CN111539778A - Dynamic pricing method and system for directional pushing - Google Patents
Dynamic pricing method and system for directional pushing Download PDFInfo
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Abstract
The invention discloses a dynamic pricing method and a dynamic pricing system for directional pushing, and relates to the field of airline income analysis. The method comprises the following steps: the method comprises the steps that a flight seat monitoring device obtains the residual seat information of a plurality of flights of an airline with profit to be optimized, an aviation operation background computing device obtains a target customer according to customer query records of the airline, a customer dynamic pricing model trained through information of historical transaction customers outputs the dynamic pricing result of the residual seats according to the input residual seat information and the information of the target customer, an aviation operation front-end device respectively pushes the dynamic pricing result to the target customer, the dynamic pricing model predicts the integral operation condition of the flights in real time according to the residual seat requirements of the flights, dynamic quotations are carried out on different passengers, the transaction amount of the passengers is improved, and accurate income management is achieved.
Description
Technical Field
The invention relates to the field of airline revenue analysis, in particular to a dynamic pricing method and a dynamic pricing system for directional pushing.
Background
With the gradual improvement of living standard of people, more and more people select airplanes as a travel transportation mode. Before traveling, the traveler usually selects advance booking tickets, and different airlines also give corresponding ticket price schemes for different time periods, so that the traveler can select the traveling time. For an airline company, an effective air ticket price management scheme is formulated, and improvement of the passenger seat rate of flights and the overall income of flights is facilitated.
The current air ticket price management scheme adopted by airlines is based on seat control, and achieves the purpose of controlling the income of an air ticket by controlling the seat number of different cabins. For a certain airline company, the price of a designated full-price cabin is set according to the distance from the takeoff time, so that ticket prices with different discounts are set, and different passengers are combined to sell tickets with different prices during ticket selling.
However, the existing air ticket price management scheme of the airline company only carries out pricing based on the consideration of the airline end, belongs to a passive pricing process, is not butted to the customer demand, greatly reduces the transaction rate of unstable customer groups, and reduces the overall income of the airline.
Disclosure of Invention
The invention provides a dynamic pricing method and system for directional pushing, aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
a dynamic pricing method for directed push, comprising:
s1, the flight seat monitoring device obtains the residual seat information of a plurality of flights of the route with the profit to be optimized;
s2, the aviation operation background computing device acquires a target customer according to the customer query record of the airline;
s3, outputting the dynamic pricing result of the remaining seats according to the input information of the remaining seats and the information of the target customer through the dynamic pricing model of the customer after the information training of the historical transaction customer;
and S4, the aviation operation front-end device respectively pushes the dynamic pricing results to the target customers.
The invention has the beneficial effects that: according to the scheme, a dynamic pricing result of the target customer is output according to the input residual seat information and the target customer through a customer dynamic pricing model after information training of historical trading customers, the dynamic pricing result is pushed to the target customer respectively, the dynamic pricing model predicts the integral operation condition of the flight in real time according to the residual seat requirement of the flight, dynamic quotations are carried out on different passengers, the trading volume of the passengers is improved, and accurate income management is achieved.
The method conforms to the concept of differential accurate marketing in NDC standards, carries out directional quotation aiming at different customers, improves the seat achievement rate, carries out directional accurate marketing on potentially unstable customer groups, improves the customer service quality, and increases the utilization rate of the rest seats and the flight profit.
Further, dividing the residual seat information into different cabins, setting different grades according to the different cabins, and confirming corresponding preferential proportions according to the grades;
the S3 specifically includes: and outputting the dynamic pricing result of the target customer according to the input residual seat information and the target customer and by combining the cabin level and the preferential proportion of the seat through a customer dynamic pricing model after the information training of the historical transaction customer.
The beneficial effect of adopting the further scheme is that: according to the scheme, the rest seat information is divided into different cabins, different grades are set according to the different cabins, and corresponding preferential proportions are confirmed according to the grades; different cabin positions have different preferential proportions, the prices of the high-price cabin and the low-price cabin are distinguished, if two customers under the same condition respectively select the high-price cabin and the low-price cabin, but the preferential proportions are still different, the seat volume is guaranteed, meanwhile, the profit of the high-price cabin is not damaged, and the overall benefit of the flight is improved.
