E-commerce sales pricing analysis decision-making system

Abstract

The invention relates to the technical field of data processing, and particularly discloses an e-commerce sales pricing analysis decision-making system, which comprises an analysis data acquisition module, a product preliminary pricing module, a market comprehensive pricing module, a competition optimization pricing module, a pricing decision-making generation module and a database. The price advantage interval boundary value is set based on industry analysis and enterprise historical data, so that the pricing decision has scientificity and perspectiveness, the overall trend of the industry and the development process and market performance of the enterprise are fully considered, the pricing risk can be effectively reduced by adopting the decision mode based on data driving, and the pricing efficiency is improved. The survival ability and the profitability of an enterprise in a fierce competition environment of the e-commerce market are improved, the enterprise is helped to flexibly cope with competition challenges in a complex and changeable market environment, the long-term stable development goal is achieved, the price is continuously adjusted according to the price optimization index, the market positioning and competition strategies of products are gradually optimized, and the market competitiveness is improved. And enterprises are promoted to continuously improve market competitiveness and economic benefits.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an e-commerce sales pricing analysis and decision-making system. Background Art

[0002] With the rapid development of Internet technology, the e-commerce industry has shown a booming trend and has become an indispensable and important part of the modern business field. Many companies have been involved in e-commerce sales, and market competition is becoming increasingly fierce. In such an environment, product pricing is no longer a simple cost-plus, but requires comprehensive consideration of multiple factors. On the one hand, the openness and transparency of the e-commerce market enable consumers to easily compare the prices and characteristics of products from different brands and merchants, which requires companies to accurately grasp the elasticity of market demand in order to formulate pricing strategies that can attract consumers and protect corporate profits. On the other hand, competitors' product advantages and pricing strategies always affect the company's own market share and profitability. Companies need to pay attention to the competitive situation and respond in a timely manner.

[0003] Traditional pricing methods often focus on cost orientation or single market factor considerations. Cost-oriented pricing determines prices based only on fixed costs, variable costs and target profits. Although it can ensure that companies achieve profit expectations to a certain extent, it is easy to ignore the dynamic changes in market demand and the impact of competitors. When market demand elasticity is large, pricing based solely on cost may lead to excessively high product prices, which will discourage consumers and thus affect sales performance. Moreover, this pricing method lacks an assessment of the comprehensive competitiveness of products in the market, making it difficult to stand out in the highly competitive e-commerce environment. In the process of e-commerce sales, companies urgently need a more comprehensive, scientific and flexible pricing analysis and decision-making system that can integrate multiple factors such as cost, market demand, and competitive situation to formulate the most optimized pricing strategy for products to adapt to the complex and changing competitive environment of the e-commerce market and achieve long-term and stable development of the company. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an e-commerce sales pricing analysis and decision-making system to solve the above-mentioned technical defects.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an e-commerce sales pricing analysis and decision-making system, including a product preliminary pricing module, a market comprehensive pricing module, a competition optimization pricing module and a pricing decision generation module; The product preliminary pricing module performs preliminary pricing calculation and analysis for e-commerce sales based on fixed costs, variable costs and target profits, and obtains preliminary pricing for each production batch of each product sold by e-commerce sales; The market comprehensive pricing module calculates and analyzes the market pricing based on the marginal cost and demand price elasticity coefficient of each production batch of each sales product corresponding to e-commerce sales, and obtains the comprehensive pricing of each production batch of each sales product corresponding to e-commerce sales; The competitive optimization pricing module obtains the price optimization index of each production batch of each sales product corresponding to e-commerce sales based on the advantage scores of each sales product corresponding to e-commerce sales and the advantage scores of competing products through comprehensive calculation and analysis; The pricing decision generation module performs a comprehensive analysis based on the comprehensive pricing of each sales product corresponding to the e-commerce sales and the price optimization index of each sales product corresponding to the e-commerce sales, and obtains the final pricing of each sales product corresponding to the e-commerce sales.

[0006] Further, it also includes: analyzing data acquisition module and database; The analysis data collection module is used to collect data on various pricing analysis parameters required for sales pricing corresponding to each sales product, and record them in real time in the database for storage.

