CN113538055A - Sales estimation method and device for Amazon platform commodities and processor - Google Patents
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Abstract
The embodiment of the application provides a sales estimation method, a sales estimation device, a processor and a storage medium for amazon platform commodities. The method comprises the following steps: aiming at each commodity of the Amazon platform, acquiring the current commodity inventory and sales ranking of the commodity at preset time intervals; determining the sales volume of the commodity according to the variation of the commodity inventory; the sales of the commodities and the corresponding sales are ranked and input into a regression prediction model, and parameter values of a polynomial regression equation included in the regression prediction model are determined; obtaining sales ranking of commodities to be predicted; the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation; determining a sales ranking calculation formula of an Amazon platform; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
Description
Technical Field
The application relates to the technical field of computers, in particular to a sales volume estimation method and device for amazon platform commodities, a storage medium and a processor.
Background
The sales volume of the commodity changes, representing the strength and weakness of the demand. Estimating the monthly sales (the last 30 days) of the commodity on amazon platform is very important for the seller to analyze and select the commodity on the market. In the prior art, the adopted sales calculation methods generally include a Review estimation method, a Feedback estimation method, a daily stock difference analysis method and the like.
However, the estimation methods of Review and Feedback have a large error, and can only be used as a reference, and basically have no great application value in the era of fine operation. In addition to the aforementioned disadvantages, the currently more general method for daily poor inventory has large deviation of sales prediction due to the fact that the established model is too simple (the model considers that the BSR ranking is only related to daily sales), the inspected factors are too few, the granularity of data is too coarse (daily sales estimation), and the like.
Disclosure of Invention
The embodiment of the application aims to provide a sales volume estimation method and device for amazon platform commodities, a storage medium and a processor.
In order to achieve the above object, a first aspect of the present application provides a sales estimation method for amazon platform commodities, including:
aiming at each commodity of the Amazon platform, acquiring the current commodity inventory and sales ranking of the commodity at preset time intervals;
determining the sales volume of the commodity according to the variation of the commodity inventory;
the sales of the commodities and the corresponding sales are ranked and input into a regression prediction model, and parameter values of a polynomial regression equation included in the regression prediction model are determined;
obtaining sales ranking of commodities to be predicted;
the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation;
determining a sales ranking calculation formula of an Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking;
and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
Optionally, the weighting coefficients are determined by inputting the sales and the sales ranking of the goods into a linear model for regression analysis.
Optionally, the sales ranking calculation formula is:
wherein y is an exponentially weighted average and xnThe sales per hour of the commodity in n hours are shown, a is an exponential function, and t is a weighting coefficient of the exponential function.
Optionally, the polynomial regression equation is: y ═ a + b ×2+c*x3+d*x4+e*x5；
Wherein y is an exponential weighted average, x is the sales ranking of the goods, and a, b, c, d, e are parameters of a polynomial regression equation.
Optionally, the preset time and the preset time period are per hour; the step of obtaining the current commodity inventory and sales ranking of the commodities at preset time intervals comprises the following steps: and acquiring the current commodity inventory and the BSR ranking of the commodities every other hour.
Optionally, in the sales ranking calculation formula, the more recent sales data are weighted more heavily and exhibit exponential variation.
The application second aspect provides a sales prediction device for amazon platform commodity, includes:
the data statistics module is used for acquiring the current commodity inventory and sales ranking of the commodities at preset time intervals for each commodity of the amazon platform, and determining the sales of the commodities according to the variation of the commodity inventory;
the parameter determining module is used for inputting sales of the commodities and corresponding sales ranks into the regression prediction model and determining parameter values of a polynomial regression equation in the regression prediction model;
the sales prediction module is used for acquiring sales ranking of the commodities to be predicted; the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation; determining a sales ranking calculation formula of an Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to be configured to perform the aforementioned sales forecasting method for amazon platform commodities.
A fourth aspect of the present application provides a processor configured to perform the aforementioned sales forecasting method for amazon platform commodities.