Another technical solution of the present invention for solving the above technical problems is as follows:
a dynamic pricing system for directed push, comprising: the system comprises a flight seat monitoring device, an aviation operation background computing device, a client dynamic pricing model and an aviation operation front-end device;
the flight seat monitoring device is used for obtaining the residual seat information of a plurality of flights of the airline with the profit to be optimized;
the aviation operation background computing device is used for acquiring a target customer according to the customer query record of the airline;
the client dynamic pricing model is used for outputting a dynamic pricing result of the rest seats according to the input rest seat information and the information of the target client;
and the aviation operation front-end device is used for pushing the dynamic pricing results to the target customers respectively.
The beneficial effect of this scheme is: according to the scheme, a dynamic pricing result of the target customer is output according to the input residual seat information and the target customer through a customer dynamic pricing model after information training of historical trading customers, the dynamic pricing result is pushed to the target customer respectively, the dynamic pricing model predicts the integral operation condition of the flight in real time according to the residual seat requirement of the flight, dynamic quotations are carried out on different passengers, the trading volume of the passengers is improved, and accurate income management is achieved.
The method conforms to the concept of differential accurate marketing in NDC standards, carries out directional quotation aiming at different customers, improves the seat achievement rate, carries out directional accurate marketing on potentially unstable customer groups, improves the customer service quality, and increases the utilization rate of the rest seats and the flight profit.
The cabin space grading module is used for dividing the residual seat information into different cabin spaces, setting different grades according to the different cabin spaces and confirming corresponding preferential proportions according to the grades;
the client dynamic pricing model is specifically used for outputting a dynamic pricing result of the target client according to the input remaining seat information and the target client and by combining the cabin level and the preferential proportion of the seat through the client dynamic pricing model trained by the information of the historical transaction clients.
The beneficial effect of adopting the further scheme is that: according to the scheme, the rest seat information is divided into different cabins, different grades are set according to the different cabins, and corresponding preferential proportions are confirmed according to the grades; different cabin positions have different preferential proportions, the prices of the high-price cabin and the low-price cabin are distinguished, if two customers under the same condition respectively select the high-price cabin and the low-price cabin, but the preferential proportions are still different, the seat volume is guaranteed, meanwhile, the profit of the high-price cabin is not damaged, and the overall benefit of the flight is improved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic flowchart of a dynamic pricing method for directional pushing according to an embodiment of the present invention;
FIG. 2 is a network framework diagram of a dynamic pricing system for directed push according to other embodiments of the invention;
fig. 3 is a block diagram of a dynamic pricing system for directional pushing according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, a dynamic pricing method for directional pushing provided by an embodiment of the present invention includes:
s1, the flight seat monitoring device 11 obtains the residual seat information of a plurality of flights of the route with the profit to be optimized;
the specific implementation manner of acquiring the remaining seat information of the flight can be as follows: the flight seat monitoring device 11 serves as a server device at the back end of the platform, and is used for polling and scanning front-end ticketing equipment, such as an online webpage end, an offline unmanned ticketing machine or ticketing window equipment, and the like in real time, confirming that returned tickets, sold tickets and the rest are used as tickets in real time, connecting the returned tickets, sold tickets and the rest as tickets with a customer dynamic pricing model, and sending the obtained information of a plurality of flight rest seats of the airline to the customer dynamic pricing model.
As shown in fig. 2, the flight seat monitoring device sends the information of the plurality of remaining seats of the flight of the airline obtained by polling and scanning the front-end ticket selling equipment to the customer dynamic pricing model; and the aviation operation background computing device sends the computed information of the target client to the client dynamic pricing model, computes the pricing of the remaining seat information according to the remaining seat information and the client dynamic pricing model of the target client after training to obtain the pricing of the remaining seat information, sends the pricing result to the aviation operation front-end device, and pushes the pricing result to the client through the aviation operation front-end device.
In a certain embodiment, the flight seat monitoring device 11 collects the remaining seats of the airline whose profit is to be optimized, divides the remaining seat information into different slots, sets different levels according to the different slots, confirms corresponding preferential proportions according to the levels, and offers seats with different preferential proportions according to the different slots. For example, in the dynamic pricing model, when the same user queries prices of different slots, offers output by the model are different from discount discounts of corresponding original price slots, and the discount degree is confirmed according to the slot grade; if the existing first-level cabin, second-level cabin and third-level cabin, the corresponding cabin full-price seat price is as follows: 5000. 4000 and 2000, the corresponding preferential proportion is 95 folds, 85 folds and 7 folds; the first client (the client conditions are consistent) selects the prices of the three cabins to be 5000 x 95%, 4000 x 85% and 2000 x 70% respectively, so that even one client corresponds to the cabins of different grades, the preferential proportion is different, and the profit of the high-price cabin and the achievement rate of the whole cabin are ensured.