[0007] Furthermore, the preliminary pricing calculation and analysis of e-commerce sales is carried out based on fixed costs, variable costs and target profits. The specific calculation and analysis method is as follows: By recording the product of the expected sales volume of each production batch of each sales product corresponding to e-commerce sales and the unit variable cost of each production batch of each sales product corresponding to e-commerce sales as the variable cost, the variable cost of each production batch of each sales product corresponding to e-commerce sales is obtained. At the same time, the target profit is set according to the profit target of each production batch of each sales product corresponding to e-commerce sales, and the target profit of each production batch of each sales product corresponding to e-commerce sales is obtained. By summing up the fixed cost, variable cost and target profit of each production batch in each sales product corresponding to e-commerce sales, and dividing the sum by the expected sales volume, we can obtain the one-time pricing for each production batch in each sales product corresponding to e-commerce sales.

[0008] Furthermore, by calculating the difference between the total cost of the nth production batch of each sales product corresponding to the e-commerce sales and the total cost of the n-1th production batch of each sales product corresponding to the e-commerce sales, and at the same time calculating the difference between the output of the nth production batch of each sales product corresponding to the e-commerce sales and the output of the n-1th production batch of each sales product corresponding to the e-commerce sales, the cost difference is divided by the output difference to calculate, and the marginal cost of each production batch of each sales product corresponding to the e-commerce sales is obtained; The marginal revenue of each production batch of each sales product corresponding to e-commerce sales is obtained by subtracting the total revenue of n production batches of each sales product corresponding to e-commerce sales and the total revenue of n-1 production batches of each sales product corresponding to e-commerce sales; By taking the output of each production batch of each sales product corresponding to e-commerce sales as the x point, and the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales as the y point, two curves representing the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales are drawn, and the output corresponding to the intersection of the marginal cost and marginal revenue curves of each production batch of each sales product corresponding to e-commerce sales is taken as the profit-maximizing output, and the pricing output of each production batch of each sales product corresponding to e-commerce sales is obtained.

[0009] Furthermore, by summing up the fixed cost, variable cost and target profit of each production batch in each sales product corresponding to e-commerce sales, and dividing the sum by the pricing output, the secondary pricing of each production batch in each sales product corresponding to e-commerce sales is obtained. Finally, by averaging the primary pricing and secondary pricing of each production batch in each sales product corresponding to e-commerce sales, the average result is recorded as the preliminary pricing of each production batch in each sales product corresponding to e-commerce sales.

[0010] Furthermore, based on the marginal cost and demand price elasticity coefficient of each production batch of each sales product corresponding to e-commerce sales, market pricing is calculated and analyzed. The specific calculation and analysis method is as follows: Obtain the historical selling price and sales volume data of each product sold by e-commerce, obtain the model parameters a and b through linear regression analysis, and establish a linear regression model Q=a-bP, where Q represents the historical sales volume of each product sold by e-commerce, and P represents the historical selling price of each product sold by e-commerce. Calculate the price elasticity coefficient T of demand for each production batch of each sales product corresponding to e-commerce sales d .

[0011] Furthermore, by comprehensively calculating and analyzing the marginal cost, demand price elasticity coefficient and preliminary pricing of each production batch of each product sold by e-commerce sales, according to the formula Calculate the comprehensive pricing P of each production batch of each product sold by e-commerce 综合 , where BC represents the marginal cost of each production batch of each product sold by e-commerce, P 初步 It indicates the preliminary pricing of each production batch of each product sold by e-commerce. and w 初步 Respectively represent the corresponding weight factors; and w 初步 They are all set manually according to the needs of the enterprise.

