Multi-product quality optimization method, system and equipment based on consumer price reference effect and medium
By establishing consumer utility function and retailer profit function, and optimizing multi-product quality strategies with the expectation maximization algorithm, the problem of ignoring the price reference effect in traditional decision-making is solved, and more accurate consumer behavior prediction and profit improvement are achieved.
Patent Information
- Application Number
- CN202510349537.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-07-29
AI Technical Summary
传统产品质量决策忽视了消费者在购买时的价格参考效应,导致无法准确预测消费者行为和提供符合偏好的产品价格和质量。
By obtaining retailers' historical transaction data, establishing consumer utility functions and retailer profit functions, estimating parameters using the expectation maximization algorithm, and optimizing multi-product quality strategies to improve prediction accuracy and profit.
It improves the accuracy of predicting consumer behavior and improves retailers' sales revenue and profits.
Smart Images
Figure CN120387720A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and particularly to a multi-product quality optimization method, system, device and medium based on the consumer price reference effect. Background Art
[0002] In order to better meet the needs of consumers, when enterprises sell on e-commerce platforms, they usually offer consumers a variety of products with different qualities and prices, so as to achieve the purpose of attracting consumers to purchase. After seeing the price of a certain product, consumers will compare it with the reference price, thus generating psychological losses or gains. Enterprises formulate their product quality strategies according to the reference effect generated by consumers' comparison behaviors.
[0003] The following problems exist in traditional product quality decisions:
[0004] 1. Ignoring the price reference effect generated by consumers when making purchase decisions.
[0005] When consumers make purchases, their choice behaviors are not only affected by the attributes of the products themselves, but also by the reference effect generated by their own comparison behaviors. This price reference effect will affect consumers' purchase decisions and thus their purchase behaviors. However, enterprises often ignore the price reference effect, making it difficult to accurately predict consumers' behaviors.
[0006] 2. Unable to accurately provide product prices and qualities that meet consumers' preferences.
[0007] In the actual operation of enterprises, due to ignoring the impact of the price reference effect on consumers' behaviors, enterprises are unable to accurately estimate consumers' demands for products, resulting in their inability to accurately provide product prices and qualities that meet consumers' preferences. Summary of the Invention
[0008] To solve the above technical problems, the present invention provides a multi-product quality optimization method, system, device and medium based on the consumer price reference effect.
[0009] To solve the above technical problems, the present invention adopts the following technical solutions:
[0010] A multi-product quality optimization method based on the consumer price reference effect, comprising:
[0011] Obtaining the historical transaction data of retailers, and establishing a consumer utility function and a retailer profit function based on the historical transaction data;
[0012] Establishing a multi-product quality optimization strategy according to the retailer profit function;
[0013] Estimate the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtain the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy.
[0014] In one embodiment, the method of obtaining the historical transaction data of the retailer and establishing the consumer utility function and the retailer profit function based on the historical transaction data specifically includes:
[0015] Crawl the historical sales data of the retailer on the e-commerce platform to obtain the historical transaction data of consumers of the online retailer;
[0016] Establish a consumer utility function according to the historical transaction data of consumers;
[0017] According to the consumer utility function, obtain the demand function for each product;
[0018] Establish a retailer profit function according to the demand function.
[0019] In one embodiment, the method of establishing a consumer utility function according to the historical transaction data of consumers specifically includes:
[0020] u i = θq i - p i - γ(p i - p r ), i = 1, …, n;
[0021] Wherein, u i represents the utility of the consumer for product i; θ represents the consumer's perception of the product quality and follows a uniform distribution on [0, 1]; q i represents the quality of product i; p i represents the price of product i; θq i - p i represents the basic utility obtained by the consumer when purchasing product i; n represents the number of products in the product line; γ(p i - p r ) represents the reference utility obtained by the consumer when comparing the price of product i with the reference price, where represents the coefficient of the price reference effect, p r represents the reference price.
[0022] In one embodiment, the method of obtaining the demand function for each product according to the consumer utility function specifically includes:
[0023]
[0024] where d i (p, q) represents the probability that a consumer chooses product i when the product price is p and the quality is q; p = (p1, p2, …, p n ); q = (q1, q2, …, q n ); represents probability, u i represents the utility of product i for the consumer, q n represents the quality of product n; p n represents the price of product n.
[0025] In one embodiment, establishing the retailer profit function according to the demand function specifically includes:
[0026]
[0027] where represents the cost required for the enterprise to produce product i, κ represents the coefficient of production cost; Π(p, q) represents the profit of the retailer when the product quality is q and the price is p; n represents the number of products in the product line; p i represents the price of product i, q i represents the quality of product i; d i (p, q) represents the probability that a consumer chooses product i when the product price is p and the quality is q.
