Multi-dimensional dynamic differential price calculation method, system and equipment

Abstract

The invention relates to the technical field of data analysis and pricing strategies, and provides a multi-dimensional dynamic differentiated price calculation method, system and equipment, and the method comprises the steps: collecting multi-dimensional condition data, and configuring differentiated price information according to the collected multi-dimensional condition data; collecting real-time transaction scene condition element information, wherein the real-time transaction scene condition element information comprises product market information and transaction scene information; and matching the acquired real-time transaction scene condition element information with the configured differentiated price information, and obtaining a differentiated price result through association relation identification and analysis calculation. According to the multi-dimensional dynamic differentiated price calculation method, system and equipment, differentiated pricing of products can be realized, and the prices of the products or services can be automatically adjusted according to various factors such as market dynamics, consumer behaviors and product characteristics so as to optimize earnings and market shares.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and pricing strategy, and in particular to a multi-dimensional dynamic differentiated price calculation method, system and device. Background Art

[0002] Pricing strategy is a key part of an enterprise's sales strategy and is closely related to maximizing sales and profits. Traditional pricing strategies usually rely on fixed cost-plus methods or market competition analysis. Due to ignoring the real-time changes in the market and the diversity of consumer demand, in actual applications, traditional pricing strategies have the following shortcomings: 1. The pricing method is not flexible. Although differentiated pricing can be performed by channel, account, and organization through logical concentration, it lacks differentiated pricing based on customer portraits, customer classification, grouping, customer behavior analysis, and other dimensions; 2. The accuracy of pricing needs to be improved. Due to the lack of flexible combination and assembly of multi-level price influencing factors, it is impossible to form an accurate differentiated price calculation solution system; 3. It is impossible to realize real-time dynamic adjustment of the pricing strategy system based on dynamic factor capture and analysis.

[0003] Therefore, how to provide a more flexible, accurate and real-time dynamically adjusted differentiated pricing method has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, in order to overcome the deficiencies of the prior art, the present invention aims to provide a multi-dimensional dynamic differentiated price calculation method, system and device.

[0005] According to a first aspect of the present invention, a multi-dimensional dynamic differentiated price calculation method is provided, the method comprising:

[0006] Collect multi-dimensional conditional data and configure differentiated price information based on the collected multi-dimensional conditional data;

[0007] Collecting real-time transaction scenario condition element information, where the real-time transaction scenario condition element information includes product market information and transaction scenario information;

[0008] Match the collected real-time transaction scenario condition factor information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

[0009] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, the multi-dimensional conditional data includes channel source data, account type data, institution type data and customer data, and the customer data includes customer portrait, customer level, customer behavior, customer asset level, customer transaction time and customer contribution data.

[0010] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, product market information includes consumer behavior data, competitor pricing data, market demand data and product characteristic data.

[0011] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, the transaction scenario information includes price caller dimensional information and transaction context information, the price caller dimensional information includes price caller level information and price caller asset information, and the transaction context information includes transaction context channel information, transaction time information and transaction location information.

[0012] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, the collected real-time transaction scenario condition element information is matched with the configured differentiated price information, including: according to a preset matching rule, the collected real-time transaction scenario condition element information is matched with the configured differentiated price information; when the collected real-time transaction scenario condition element information and the configured differentiated price information meet the preset matching rule, the collected real-time transaction scenario condition element information is determined to be successfully matched with the configured differentiated price information.

[0013] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, differentiated price results are obtained through association relationship identification and analysis calculation, including: when the collected real-time transaction scenario condition element information and the configured differentiated price information are successfully matched, the correlation between each element information in the real-time transaction scenario condition element information and the differentiated price information is identified, and the differentiated price result is obtained according to the identified correlation calculation.