According to the sales volume estimation method for amazon platform commodities, the collected data are BSR and inventory information acquired every hour, so that the established model and parameters can be updated every hour. And in the prior art, only daily updating can be realized at most. Therefore, the model established in the application is more accurate, the iteration and optimization frequency is higher, and the sales prediction of the commodity to be predicted is more accurate. Furthermore, by means of updating every hour, not only the influence of the current sales volume on the BSR is considered, but also the influence of the sales speed (the rate of change of the sales volume) for the past 14 days on the BSR is subjected to correlation analysis, and this influence cannot be ignored in practice. In addition, the sales volume estimation model established in the application has the advantages that the completeness and the updating frequency of data are obviously improved compared with those of the prior art, meanwhile, the influence of ASIN sales speed is used as an important factor influencing BSR ranking, and the Amazon BSR algorithm is highly reduced, so that the obtained estimated sales volume is closer to the actual sales volume, and the prediction accuracy is obviously improved.
Additional features and advantages of embodiments of the present application will be described in detail in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the embodiments of the disclosure, but are not intended to limit the embodiments of the disclosure. In the drawings:
FIG. 1 is a schematic flow chart illustrating a sales forecasting method for Amazon platform commodities according to an embodiment of the present application;
FIG. 2 is a block diagram schematically illustrating a sales forecasting apparatus for Amazon platform merchandise according to an embodiment of the present disclosure;
fig. 3 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the embodiments of the application, are given by way of illustration and explanation only, not limitation.
In the prior art, there are several calculation methods for calculating the BSR rank of a commodity on amazon:
1. review estimation method
The average us consumer has a retention rating between 3% and 5% on the amazon platform, and therefore the sales are deduced from this. And the statistics and display of the commodity Review on amazon are started from the Listing shelf, and the data before three years does not have too much referential property for the current operation, so that when the Review data is counted, the sales volume estimation only by taking the last three months or half a year as a time scale is closer to the real data. For example, by counting the number of scores in the last three months (by 90 days), it is inferred according to a ratio of 1: 300 or 1: 500. If the number of the last three months is 10, then the total sales for the last 90 days is approximately 3000, then 3000/90 ≈ 33, i.e. 33 daily sales, in a 1: 300 ratio; if the ratio is 1: 500, the daily sales of the product can be estimated to be 55.
2. Feedback estimation method
And directly finding the number of feedbacks of the shop corresponding to the commodities for the last 30 days, 5 and the average daily sales volume. If there are many vendors to refresh the view because of the needs of the operation, then only few vendors will do the refresh of the Feedback. Because the quality and the help of the Feedback quantity to the operation are small and even have no influence, few sellers can brush the Feedback, which means that the Feedback quantity is closer to the real data. Empirically, if the sales of a store is substantially constant, the number of feedbacks in the store for 30 days is 4-5 times the number of orders in the store for one day. Knowing the total order number of the shop, entering the shop to check, because the default of the commodities in the shop is sorted according to the sales volume, checking the position of the Listing which is being evaluated in the shop, and then roughly evaluating the sales volume of the Listing according to the evaluation of the Review number.
3. Daily stock difference analysis method
The current inventory of the item is known by adding the item to the shopping cart and setting the order quantity to a maximum quantity of 999 and amazon indicating how much is available if the inventory of the other party is less than 999. Repeating the above operations the next day for one or two consecutive weeks, and averaging the recorded data, it is possible to estimate the average daily sales of the Listing. This approach has several disadvantages. First, if the inventory is greater than 999, or the seller may set the maximum Order Quantity (Max Order Quantity) for the limited purchase of the goods, the method of estimating the sales volume by the inventory difference is not effective. Secondly, a premise of this method is that there are only 1 selling seller, which is not applicable to Listing with many people. Third, this method usually finds the ASIN with a large BSR ranking of 50 ten thousand or less in Amazon station at the beginning of each month, and then estimates the monthly sales volume of the ASIN in the last natural month based on various models, and such inclusion principle brings many problems, for example:
1) for the categories of clothing, many SKUs outdoors, many ASIN are not included.
2) The first 50 thousands of new products, followed by surge, were not collected at the beginning of the month BSR.
3) For those variants that do not share a parent BSR, the sales may be small and no BSR and no collection.
4) Some small categories, such as Tablet standards (ipad stent, amazon in-station search: ipad holder), only the minor BSR, and therefore, is not included.
