Cross-border e-commerce operation method and system based on SaaS

Abstract

The invention discloses a cross-border e-commerce operation method and system based on SaaS, and relates to the technical field of cross-border e-commerce operation, and the method comprises the steps: carrying out the calculation and analysis of commodity sales information, calculating a commodity reference value, and outputting a plurality of test prices; operation is carried out at different test prices, and the actual sales volume of each operation cycle is recorded; calculating and analyzing data in the scatter diagram, and outputting a change slope; calculating and analyzing the change slope and data in the scatter diagram, and outputting operation reference information based on a calculation and analysis result; the method is used for solving the problems that in the prior art, a method for analyzing market sales data of commodities to provide reference information is lacked, manual market research analysis is needed, the market research efficiency is low, and the reference of a final result obtained through analysis is not comprehensive enough.

Description

Technical Field

[0001] The present invention relates to the field of cross-border e-commerce operation technology, and specifically to a cross-border e-commerce operation method and system based on SaaS. Background Art

[0002] The Internet is a borderless medium with global and decentralized characteristics. Cross-border e-commerce that occurs on the Internet also has global and decentralized characteristics. Compared with traditional trading methods, an important feature of e-commerce is that it is a borderless transaction that loses the geographical factors of traditional transactions. Therefore, the management of cross-border e-commerce requires a set of effective operational methods.

[0003] Existing operational improvements for cross-border e-commerce usually formulate corresponding operational activities based on the product information browsed by users. For example, in the Chinese patent with authorization announcement number CN117611245B, a data analysis management system and method for e-commerce operational activity planning are disclosed. This solution is to understand the user's browsing intention and demand preference by performing semantic analysis and cross-modal fusion on the text description and picture part in the user's browsing record information, so as to determine the theme of operational activity planning, and provide users with more personalized and attractive operational activities; while for merchants who have no operational experience but want to operate cross-border e-commerce, they need to spend a lot of time to conduct market research, and then manually analyze the research results to obtain the final product pricing. There is a lack of a method that can automatically analyze the market sales data of the product and finally provide reference information, which will result in slow manual market research and the calculation results are more dependent on analysts. In view of this, it is necessary to improve the existing cross-border e-commerce operation methods. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. By improving cross-border e-commerce operations, it is used to solve the problem that the prior art lacks a method for analyzing the market sales data of commodities to provide reference information, and that manual market research and analysis are required, resulting in low market research efficiency and the reference of the final results obtained by the analysis is not comprehensive enough.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a cross-border e-commerce operation method based on SaaS, comprising: Acquire a first amount of commodity sales information, perform calculation and analysis on the commodity sales information, and output a commodity reference value based on the calculation and analysis result; Calculate the reference value of the product, output the reference average value and the adjusted price; calculate the reference average value based on the adjusted price, and output multiple test prices; Based on multiple test prices, operate at different test prices in multiple operating cycles and record the actual sales volume in each operating cycle; A scatter plot is created based on actual sales volume and test price, and the data in the scatter plot is calculated and analyzed to output the slope of change; the slope of change and the data in the scatter plot are calculated and analyzed, and operational reference information is output based on the calculation and analysis results.

[0006] Further, obtaining the first amount of commodity sales information, performing calculation and analysis on the commodity sales information, and outputting the commodity reference value based on the calculation and analysis result includes: Mark the goods that the cross-border e-commerce needs to sell as test goods, and obtain the first quantity of goods with the highest sales volume and the same as the test goods on the cross-border e-commerce platform through network communication, wherein the goods sales information includes the price and sales volume of the goods; Calculate the average of all product prices and mark it as average selling price; Calculate the average of all sales and label it as average sales; For each commodity sales information, the commodity price, sales volume, average selling price and average sales volume are calculated using the commodity reference formula, and the commodity reference value of each commodity sales information is output; The reference formula configuration of the product is: ; Where SZ is the reference value of the product, SJ is the selling price of the product, PJ is the average selling price, SL is the sales volume, and PL is the average sales volume.

[0007] Further, the reference value of the product is calculated, and the reference average value and the adjusted price are outputted, including: Sort the commodity reference values ​​in ascending order to obtain a reference sequence; Calculate the median of the reference sequence and delete the reference values ​​of commodities that are smaller than the median. Get the commodity prices corresponding to the reference values ​​of the remaining commodities, calculate the average of the commodity prices, and mark it as the reference average; Calculate the difference between the largest and smallest commodity prices among the commodity prices, and mark it as the adjusted price; Use the selling price adjustment formula to calculate the adjusted price and output the unit test price; The selling price adjustment formula is configured as: ; Where TJ is the unit test price, CJ is the adjusted price, and D is the first quantity.