[0014] Preferably, in the multi-dimensional dynamic differentiated price calculation method of the present invention, a differentiated price result is obtained according to the identified correlation calculation, including: setting a weighted value and a differentiated price contribution value for each element information in the real-time transaction scenario condition element information according to the identified correlation, and performing weighted calculation on each element in the real-time transaction scenario condition element information according to the set weighted value and differentiated price contribution value to obtain a differentiated price result.

[0015] According to a second aspect of the present invention, a multi-dimensional dynamic differentiated price calculation system is provided, the system includes a differentiated price calculation server, the differentiated price calculation server is used to: collect multi-dimensional condition data, and configure differentiated price information according to the collected multi-dimensional condition data; collect real-time transaction scenario condition element information, the real-time transaction scenario condition element information includes product market information and transaction scenario information; match the collected real-time transaction scenario condition element information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

[0016] Preferably, in the multi-dimensional dynamic differentiated price calculation system of the present invention, the differentiated price calculation server includes:

[0017] A differentiated price information configuration module is used to collect multi-dimensional condition data and configure differentiated price information according to the collected multi-dimensional condition data;

[0018] A real-time transaction scenario condition factor information collection module is used to collect real-time transaction scenario condition factor information, where the real-time transaction scenario condition factor information includes product market information and transaction scenario information;

[0019] The matching analysis and calculation module is used to match the collected real-time transaction scenario condition factor information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

[0020] According to a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect of the present invention when executing the program.

[0021] The multi-dimensional dynamic differentiated price calculation method, system and device of the present invention realizes differentiated pricing of products based on the synergy of a differentiated price information configuration module, a real-time transaction scenario condition factor information collection module and a matching analysis and calculation module. It can automatically adjust the price of products or services to optimize revenue and market share based on various factors such as market dynamics, consumer behavior, and product characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of 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.

[0023] Figure 1 This is an example diagram of the architecture of a multi-dimensional dynamic differentiated price calculation system according to an embodiment of the present invention;

[0024] Figure 2 This is an example diagram of the architecture of a differentiated price calculation server of a multi-dimensional dynamic differentiated price calculation system according to an embodiment of the present invention;

[0025] Figure 3 A flowchart of a multi-dimensional dynamic differentiated price calculation method according to an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of the structure of the device provided by the present invention. DETAILED DESCRIPTION

[0027] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0028] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0029] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0030] Figure 1 FIG. 1 is a schematic diagram of an architecture of a multi-dimensional dynamic differentiated price calculation system according to an embodiment of the present invention. Figure 1 As shown, the system may include a differential price calculation server 101, a communication network 102 and / or one or more differential price calculation clients 103. Figure 1 The example in FIG. 1 is a plurality of differentiated price calculation clients 103 .

[0031] The differentiated price calculation server 101 can be any appropriate server for storing information, data, programs and / or any other suitable type of content. In some embodiments, the differentiated price calculation server 101 can perform appropriate functions. For example, in some embodiments, the differentiated price calculation server 101 can be used for multi-dimensional dynamic differentiated price calculation. As an optional example, in some embodiments, the differentiated price calculation server 101 can be used to: collect multi-dimensional condition data, configure differentiated price information based on the collected multi-dimensional condition data; collect real-time transaction scenario condition element information, the real-time transaction scenario condition element information includes product market information and transaction scenario information; match the collected real-time transaction scenario condition element information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis calculation.

[0032] Figure 2FIG. 4 is an example diagram of the architecture of a differentiated price calculation server of a multi-dimensional dynamic differentiated price calculation system according to an embodiment of the present invention; Figure 2 As shown, the differentiated price calculation server includes:

[0033] A differentiated price information configuration module is used to collect multi-dimensional condition data and configure differentiated price information according to the collected multi-dimensional condition data;

[0034] A real-time transaction scenario condition factor information collection module is used to collect real-time transaction scenario condition factor information, where the real-time transaction scenario condition factor information includes product market information and transaction scenario information;

[0035] The matching analysis and calculation module is used to match the collected real-time transaction scenario condition factor information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

[0036] As another example, in some embodiments, the differentiated price calculation server 101 may send the multi-dimensional dynamic differentiated price calculation method to the differentiated price calculation client 103 for user use based on a request of the differentiated price calculation client 103 .

