CN110135878B - Method and device for determining sales price - Google Patents
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Abstract
The application discloses a method and a device for determining sales price. The method relates to the field of computer information processing, and comprises the following steps: establishing a predicted sales volume model according to historical sales data of an item, wherein the predicted sales volume model is a function related to future item sales price; establishing a conventional sales volume model according to the historical sales data of the article, wherein the conventional sales volume model is a function related to the selling price of the conventional article; determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and determining the future item selling price through an optimal solution of the optimization target model. The method and the device for determining the sales price can obtain the optimal sales promotion price of the article, so that the article can maintain high sales in a longer time range.
Description
Technical Field
The present application relates to the field of computer information processing, and in particular, to a method and apparatus for determining sales prices.
Background
The existing promotion management systems have a plurality of problems, which cause difficulty in managing a large number of commodities. First, promotions typically consider only the current promotion period, and do not consider the impact on regular sales after adjustment of a single SKU promotion period. For example, for some durable goods, a sales promotion for a certain SKU may result in a large amount of stock by the consumer during the sales promotion period, resulting in a dramatic drop in the regular sales after sales promotion; if oversubscribed at the promotion, it may result in a reduction in the overall sales. The promotion needs to be optimized from the whole rather than just considering sales for the promotion period. Secondly, since the sales promotion pricing method is only roughly based on some attributes of the SKU, the sales promotion is carried out according to the unified discount on the basis of the original price, and the influence of the sales promotion price on the sales in the current sales promotion period and the sales in the future non-sales promotion period by deeply researching the historical sales data by using an analysis technology is ignored.
At present, for large-scale commodity pricing management, commodity classification methods are mainly utilized to distribute different sales collectors for different classified commodities for management. The commodity flow is mainly used for dividing the commodity into a key commodity and a non-key commodity respectively. The more price management sales personnel are allocated to the key commodity; for non-critical goods, the fewer the price management sales personnel they are assigned. During the promotion period, price management sales personnel typically make lower prices for key goods than friends to ensure high sales; while maintaining the original price for non-critical goods to ensure a high profit.
The existing large-scale sales promotion is too dependent on manual control, and a large amount of manpower resources are needed due to the large number of SKUs. Existing promotions typically consider only the next sales cycle, which may be followed by a post-promotion period resulting in a tired sales of the next cycle due to the effects of the cross-cycle ingestion effect (e.g., consumer's goods handling activities). If the current period is promoted at an excessively low price, the low profit at the current period and the low sales at the next several periods will result in low yield for the overall sales, although the sales volume at the current period is high. In addition, the prior art fails to correlate the expected sales force with the expected sales results, lacks a systematic assessment scheme, and thus results in no more accurate inventory planning. To prevent the backdrop of sales, current sales methods are often over-stocked, thus incurring significant inventory costs.
Thus, there is a need for a new method and apparatus for determining sales prices.
The above information disclosed in the background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present application provides a method and apparatus for determining sales prices that enables optimal promotional prices for items to be obtained so that items remain at high sales for a longer period of time.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.
According to an aspect of the present application, a method for determining a sales price is presented, the method comprising: establishing a predicted sales volume model according to historical sales data of an item, wherein the predicted sales volume model is a function related to future item sales price; establishing a conventional sales volume model according to the historical sales data of the article, wherein the conventional sales volume model is a function related to the selling price of the conventional article; determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and determining the future item selling price through an optimal solution of the optimization target model.
In an exemplary embodiment of the present disclosure, further comprising: and filtering the abnormal value of the article sales data to obtain historical sales data.
In an exemplary embodiment of the present disclosure, the filtering the item sales data for outlier filtering to obtain historical sales data includes: the item sales data that decreases in the price of the deal due to the discount by more than a predetermined threshold is filtered out to obtain the historical sales data.
In an exemplary embodiment of the present disclosure, the filtering the abnormal value of the item sales data to obtain historical sales data further includes: and filtering historical data with residual values outside a preset range through a robust regression algorithm to obtain the historical sales data.