[0012] Furthermore, based on the advantage scores of each product sold and the advantage scores of competing products corresponding to e-commerce sales, a comprehensive calculation and analysis is performed. The specific calculation and analysis method is as follows: A product advantage evaluation index system is constructed for each sales product corresponding to e-commerce sales, wherein the product advantage evaluation index system includes the quality dimension, function dimension, brand dimension and service dimension of each sales product. A weight is assigned to each indicator by the internal product manager of the enterprise in combination with market research. At the same time, each indicator in the product advantage evaluation index system is scored and multiplied by the corresponding indicator weight. Finally, the product results of each indicator and the indicator weight are summed to obtain the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products respectively. The difference between the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products is calculated. At the same time, the difference between the comprehensive pricing of each sales product corresponding to e-commerce sales and the selling price of competitive products is calculated. Finally, the advantage score difference is divided by the selling price difference to obtain the price optimization index of each sales product corresponding to e-commerce sales.

[0013] Furthermore, a comprehensive analysis is performed based on the comprehensive pricing of each sales product corresponding to the e-commerce sales and the price optimization index of each sales product corresponding to the e-commerce sales. The specific comprehensive analysis method is as follows: Through industry analysis and the company's historical data of each sales product, the price advantage range boundaries are set, and the price advantage range is divided into an advantageous range, a medium range and a disadvantageous range.

[0014] Furthermore, if the price optimization index of each sales product corresponding to the e-commerce sales is in the advantageous range, it means that the advantages and price combinations of each sales product corresponding to the e-commerce sales have certain competitiveness, and the price of each sales product corresponding to the e-commerce sales is increased by 5 percentage points to obtain the final pricing of each sales product corresponding to the e-commerce sales; If the price optimization index of each sales product corresponding to e-commerce sales is in the middle range, it means that the advantages and price combinations of each sales product corresponding to e-commerce sales have no obvious competitiveness, and the price of each sales product corresponding to e-commerce sales is adjusted up or down by 1 percentage point to obtain the final pricing of each sales product corresponding to e-commerce sales; If the price optimization index of each sales product corresponding to the e-commerce sales is in the disadvantageous range, it means that the advantages and price combination of each sales product corresponding to the e-commerce sales is at a disadvantage. The price of each sales product corresponding to the e-commerce sales is reduced by 3 percentage points to obtain the final pricing of each sales product corresponding to the e-commerce sales.

[0015] Beneficial effects of the present invention: 1. The present invention firstly calculates a price based on fixed costs, variable costs and target profits, which provides a basic reference for product pricing and can preliminarily ensure that prices are determined on the premise of achieving target profits, so that the company has basic expectations for profits. Secondly, the marginal cost and marginal revenue are calculated and a curve is drawn to find the pricing output corresponding to the profit-maximizing output, which helps the company to deeply understand the changing relationship between cost and revenue with output, accurately locate the production and sales scale for profit maximization, thereby optimizing resource allocation and avoiding blind production and pricing. Finally, the initial pricing is obtained by averaging the primary pricing and the secondary pricing based on profit maximization, which takes into account both the realization of target profits and the need for profit maximization in market dynamics, making pricing more scientific and flexible, and better adapting to the complex and changeable competitive environment of the e-commerce market, balancing the relationship between corporate profits and market share expansion, and improving the overall economic benefits and market competitiveness of the company.

[0016] 2. The present invention uses the historical selling price and historical sales volume data of e-commerce products to perform linear regression analysis to determine model parameters and construct a model, which provides a scientific basis for in-depth understanding of the market demand law of products. This model can quantify the relationship between price and sales volume, thereby accurately calculating the demand price elasticity coefficient. The demand price elasticity coefficient intuitively reflects the consumer's sensitivity to price changes, which helps companies to fully grasp market dynamics and consumer behavior preferences. In addition, comprehensive pricing is calculated based on marginal cost, demand price elasticity coefficient and preliminary pricing, which closely combines the company's production cost with market demand conditions. Marginal cost reflects the company's Resource input and cost changes, while demand price elasticity focuses on the response of consumers on the market side. The comprehensive pricing formulated by combining the two can not only ensure that the company's costs in the production process are reasonably covered and avoid losses due to too low pricing, but also fully consider the market demand elasticity and avoid large-scale consumer loss due to too high pricing. In this way, companies can formulate more market-oriented and competitive pricing strategies, maximize their own economic benefits while meeting consumer price expectations, effectively balance the relationship between corporate profit pursuit and market share expansion, enhance the company's adaptability and competitiveness in the e-commerce market, and lay a solid foundation for the company's long-term and stable development.