S2, the aviation operation background computing device 12 acquires a target customer according to the customer inquiry record of the airline;
the embodiment of obtaining the target client may be: the airline operation background computing device 12 is configured to collect a customer query record of the passenger for the airline, for example, a record of the passenger querying the airline at a web end or an APP end of an airline operation platform, or a query record of the passenger locating to the airline when querying sales promotion information such as special airline tickets, where the query record mainly collects personal information and query content of the customer, and can query and obtain historical travel information corresponding to the personal information of the customer according to the collected personal information of the customer, for example, historical order information such as flights selected by historical travel, fare information of purchased airline tickets, and travel time. The aviation operation background computing device 12 is connected with the client dynamic pricing model and sends the acquired client query record information to the client dynamic pricing model.
In a certain embodiment, S2 specifically includes: the aviation operation background computing device 12 acquires inquiry content and historical travel information corresponding to each customer according to the customer inquiry record of the airline;
calculating the virtual achievement rate of each customer through a particle swarm algorithm, the acquired query content and the acquired historical travel information, and taking the customer with the virtual achievement rate higher than the preset virtual achievement rate threshold value of the airline as a target customer, wherein the virtual achievement rate is used for indicating the probability of the customer purchasing the flight seat of the airline.
And setting parameters of a virtual achievement rate threshold value of the route in the aviation operation background computing device 12, wherein the virtual achievement rate represents the probability of the customer purchasing the flight seat of the route according to the collected personal information, historical travel information and inquiry content of the customer, and the virtual achievement rate threshold value can be determined according to target customer data required by the route and the residual seats.
The specific implementation of calculating the virtual achievement rate may be: the aviation operation background computing device 12 calculates the virtual achievement rate of each customer through a particle swarm algorithm according to the historical travel information and the query content of each corresponding customer in the customer query record and the query record of the airline, and takes the customer corresponding to the virtual achievement rate higher than the virtual achievement rate threshold value as a target customer.
Through simple behaviors of particle individuals, namely historical transaction behaviors and query contents of each client, information interaction in a group is associated, the group is a client who has historical transaction on the airline and a client who queries information of the airline, parameters of a PSO (particle swarm optimization) are adjusted to balance global detection and local exploitation capacity of an algorithm, for example, inertial weight is introduced into a speed item of the PSO algorithm by Shi and Eberhart, linear (or nonlinear) dynamic adjustment is carried out on the inertial weight according to an iteration process and a particle flight condition to balance the global property and convergence rate of search, actual adjustment can be selected according to data mining depth, and the individual behaviors and the group behaviors are linked through a particle swarm algorithm to obtain the clients who most possibly achieve transaction in the user who queries the airline, wherein the particle swarm algorithm belongs to the prior art, and the specific calculation process is not repeated here.
According to the scheme, the parameters of the virtual achievement rate threshold value are set firstly, then the virtual achievement rate of each customer is obtained through the particle swarm algorithm according to the customer query record of the airline and the historical travel information of each customer, the customer higher than the virtual achievement rate threshold value is used as a target customer, the customer is selected at will to push in numerous login customers, the achievement rate is extremely low, a large number of customers are pushed for preferential quotation, profits can be damaged, the data processing pressure of the system can be increased, accurate high-quality target customers can be obtained through the particle swarm algorithm, the achievement rate is effectively improved, and the total profits are guaranteed.
S3, outputting the dynamic pricing result of the rest seats according to the input rest seat information and the information of the target customer through the customer dynamic pricing model 13 trained by the information of the historical transaction customers;
in one embodiment, the customer dynamic pricing model 13 is built based on time to takeoff from distance, historical sales prices for airlines, contemporaneous sales prices for airlines, booking rates for all flights on an airline, and customer information.