In one exemplary embodiment of the present disclosure, the establishing a model of predicted sales volume based on historical sales data of an item includes: establishing the predicted sales volume model through data fitting by a ridge regression algorithm and the historical sales data; and establishing the predicted sales model through data fitting by means of a lasso regression algorithm and the historical sales data.
In an exemplary embodiment of the present disclosure, the determining an optimization objective model by the predicted sales model and the regular sales model includes:
GMV SUM (x t )＝GMV t (x t )+GMV t+1 (x 0 )；
wherein, GMV SUM (x t ) For the optimization objective model, GMV t (x t ) For the predictive sales model, GMV t+1 (x t ) For the conventional sales model, x t Price for future goods, x 0 Selling prices for conventional items.
In an exemplary embodiment of the present disclosure, the determining the future item selling price by the optimal solution of the optimization objective model includes: solving an optimal solution of the optimization target model by a Newton-Lawson method; and determining the future item selling price from the optimal solution.
In an exemplary embodiment of the present disclosure, in the process of solving an optimal solution of an optimization target model, a constraint condition of the optimal solution is: and the second derivative of the optimal solution is less than or equal to 0.
According to an aspect of the present application, there is provided an apparatus for determining a sales price, the apparatus comprising: a predicted sales module for building a predicted sales model based on historical sales data for an item, the predicted sales model being a function related to future item sales prices; a conventional sales module for establishing a conventional sales model according to historical sales data of the item, wherein the conventional sales model is a function related to the selling price of the conventional item; the optimization target module is used for determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and the optimal solution module is used for determining the selling price of the future object through the optimal solution of the optimal target model.
In an exemplary embodiment of the present disclosure, further comprising: and the data filtering module is used for filtering the abnormal value of the article sales data to obtain historical sales data.
According to an aspect of the present application, there is provided an electronic device including: one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods as described above.
According to an aspect of the present application, a computer-readable medium is presented, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the method and the device for determining the sales price, the optimal sales promotion price of the article can be obtained, so that the article can maintain high sales in a longer time range.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The above and other objects, features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the present application and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a system block diagram illustrating a method for determining sales prices according to an example embodiment.
FIG. 2 is a flowchart illustrating a method for determining a sales price according to an example embodiment.
FIG. 3 is a block diagram illustrating an apparatus for determining a sales price according to an example embodiment.
Fig. 4 is a block diagram of an electronic device, according to an example embodiment.
FIG. 5 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments, and that the modules or flows in the drawings are not necessarily required to practice the present application, and therefore, should not be taken to limit the scope of the present application.
FIG. 1 is a system block diagram illustrating a method for determining sales prices according to an example embodiment.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user using the terminal devices 101, 102, 103. The background management server can analyze and process the received data such as product information purchase and the like, and feed back the processing results (such as target push information and article information) to the terminal equipment.
It should be noted that, the method for predicting sales volume provided in the embodiments of the present application is generally executed by the server 105, and accordingly, the web page generating device for assisting the user to browse is generally disposed in the client 101.
FIG. 2 is a flowchart illustrating a method for determining a sales price according to an example embodiment.
As shown in fig. 2, in S202, a predicted sales model is established based on historical sales data for an item, the predicted sales model being a function of future item sales. For example, historical sales data for an item is obtained, the historical sales data including historical prices for the item and sales data corresponding to the historical prices. The historical time is divided into different sales cycles and future prediction parameters extracted from the sales cycles may be as shown in table 1, for example.
| Variable name | Data |
| x 5 | Is the present sales period contain shopping knots? |
| x 6 | Is the lower sales period contain shopping knots? |
| x 7 | Is the present sales period contain holidays? |
| x 8 | Is the lower sales cycle contain holidays? |
| x 9 | Is the present sales period contained spring festival? |
| x t | Selling price of the sales period |
And establishing a predicted sales model by using the parameters and the historical sales data through a regression algorithm in a data fitting mode. The predicted sales model may be established, for example, by a ridge regression algorithm with the historical sales data, by data fitting; and establishing the predicted sales model through data fitting by means of a lasso regression algorithm and the historical sales data.