[0008] Further, the reference average is calculated based on the adjusted price, and multiple test prices are output including: Set the reference mean value to the median of the interval, and use the left interval calculation formula to calculate the left endpoint of the interval; use the right interval calculation formula to calculate the right endpoint of the interval; The left interval calculation formula is configured as: CZ2=CZ1-k×TJ; where CZ1 is the reference average value and CZ2 is the left endpoint of the interval; The right interval calculation formula is configured as CZ3=CZ1+k×TJ; wherein CZ3 is the right endpoint of the interval; The test price value interval [CZ3, CZ2] is obtained, where the difference between two adjacent test prices is TJ, and there are 2k+1 test prices in the interval.

[0009] Furthermore, based on the multiple test prices, operations are performed at different test prices in multiple operating cycles, and the actual sales volume of each operating cycle is recorded, including: Set the operating cycle with the first day as the unit, set the selling price of the test product to CZ3 before the start of the first operating cycle, and execute the sales statistics cycle; The sales statistics cycle includes: putting the test product on the e-commerce platform for sale; recording the sales volume of the test product in the current operating cycle on the last day of the operating cycle and marking it as the actual sales volume; then removing the test product from the shelf, adjusting the selling price to CZ3 minus TJ, and repeating the sales statistics cycle.

[0010] Furthermore, a scatter plot is established based on the actual sales volume and the test price, and the data in the scatter plot is calculated and analyzed, and the output change slope includes: Create a scatter plot with the selling price of the test product as the horizontal axis and the actual sales volume as the vertical axis; Calculate the absolute value of the slope of the line segment connecting two adjacent points, marked as the change slope; The largest change slope is marked as the analysis slope, the line segment corresponding to the analysis slope in the scatter plot is marked as the dividing line segment, the change slope corresponding to the line segment on the left side of the dividing line segment is divided into the first slope group, and the change slope corresponding to the line segment on the right side of the dividing line segment is divided into the second slope group.

[0011] Furthermore, the change slope and the data in the scatter plot are calculated and analyzed, and the operation reference information output based on the calculation and analysis results includes: Calculate the average of the change slopes in the first slope group, marked as the left slope; Calculate the average of the change slopes in the second slope grouping, labeled as the right slope; Calculate the ratio of the left slope to the right slope, labeled as the slope analysis ratio; If the slope analysis ratio is greater than or equal to the slope change threshold, a first prediction process is performed; The slope analysis ratio is analyzed, and if the slope analysis ratio is less than the slope change threshold, a second prediction process is performed.

[0012] Further, the first prediction process includes: Mark the points on the line segment corresponding to the analysis slope and on the right side of the line segment as predicted points; Get the actual sales volume and test price of the forecast point, calculate the product of the actual sales volume and the test price at each forecast point, and mark it as the forecast income; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

[0013] Further, the second prediction process includes: Calculate the product of actual sales volume and test price for each point in the scatter plot and mark it as predicted revenue; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

[0014] In a second aspect, the present invention further provides a cross-border e-commerce operation system based on SaaS, including a market analysis module, an operation test module and a price analysis module; The market analysis module is used to obtain a first amount of commodity sales information, perform calculation and analysis on the commodity sales information, and output a commodity reference value based on the calculation and analysis result; The market analysis module is also used to calculate the commodity reference value, output the reference average value and the adjusted price; calculate the reference average value based on the adjusted price, and output multiple test prices; The operation test module is used to operate at different test prices in multiple operation cycles based on multiple test prices, and record the actual sales volume of each operation cycle; The selling price analysis module is used to establish a scatter plot based on actual sales volume and test price, perform calculation and analysis on the data in the scatter plot, and output the change slope; perform calculation and analysis on the change slope and the data in the scatter plot, and output operation reference information based on the calculation and analysis results.