It can be seen that there are various disadvantages to the prior art described above. For example, the estimation methods of Review and Feedback have a large error, and can only be used as a reference, and basically have no great application value in the era of fine operation. In addition to the aforementioned disadvantages, the currently more general method for daily poor inventory has large deviation of sales prediction due to the fact that the established model is too simple (the model considers that the BSR ranking is only related to daily sales), the inspected factors are too few, the granularity of data is too coarse (daily sales estimation), and the like. For example:
1) since the BSR is based on order volume, rather than sales volume, an order containing 10 items and an order containing 1 item would be the same for the promotion of BSR. So when a user of a certain product often purchases more than 1 piece, the forecast deviation of sales amount is larger.
2) If the BSR changes dramatically during the same day due to promotion such as killing by seconds, for example, the average value of the current day is about 2 thousands (the average value is calculated by four time intervals in one day) when the number of 10 thousands is suddenly increased to 2000.
3) If the goods are out of stock, the BSR steadily decreases but is valued, the system can predict that the sales volume is in the same day, and the actual sales volume is 0.
4) Because data monitoring is not timely, for example, some ASIN cannot monitor for one or more times in a day, so that no BSR exists in the day, the average value of the day can be predicted only by using BSRs in the last few days.
5) Some small categories, such as Tablet standards (ipad stent, amazon in-station search: ipad holder), there is substantially no big BSR, and prediction based on big BSR is not possible. Sales may be off-spectrum if inadvertently recorded and predicted.
6) The overall increase in sales for a category, such as amazon Prime Day and black five, resulted in the same BSR ranked ASIN, which was less predictable when the daily sales were more drastic than the monthly overall.
7) For head goods, especially those with BSR within 500, the prediction error may be relatively large. Since the BSR is based on ranking, such as the top 10 product of a category, the ranking is substantially stable, but the BSR ranking for an ASIN increases from 8 th to 5 th, since the daily sales for several days increases dramatically from 300 to 1000, and the actual daily sales increases from 300 to 500, as does the BSR ranking.
8) The categories of ASIN changed abruptly from Beauty & Personal Care to Health & Household, and sales may be inaccurate by several days.
In view of the above, in an embodiment of the present application, a sales estimation method for amazon platform commodities is provided, as shown in fig. 1, the method includes the following steps:
And step 102, determining the sales volume of the goods according to the variation of the inventory of the goods.
103, inputting the sales of the plurality of commodities and the corresponding sales ranking into the regression prediction model, and determining the parameter values of the polynomial regression equation included in the regression prediction model.
And 104, acquiring the sales volume ranking of the commodity to be predicted.
And 105, inputting the sales ranking of the to-be-predicted commodity into a regression prediction model so as to determine the exponential weighted average of the to-be-predicted commodity according to a polynomial regression equation.
And 106, determining a sales ranking calculation formula of the Amazon platform, wherein the independent variables of the sales ranking calculation formula comprise sales of the commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and the dependent variable is the sales ranking.
And 107, performing reverse engineering processing on the sales ranking calculation formula to determine the sales of the to-be-predicted commodity in a preset time period according to the exponential weighted average of the to-be-predicted commodity.
First, for each commodity of the amazon platform, the current commodity inventory and sales ranking of the commodity can be obtained at intervals of a preset time. Specifically, the preset time may refer to hourly, and the sales ranking refers to the BSR ranking of amazon. The BSR ranking refers to the best-selling ranking of the commodities on the Amazon platform, and the BSR ranking is updated continuously, usually once every hour. Wherein, the lower the number of BSR ranking of the goods, the higher the ranking. Due to the fact that commodities on the amazon platform are numerous and data needs to be acquired once an hour, a crawler server group with mass data processing capacity can be established, and basic commodity information of amazon sites, including but not limited to inventory and BSR ranking of commodities, can be crawled every hour. After the inventory of the commodity is obtained, the sales volume of the commodity can be calculated according to the variation of the inventory of the commodity. For example, if the stock quantity of the article a is 2000 pieces at 19:00 time and 1000 pieces at 20:00 time, the sales volume of the article a in this one hour can be determined to be 1000 pieces. Then, based on these data, a regression prediction model can be established, using the sales and BSR ranking of each period in the past period as independent variable and dependent variable, respectively, for example, using the sales and BSR ranking of each period in the past 14 days as independent variable and dependent variable, respectively.