In some embodiments, ridge regression is a biased estimation regression method dedicated to the analysis of co-linear data, and is essentially an improved least squares estimation method, in which the regression coefficients are obtained more in line with the actual, more reliable regression method at the cost of losing part of the information and reducing the accuracy by giving up the unbiasedness of the least squares method, and the fitting to the pathological data is stronger than the least squares method. In the present application, it is preferable to use a ridge regression model with a penalty coefficient λ=1e—1 as the predicted sales model.
In some embodiments, lasso regression is sometimes referred to as L1 regularization of linear regression, with Lasso regression being L1 regularization. Lasso regression makes some coefficients smaller, even some coefficients with smaller absolute values directly become 0, and is therefore particularly suitable for parameter number reduction and parameter selection, and thus is used to estimate a linear model of sparse parameters.
In S204, a conventional sales model is established based on historical sales data for the item, the conventional sales model being a function related to a sales price of the conventional item. For example, historical sales data for an item is obtained, the historical sales data including historical prices for the item and sales data corresponding to the historical prices. The historical time is divided into different sales cycles and the historical prediction parameters extracted from the sales cycles may be as shown in Table 2, for example.
| Variable name | Data |
| x 1 | Sales average over 5 sales cycles |
| x 2 | Sales of the sales period in the last year |
| x 3 | Rate of increase YOY in the previous month-chronous sales |
| x 4 | Recent month YOY growth rate-chronous sales |
| x t-1 | Selling price of last selling period |
And establishing a conventional sales model according to the historical prediction parameters. The establishment method may refer to the content in S204, for example, and will not be described herein.
In S206, an optimization objective model is determined from the predicted sales model and the regular sales model. In an exemplary embodiment of the present disclosure, the determining an optimization objective model by the predicted sales model and the regular sales model includes:
GMV SUM (x t )＝GMV t (x t )+GMV t+1 (x 0 )；
wherein, GMV SUM (x t ) For the optimization objective model, GMV t (x t ) For the predictive sales model, GMV t+1 (x 0 ) For the conventional sales model, x t Price for future goods, x 0 Selling prices for conventional items.
In S208, the future item selling price is determined by an optimal solution of the optimization objective model. In an exemplary embodiment of the present disclosure, the determining the future item selling price by the optimal solution of the optimization objective model includes: solving an optimal solution of the optimization target model by a Newton-Lawson method; and determining the future item selling price from the optimal solution.
Among them, the ton-Lawson method, also called Newton's method, is a method proposed by Newton in the 17 th century to solve equations approximately in the real-number domain and the complex-number domain. Most equations do not have root-finding formulas, so it is very difficult, if not impossible, to find an approximate root of the equation. The method uses the first few of the taylor series of function f (x) to find the root of equation f (x) =0. Newton's iteration method is one of the important methods for solving the root of the equation, and has the greatest advantage of square convergence around a single root of equation f (x) =0, and can also be used to solve the repeated root, complex root of the equation, where linear convergence, but can be changed into super-linear convergence by some methods.
In some embodiments, in the process of solving the optimal solution of the optimization target model, the constraint condition of the optimal solution is: and the second derivative of the optimal solution is less than or equal to 0.
According to the method for determining the sales price, the predicted sales volume corresponding to the sales promotion price is obtained through establishing the predicted sales model, the conventional sales volume corresponding to the conventional price is obtained through the conventional sales model, the sum of the predicted sales volume and the conventional sales volume is further used as the objective function, and the optimal sales promotion price of the article can be obtained by obtaining the optimal solution of the objective function, so that the article can maintain the high sales volume in a longer time range.
According to the method for determining the sales price, the negative influence of self-swallowing effect generated by sales promotion on sales can be prevented, a more reasonable sales promotion plan is formulated, and the improvement of overall sales is realized.
According to the method for determining the sales price, sales staff can be helped to better know the relationship between the sales promotion period and the conventional sales period of the product, so that a better data basis is provided for developing a sales promotion plan. Manpower resources can be effectively saved, and the scheduling results can be rapidly analyzed and summarized.