[0017] 3. The present invention covers multiple dimensions such as quality, function, brand and service, and combines the professional product managers within the enterprise with in-depth market research to determine the weight of each indicator, which can accurately and objectively quantify the comprehensive advantages of the product. This not only helps the enterprise to deeply understand the true competitiveness of its own products in the market, but also provides solid data support for the formulation of subsequent pricing strategies. The product advantage score and the competitive product advantage score calculated based on a scientific and reasonable evaluation system provide enterprises with a clear reference in competitive comparison, and can keenly capture their own advantages and disadvantages, so as to formulate price optimization strategies in a targeted manner.

[0018] 4. The e-commerce sales pricing analysis and decision-making system proposed in the present invention sets the price advantage interval boundary based on industry analysis and enterprise historical data, so that pricing decisions are scientific and forward-looking, and fully consider the overall industry trend as well as the company's own development history and market performance. The data-driven decision-making method can effectively reduce pricing risks, improve the company's ability to survive and profitability in the fierce competition environment of the e-commerce market, help companies flexibly respond to competitive challenges in a complex and changing market environment, and achieve long-term and stable development goals. In addition, by continuously adjusting pricing according to the price optimization index, it gradually optimizes the product's market positioning and competition strategy, and promotes companies to continuously improve their market competitiveness and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Figure 1 This is a functional block diagram of an e-commerce sales pricing analysis and decision-making system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0022] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0023] Although the present invention has made various references to certain modules in the system according to an embodiment of the present invention, any number of different modules can be used and run on a user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0024] The present invention uses a flow chart to illustrate the operations performed by the system according to an embodiment of the present invention. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0025] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0026] Embodiment 1: See also Figure 1 As shown, the e-commerce sales pricing analysis and decision-making system includes: an analysis data collection module, a product preliminary pricing module, a market comprehensive pricing module, a competition optimization pricing module, a pricing decision generation module and a database.

[0027] The analysis data collection module is used to collect data on various pricing analysis parameters required for sales pricing corresponding to each sales product, and record them in the database in real time for storage; The product preliminary pricing module performs preliminary pricing calculation and analysis for e-commerce sales based on fixed costs, variable costs, and target profits, and obtains preliminary pricing for each production batch of each product sold by e-commerce sales. The specific calculation and analysis method is as follows: By recording the product of the expected sales volume of each production batch of each sales product corresponding to e-commerce sales and the unit variable cost of each production batch of each sales product corresponding to e-commerce sales as the variable cost, the variable cost of each production batch of each sales product corresponding to e-commerce sales is obtained. At the same time, the target profit is set according to the profit target of each production batch of each sales product corresponding to e-commerce sales, and the target profit of each production batch of each sales product corresponding to e-commerce sales is obtained. The fixed cost, variable cost and target profit of each production batch of each sales product corresponding to the e-commerce sales are calculated by summing up the sum and dividing the result by the expected sales volume to obtain the one-time pricing of each production batch of each sales product corresponding to the e-commerce sales; The marginal cost of each production batch of each sales product corresponding to e-commerce sales is calculated by subtracting the total cost of the n-1 production batch of each sales product corresponding to e-commerce sales, and subtracting the output of the n-1 production batch of each sales product corresponding to e-commerce sales, and dividing the cost difference by the output difference to obtain the marginal cost of each production batch of each sales product corresponding to e-commerce sales; The marginal revenue of each production batch of each sales product corresponding to e-commerce sales is obtained by subtracting the total revenue of n production batches of each sales product corresponding to e-commerce sales and the total revenue of n-1 production batches of each sales product corresponding to e-commerce sales; By taking the output of each production batch of each sales product corresponding to e-commerce sales as the x point, and taking the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales as the y point, two curves representing the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales are drawn, and the output corresponding to the intersection of the marginal cost and marginal revenue curves of each production batch of each sales product corresponding to e-commerce sales is taken as the profit-maximizing output, and the pricing output of each production batch of each sales product corresponding to e-commerce sales is obtained; The secondary pricing of each production batch of each sales product corresponding to e-commerce sales is obtained by summing up the fixed cost, variable cost and target profit, and dividing the sum by the pricing output. Finally, the primary pricing and secondary pricing of each production batch of each sales product corresponding to e-commerce sales are averaged, and the average result is recorded as the preliminary pricing of each production batch of each sales product corresponding to e-commerce sales.