It should be noted that, an airline company determines a certain airline A, which has a plurality of flights, wherein flight a, determined takeoff time Ta, is subdivided according to the difference between the ticket buying date and the purchased space level of a passenger to generate a multi-level space and a multi-level fare system, and then is optimized according to the information in the revenue management database to determine the acceptable booking number and corresponding contemporaneous sale price of each level of each flight;
in a revenue management system of an airline company, by using historical booking numbers and corresponding historical selling prices in a revenue management database, an airline booking prediction model is combined with historical data to conjecture market demands and determine the predetermined rate of all flights on an airline; the airline reservation forecasting model calculates a predetermined rate and obtains the rate according to historical sales information and by combining weighted seasonal reservation fluctuation rate.
The reservation system acquires the flight reservation condition in sale in time, compares the historical sale data with the planned sale target, and determines the discount of the ticket price by the marketing management personnel of the airline company by combining the prediction information of the prediction subsystem, so that the airline company can flexibly allocate the spaces, and performs price reduction processing or space transfer on the spaces with less demand and slow reservation to realize the optimization of the reservation rate of the flight;
basic variables of the customer dynamic pricing model: determining the acceptable booking number of each grade and the corresponding contemporaneous sale price according to the time of the distance takeoff; determining the booking rate of all flights on the airline according to the historical booking number and the corresponding historical sale price; and adjusting the seat information behind the cabin.
Important influencing variables of the customer dynamic pricing model output: the target client and the system ticket price correspond to the achievement rate.
In one embodiment, the dynamic pricing model is an airline a, wherein one flight a of the airline has a takeoff time of Ta, the number of seats acceptable for each level of the flight a is 50, the corresponding original price sale price is 5000 yuan, the number of seats of three levels of the flight is adjusted to be 20, 50 and 80 according to the number of seats allocated by airline management personnel, the optimal predetermined rate of the flight is determined to be 75%, namely, the rest seats to be optimized for profit are 25% of the flight, the number of seats of three levels is respectively 5, 12 and 20, and the optimal price of the flight corresponding to the target customer in the seats of different levels is determined by combining the time difference T between the preset time of the target customer and the time of Ta and the achievement rate of the price after the original price is discounted.
Wherein the trained model outputs the optimal price, in a certain embodiment, for example, in a certain embodiment, the historical journey information of the customer a includes information of a plurality of different routes, wherein route 1 is our target route, that is, there are remaining seats, and belongs to the route whose profit is to be optimized, in the journey of route 1, the customer a takes 50 times, and the taking price has three grades, one grade: 4000. and (2) second stage: 6000 and three stages: 10000, wherein the number of taking for the first level is 10, the number of taking for the second level is 40, and the number of taking for the third level is 10, so that the probability of taking for the first level at the price is: 10/50 × 100%, secondary valence ride probability: 40/50 × 100% and the tertiary valence ride probability: 10/50 × 100%, it can be seen that the secondary price achievement rate is highest for route 1, and the trained model will price the customer according to the secondary price as the optimal price of route 1 and in combination with the discount corresponding to the predetermined time as the reference, and this optimal price reference represents that if the remaining targets are seats without secondary price, the seat price closest to the secondary price will be the final output pricing result of the model.
According to the scheme, a model is established according to the time of distance takeoff, the historical sales price of the airline, the contemporaneous sales price of the airline, the reservation rate of all flights on the airline and the customer information, so that the customer dynamic pricing can be combined with various influence factors, the precision of directional pricing is improved, and the achievement rate of seats of the flights is improved.
Wherein the customer information includes: personal information of the customer, historical trip information, and historical seat purchase information.
Through the personal information, the historical travel information and the historical seat purchasing information of the customers, the dynamic pricing model fully excavates the actual demands of the customers, quotes according to the real demands of the customers, so that the quoted results more accord with the idea of accurate marketing, and the flight income is improved.
In one embodiment, based on the historical travel information of the customer and the fare information corresponding to the travel, the customer dynamic pricing model 13 is trained, fare information corresponding to flights with the achievement rate not less than the preset achievement rate is output, and the trained customer dynamic pricing model 13 is obtained for finishing the training, wherein the flights represent flight information of routes corresponding to the historical travel; if the achievement rate is less than the preset achievement rate, continuing training. The preset achievement rate can be determined according to the profit standard of the flight, different achievement rates can be obtained according to historical information by different pricing, different prices have different profit values, and the proper preset achievement rate can be determined according to different profit standards of different seasons.