[0037] As an optional example, in some embodiments, the differentiated price calculation client 103 is used to provide a visual differentiated price calculation interface, which is used to receive a user's selection input operation for multi-dimensional dynamic differentiated price calculation, and, in response to the selection input operation, obtain a differentiated price calculation interface corresponding to the option selected by the selection input operation from the differentiated price calculation server 101 and display the differentiated price calculation interface, wherein the differentiated price calculation interface at least displays information on multi-dimensional dynamic differentiated price calculation and operation options for the multi-dimensional dynamic differentiated price calculation information.

[0038] In some embodiments, the communication network 102 can be any suitable combination of one or more wired and / or wireless networks. For example, the communication network 102 can include any one or more of the following: the Internet, an intranet, a wide area network (WAN), a local area network (LAN), a wireless network, a digital subscriber line (DSL) network, a frame relay network, an asynchronous transfer mode (ATM) network, a virtual private network (VPN), and / or any other suitable communication network. The differential price calculation client 103 can be connected to the communication network 102 via one or more communication links (e.g., communication link 104), and the communication network 102 can be linked to the differential price calculation server 101 via one or more communication links (e.g., communication link 105). The communication link can be any communication link suitable for transmitting data between the differential price calculation client 103 and the differential price calculation server 101, such as a network link, a dial-up link, a wireless link, a hard-wired link, any other suitable communication link, or any suitable combination of such links.

[0039] The differential price calculation client 103 may include any one or more clients that present an interface related to multi-dimensional dynamic differential price calculation in an appropriate form for use and operation by a user. In some embodiments, the differential price calculation client 103 may include any suitable type of device. For example, in some embodiments, the differential price calculation client 103 may include a mobile device, a tablet computer, a laptop computer, a desktop computer, and / or any other suitable type of client device.

[0040] Although the differential price calculation server 101 is illustrated as one device, in some embodiments, any appropriate number of devices may be used to perform the functions performed by the differential price calculation server 101. For example, in some embodiments, multiple devices may be used to implement the functions performed by the differential price calculation server 101. Alternatively, the functions of the differential price calculation server 101 may be implemented using a cloud service.

[0041] Based on the above system, an embodiment of the present invention provides a multi-dimensional dynamic differentiated price calculation method, which is described below through the following embodiments.

[0042] Figure 3 The multi-dimensional dynamic differentiated price calculation method according to an embodiment of the present invention is a flowchart of the steps. The multi-dimensional dynamic differentiated price calculation method of this embodiment can be executed on a differentiated price calculation server, and the multi-dimensional dynamic differentiated price calculation method includes the following steps:

[0043] Step S201: Collect multi-dimensional condition data, and configure differentiated price information according to the collected multi-dimensional condition data.

[0044] As an optional example, in this embodiment, the multi-dimensional conditional data includes channel source data, account type data, institution type data and customer data, and the customer data includes customer portrait, customer level, customer behavior, customer asset level, customer transaction time and customer contribution data.

[0045] Step S202: Collect real-time transaction scenario condition element information, where the real-time transaction scenario condition element information includes product market information and transaction scenario information.

[0046] As optional examples, product market information includes consumer behavior data, competitor pricing data, market demand data, and product feature data.

[0047] As an optional example, the transaction scenario information includes the price caller dimension information and the transaction context information. The price caller dimension information includes the price caller level information and the price caller asset information. The transaction context information includes the transaction context channel information, the transaction time information and the transaction location information. In practical applications, the method of this embodiment can also be applied to a cloud platform or an enterprise's own server, connected to an e-commerce platform, a CRM system and other business systems through an API, and real-time acquisition and analysis of data. For example, on an e-commerce platform, the price of goods can be dynamically adjusted to maximize sales or profits based on the user's shopping history, search behavior, click-through rate and other data, combined with the price fluctuations of similar products on the market.