[0028] In a specific embodiment, the present invention first calculates a price based on fixed costs, variable costs and target profits, which provides a basic reference for product pricing and can preliminarily ensure that prices are determined on the premise of achieving target profits, so that the company has basic expectations for profits. Secondly, the marginal cost and marginal revenue are calculated and a curve is drawn to find the pricing output corresponding to the profit-maximizing output, which helps the company to deeply understand the changing relationship between cost and revenue with output, accurately locate the production and sales scale for profit maximization, thereby optimizing resource allocation and avoiding blind production and pricing. Finally, the initial pricing is obtained by averaging the primary pricing and the secondary pricing based on profit maximization, which takes into account both the realization of target profits and the need for profit maximization in market dynamics, making pricing more scientific and flexible, and better adapting to the complex and changing competitive environment of the e-commerce market, balancing the relationship between corporate profits and market share expansion, and improving the overall economic benefits and market competitiveness of the company.

[0029] The market comprehensive pricing module calculates and analyzes market pricing based on the marginal cost and demand price elasticity coefficient of each production batch of each sales product corresponding to e-commerce sales, and obtains the comprehensive pricing of each production batch of each sales product corresponding to e-commerce sales. The specific calculation and analysis method is as follows: Obtain the historical selling price and sales volume data of each product sold by e-commerce, obtain the model parameters a and b through linear regression analysis, and establish a linear regression model Q=a-bP, where Q represents the historical sales volume of each product sold by e-commerce, and P represents the historical selling price of each product sold by e-commerce. Calculate the price elasticity coefficient T of demand for each production batch of each sales product corresponding to e-commerce sales d ; By comprehensively calculating and analyzing the marginal cost, demand price elasticity coefficient and preliminary pricing of each production batch of each product sold by e-commerce sales, according to the formula Calculate the comprehensive pricing P of each production batch of each product sold by e-commerce 综合 , where BC represents the marginal cost of each production batch of each product sold by e-commerce, P 初步 It indicates the preliminary pricing of each production batch of each product sold by e-commerce. and w 初步 Respectively represent the corresponding weight factors; and w 初步 They are all set manually according to the needs of the enterprise.

[0030] In a specific embodiment, the present invention uses the historical selling price and historical sales volume data of e-commerce products to perform linear regression analysis to determine model parameters and construct a model, which provides a scientific basis for in-depth understanding of the market demand law of products. This model can quantify the relationship between price and sales volume, thereby accurately calculating the demand price elasticity coefficient. The demand price elasticity coefficient intuitively reflects the consumer's sensitivity to price changes, which helps companies to fully grasp market dynamics and consumer behavior preferences. In addition, comprehensive pricing is calculated based on marginal cost, demand price elasticity coefficient and preliminary pricing, which closely combines the company's production cost with market demand conditions. Marginal cost reflects the company's production The resource input and cost changes in the process, while the demand price elasticity focuses on the response of consumers on the market side. The comprehensive pricing formulated by combining the two can not only ensure that the company's costs in the production process are reasonably covered and avoid losses due to too low pricing, but also fully consider the market demand elasticity and avoid a large number of consumers leaving due to too high pricing. In this way, companies can formulate more competitive pricing strategies that are more in line with market realities, maximize their own economic benefits while meeting consumers' price expectations, effectively balance the relationship between corporate profit pursuit and market share expansion, enhance the company's adaptability and competitiveness in the e-commerce market, and lay a solid foundation for the company's long-term and stable development.