It should be clearly understood that this application describes how to make and use particular examples, but the principles of this application are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
In an exemplary embodiment of the present disclosure, further comprising: and filtering the abnormal value of the article sales data to obtain historical sales data. Comprising the following steps: the item sales data that decreases in the price of the deal due to the discount by more than a predetermined threshold is filtered out to obtain the historical sales data. The historical data is subjected to outlier filtering, and an outlier (Outliers) refers to an individual value in a sample, the value of which deviates significantly from the rest of the observed values of the sample to which it (or they) belongs, also called outlier data, outliers. In the process of carrying out quantitative relation regression, the existence of an abnormal value can influence the fitting effect of the demand function to a certain extent. Outlier filtering principles may be, for example:
1. filtering out records where discounts result in a decrease in new-achievement price of greater than 50%;
2. robust regression (log (sales) =log (price) +c) was performed, followed by elimination of records with residuals outside 2 standard deviations of the mean. Where log refers to log-taking calculations and C refers to regression constant terms.
3. SKUs retaining more than 30 pieces of sales data.
In some embodiments, further comprising: and filtering historical data with residual values outside a preset range through a robust regression algorithm to obtain the historical sales data. Robust regression (robust regression) is one method in statistically robust estimation, the main idea of which is to modify the objective function in classical least squares regression, which is very sensitive to outliers. Classical least squares regression minimizes the sum of squares of the errors as its objective function. Since the variance is an unstable statistic, least squares regression is an unstable method. Different objective functions define different robust regression methods. Common robust regression methods are: the least squares (least median square; LMS) method, M estimation method, etc., are not limited thereto.
In some embodiments, all non-promotional discount variables are represented by vector x, i.e
x＝(x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 ,x 8 ,x 9 ,x t-1 ,x t )；
The sales discount fitting model is as follows:
by usingRepresenting regression coefficient vectors.
By x t Indicating the selling price of the next promotional period,features indicating promotional periods, Q t Indicating the predicted sales for the promotional period. By x 0 Representing the original price of the regular sales period after the sales promotion period,/->Features representing regular sales periods after the promotion period, Q t+1 Representing the predicted sales volume for a regular sales period after a promotional period. The system performs regression prediction on sales revenue (GMV) based on the resulting fit equation.
Since the decision variables are only (x t ) Thus the GMV fitting equation can be reduced to
Wherein the method comprises the steps ofThe goal of the system is to optimize the GMV sum of the upsell and non-upsell periods, so the goal equation of the system is
For GMV SUM (x t ) Taking the first derivative, the system gets the optimal discountConditions that should be satisfied
The system gets the optimal discountShould satisfy->The second derivative condition is
Combining the results of the first order condition, optimal discountThe second derivative condition that should be satisfied can be reduced to
In this application, equation (1) is solved using the newton-radson method, and the constraint condition thereof needs to satisfy equation (2). The obtained optimal solution can be used as the optimal price.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. When executed by a CPU, performs the functions defined by the above methods provided herein. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
FIG. 3 is a block diagram illustrating an apparatus for determining a sales price according to an example embodiment. The means 30 for determining a sales price comprises: the forecast sales module 302, the conventional sales module 304, the optimization objective module 306, and the optimal solution module 308.
Wherein the predicted sales module 302 is configured to build a predicted sales model based on historical sales data of an item, the predicted sales model being a function of future item sales. For example, historical sales data for an item is obtained, the historical sales data including historical prices for the item and sales data corresponding to the historical prices. The historical time is divided into different sales periods, and future prediction parameters are extracted according to the sales periods. And establishing a predicted sales model by using the parameters and the historical sales data through a regression algorithm in a data fitting mode.
The conventional sales module 304 is configured to build a conventional sales model based on historical sales data for the item, the conventional sales model being a function of a conventional item sales price. For example, historical sales data for an item is obtained, the historical sales data including historical prices for the item and sales data corresponding to the historical prices. Dividing the historical time into different sales periods, and establishing a conventional sales volume model according to the historical prediction parameters extracted from the sales periods and the historical prediction parameters.
