion module obtains the predicted average room rate of the day, and makes targeted adjustments based on the actual situation of the day, giving the best price suggestion, which effectively helps the hotel implement dynamic pricing, so that the hotel will not lose guests due to too high prices, nor lose income due to too low prices.

[0051] In actual application, the overbooking module automatically makes recommendations on the number of room nights for each room type that are overbooked on fully booked days, and records and counts the results and benefits of overbooking.

[0052] In actual application, when the hotel is fully booked (when the predicted occupancy rate exceeds 95%), overbooking can make up for the loss of vacant rooms due to failure to book a room, temporary cancellation or early departure.

[0053] In practical applications, the business replacement module performs business replacement analysis, calculates the impact of each business on the hotel's overall revenue and profit, and proposes an acceptable minimum quotation and sales strategy.

[0054] In actual application, we should change the outdated and backward concepts of first-come-first-served and quotation based on experience, provide dynamic quotation for the team, reserve rooms reasonably, and set upper and lower limits of prices.

[0055] In actual applications, the budget management module scientifically predicts and optimizes the budget based on historical data, market environment, and corporate goals (MPI), rather than based on the subjective experience of the budget maker. The budget is very accurate, greatly reducing the workload of manual budgeting.

[0056] In practical applications, the market performance index report generated by the report generation module includes market share index (MPI), price index (ARI), single room return index (RGI), etc.; it is automatically generated, reliable, trouble-free and cost-saving.

[0057] In actual applications, the system also provides trends in historical operating data of hotel sales statistics, making it easier for hotels to upgrade sales; increase value-added promotions; dynamically adjust room price differences; and optimize room structure and content.

[0058] In actual application, the system also displays hotel inventory data, which is conducive to hotel inventory allocation management, can control booking progress, upgrade sales, and tap into horizontal benefits.

[0059] This embodiment provides a hotel revenue management system that accurately predicts the number of room nights, average room rate and room revenue for each market segment through analysis of hotel data, makes price recommendations based on the predicted data, and provides functions such as overbooking and business replacement, and provides support for budget management, which can bring the greatest benefits to the hotel's operations.

[0060] like Figure 2 As shown, the present invention also discloses a hotel revenue management method based on the hotel revenue management system, and the hotel revenue management method comprises the following steps:

[0061] S1: The hotel management system PMS transmits hotel data to the data collection layer;

[0062] In practical applications, the hotel data is transmitted to the data collection layer through an interface, and the hotel data includes code property data and hotel operation data.

[0063] In actual applications, hotel data is synchronized once every morning without interruption.

[0064] In actual applications, the system is run repeatedly to re-upload data that was not uploaded in time.

[0065] S2: The data collection layer categorizes and organizes the data;

[0066] In actual applications, for code-type data, the uploaded data is matched. If the code data already exists in the database, the original data is updated according to the new data. If the code data does not exist in the database, the system adds such data to the database.

[0067] In actual applications, for hotel operation data and order data, the order is split. For example, if there are orders for multiple rooms within a period of time, the system will split the order into one record per room per day. For consumption and other data, the system will add data to the database every day.

[0068] In actual applications, the system aggregates and counts the split data according to the hotel operating indicator data, and summarizes and counts the number of room nights, occupancy rate, average room rate, single room revenue, room revenue, number of reservations in hand, number of available rooms, etc. according to each market segment, channel, and room type of the hotel, and solidifies the data.

[0069] In actual applications, the analyzed and sorted data is stored in the Mongo database.

[0070] S3: The analysis and prediction layer predicts and optimizes the classified and organized data;

[0071] The prediction and optimization include the following steps: 1. Fitting the price elasticity curve according to the number of room nights and obtaining the corresponding parameters; 2. Obtaining the maximum protection capacity of each day for different markets, room types and channels; 3. Regularizing the order data in the format of 45 days in advance to the 0th day; 4. Predicting the number of room nights for different market room types in the future according to the GDBT algorithm; 5. Calculating the historical average price of different room types and channels; 6. Calculating the price response curve parameters according to the price elasticity curve, the predicted room night results and the maximum capacity; 7. Dividing the coarser-grained room types and market room nights into finer-grained markets and room types; 8. Calculating the optimal pricing, protection capacity, average number of arrivals, and unrestricted demand; 9. Calculating the optimal pricing, protection capacity, average number of arrivals, and unrestricted demand at a finer granularity.

[0072] S4: Import the optimized data into the database and wait for user calls.

[0073] In actual applications, the optimized data is saved in the Mongo database. Hotel users finally access the data in the Mongo database through the front end of the hotel revenue management system to obtain the data that the hotel needs to see.

[0074] In practical applications, the framework diagram adopted by the hotel revenue management method of the present invention is as follows: Figure 3 As shown:

[0075] 1. The hotel PMS management system synchronizes hotel data to the file data in the revenue management system through multiple external interfaces. Multiple file data can be synchronized to ensure error-free;

[0076] 2. Clean and analyze the data in each file, and store the analyzed data in multiple SQL servers respectively;

[0077] 3. Model and analyze the data in SQL Server. The modeling and analysis data is structured data stored in SQL Server. Mongo is used to store other unstructured data, such as exhibitions and external price data.