[0031] The competitive optimization pricing module performs comprehensive calculation and analysis based on the advantage scores of each product sold by e-commerce sales and the advantage scores of competing products, and obtains the price optimization index of each production batch of each product sold by e-commerce sales. The specific calculation and analysis method is as follows: A product advantage evaluation index system is constructed for each sales product corresponding to e-commerce sales, wherein the product advantage evaluation index system includes the quality dimension, function dimension, brand dimension and service dimension of each sales product. A weight is assigned to each indicator by the internal product manager of the enterprise in combination with market research. At the same time, each indicator in the product advantage evaluation index system is scored and multiplied by the corresponding indicator weight. Finally, the product results of each indicator and the indicator weight are summed to obtain the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products respectively. The difference between the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products is calculated. At the same time, the difference between the comprehensive pricing of each sales product corresponding to e-commerce sales and the selling price of competitive products is calculated. Finally, the advantage score difference is divided by the selling price difference to obtain the price optimization index of each sales product corresponding to e-commerce sales.

[0032] The pricing decision generation module performs a comprehensive analysis based on the comprehensive pricing of each sales product corresponding to e-commerce sales and the price optimization index of each sales product corresponding to e-commerce sales, and obtains the final pricing of each sales product corresponding to e-commerce sales. The specific comprehensive analysis method is as follows: The price advantage interval boundary is set through industry analysis and the historical data of each sales product of the enterprise, and the price advantage interval is divided into an advantage interval, a medium interval and a disadvantage interval. If the price optimization index of each sales product corresponding to e-commerce sales is in the advantage interval, it means that the advantage and price combination of each sales product corresponding to e-commerce sales has a certain competitiveness. The price of each sales product corresponding to e-commerce sales is increased by 5 percentage points to obtain the final pricing of each sales product corresponding to e-commerce sales; If the price optimization index of each sales product corresponding to e-commerce sales is in the middle range, it means that the advantages and price combinations of each sales product corresponding to e-commerce sales have no obvious competitiveness, and the price of each sales product corresponding to e-commerce sales is adjusted up or down by 1 percentage point to obtain the final pricing of each sales product corresponding to e-commerce sales; If the price optimization index of each sales product corresponding to the e-commerce sales is in the disadvantageous range, it means that the advantages and price combination of each sales product corresponding to the e-commerce sales is at a disadvantage. The price of each sales product corresponding to the e-commerce sales is reduced by 3 percentage points to obtain the final pricing of each sales product corresponding to the e-commerce sales.

[0033] In a specific embodiment, the present invention covers multiple dimensions such as quality, function, brand and service, and combines the professional product managers within the enterprise with in-depth market research to determine the weight of each indicator, so as to accurately and objectively quantify the comprehensive advantages of the product. This not only helps the enterprise to deeply understand the real competitiveness level of its own products in the market, but also provides solid data support for the formulation of subsequent pricing strategies. The product advantage scores and competitive product advantage scores calculated based on a scientific and reasonable evaluation system provide enterprises with a clear reference in competitive comparison, and can keenly capture their own advantages and disadvantages, so as to formulate price optimization strategies in a targeted manner. In addition, by calculating the price optimization index, the product advantages and price factors are cleverly integrated into a quantitative indicator. The price optimization index can intuitively reflect the price-performance ratio of the product under the market competition pattern. Based on the index, enterprises can quickly determine the position of their own products in terms of price and advantage combination, that is, whether they are in the advantageous, medium or disadvantageous range, and formulate differentiated pricing adjustment strategies for different ranges, which reflects the flexibility and adaptability of the plan. Raising prices in the advantageous range can not only further increase profit margins, but also highlight the high-end positioning of products and consolidate brand image; making moderate price adjustments in the medium range can explore a better balance between product advantages and prices while maintaining a stable market share; lowering prices in the disadvantageous range can quickly enhance the price competitiveness of products and attract more price-sensitive consumers, thereby effectively preventing excessive loss of market share; To sum up, the entire plan sets the price advantage interval boundaries based on industry analysis and corporate historical data, making pricing decisions scientific and forward-looking, and fully considering the overall industry trends as well as the company's own development history and market performance. The use of data-driven decision-making methods can effectively reduce pricing risks, improve the company's ability to survive and profitability in the fierce competition in the e-commerce market, and help companies flexibly respond to competitive challenges in a complex and changing market environment and achieve long-term and stable development goals. In addition, by continuously adjusting pricing based on the price optimization index, it gradually optimizes the product's market positioning and competitive strategy, and promotes companies to continuously improve their market competitiveness and economic benefits.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The size of the coefficient is to quantify each parameter to obtain a specific value. Regarding the size of the coefficient, it is acceptable as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0035] In addition, those skilled in the art will appreciate that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0036] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0037] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. E-commerce sales pricing analysis and decision-making system, including product preliminary pricing module, market comprehensive pricing module, competition optimization pricing module and pricing decision generation module; its characteristics are: The product preliminary pricing module performs preliminary pricing calculation and analysis for e-commerce sales based on fixed costs, variable costs and target profits, and obtains preliminary pricing for each production batch of each product sold by e-commerce sales; The market comprehensive pricing module calculates and analyzes the market pricing based on the marginal cost and demand price elasticity coefficient of each production batch of each sales product corresponding to e-commerce sales, and obtains the comprehensive pricing of each production batch of each sales product corresponding to e-commerce sales; The competitive optimization pricing module performs comprehensive calculation and analysis based on the advantage scores of each sales product corresponding to e-commerce sales and the advantage scores of competing products, and obtains the price optimization index of each production batch of each sales product corresponding to e-commerce sales; The pricing decision generation module performs a comprehensive analysis based on the comprehensive pricing of each sales product corresponding to the e-commerce sales and the price optimization index of each sales product corresponding to the e-commerce sales, and obtains the final pricing of each sales product corresponding to the e-commerce sales.