It should be noted that the achievement rate refers to a relationship between a historical itinerary of a customer and a corresponding fare, which kind of price is the highest in the customer purchase probability for the same airline itinerary, each airline has multiple exact actual probability values calculated, and the trained model determines the most reasonable price according to a condition that the selected airline has the highest probability value and meets a preset achievement rate. For example, in one embodiment, the historical itinerary information of customer a includes information of a plurality of different airlines, where airline 1 is our target airline, i.e., has remaining seats, and belongs to the airline whose profit is to be optimized, and in the itinerary of airline 1, customer a takes 50 times, and the price of taking is in one of three levels: 4000. and (2) second stage: 6000 and three stages: 10000, wherein the number of taking for the first level is 10, the number of taking for the second level is 40, and the number of taking for the third level is 10, so that the probability of taking for the first level at the price is: 10/50 × 100%, secondary valence ride probability: 40/50 × 100% and the tertiary valence ride probability: 10/50 × 100%, it can be seen that for airline 1, the secondary price achievement rate is the highest, and the trained model will price the customer according to the secondary price as the optimal price reference for airline 1, which represents the final output pricing result of the model with the seat price closest to the secondary price if the remaining targets are seats without the secondary price.
According to the scheme, based on historical travel information of a client and fare information corresponding to a travel, a dynamic pricing model 13 of the client is trained, fare information corresponding to flights with an achievement rate not less than a preset achievement rate is output, and a model is trained through historical trading data of the user, so that the trained model can output the price with the highest purchasing rate of the flights according to the flight information and the user information, the price quoted to the client through the trained model is most reasonable, and the achievement rate is highest.
Preferably, the rest seat information is divided into different cabins, different grades are set according to the different cabins, and corresponding preferential proportions are confirmed according to the grades;
s3 specifically includes: and outputting a dynamic pricing result of the target customer according to the input residual seat information and the target customer by the customer dynamic pricing model 13 trained by the information of the historical transaction customer and by combining the cabin level and the preferential proportion of the seat.
According to the scheme, the rest seat information is divided into different cabins, different grades are set according to the different cabins, and corresponding preferential proportions are confirmed according to the grades; the high-price cabin and the low-price cabin are respectively selected by two customers under the same condition, but the preferential proportions are still different, so that the seat volume is guaranteed, meanwhile, the profit of the high-price cabin is not damaged, and the overall benefit of the flight is improved.
And S4, the aviation operation front-end device 14 respectively pushes the dynamic pricing results to the target customers. The pushing mode can be that the customer sends out in a bounce window mode when the customer inquires on the platform, or directly displays the results in the customer inquiry, or sends out the pricing results in a short message mode; the pushing mode can be confirmed according to the query record of the customer, for example, when the customer directly queries the information of a certain airline, the pricing result can be directly displayed in the query result by inputting the exact airline information; if the customer is located on the route in other travel information or promotion discount information, the information can be sent to the customer in a bouncing window or short message mode.
According to the scheme, a dynamic pricing result of a target customer is output according to input remaining seat information and the target customer through a customer dynamic pricing model 13 after information training of historical trading customers, the dynamic pricing result is pushed to the target customer respectively, the dynamic pricing model predicts the whole operation condition of a flight in real time according to the remaining seat requirements of the flight, dynamic quotations are carried out on different passengers, the trading volume of the passengers is improved, and accurate income management is achieved.
The method conforms to the concept of differential accurate marketing in NDC (New Distribution capability) standards, carries out directional quotation aiming at different customers, improves the seat achievement rate, carries out directional accurate marketing on potentially unstable customer groups, improves the service quality of the customers, and increases the utilization rate of the rest seats and the flight profits.
In one embodiment, as shown in fig. 3, a dynamic pricing system for directed push, the system comprising: the system comprises a flight seat monitoring device 11, an airline operation background computing device 12, a client dynamic pricing model 13 and an airline operation front-end device 14;
the flight seat monitoring device 11 is used for obtaining the residual seat information of a plurality of flights of the route with the profit to be optimized;
the aviation operation background computing device 12 is used for acquiring a target customer according to the customer query record of the airline;
the client dynamic pricing model 13 is used for outputting the dynamic pricing result of the rest seats according to the input rest seat information and the target client;
the aviation operation front-end device 14 is used for the aviation operation front-end device 14 to respectively push the dynamic pricing results to the target customers.