[0078] 4. The structured data in the algorithm comes from SQL Server. The algorithm needs to obtain some unstructured data from Mongo, and the generated algorithm structure is stored in SQL Server.

[0079] 5. Finally, the generated modeling analysis data, algorithm data, and some unprocessed unstructured data will be synchronized to the display database MySQL for front-end web page calls.

[0080] In actual applications, by setting up multiple databases, data can be backed up layer by layer, data is not easily lost, and reliability is greatly increased.

[0081] In actual applications, considering the disaster recovery environment, multiple environments are required for incremental backup and data synchronization at the same time.

[0082] Advantages of the implementation of the present invention: A hotel revenue management system of the present invention includes: a data collection layer, which obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer; an analysis and prediction layer, which analyzes the analysis charts and predicts the hotel's future business data in different dimensions; an operation optimization layer, which optimizes the hotel's business plan; a budget management module, which scientifically manages and optimizes the hotel's business budget; and a report generation module, which generates various market performance index reports. According to the algorithm, a reference price is provided to the hotel based on the market supply and demand relationship and the hotel's daily revenue target, which facilitates the hotel to implement dynamic pricing; improves the price system and pricing level; the system's prediction algorithm provides the hotel with room occupancy rate, number of hotel guests, number of check-in and check-out rooms and time periods, number of diners, per-guest consumption, etc., and accurately schedules shifts, prepares materials and arranges work on this basis; so that the hotel can increase its hotel revenue by 5-7% and its profit by more than 50% without increasing or increasing its investment, and its market share is greatly improved.

[0083] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A hotel revenue management system, It is characterized in that include: The data collection layer obtains hotel data and generates business data analysis charts of different dimensions and transmits them to the analysis and prediction layer; The analysis and prediction layer analyzes the analytical charts and predicts the hotel's future operating data in different dimensions; The operation optimization layer optimizes the hotel operation plan; Budget management module, scientifically manage and optimize the hotel operating budget; Report generation module, generating various market performance index reports; The analysis and prediction layer has built-in room nights prediction module and house price prediction module, and the room nights prediction module includes an algorithm module and an exhibition report module; the algorithm module configures corresponding algorithms according to the obtained analysis charts to respectively predict the number of room nights of the hotel in different dimensions in the future; the exhibition report module obtains useful information about events that affect the hotel market demand every day in the city where the hotel is located through data mining, and predicts the demand for room nights of the hotel in the city brought by the event; the results of the algorithm module and the exhibition report module are integrated to accurately predict the number of room nights of the hotel in different dimensions in the future; the house price prediction module fits the price elasticity curve according to the relationship between the number of nights and the average house price of the historical hotel operation data, and then calculates the optimal price as the average house price prediction result according to the number of nights predicted by the room nights prediction module, and the product of the number of room nights and the average house price is the room revenue prediction result; The business optimization layer has built-in price suggestion module, overbooking module and business replacement module; the price suggestion module gives price suggestion according to the business data forecast result, comprehensively considers the market demand, the reservation status on hand and the pricing of competitors on that day; the overbooking module automatically makes suggestion on the number of room nights of each room type on the fully occupied day and overbooked guest rooms, and records and counts the results and revenue of overbooking; the business replacement module conducts business replacement analysis, calculates the impact of each business on the overall revenue and profit of the hotel, and proposes the lowest acceptable quotation and sales strategy.

2. The hotel revenue management system according to claim 1, It is characterized in that The data collection layer obtains hotel data from the hotel management system PMS and / or the Internet. The operating data includes room nights, average room rate and room revenue, and generates different operating data analysis charts in terms of room type, channel and market segment dimensions.

3. A hotel revenue management method based on the hotel revenue management system according to any one of claims 1 to 2, It is characterized in that The hotel revenue management method comprises the following steps: The hotel management system PMS transmits hotel data to the data collection layer; The data collection layer categorizes and organizes the data; The analysis and prediction layer predicts and optimizes the classified and organized data; Import the optimized data into the database and wait for user calls; The prediction and optimization include the following steps: fitting a price elasticity curve according to the number of room nights and obtaining corresponding parameters; obtaining the maximum protection capacity of each day for different markets, room types and channels; organizing the order data in a format from 45 days in advance to day 0; predicting the number of room nights for different market room types in the future according to the GDBT algorithm; calculating the historical average price of different room types and channels; calculating the price response curve parameters according to the price elasticity curve, the predicted room night results and the maximum capacity; dividing the coarser-grained room types and market room nights into finer-grained markets and room types; calculating the optimal pricing, protection capacity, average number of arrivals, and unrestricted demand; calculating the optimal pricing, protection capacity, average number of arrivals, and unrestricted demand at a finer granularity.

4. The hotel revenue management method according to claim 3, It is characterized in that The hotel data is transmitted to the data collection layer through an interface, and the hotel data includes code property data and hotel operation data.

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