[0048] Step S203: Match the collected real-time transaction scenario condition element information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

[0049] As an optional example, this embodiment matches the collected real-time transaction scenario condition element information with the configured differentiated price information according to the preset matching rules. When the collected real-time transaction scenario condition element information and the configured differentiated price information meet the preset matching rules, the collected real-time transaction scenario condition element information is determined to be successfully matched with the configured differentiated price information. When the collected real-time transaction scenario condition element information and the configured differentiated price information are successfully matched, the correlation between each element information in the real-time transaction scenario condition element information and the differentiated price information is identified, and the differentiated price result is obtained according to the identified correlation calculation.

[0050] As an optional example, this embodiment sets a weighted value and a differentiated price contribution value for each element information in the real-time transaction scenario condition element information according to the identified correlation, and performs weighted calculation on each element in the real-time transaction scenario condition element information according to the set weighted value and differentiated price contribution value to obtain a differentiated price result. It should be noted that in the method of this embodiment, the real-time transaction scenario refers to online retail, stock trading or other trading environments that require real-time decision-making. Condition elements refer to various factors that affect the transaction price, such as the demand, supply, market trends, etc. of the goods. The weighted value and differentiated price contribution value are the values ​​used to calculate the final transaction price, which reflect the degree of influence of each element on the price.

[0051] The method of this embodiment is further described in detail below in a specific application scenario to illustrate how to set weighted values ​​and differentiated price contribution values ​​for real-time transaction scenario condition factor information according to the identified relevance, and perform weighted calculation to obtain differentiated price results:

[0052] Case Background

[0053] An online retailer wants to dynamically adjust its product prices by analyzing market data in real time to maximize profits and maintain competitiveness. The method of this embodiment analyzes various factors that affect product prices and adjusts prices accordingly.

[0054] Conditional elements include:

[0055] Demand: The amount of a product that consumers demand.

[0056] Supply: The supply of the product in the market.

[0057] Competitor prices: The prices charged by competitors for similar products.

[0058] Seasonality: The effect of a particular season on the demand for a commodity.

[0059] Promotions: The impact of ongoing promotions on prices.

[0060] The weighted value and differentiated price contribution value settings include the following:

[0061] Demand: weighted value 0.3, differentiated price contribution value +5%;

[0062] Supply: weighted value 0.2, differentiated price contribution value -3%;

[0063] Competitor price: weighted value 0.15, differentiated price contribution value +2%;

[0064] Seasonality factor: weighted value 0.2, differentiated price contribution value +10%;

[0065] Promotional activities: weighted value 0.15, differentiated price contribution value -15%;

[0066] The weighted calculation process is as follows:

[0067] Assume that the current demand is 100, the supply is 50, the average competitor price is 100 yuan, the current seasonal demand increase results in a seasonality factor score of 1.2 (relative to the baseline value of 1), and the promotional activities result in a score of 0.85.

[0068] Demand calculation: 100*0.3*5%=1.5;

[0069] Supply calculation: 50*0.2*-3%=-0.3;

[0070] Competitor price calculation: 100*0.15*2%=0.3;

[0071] Seasonal factor calculation: 1.2*0.2*10%=0.24;

[0072] Promotion calculation: 0.85*0.15*-15%=-0.1275;

[0073] The differentiated price results are as follows:

[0074] Add up all the calculations to get the final price adjustment:

[0075] 1.5-0.3+0.3+0.24-0.1275=1.6225

[0076] Assuming that the base price of the product is 100 yuan, based on the above calculation, the final differentiated price is: 100+1.6225=101.62 yuan.

[0077] Through Shanshu's real-time analysis and weighted calculations, online retailers are able to dynamically adjust their product prices based on real-time changes in market conditions to adapt to market demand and optimize profits.