The optimization objective module 306 is configured to determine an optimization objective model from the predicted sales volume model and the regular sales volume model. The determining an optimization objective model by the predicted sales volume model and the regular sales volume model includes:
GMV SUM (x t )＝GMV t (x t )+GMV t+1 (x 0 )；
wherein, GMV SUM (x t ) For the optimization objective model, GMV t (x t ) For the predictive sales model, GMV t+1 (x t ) For the conventional sales model, x t Price for future goods, x 0 Selling prices for conventional items.
The optimal solution module 308 is configured to determine the future selling price of the item through an optimal solution of the optimization objective model. For example, solving an optimal solution of the optimization target model by a Newton-Lafreon method; and determining the future item selling price from the optimal solution.
In an exemplary embodiment of the present disclosure, further comprising: a data filtering module (not shown in the figure) for filtering the abnormal value of the article sales data to obtain the historical sales data.
According to the device for determining the sales price, the predicted sales quantity corresponding to the sales promotion price is obtained through establishing the predicted sales model, the conventional sales quantity corresponding to the conventional price is obtained through the conventional sales model, the sum of the predicted sales quantity and the conventional sales quantity is further used as the objective function, and the optimal sales promotion price of the article can be obtained by obtaining the optimal solution of the objective function, so that the article can maintain high sales quantity in a longer time range.
Fig. 4 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 200 according to this embodiment of the present application is described below with reference to fig. 4. The electronic device 200 shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments herein.
As shown in fig. 4, the electronic device 200 is in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting the different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 such that the processing unit 210 performs the steps according to various exemplary embodiments of the present application described in the above-described electronic prescription flow processing methods section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2.
The memory unit 220 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 2201 and/or cache memory 2202, and may further include Read Only Memory (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 230 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 200, and/or any device (e.g., router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter 260. Network adapter 260 may communicate with other modules of electronic device 200 via bus 230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 200, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
Fig. 5 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure.
Referring to fig. 5, a program product 500 for implementing the above-described method according to an embodiment of the present application is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: establishing a predicted sales volume model according to historical sales data of an item, wherein the predicted sales volume model is a function related to future item sales price; establishing a conventional sales volume model according to the historical sales data of the article, wherein the conventional sales volume model is a function related to the selling price of the conventional article; determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and determining the future item selling price through an optimal solution of the optimization target model.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and include several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present application.
Exemplary embodiments of the present application are specifically illustrated and described above. It is to be understood that this application is not limited to the details of construction, arrangement or method of implementation described herein; on the contrary, the application is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
In addition, the structures, proportions, sizes, etc. shown in the drawings in the specification are used for the understanding and reading of the disclosure, and are not intended to limit the applicable limitations of the disclosure, so that any structural modification, change in proportion, or adjustment of size is not technically significant, and yet falls within the scope of the disclosure without affecting the technical effects and the objects that can be achieved by the disclosure. Also, the terms "upper", "first", "second", and "a" and the like recited in the present specification are also for descriptive purposes only and are not intended to limit the scope of the disclosure in which the relative relationships are altered or modified without materially altering the technical context to be considered within the scope of the application in which the invention may be practiced.
Claims (11)
1. A method for determining a sales price, comprising:
establishing a predicted sales volume model according to historical sales data of an item, wherein the predicted sales volume model is a function related to the selling price of a future item, and is used for predicting sales volume based on the selling price of the future item in a promotion period, wherein the historical time is divided into different sales periods, and the predicted sales volume model is established according to future predicted parameters extracted from the sales periods and the historical sales data;
establishing a conventional sales volume model according to historical sales data of the items, wherein the conventional sales volume model is a function related to conventional item sales prices, and is used for predicting sales volume based on the conventional item sales prices in the next period of the promotion period, wherein historical time is divided into different sales periods, a non-promotion discount variable is constructed according to historical prediction parameters extracted from the sales periods and the future prediction parameters, and the conventional sales volume model is constructed based on the non-promotion discount variable;
determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and
determining the future item selling price through the optimal solution of the optimization target model comprises the following steps: solving an optimal solution of the optimization target model by a Newton-Lawson method; and determining the future item selling price from the optimal solution.