According to the scheme, a dynamic pricing result of the target customer is output according to input remaining seat information and information of the target customer through a customer dynamic pricing model 13 after information training of historical trading customers, the dynamic pricing result is pushed to the target customer respectively, the dynamic pricing model predicts the whole operation condition of the flight in real time according to the remaining seat requirements of the flight, dynamic quotations are carried out on different passengers, the trading volume of the passengers is improved, and accurate income management is achieved.
The method conforms to the concept of differential accurate marketing in NDC standards, carries out directional quotation aiming at different customers, improves the seat achievement rate, carries out directional accurate marketing on potentially unstable customer groups, improves the customer service quality, and increases the utilization rate of the rest seats and the flight profit.
Preferably, in any of the above embodiments, the airline operation background computing device 12 is specifically configured to obtain a preset virtual achievement rate threshold of the airline;
the aviation operation background computing device 12 obtains the virtual achievement rate of each customer through a particle swarm algorithm according to the customer query record of the airline and the historical travel information of each customer, and takes the customer with the virtual achievement rate higher than the virtual achievement rate threshold value as a target customer.
According to the scheme, the parameters of the virtual achievement rate threshold value are set firstly, then the virtual achievement rate of each customer is obtained through the particle swarm algorithm according to the customer query record of the airline and the historical travel information of each customer, the customer higher than the virtual achievement rate threshold value is used as a target customer, the customer is selected at will to push in numerous login customers, the achievement rate is extremely low, a large number of customers are pushed for preferential quotation, profits can be damaged, the data processing pressure of the system can be increased, accurate high-quality target customers can be obtained through the particle swarm algorithm, the achievement rate is effectively improved, and the total profits are guaranteed.
Preferably, in any of the above embodiments, further comprising: the model training module is used for training the customer dynamic pricing model 13 based on the historical journey information of the customer and the fare information corresponding to the journey, outputting the fare information corresponding to the flight with the achievement rate not less than the preset achievement rate, and obtaining the trained customer dynamic pricing model 13 for finishing training, wherein the flight represents the flight information of the airline corresponding to the historical journey; if the achievement rate is less than the preset achievement rate, continuing training.
According to the scheme, based on historical travel information of a client and fare information corresponding to a travel, a dynamic pricing model 13 of the client is trained, fare information corresponding to flights with an achievement rate not less than a preset achievement rate is output, and a model is trained through historical trading data of the user, so that the trained model can output the price with the highest purchasing rate of the flights according to the flight information and the user information, the price quoted to the client through the trained model is most reasonable, and the achievement rate is highest.
Preferably, in any of the above embodiments, further comprising: and the model building module is used for building a client dynamic pricing model 13 according to the time of distance takeoff, the historical sale price of the airline, the contemporaneous sale price of the airline, the booking rate of all flights on the airline and the client information.
According to the scheme, a model is established according to the time of distance takeoff, the historical sales price of the airline, the contemporaneous sales price of the airline, the reservation rate of all flights on the airline and the customer information, so that the customer dynamic pricing can be combined with various influence factors, the precision of directional pricing is improved, and the achievement rate of seats of the flights is improved.
Preferably, in any of the above embodiments, the customer information comprises: personal information of the customer, historical trip information, and historical seat purchase information.
The scheme comprises the following steps of through client information: the personal information, the historical travel information and the historical seat purchasing information of the customers enable the dynamic pricing model to fully mine the actual requirements of the customers and quote according to the actual requirements of the customers, so that the quoted results are more in line with the idea of accurate marketing, and the flight income is improved.
Preferably, in any of the above embodiments, the system further includes a cabin space grading module, configured to divide the remaining seat information into different cabin spaces, set different grades according to the different cabin spaces, and determine corresponding preferential proportions according to the grades;
the customer dynamic pricing model is specifically used for outputting a dynamic pricing result of the target customer according to the input residual seat information and the target customer and by combining the cabin level and the preferential proportion of the seat through the customer dynamic pricing model 13 trained by the information of the historical transaction customer.
According to the scheme, the rest seat information is divided into different cabins, different grades are set according to the different cabins, and corresponding preferential proportions are confirmed according to the grades; different cabin spaces have different preferential proportions, the prices of the high-price cabin and the low-price cabin are distinguished, for example, two customers under the same condition respectively select the high-price cabin and the low-price cabin, but the preferential proportions are still different, so that the seat volume is guaranteed, meanwhile, the profit of the high-price cabin is not damaged, and the overall benefit of the flight is improved.