[0028] In one embodiment, establishing the multi-product quality optimization strategy according to the retailer profit function specifically includes:
[0029] Solving the retailer profit function to obtain the multi-product quality optimization strategy of the enterprise based on the consumer price reference effect:
[0030]
[0031] where represents the optimal quality difference between two adjacent products; p i represents the price of product i, q i represents the quality of product i, κ represents the coefficient of production cost; γ is a parameter in the consumer utility function, and n represents the number of products in the product line.
[0032] In one embodiment, estimating the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtaining the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy, specifically includes:
[0033] Obtain consumer historical transaction data based on the retailer's historical transaction data; estimate the parameters γ and the consumer arrival rate λ in the consumer utility function using the Expectation-Maximization algorithm based on the consumer historical transaction data and the consumer utility function;
[0034] According to the parameters γ and the consumer arrival rate λ in the consumer utility function, obtain the final product quality decision through the multi-product quality optimization strategy and obtain the retailer's revenue.
[0035] A multi-product quality optimization system based on the consumer price reference effect, comprising:
[0036] A function construction module, configured to obtain the retailer's historical transaction data and establish a consumer utility function and a retailer profit function based on the historical transaction data;
[0037] A multi-product quality optimization module, configured to establish a multi-product quality optimization strategy according to the retailer profit function;
[0038] A final decision module, configured to estimate the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtain the final product quality decision and the retailer's revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy.
[0039] An electronic device, comprising:
[0040] One or more processors;
[0041] A memory, configured to store one or more computer programs;
[0042] Wherein, when the one or more computer programs are executed by the one or more processors, the methods in any embodiment can be implemented.
[0043] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the methods in any embodiment are implemented.
[0044] Compared with the prior art, the beneficial technical effects of the present invention are:
[0045] From the perspective of the retailer, the present invention considers the influence of the price reference effect on consumer behavior, optimizes the quality and price decisions of each product of the enterprise, can better attract consumers to purchase, and improve the retailer's profit. In addition, from the perspective of practical application, the present invention provides a parameter estimation method for the demand model, namely the Expectation-Maximization algorithm, which can effectively improve the prediction accuracy of consumer behavior and increase the retailer's profit. Description of the Drawings
[0046] Figure 1 This is a schematic flowchart of a multi-product quality optimization method based on the consumer price reference effect in an embodiment of the present invention.
[0047] Figure 2 This is a schematic flowchart of a multi-product quality optimization system based on the consumer price reference effect in an embodiment of the present invention. Detailed implementation manners
[0048] The following will give a detailed description of a preferred implementation manner of the present invention with reference to the accompanying drawings.
[0049] As Figure 1 shown, the present invention discloses a multi-product quality optimization method based on the consumer price reference effect, including the following steps:
[0050] S1: Obtain the historical transaction data of the retailer, and establish a consumer utility function and a retailer profit function based on the historical transaction data;
[0051] S2: Establish a multi-product quality optimization strategy according to the retailer profit function;
[0052] S3: Estimate the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtain the final product quality decision and the retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate and the multi-product quality optimization strategy.
[0053] In one embodiment, obtaining the historical transaction data of the retailer in step S1 and establishing a consumer utility function and a retailer profit function based on the historical transaction data specifically include the following steps:
[0054] S11: Crawl the historical sales data of the retailer on the e-commerce platform to obtain the historical transaction data of the consumers of the online retailer.
[0055] For example, the transaction situations of two products of retailer TL on the Tmall platform are relatively good, namely 500g caramelized melon seeds and 2000g caramelized melon seeds. The embodiment of the present invention obtains the transaction order data of these two kinds of melon seeds from May 2018 to April 2019, and the specific fields include the transaction date, the delivery date, the transaction quantity, the transaction price, etc. of the product.
[0056] S12: Establish a consumer utility function according to the historical transaction data of the consumers.
[0057] In one embodiment, establishing a consumer utility function according to the historical transaction data of the consumers in step S12 specifically includes:
[0058] ui = θq i - p i - γ(p i - p r ), i = 1, …, n;
[0059] Among them, u i represents the utility of consumer for product i; θ represents the perception of product quality by consumers and follows a uniform distribution on [0, 1]; q i represents the quality of product i; p i represents the price of product i; θq i - p i represents the basic utility obtained by consumers when purchasing product i; n represents the number of products in the product line, and the condition n ≥ 2 needs to be satisfied; γ(p i - p r ) represents the reference utility obtained by consumers when purchasing product i by comparing the price of product i with the reference price, where represents the coefficient of the price reference effect; p r represents the reference price, which is defined as the lowest price among n products.
[0060] S13: According to the consumer utility function, obtain the demand function of consumers for each product.
[0061] In one embodiment, the obtaining the demand function of consumers for each product according to the consumer utility function in step S13 specifically includes:
[0062]
[0063] Among them, d i (p, q) represents the probability that consumers choose product i when the product price is p and the quality is q; p = (p1, p2, …, p n ); q = (q1, q2, …, q n ); represents probability, u i represents the utility of consumer for product i, q n represents the quality of product n; p n represents the price of product n.