Specifically, amazon has twenty billions of ASIN (amazon commodity is a special coded identifier, and each commodity is different), and a large number of crawler servers are used for acquiring inventory and BSR ranking of all ASIN every hour, and the sales volume and BSR ranking of each ASIN in each hour can be calculated. The obtained ASIN may be sampled and the sampling probability may be determined based on the historical sales or ranking of the ASIN. A sample ASIN representing a high sales volume and a sample ASIN representing a low sales volume may be drawn from all the ASINs, with higher sales and higher sampling probability, and the sample ASIN may be obtained. For example, product a has sold 8 products in one hour, product B has sold 4 products in one hour, and product C has sold 1 product in one hour. In sampling, the probability of product A being drawn is 20%, the probability of product B being drawn is 10%, and the probability of product C being drawn is 2.5%. Based on such a sampling probability model, a library of sample ASIN's can be obtained, which the crawler acquires and BSR's every hour. The difference in inventory, which is the sales of the ASIN at that hour, is obtained by subtracting the inventory at the previous hour from the inventory at that hour.
Regression analysis is a method of predictive modeling techniques that studies the relationship between dependent variables (targets) and independent variables (predictors). This technique is used for prediction, time series modeling and finding causal relationships between variables. For example, the relationship between reckless driving of a driver and the occurrence frequency of traffic accidents can be solved through regression analysis. Regression analysis can estimate the relationship between two or more variables and is an important tool for data modeling and analysis. There are many benefits to using regression models. It can reveal significant relationships between dependent variables and independent variables, effectively measure the influence of multiple independent variables on a single dependent variable, and allow us to compare the influence of variables measured on different scales, such as the influence of price changes and the influence of the number of promotional programs, and can help market researchers, data analysts and data scientists to evaluate and select the best set of variables for building predictive models.
Regression analysis may also be used in this application to determine the functional relationship between BSR hourly ranking and hourly sales. In the regression prediction model, the dependent variable is continuous, and the independent variable may be continuous or discrete. The nature of the regression is linear. Linear regression establishes a relationship between a dependent variable (Y) and one or more independent variables (X) by using the best-fit straight line (also known as a regression line). Its basic expression is: y ═ a + b × X + e, where a is the straight line intercept, b is the straight line slope, and e is the error term. If the independent variable X is given, the predicted value, namely the dependent variable Y, can be calculated through the linear regression expression. Unary and multiple linear regressions differ in that multiple linear regressions have more than 1 independent variable, whereas unary linear regressions have only 1 independent variable.
Further, sales and BSR rankings of the samples ASIN in a plurality of time periods may be input into the regression prediction model, and a functional relationship between the BSR hourly ranking and hourly sales may be determined by the regression prediction model. In the regression model used in this example, the Least Square Method (Least Square Method) was used to obtain the best-fit straight line. The least squares method is a commonly used algorithm to fit a regression line. It calculates the best-fit straight line by minimizing the sum of the squares of the vertical error of each data point and the predicted straight line. Because the sum of the squares of the errors is calculated, the positive and negative values of the errors do not cancel each other. Specifically, the regression prediction model in the present embodiment uses a polynomial regression equation. Polynomial Regression (Polynomial Regression) can be performed using a Polynomial Regression fit BSR and an exponentially weighted average y. For a regression equation, if the exponent of the argument is greater than 1, it is a polynomial regression equation.
In one embodiment, the polynomial regression equation is: y ═ a + b ×2+c*x3+d*x4+e*x5(ii) a Wherein y is an exponential weighted average, x is the sales ranking of the goods, and a, b, c, d, e are parameters of a polynomial regression equation. The parameter values of the polynomial regression equation, i.e. a, b, c, d, e, are calculated by continuously substituting the known sales ranking x.