2. The e-commerce sales pricing analysis and decision-making system according to claim 1 is characterized in that: Also includes: Analyze data acquisition modules and databases; The analysis data collection module is used to collect data on various pricing analysis parameters required for sales pricing corresponding to each sales product, and record them in real time in the database for storage.

3. The e-commerce sales pricing analysis and decision-making system according to claim 1, characterized in that: The preliminary pricing calculation and analysis of e-commerce sales is based on fixed costs, variable costs and target profits. The specific calculation and analysis methods are as follows: By recording the product of the expected sales volume of each production batch of each sales product corresponding to e-commerce sales and the unit variable cost of each production batch of each sales product corresponding to e-commerce sales as the variable cost, the variable cost of each production batch of each sales product corresponding to e-commerce sales is obtained. At the same time, the target profit is set according to the profit target of each production batch of each sales product corresponding to e-commerce sales, and the target profit of each production batch of each sales product corresponding to e-commerce sales is obtained. By summing up the fixed cost, variable cost and target profit of each production batch in each sales product corresponding to e-commerce sales, and dividing the sum by the expected sales volume, we can obtain the one-time pricing for each production batch in each sales product corresponding to e-commerce sales.

4. The e-commerce sales pricing analysis and decision-making system according to claim 3 is characterized by: The marginal cost of each production batch of each sales product corresponding to e-commerce sales is calculated by subtracting the total cost of the n-1 production batch of each sales product corresponding to e-commerce sales, and subtracting the output of the n-1 production batch of each sales product corresponding to e-commerce sales, and dividing the cost difference by the output difference to obtain the marginal cost of each production batch of each sales product corresponding to e-commerce sales; The marginal revenue of each production batch of each sales product corresponding to e-commerce sales is obtained by subtracting the total revenue of n production batches of each sales product corresponding to e-commerce sales and the total revenue of n-1 production batches of each sales product corresponding to e-commerce sales; By taking the output of each production batch of each sales product corresponding to e-commerce sales as the x point, and the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales as the y point, two curves representing the marginal cost and marginal revenue of each production batch of each sales product corresponding to e-commerce sales are drawn, and the output corresponding to the intersection of the marginal cost and marginal revenue curves of each production batch of each sales product corresponding to e-commerce sales is taken as the profit-maximizing output, and the pricing output of each production batch of each sales product corresponding to e-commerce sales is obtained.

5. The e-commerce sales pricing analysis and decision-making system according to claim 4 is characterized by: The secondary pricing of each production batch of each sales product corresponding to e-commerce sales is obtained by summing up the fixed cost, variable cost and target profit, and dividing the sum by the pricing output. Finally, the primary pricing and secondary pricing of each production batch of each sales product corresponding to e-commerce sales are averaged, and the average result is recorded as the preliminary pricing of each production batch of each sales product corresponding to e-commerce sales.