[0064] The embodiment of the present invention selects two products: 500g caramelized melon seeds and 2000g caramelized melon seeds, so n = 2.
[0065] S14: According to the demand function, establish the retailer profit function.
[0066] In one embodiment, the establishing the retailer profit function according to the demand function specifically includes:
[0067]
[0068] Among them, represents the cost required for the enterprise to produce product i, and κ represents the coefficient of production cost; ∏(p, d) represents the profit of the retailer when the product quality is q and the price is p; n represents the number of products in the product line; p i represents the price of product i, and q i represents the quality of product i; d i (p, q) represents the probability that consumers choose product i when the product price is p and the quality is q.
[0069] In one embodiment, establishing a multi-product quality optimization strategy according to the retailer profit function in step S2 specifically includes:
[0070] Solving the retailer profit function to obtain a multi-product quality optimization strategy of the enterprise based on the consumer price reference effect:
[0071]
[0072] Among them, represents the optimal quality difference between two adjacent products; p i represents the price of product i, and q i represents the quality of product i, κ represents the coefficient of production cost; γ is a parameter in the consumer utility function, and n represents the number of products in the product line.
[0073] In one embodiment, estimating the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data in step S3, and obtaining the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy specifically includes:
[0074] Step S31: Obtain consumer historical transaction data based on the retailer's historical transaction data; estimate the parameter γ and the consumer arrival rate λ in the consumer utility function using the expectation maximization algorithm based on the consumer historical transaction data and the consumer utility function;
[0075] Let N t represent the choice set of products on the t-th day, represent the choice vector of consumers on the t-th day, represent the total number of consumers who choose to purchase, n0 = (n 0t ) t represent the vector of consumers who do not purchase, Θ represents the parameters to be estimated γ, λ, then the likelihood function that can be obtained is as follows:
[0076]
[0077] The Expectation-Maximization algorithm is used to estimate the parameters. As Figure 2 shown, it specifically includes the following steps:
[0078] 1) Initialize the parameters Set the termination condition ε > 0 and let m = 1;
[0079] 2) Update the parameters
[0080] 3) Calculate the purchase probability according to the parameters of the previous period
[0081] 4) Update the number of consumers who choose the external option. The formula is as follows:
[0082] 5) If then output and stop. Otherwise, return to the second step and let m ← m + 1.
[0083] In this embodiment, two types of products of TL are analyzed in total, and the parameters of these two types of products are estimated. The results are shown in Table 1.
[0084] Table 1 Parameter Estimation of the Consumer Utility Function
[0085]
[0086] Note: *** indicates that the significance level is 0.001.
[0087] Step S32: According to the parameters γ and the consumer arrival rate λ in the consumer utility function, obtain the final product quality decision through the multi-product quality optimization strategy and obtain the retailer's revenue.
[0088] According to the product quality strategy and the parameter estimation results, predict the consumer demand and optimize the product quality decision of TL. Based on this, calculate the sales revenue of TL. The prediction and calculation results of the embodiments of the present invention are shown in Table 2.
[0089] Table 2 Retailer's Revenue
[0090]
[0091] By comparing with the sales revenue without considering the price reference effect, it can be found that the method provided by the present invention can effectively improve the retailer's sales revenue, with an average increase of 2.182%.
[0092] A multi-product quality optimization method based on the consumer price reference effect disclosed by the present invention starts from the perspective of retailers, considers the price reference effect, optimizes the quality and price decisions of each product by enterprises, and can better attract consumers to make purchases. In addition, from the perspective of practical applications, the present invention provides a parameter estimation method, namely the Expectation-Maximization algorithm, which can effectively improve the prediction accuracy of consumer behavior and increase the profits of retailers.
[0093] The present invention also provides a multi-product quality optimization system based on the consumer price reference effect. The system may include a system (including a distributed system), software (application), module, component, server, client, etc. that uses the above method and combines necessary implementation hardware. Since the implementation solutions of the system to solve problems are similar to those of the method, the implementation of the specific system in the embodiments of this specification may refer to the implementation of the foregoing method. The term "module" used hereinafter is a combination of software and / or hardware that can implement a predetermined function. Although the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.
[0094] As Figure 2 shown, the system may include:
[0095] A function construction module 100, configured to obtain historical transaction data of a retailer and establish a consumer utility function and a retailer profit function based on the historical transaction data;
[0096] A multi-product quality optimization module 200, configured to establish a multi-product quality optimization strategy according to the retailer profit function;
[0097] A final decision module 300, configured to estimate parameters of the consumer utility function and a consumer arrival rate based on the historical transaction data, and obtain a final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy.