Further, for the commodity to be predicted, the sales ranking (BSR ranking) of the commodity to be predicted can be directly obtained on the amazon platform, and then the sales ranking is substituted into the polynomial regression equation, that is, the exponentially weighted average y can be calculated according to the calculated parameters and the substituted sales ranking. Then, as can be seen from the analysis of hourly sales and BSR ranking of amazon platform commodities, the amazon BSR algorithm ranking is calculated according to an exponentially weighted average y of n hours of hourly sales x, and the smaller the time t, the larger the weight is, the smaller the time t isAnd are exponential. Therefore, the sales ranking calculation formula of the amazon platform can be determined as follows:
wherein y is an exponentially weighted average and xnThe sales per hour of the commodity in n hours are shown, a is an exponential function, and t is a weighting coefficient of the exponential function. It can be seen that the independent variables of the sales ranking calculation formula include sales of the goods in a preset time period, an exponentially weighted average, an exponential function, and a weighting coefficient of the exponential function, and the dependent variable is the sales ranking. The weighting coefficient t is determined by inputting the sales volume and the sales volume ranking of the commodities into a linear model for regression analysis.
It can be determined through extensive data analysis that the amazon BSR ranking updated every hour not only reflects the sales of the current ASIN, but also is related to the rate of change of sales or historical data on sales. An ASIN has a sales volume of 10 units in an hour but drops to only one unit in the next hour, which does not mean that it automatically drops to a BSR that sells only one product per hour. A number of examples show that the rate of sales, or rate of change of sales, of ASIN affects its BSR ranking. For example, commodity A is ranked 500 th in the Patio, Lawn & Garden category of Amazon, which sold 8 products in the past hour. Meanwhile, item B ranks 1000 th in the same category, and only 4 products were sold in the past hour. In the next hour, a sells only 4 products and B sells 8 products. If the sales speed factor is not considered, it appears that the two will swap the BSR rankings, i.e., A will be ranked 1000 and B will be ranked 500. In practice we find that the BSR for commodity a becomes around 600, while the BSR for commodity B is programmed around 900. This indicates that although the hourly sales of the two commodities A and B are "swap locations", their Amazon BSR does not fluctuate dramatically, the inherent reason being that the Amazon BSR algorithm takes into account the historical sales of ASIN.
In practice, when amazon sellers promote sales or coupons at discounted prices, even if sales are high, the BSR of ASIN will not change very quickly, which is considered as that amazon platform prevents sellers from operating BSR ranking system by using short-term means such as promotion of gifts to promote sales. Through experimentation, error and analysis of large amounts of data, it can be determined that:
1) although the BSR is recalculated every hour, there is a 2-3 hour lag between the update time and the sales time;
2) the calculation of the BSR considers more recent sales, but the historical sales still have a considerable proportion in the calculation;
3) new goods without sales have no BSR;
4) some variants do not have their own BSR, sales will be accounted for on the parent ASIN, but others have their own BSR;
5) the BSR depends on the order volume, independent of the sales volume. For example, an order with 90 items has the same big impact as an order for one item.
Based on the above, the amazon BSR algorithm ranking criterion is calculated by an exponential weighted average y of n hours of sales x per hour, and the smaller the time t is, the larger the weight is, and the exponential relationship is formed. The sales ranking calculation formula may then be reverse engineered. At this time, the unknown quantity y becomes a known quantity, and the known quantity x becomes an unknown quantity, so x can be reversely solved by y. Therefore, the calculated exponential weighted average value can be substituted into the sales ranking calculation formula, that is, the sales of the to-be-predicted commodity in the preset time period can be reversely obtained, that is, the sales of the to-be-predicted commodity in the time period can be determined according to the BSR ranking of the to-be-predicted commodity.
According to the sales volume estimation method for amazon platform commodities, the collected data are BSR and inventory information acquired every hour, so that the established model and parameters can be updated every hour. And in the prior art, only daily updating can be realized at most. Therefore, the model established in the application is more accurate, the iteration and optimization frequency is higher, and the sales prediction of the commodity to be predicted is more accurate. Furthermore, by means of updating every hour, not only the influence of the current sales volume on the BSR is considered, but also the influence of the sales speed (the rate of change of the sales volume) for the past 14 days on the BSR is subjected to correlation analysis, and this influence cannot be ignored in practice. In addition, the sales volume estimation model established in the application has the advantages that the completeness and the updating frequency of data are obviously improved compared with those of the prior art, meanwhile, the influence of ASIN sales speed is used as an important factor influencing BSR ranking, and the Amazon BSR algorithm is highly reduced, so that the obtained estimated sales volume is closer to the actual sales volume, and the prediction accuracy is obviously improved.