6. The e-commerce sales pricing analysis and decision-making system according to claim 1, characterized in that: Based on the marginal cost and demand price elasticity coefficient of each production batch of each sales product in e-commerce sales, market pricing is calculated and analyzed. The specific calculation and analysis method is as follows: Obtain the historical selling price and sales volume data of each product sold by e-commerce, obtain the model parameters a and b through linear regression analysis, and establish a linear regression model Q=a-bP, where Q represents the historical sales volume of each product sold by e-commerce, and P represents the historical selling price of each product sold by e-commerce. Calculate the price elasticity coefficient T of demand for each production batch of each sales product corresponding to e-commerce sales d .

7. The e-commerce sales pricing analysis and decision-making system according to claim 6, characterized in that: By comprehensively calculating and analyzing the marginal cost, demand price elasticity coefficient and preliminary pricing of each production batch of each product sold by e-commerce sales, according to the formula Calculate the comprehensive pricing P of each production batch of each product sold by e-commerce 综合 , where BC represents the marginal cost of each production batch of each product sold by e-commerce, P 初步 It indicates the preliminary pricing of each production batch of each product sold by e-commerce. and w 初步 Respectively represent the corresponding weight factors; and w 初步 They are all set manually according to the needs of the enterprise.

8. The e-commerce sales pricing analysis and decision-making system according to claim 1, characterized in that: Based on the advantage scores of each product sold and the advantage scores of competing products corresponding to e-commerce sales, and comprehensive calculation and analysis, the specific calculation and analysis method is as follows: A product advantage evaluation index system is constructed for each sales product corresponding to e-commerce sales, wherein the product advantage evaluation index system includes the quality dimension, function dimension, brand dimension and service dimension of each sales product. A weight is assigned to each indicator by the internal product manager of the enterprise in combination with market research. At the same time, each indicator in the product advantage evaluation index system is scored and multiplied by the corresponding indicator weight. Finally, the product results of each indicator and the indicator weight are summed to obtain the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products respectively. The difference between the advantage score of each sales product corresponding to e-commerce sales and the advantage score of competitive products is calculated. At the same time, the difference between the comprehensive pricing of each sales product corresponding to e-commerce sales and the selling price of competitive products is calculated. Finally, the advantage score difference is divided by the selling price difference to obtain the price optimization index of each sales product corresponding to e-commerce sales.

9. The e-commerce sales pricing analysis and decision-making system according to claim 1, characterized in that: Based on the comprehensive pricing of each sales product corresponding to e-commerce sales and the price optimization index of each sales product corresponding to e-commerce sales, a comprehensive analysis is conducted. The specific comprehensive analysis method is as follows: Through industry analysis and the company's historical data of each sales product, the price advantage range boundaries are set, and the price advantage range is divided into an advantageous range, a medium range and a disadvantageous range.

10. The e-commerce sales pricing analysis and decision-making system according to claim 9, characterized in that: If the price optimization index of each sales product corresponding to e-commerce sales is in the advantageous range, it means that the advantages and price combinations of each sales product corresponding to e-commerce sales have certain competitiveness, and the price of each sales product corresponding to e-commerce sales is increased by 5 percentage points to obtain the final pricing of each sales product corresponding to e-commerce sales; If the price optimization index of each sales product corresponding to e-commerce sales is in the middle range, it means that the advantages and price combinations of each sales product corresponding to e-commerce sales have no obvious competitiveness, and the price of each sales product corresponding to e-commerce sales is adjusted up or down by 1 percentage point to obtain the final pricing of each sales product corresponding to e-commerce sales; If the price optimization index of each sales product corresponding to the e-commerce sales is in the disadvantageous range, it means that the advantages and price combination of each sales product corresponding to the e-commerce sales is at a disadvantage. The price of each sales product corresponding to the e-commerce sales is reduced by 3 percentage points to obtain the final pricing of each sales product corresponding to the e-commerce sales.

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