[0078] It should be noted that, in actual applications, the weighted value and the differentiated price contribution value may be adjusted according to specific application scenarios, and this embodiment does not limit this.

[0079] According to the multi-dimensional dynamic differentiated price calculation method and system of the embodiments of the present invention, differentiated pricing of products is achieved based on the synergy of the differentiated price information configuration module, the real-time transaction scenario condition factor information collection module and the matching analysis and calculation module. It can automatically adjust the price of products or services to optimize revenue and market share based on various factors such as market dynamics, consumer behavior, and product characteristics.

[0080] like Figure 4As shown, the present invention also provides a device, including a processor 310, a communication interface 320, a memory 330 for storing a computer program executable by the processor, and a communication bus 340. The processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 implements the above-mentioned multi-dimensional dynamic differentiated price calculation method by running the executable computer program.

[0081] Among them, the computer program in the memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0082] The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected based on actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0084] 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 a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A multi-dimensional dynamic differentiated price calculation method, characterized in that: The method comprises: Collect multi-dimensional conditional data and configure differentiated price information based on the collected multi-dimensional conditional data; Collecting real-time transaction scenario condition element information, wherein the real-time transaction scenario condition element information includes product market information and transaction scenario information; Match the collected real-time transaction scenario condition factor information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

2. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: The multi-dimensional conditional data includes channel source data, account type data, institution type data and customer data. The customer data includes customer portrait, customer level, customer behavior, customer asset level, customer transaction time and customer contribution data.

3. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: Product market information includes consumer behavior data, competitor pricing data, market demand data, and product feature data.

4. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: The transaction scenario information includes the price caller dimension information and the transaction context information. The price caller dimension information includes the price caller level information and the price caller asset information. The transaction context information includes the transaction context channel information, the transaction time information and the transaction location information.

5. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: Matching the collected real-time transaction scenario condition element information with the configured differentiated price information, including: matching the collected real-time transaction scenario condition element information with the configured differentiated price information according to a preset matching rule, and when the collected real-time transaction scenario condition element information and the configured differentiated price information meet the preset matching rule, determining that the collected real-time transaction scenario condition element information is successfully matched with the configured differentiated price information.

6. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: The differentiated price results are obtained through association relationship identification and analysis and calculation, including: when the collected real-time transaction scenario condition factor information is successfully matched with the configured differentiated price information, the correlation between each factor information in the real-time transaction scenario condition factor information and the differentiated price information is identified, and the differentiated price results are obtained based on the identified correlation calculation.

7. The multi-dimensional dynamic differentiated price calculation method according to claim 1, characterized in that: The differentiated price result is obtained according to the identified correlation calculation, including: setting a weighted value and a differentiated price contribution value for each element information in the real-time transaction scenario condition element information according to the identified correlation, and performing a weighted calculation on each element in the real-time transaction scenario condition element information according to the set weighted value and differentiated price contribution value to obtain the differentiated price result.

8. A multi-dimensional dynamic differentiated price calculation system, characterized in that: The system includes a differentiated price calculation server, which is used to: collect multi-dimensional condition data and configure differentiated price information based on the collected multi-dimensional condition data; collect real-time transaction scenario condition element information, wherein the real-time transaction scenario condition element information includes product market information and transaction scenario information; match the collected real-time transaction scenario condition element information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

9. The multi-dimensional dynamic differentiated price calculation system according to claim 8, characterized in that: The differentiated price calculation server includes: A differentiated price information configuration module is used to collect multi-dimensional condition data and configure differentiated price information according to the collected multi-dimensional condition data; A real-time transaction scenario condition factor information collection module, used to collect real-time transaction scenario condition factor information, wherein the real-time transaction scenario condition factor information includes product market information and transaction scenario information; The matching analysis and calculation module is used to match the collected real-time transaction scenario condition factor information with the configured differentiated price information, and obtain differentiated price results through association relationship identification and analysis and calculation.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.

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