2. The method as recited in claim 1, further comprising:
and filtering the abnormal value of the article sales data to obtain historical sales data.
3. The method of claim 2, wherein said outlier filtering the item sales data to obtain historical sales data comprises:
the item sales data that decreases in the price of the deal due to the discount by more than a predetermined threshold is filtered out to obtain the historical sales data.
4. The method of claim 2, wherein the outlier filtering the item sales data to obtain historical sales data further comprises:
and filtering historical data with residual values outside a preset range through a robust regression algorithm to obtain the historical sales data.
5. The method of claim 1, wherein said building a model of predicted sales volume based on historical sales data for an item comprises:
establishing the predicted sales volume model through data fitting by a ridge regression algorithm and the historical sales data; and
and establishing the predicted sales model through data fitting by a lasso regression algorithm and the historical sales data.
6. The method of claim 1, wherein the determining an optimization objective model from the predicted sales model and the conventional sales model comprises:
GMV SUM (x t )＝GMV t (x t )+GMV t+1 (x 0 )；
wherein, GMV SUM (x t ) For the optimization objective model, GMV t (x t ) For the predictive sales model, GMV t+1 (x t ) For the conventional sales model, x t Price for future goods, x 0 Selling prices for conventional items.
7. The method of claim 1, wherein in the process of solving for the optimal solution of the optimization objective model, the constraint on the optimal solution is:
and the second derivative of the optimal solution is less than or equal to 0.
8. An apparatus for determining a sales price, comprising:
a predicted sales volume module, configured to establish a predicted sales volume model according to historical sales data of an item, where the predicted sales volume model is a function related to a sales price of a future item, and the predicted sales volume model is configured to predict sales volumes based on the sales price of the future item in a promotion period, where historical time is divided into different sales periods, and the predicted sales volume model is established according to future prediction parameters extracted from the sales periods and the historical sales data;
a conventional sales volume module for establishing a conventional sales volume model according to historical sales data of the item, the conventional sales volume model being a function related to a sales price of the conventional item, the conventional sales volume model being used for predicting sales volume based on the sales price of the conventional item in a next cycle of the promotion cycle, wherein historical time is divided into different sales cycles, a non-promotion discount variable is constructed according to historical prediction parameters extracted from the sales cycles and the future prediction parameters, and the conventional sales volume model is constructed based on the non-promotion discount variable;
the optimization target module is used for determining an optimization target model through the predicted sales volume model and the conventional sales volume model; and
an optimal solution module, configured to determine the future selling price of the item through an optimal solution of the optimization target model, including: solving an optimal solution of the optimization target model by a Newton-Lawson method; and determining the future item selling price from the optimal solution.
9. The apparatus as recited in claim 8, further comprising:
and the data filtering module is used for filtering the abnormal value of the article sales data to obtain historical sales data.
10. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
11. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.
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| CN111260142B (en) * | 2020-01-19 | 2023-06-09 | 杭州网易再顾科技有限公司 | Commodity index data prediction method and device, storage medium and electronic equipment |
| CN111754257A (en) * | 2020-05-13 | 2020-10-09 | 苏州明池纺织科技有限公司 | Textile fabric selling price making system |
| CN112258268B (en) * | 2020-10-16 | 2023-11-07 | 百度国际科技（深圳）有限公司 | Method, apparatus, device and medium for determining recommendation model and determining price of article |
| CN112819533A (en) * | 2021-01-29 | 2021-05-18 | 深圳脉腾科技有限公司 | Information pushing method and device, electronic equipment and storage medium |
| CN115619507A (en) * | 2022-12-05 | 2023-01-17 | 阿里健康科技(杭州)有限公司 | Method, device and equipment for determining target resource exchange amount of data object |
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