It is understood that some or all of the alternative embodiments described above may be included in some embodiments.
It should be noted that the above embodiments are product embodiments corresponding to the previous method embodiments, and for the description of each optional implementation in the product embodiments, reference may be made to corresponding descriptions in the above method embodiments, and details are not described here again.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
The above method, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A dynamic pricing method for directional pushing is characterized by comprising the following steps:
the flight seat monitoring device obtains the residual seat information of a plurality of flights of the route with the profit to be optimized;
the aviation operation background computing device acquires a target customer according to the customer query record of the airline;
outputting a dynamic pricing result of the rest seats according to the input information of the rest seats and the information of the target customer through a dynamic pricing model of the customer after the information training of the historical transaction customer;
and the aviation operation front-end device respectively pushes the dynamic pricing results to the target customers.
2. The dynamic pricing method for directional pushing according to claim 1, wherein the obtaining of the target customer by the airline operation background computing device according to the customer query record of the airline specifically comprises:
the aviation operation background computing device acquires inquiry content and historical travel information corresponding to each customer according to the customer inquiry record of the airline;
calculating the virtual achievement rate of each customer through a particle swarm algorithm, the acquired query content and the acquired historical travel information, and taking the customer with the virtual achievement rate higher than the preset virtual achievement rate threshold value of the airline as a target customer, wherein the virtual achievement rate is used for indicating the probability of the customer purchasing the flight seat of the airline.
3. A dynamic pricing method for directed push according to claim 1 or 2, characterized by further comprising: training a dynamic client pricing model based on historical travel information and corresponding historical fare information of a historical transaction client, outputting fare information corresponding to flights with an achievement rate not less than a preset achievement rate, and obtaining the trained dynamic client pricing model for finishing training, wherein the flights represent flight information of routes corresponding to the historical travel; if the achievement rate is less than the preset achievement rate, continuing training.
4. The dynamic pricing method for directed pushing according to claim 3, further comprising: and establishing the customer dynamic pricing model according to the time of distance takeoff, the historical sales price of the airline, the contemporaneous sales price of the airline, the booking rate of all flights on the airline and customer information.
5. The dynamic pricing method for directed pushing according to claim 4, wherein the customer information comprises: personal information of the customer, historical trip information, and historical seat purchase information.
6. A dynamic pricing system for directed push, comprising: the system comprises a flight seat monitoring device, an aviation operation background computing device, a client dynamic pricing model and an aviation operation front-end device;
the flight seat monitoring device is used for obtaining the residual seat information of a plurality of flights of the airline with the profit to be optimized;
the aviation operation background computing device is used for acquiring a target customer according to the customer query record of the airline;
the client dynamic pricing model is used for outputting a dynamic pricing result of the rest seats according to the input rest seat information and the information of the target client;
and the aviation operation front-end device is used for pushing the dynamic pricing results to the target customers respectively.
7. The dynamic pricing system for directed pushing according to claim 6, wherein the airline operations background computing device is specifically configured to
Acquiring inquiry content and historical travel information corresponding to each customer according to the customer inquiry record of the airline;
calculating the virtual achievement rate of each customer through a particle swarm algorithm, the acquired query content and the acquired historical travel information, and taking the customer with the virtual achievement rate higher than the preset virtual achievement rate threshold value of the airline as a target customer, wherein the virtual achievement rate is used for indicating the probability of the customer purchasing the flight seat of the airline.
8. A dynamic pricing system for directed pushing according to claim 6 or 7, further comprising: the model training module is used for training a dynamic pricing model of a customer based on historical travel information and corresponding historical fare information of a historical transaction customer, outputting fare information corresponding to a flight with an achievement rate not less than a preset achievement rate, and obtaining the trained dynamic pricing model of the customer for finishing training, wherein the flight represents flight information of an airline corresponding to the historical travel; if the achievement rate is less than the preset achievement rate, continuing training.
9. The dynamic pricing system for directed pushing according to claim 8, further comprising: and the model building module is used for building the client dynamic pricing model according to the time of distance takeoff, the historical sales price of the airline, the contemporaneous sales price of the airline, the booking rate of all flights on the airline and the client information.
10. The dynamic pricing system for directed pushing according to claim 9, wherein the customer information comprises: personal information of the customer, historical trip information, and historical seat purchase information.
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