In one embodiment, as shown in fig. 2, there is provided a sales forecasting apparatus for amazon platform commodities, comprising:
the data statistics module 201 is configured to obtain, for each commodity of the amazon platform, a current commodity sales amount and a sales amount rank of the commodity every preset time, and determine a sales amount of the commodity according to the variation of the commodity inventory;
the parameter determining module 202 is configured to input sales of the plurality of commodities and corresponding sales ranks into the regression prediction model, and determine parameter values of a polynomial regression equation included in the regression prediction model;
the sales prediction module 203 is used for acquiring sales ranking of the commodities to be predicted; the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation; determining a sales ranking calculation formula of an Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
In one embodiment, the weighting coefficients are determined by inputting the sales and sales ranking of the goods into a linear model for regression analysis.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the sales volume estimation method for the amazon platform commodity is realized by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the application provides a storage medium, wherein a program is stored on the storage medium, and when the program is executed by a processor, the sales volume estimation method for the amazon platform commodity is realized.
The embodiment of the application provides a processor, wherein the processor is used for running a program, and the program executes the sales volume estimation method for the amazon platform commodity during running.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor a01, a network interface a02, a memory (not shown), and a database (not shown) connected by a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises an internal memory a03 and a non-volatile storage medium a 04. The non-volatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a 04. The database of the computer device is used for storing commodity inventory and sales volume ranking of commodities. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 when executed by the processor a01 implements a sales prediction method for amazon platform commodity.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the application provides equipment, the equipment comprises a processor, a memory and a program which is stored on the memory and can run on the processor, and the following steps are realized when the processor executes the program: aiming at each commodity of the Amazon platform, acquiring the current commodity inventory and sales ranking of the commodity at preset time intervals; determining the sales volume of the commodity according to the variation of the commodity inventory; the sales of the commodities and the corresponding sales are ranked and input into a regression prediction model, and parameter values of a polynomial regression equation included in the regression prediction model are determined; obtaining sales ranking of commodities to be predicted; the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation; determining a sales ranking calculation formula of an Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
In one embodiment, the weighting coefficients are determined by inputting the sales and sales ranking of the goods into a linear model for regression analysis.
In one embodiment, the sales ranking calculation formula is:
wherein y is an exponentially weighted average and xnThe sales per hour of the commodity in n hours are shown, a is an exponential function, and t is a weighting coefficient of the exponential function.
In one embodiment, the polynomial regression equation is: y ═ a + b ×2+c*x3+d*x4+e*x5(ii) a Wherein y is an exponential weighted average, x is the sales ranking of the goods, and a, b, c, d, e are parameters of a polynomial regression equation.
In one embodiment, the preset time and the preset time period are hourly; the step of obtaining the current commodity inventory and sales ranking of the commodities at preset time intervals comprises the following steps: and acquiring the current commodity inventory and the BSR ranking of the commodities every other hour.
In one embodiment, the more recent sales data in the sales ranking calculation formula is weighted more heavily and exhibits exponential variations.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: aiming at each commodity of the Amazon platform, acquiring the current commodity inventory and sales ranking of the commodity at preset time intervals; determining the sales volume of the commodity according to the variation of the commodity inventory; the sales of the commodities and the corresponding sales are ranked and input into a regression prediction model, and parameter values of a polynomial regression equation included in the regression prediction model are determined; obtaining sales ranking of commodities to be predicted; the sales ranking of the goods to be predicted is input into a regression prediction model so as to determine an exponential weighted average value of the goods to be predicted according to a polynomial regression equation; determining a sales ranking calculation formula of an Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodities in a preset time period according to the exponential weighted average of the to-be-predicted commodities.
In one embodiment, the weighting coefficients are determined by inputting the sales and sales ranking of the goods into a linear model for regression analysis.