[0098] In one embodiment, the present invention provides an electronic device, which may include one or more processors, a memory, and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store the data used in the above method. The network interface of the electronic device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the above method.
[0099] In an exemplary embodiment, the present invention further provides a computer-readable storage medium storing a computer program, such as a memory storing a computer program. The above computer program can be executed by a processor to complete the above method. The storage medium may be a non-transitory computer-readable storage medium or a transitory computer-readable storage medium.
[0100] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention, and any reference signs in the claims should not be regarded as limiting the claims involved.
[0101] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A multi-product quality optimization method based on the consumer price reference effect, characterized in that Including: Obtain the historical transaction data of the retailer, and establish a consumer utility function and a retailer profit function based on the historical transaction data; Establish a multi-product quality optimization strategy according to the retailer profit function; Estimate the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtain the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy.
2. The multi-product quality optimization method based on the consumer price reference effect according to claim 1, wherein The obtaining of the historical transaction data of the retailer and the establishment of the consumer utility function and the retailer profit function based on the historical transaction data specifically include: Crawl the historical sales data of the retailer on the e-commerce platform to obtain the historical transaction data of consumers of the online retailer; Establish a consumer utility function according to the historical transaction data of consumers; According to the consumer utility function, obtain the demand function of consumers for each product; Establish a retailer profit function according to the demand function.
3. The multi-product quality optimization method based on the consumer price reference effect according to claim 2, wherein The establishment of the consumer utility function according to the historical transaction data of consumers specifically includes: u i = θq i - p i - γ(p i - p r ), i = 1, …, n; Among them, u i represents the utility of product i for consumers; θ represents the consumers' perception of product quality and follows a uniform distribution on [0, 1]; q i represents the quality of product i; p i represents the price of product i; θq i -p i represents the basic utility obtained by consumers when purchasing product i; n represents the number of products in the product line; γ(p i -p r ) represents the reference utility obtained by consumers when comparing the price of product i with the reference price when purchasing product i, where represents the coefficient of the price reference effect, p r represents the reference price.
4. The multi-product quality optimization method based on the consumer price reference effect according to claim 2, characterized in that, The obtaining of the demand function of consumers for each product according to the consumer utility function specifically includes: where q i (p, q) represents the probability that consumers choose product i when the product price is p and the quality is q; p = (p1, p2, …, p n ); q = (q1, q2, …, q n ); represents probability, u i represents the utility of product i for consumers, q n represents the quality of product n; p n represents the price of product n.
5. The multi-product quality optimization method based on the consumer price reference effect according to claim 2, characterized in that The establishment of the retailer profit function according to the demand function specifically includes: Among them, represents the cost required for the enterprise to produce product i, κ represents the coefficient of production cost; ∏(p, d) represents the profit of the retailer when the product quality is q and the price is p; n represents the number of products in the product line; p i represents the price of product i, q i represents the quality of product i; d i (p, q) represents the probability that consumers choose product i when the product price is p and the quality is q.
6. The multi-product quality optimization method based on the consumer price reference effect according to claim 1, characterized in that, The establishment of the multi-product quality optimization strategy according to the retailer profit function specifically includes: Solve the retailer profit function to obtain a multi-product quality optimization strategy of the enterprise based on the consumer price reference effect: Among them, represents the optimal quality difference between two adjacent products; p i represents the price of product i, q i represents the quality of product i, κ represents the coefficient of production cost; γ is a parameter in the consumer utility function, and n represents the number of products in the product line.
7. The multi-product quality optimization method based on the consumer price reference effect according to claim 1, characterized in that The estimating of the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and the obtaining of the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy specifically include: Obtain the historical transaction data of consumers based on the historical transaction data of the retailer; based on the historical transaction data of consumers and the consumer utility function, use the expectation maximization algorithm to estimate the parameter γ and the consumer arrival rate λ in the consumer utility function; According to the parameter γ and the consumer arrival rate λ in the consumer utility function, obtain the final product quality decision through the multi-product quality optimization strategy, and obtain the retailer revenue.
8. A multi-product quality optimization system based on the consumer price reference effect, characterized in that Including: A function construction module, configured to obtain the historical transaction data of the retailer, and establish a consumer utility function and a retailer profit function based on the historical transaction data; A multi-product quality optimization module, configured to establish a multi-product quality optimization strategy according to the retailer profit function; A final decision module, configured to estimate the parameters of the consumer utility function and the consumer arrival rate based on the historical transaction data, and obtain the final product quality decision and retailer revenue according to the parameters of the consumer utility function, the consumer arrival rate, and the multi-product quality optimization strategy.
9. An electronic device, characterized in that, Including: One or more processors; A memory, configured to store one or more computer programs; Wherein, when the one or more computer programs are executed by the one or more processors, the method according to any one of claims 1 to 7 can be implemented.
10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.