Dynamic pricing method and system based on opponent prices and relative sorting of big data, electronic equipment and readable storage medium

Abstract

The invention provides a dynamic pricing method for opponent prices and relative sorting based on big data, and the method comprises the following steps: the current selling price of a current product to be priced is known as P0, and the current selling prices of a plurality of known opponent products of the same type (n is greater than or equal to 3) are selected to form the real-time selling price of a group; sorting the selected current product and the products of the same type of the n opponents according to the sequence of the product selling prices from large to small; and according to the relative order of the prices of the current product and the products of the same type of the n opponents and the selling prices, obtaining a dynamic price P'of opponent prices and relative sorting based on the big data. The pricing efficiency of a user can be improved, and the total income of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a dynamic pricing method, system, electronic device and readable storage medium based on big data opponent prices and relative rankings. Background Art

[0002] In the field of revenue management, dynamic pricing is a method to achieve target revenue in a changing and complex market environment. Currently, dynamic pricing is used to assist revenue management in electric vehicles, advertising bidding, renewable energy management, ticket oversale and seat control. Existing dynamic pricing methods mainly focus on predicting demand or learning the dynamic pricing strategy of air tickets with the maximum expected return from historical data based on strategy learning. However, their pricing efficiency is poor and their effect on improving the total revenue of users is not ideal. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems raised in the above-mentioned background technology, and provide a dynamic pricing method based on the opponent prices and relative rankings of big data, which can improve the pricing efficiency of users and achieve the purpose of increasing the total revenue of users.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A dynamic pricing method based on big data opponent prices and relative rankings, comprising the following steps:

[0006] The current selling price of the current product to be priced is known to be P0, and the current selling prices of several (n≥3) known competitors' products of the same type are selected to form the real-time selling price of the group;

[0007] Sort the selected current product and the same type products of n competitors in descending order according to the price of the products. θ represents the relative position of the same type products of n selected competitors in the sorting according to the price, and θ0 is the relative position of the price of the current product. The relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1.

[0008] According to the relative ranking and selling price of the current product and the same type of products of n competitors, the dynamic pricing P′ based on the competitor's price and relative ranking based on big data is obtained:

[0009]

[0010] In the formula, is the average price of all selected products, It is the average of the relative rankings of all products.

[0011] Furthermore, the same type of products are items of the same brand, grade or similar functions, or hotel rooms of the same room type with similar brand positioning, grade, geographical location, transportation and other factors.

[0012] The present invention also provides a dynamic pricing system based on big data competitor prices and relative rankings, comprising:

[0013] Selection module: used to obtain the current selling price P0 of the current product to be priced, and select the current selling prices of several (n≥3) known competitors' products of the same type to form the real-time selling price of the group;

[0014] Sorting module: Sort the current product and the same type products of n competitors in descending order according to the price of the products. θ represents the relative position of the same type products of n competitors in the sorting according to the price, and θ0 is the relative position of the price of the current product. The relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1.

[0015] Calculation module: The calculation module stores the following formula (1). The calculation module substitutes the relative ranking and selling price of the current product and the same type of products of n competitors into the formula (1) to obtain the dynamic pricing P′ based on the competitor prices and relative rankings of big data:

[0016]

[0017] In the formula, is the average price of all selected products, It is the average of the relative rankings of all products.

[0018] The present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described above when executing the computer program.

[0019] The present invention also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the methods described are executed.

[0020] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0021] The dynamic pricing method based on opponent prices and relative ranking of big data of the present invention selects several homogeneous opponent prices in real time and dynamically based on the price data of big data from the perspective of homogeneous opponent ranking, and gives the optimal pricing suggestion within a reasonable range through the dynamic pricing method of relative ranking. On the basis of the original ranking and positioning, the floating range is reasonably controlled, which can avoid the lag and blindness caused by autonomous pricing to a certain extent, reduce the subjectivity and inefficiency of autonomous pricing, improve the pricing efficiency of users, and achieve the purpose of increasing the total revenue of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a method for dynamic pricing based on big data competitor prices and relative rankings according to a preferred embodiment of the present invention;

[0023] Figure 2 This is a module diagram of a dynamic pricing system based on big data competitor prices and relative rankings in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a component centered. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a component centered. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a component centered. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0027] See also Figure 1A preferred embodiment of the present invention provides a dynamic pricing method based on big data opponent prices and relative rankings, which is suitable for scenarios where the real-time changes and price fluctuations of multiple (n≥3) similar products / merchants are known, such as Meituan, OTA and other platforms. This pricing method uses the average price of similar products as the base number, innovatively applies the idea of ​​relative ranking to dynamic pricing, and calculates the real-time reference price of the target product by ranking the target priced product among similar competitive products. Specifically, the dynamic pricing method based on big data opponent prices and relative rankings includes the following steps:

[0028] S1, the current selling price of the current product to be priced is known to be P0, select the current selling prices of several (n≥3) known competitors' products of the same type to form the real-time selling price of the group, the current selling prices of the n known competitors' products of the same type are P1, P2, ... P n .

[0029] S2, sort the selected current product and the same type products of n competitors in descending order according to the product price. θ represents the relative position of the same type products of n selected competitors in the sorting according to the price. θ0 is the relative position of the price of the current product. When sorting, the relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1.