In one embodiment, the sales ranking calculation formula is:
wherein y is an exponentially weighted average and xnThe sales per hour of the commodity in n hours are shown, a is an exponential function, and t is a weighting coefficient of the exponential function.
In one embodiment, the polynomialThe regression equation is: y ═ a + b ×2+c*x3+d*x4+e*x5(ii) a Wherein Y is an exponential weighted average, x is the sales ranking of the goods, and a, b, c, d, e are parameters of a polynomial regression equation.
In one embodiment, the preset time and the preset time period are hourly; the step of obtaining the current commodity inventory and sales ranking of the commodities at preset time intervals comprises the following steps: and acquiring the current commodity inventory and the BSR ranking of the commodities every other hour.
In one embodiment, the more recent sales data in the sales ranking calculation formula is weighted more heavily and exhibits exponential variations.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A sales forecasting method for Amazon platform commodities is characterized by comprising the following steps:
aiming at each commodity of the amazon platform, acquiring the current commodity inventory and sales ranking of the commodity at preset time intervals;
determining the sales volume of the commodity according to the variation of the commodity inventory;
inputting the sales of the commodities and corresponding sales ranks into a regression prediction model, and determining parameter values of a polynomial regression equation included in the regression prediction model;
obtaining sales ranking of commodities to be predicted;
inputting the sales ranking of the to-be-predicted commodity into the regression prediction model so as to determine an exponential weighted average value of the to-be-predicted commodity according to the polynomial regression equation;
determining a sales ranking calculation formula of the Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking;
and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodity in a preset time period according to the exponential weighted average value of the to-be-predicted commodity.
2. The method of claim 1, wherein the weighting coefficients are determined by inputting sales and sales ranking of the commodity into a linear model for regression analysis.
3. The method of claim 1, wherein the sales ranking is calculated by the formula:
wherein y is an exponentially weighted average and xnThe sales per hour of the commodity in n hours are shown, a is an exponential function, and t is a weighting coefficient of the exponential function.
4. The method of claim 1, wherein the polynomial regression equation is:
y＝a+b*x2+c*x3+d*x4+e*x5；
wherein y is an exponential weighted average, x is the sales ranking of the goods, and a, b, c, d, e are parameters of the polynomial regression equation.
5. The method according to claim 1, wherein the preset time and the preset time period are hourly; the step of obtaining the current commodity inventory and sales ranking of the commodities at preset time intervals comprises the following steps:
and acquiring the current commodity inventory of the commodities and the BSR ranking of the commodities every other hour.
6. The method of claim 1, wherein the sales ranking calculation formula weights more and exhibits exponential changes for sales data that are closer in time.
7. A sales forecasting apparatus for Amazon platform commodities, the apparatus comprising:
the data statistics module is used for acquiring the current commodity inventory and sales ranking of the commodities at preset time intervals for each commodity of the amazon platform, and determining the sales of the commodities according to the variation of the commodity inventory;
the parameter determining module is used for inputting sales of the commodities and corresponding sales ranks into a regression prediction model and determining parameter values of a polynomial regression equation in the regression prediction model;
the sales prediction module is used for acquiring sales ranking of the commodities to be predicted; inputting the sales ranking of the to-be-predicted commodity into the regression prediction model so as to determine an exponential weighted average value of the to-be-predicted commodity according to the polynomial regression equation; determining a sales ranking calculation formula of the Amazon platform, wherein independent variables of the sales ranking calculation formula comprise sales of commodities in a preset time period, an exponential weighted average value, an exponential function and a weighting coefficient of the exponential function, and a dependent variable is a sales ranking; and performing reverse engineering treatment on the sales ranking calculation formula to determine the sales of the to-be-predicted commodity in a preset time period according to the exponential weighted average value of the to-be-predicted commodity.
8. The apparatus of claim 7, wherein the weighting coefficients are determined by inputting sales inventory and sales ranking of the product into a linear model for regression analysis.
9. A machine-readable storage medium having instructions stored thereon, which when executed by a processor, cause the processor to be configured to perform the sales forecasting method for amazon platform commodity according to any one of claims 1 to 6.
10. A processor configured to perform the sales prediction method for amazon platform commodity according to any one of claims 1 to 6.
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