[0030] In this embodiment, the same type of products are items of the same brand grade or similar functions, or hotel rooms of the same room type with similar brand positioning, grade, geographical location, transportation and other factors.

[0031] S3, according to the relative ranking and selling price of the current product and the same type of products of n competitors, obtain the dynamic pricing P′ based on the competitor's price and relative ranking based on big data:

[0032]

[0033] In this embodiment, the dynamic price P' is calculated using formula (1), where: is the average price of all selected products, It is the average of the relative rankings of all products.

[0034] A preferred embodiment of the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method when executing the computer program.

[0035] A preferred embodiment of the present invention further provides a readable storage medium, wherein a computer program is stored on the readable storage medium, and the computer program is executed by a processor to perform the steps of the method.

[0036] See also Figure 2 The preferred embodiment of the present invention also provides a dynamic pricing system based on big data competitor prices and relative rankings, including:

[0037] Selection module: used to obtain the current selling price P0 of the current product to be priced, and select the current selling prices of several (n≥3) known competitors' products of the same type to form the real-time selling price of the group;

[0038] Sorting module: Sort the current product and the same type products of n competitors in descending order according to the price of the products. θ represents the relative position of the same type products of n competitors in the sorting according to the price, and θ0 is the relative position of the price of the current product. The relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1.

[0039] Calculation module: The calculation module stores the following formula (1). The calculation module substitutes the relative ranking and selling price of the current product and the same type of products of n competitors into the formula (1) to obtain the dynamic pricing P′ based on the competitor prices and relative rankings of big data:

[0040]

[0041] In the formula, is the average price of all selected products, It is the average of the relative rankings of all products.

[0042] The present invention also applies the pricing method to actual scenarios for feasibility verification, and selects Wuhan AS Hotel as the verification object for revenue calculation. The test period is from August 1, 2022 to August 31, 2022. The test results are shown in Table 1.

[0043] Table 1 Wuhan AS Hotel adopts dynamic pricing method test data

[0044]

[0045] As shown in Table 1, the hotel room pricing was adjusted for one month using the dynamic pricing method. By comparing the average room rate and sales volume of the previous month and the same period last year, the average room rate increased by 10.51 yuan and 24.09 yuan respectively, and the monthly turnover increased by 39,100 yuan and 40,300 yuan respectively. From the data, the dynamic pricing method is feasible and effectively improves the pricing efficiency and revenue quality of users in the context of dynamic changes in market prices.

[0046] The dynamic pricing method based on opponent prices and relative ranking of big data of the present invention selects several homogeneous opponent prices in real time and dynamically based on the price data of big data from the perspective of homogeneous opponent ranking, and gives the optimal pricing suggestion within a reasonable range through the dynamic pricing method of relative ranking. On the basis of the original ranking and positioning, the floating range is reasonably controlled, which can avoid the lag and blindness caused by autonomous pricing to a certain extent, reduce the subjectivity and inefficiency of autonomous pricing, improve the pricing efficiency of users, and achieve the purpose of increasing the total revenue of users.

[0047] The above description is a detailed description of the preferred feasible embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modified changes completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A dynamic pricing method based on big data of opponent prices and relative rankings, characterized in that: The following steps are involved: The current selling price of the current product to be priced is known to be P0, and the current selling prices of several (n≥3) known competitors' products of the same type are selected to form the real-time selling price of the group; Sort the selected current product and the same type products of n competitors in descending order according to the price of the products. θ represents the relative position of the same type products of n selected competitors in the sorting according to the price, and θ0 is the relative position of the price of the current product. The relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1. According to the relative ranking and selling price of the current product and the same type of products of n competitors, the dynamic pricing P′ based on the competitor's price and relative ranking based on big data is obtained: In the formula, is the average price of all selected products, It is the average of the relative rankings of all products.

2. The method for dynamic pricing based on big data opponent prices and relative rankings as claimed in claim 1, characterized in that: The same type of products are items of the same brand, grade or similar functions, or hotel rooms of the same type with similar brand positioning, grade, geographical location, transportation and other factors.

3. A dynamic pricing system based on big data of opponent prices and relative rankings, characterized in that: include: Selection module: used to obtain the current selling price P0 of the current product to be priced, and select the current selling prices of several (n≥3) known competitors' products of the same type to form the real-time selling price of the group; Sorting module: Sort the current product and the same type products of n competitors in descending order according to the price of the products. θ represents the relative position of the same type products of n competitors in the sorting according to the price, and θ0 is the relative position of the price of the current product. The relative position of the highest price is recorded as 1, the relative position of the second highest price is recorded as 2, and so on. The relative position of the lowest price is recorded as n+1. Calculation module: The calculation module stores the following formula (1). The calculation module substitutes the relative ranking and selling price of the current product and the same type of products of n competitors into the formula (1) to obtain the dynamic pricing P′ based on the competitor prices and relative rankings of big data: In the formula, is the average price of all selected products, It is the average of the relative rankings of all products.

4. The method for dynamic pricing based on big data opponent prices and relative rankings as claimed in claim 3, characterized in that: The same type of products are items of the same brand, grade or similar functions, or hotel rooms of the same type with similar brand positioning, grade, geographical location, transportation and other factors.

5. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 2 when executing the computer program.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are executed.

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