[0015] Beneficial effects of the present invention: The present invention obtains the commodity sales analysis of the top-selling commodities on the cross-border e-commerce platform, and then calculates the commodity reference value of each commodity sales analysis to screen the commodity sales analysis; then calculates the filtered commodity sales information to obtain multiple test prices for testing; the advantage of this is that the data used for analysis is the commodity data with the largest sales volume on the platform, which ensures the reliability of the obtained results; at the same time, the commodity reference value is calculated for re-screening, which can further reduce the number of commodities that need to be analyzed, obtain commodities with more analytical significance, and improve the efficiency of subsequent analysis; The present invention also operates at different test prices during multiple operating cycles, establishes a scatter plot based on the recorded data, performs calculations and analysis on the data in the scatter plot, and outputs operating reference information based on the final calculation and analysis results; the advantage of this is that testing is performed at an appropriate number of test prices to ensure that the time length is appropriate; by analyzing the data in the scatter plot, it is possible to determine the impact of price changes on commodity sales volume, and if price changes have different impacts on commodity sales volume, classification analysis can be performed, and data with reference significance can be automatically and comprehensively displayed to merchants, thereby improving the efficiency of market research and the accuracy and comprehensiveness of the reference information ultimately obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a functional block diagram of the system of the present invention; Figure 2 is a flow chart of the steps of the method of the present invention; Figure 3 A scatter plot of a possible case of the present invention; Figure 4 This is a scatter plot in another possible case of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1 In a first aspect, the present invention provides a cross-border e-commerce operation system based on SaaS, including a market analysis module, an operation test module and a price analysis module; The market analysis module is used to obtain the first quantity of commodity sales information, perform calculation and analysis on the commodity sales information, and output commodity reference values ​​based on the calculation and analysis results; it is also used to calculate the commodity reference values, output reference average values ​​and adjusted prices; calculate the reference average values ​​based on the adjusted prices, and output multiple test prices; The market analysis module is configured with commodity analysis strategies, which include: Mark the goods that the cross-border e-commerce needs to sell as test goods, and use network communication to obtain the product sales information of the first quantity, the highest sales volume, and the same as the test goods on the cross-border e-commerce platform, the product sales information including the product price and sales volume; In the specific implementation, the first number is set to 10, which means obtaining the sales information of the top ten same-type products on the cross-border e-commerce platform; if the first number is set too small, it will lead to insufficient sample size, which will lead to unreliable analysis results; on the contrary, if the first number is set too large, the obtained product sales information will be significantly less valuable and meaningful than the sales information of the top ten products, and may also obtain sales information of products with poor sales, increasing the amount of calculation in the subsequent analysis process; considering the two aspects, it is better to set the value of the first number to 10; Calculate the average of all product prices and mark it as average selling price; Calculate the average of all sales and label it as average sales; For each commodity sales information, the commodity price, sales volume, average selling price and average sales volume are calculated using the commodity reference formula, and the commodity reference value of each commodity sales information is output; The reference formula configuration for commodities is: ; Where SZ is the reference value of the product, SJ is the selling price of the product, PJ is the average selling price, SL is the sales volume, and PL is the average sales volume.

[0019] The market analysis module is also configured with data screening strategies, which include: Sort the commodity reference values ​​in ascending order to obtain a reference sequence; Calculate the median of the reference sequence and delete the reference values ​​of commodities that are smaller than the median. It should be noted that the commodity reference value is a value used to comprehensively analyze the selling price and sales volume of the commodity. The purpose of deleting the commodity reference value that is less than the sequence median is to perform a secondary screening of the 10 groups of commodity sales information and obtain 5 groups of commodity sales information that are more meaningful for reference; For example, the selling price is graded into three categories: high selling price, medium selling price, and low selling price; the sales volume is analyzed into three categories: high sales volume, medium sales volume, and low sales volume; the selling price and sales volume are combined, and there are 9 combinations in total; if there are 9 combinations among the 10 groups of product sales information obtained, then by calculating the product reference value and performing subsequent deletion processing, the product sales information corresponding to the combinations [low selling price, low sales volume], [low selling price, medium sales volume], and [medium selling price, low sales volume] will definitely be deleted, and the remaining ones that need to be deleted will be specifically analyzed based on the calculated product reference value.

[0020] The market analysis module is also configured with a price analysis strategy, which includes: Get the commodity prices corresponding to the reference values ​​of the remaining commodities, calculate the average of the commodity prices, and mark it as the reference average; Calculate the difference between the largest and smallest commodity prices among the commodity prices, and mark it as the adjusted price; Use the selling price adjustment formula to calculate the adjusted price and output the unit test price; The price adjustment formula is configured as: ; Where TJ is the unit test price, CJ is the adjusted price, and D is the first quantity; In specific implementation, if the largest price of the remaining products is 150 yuan and the smallest is 130 yuan, the adjusted price is 20 yuan and the unit test price is 4 yuan; the adjusted price means that the product's selling price needs to be further analyzed within the range of [reference average + 10, reference average - 10]; Set the reference mean value to the median of the interval, and use the left interval calculation formula to calculate the left endpoint of the interval; use the right interval calculation formula to calculate the right endpoint of the interval; The left interval calculation formula is configured as: CZ2=CZ1-k×TJ; where CZ1 is the reference average value and CZ2 is the left endpoint of the interval; The right interval calculation formula is configured as CZ3=CZ1+k×TJ; where CZ3 is the right endpoint of the interval; The test price value interval [CZ3, CZ2] is obtained, where the difference between two adjacent test prices is TJ, and there are 2k+1 test prices in the interval; In the specific implementation, the value of k is set to 2; k is set to 2, and finally 2×2+1=5 test prices can be obtained; the more the number of test prices, the more accurate the subsequent analysis between price and sales volume can be, but too many test prices require too many operating cycles, and the span in the time dimension is large, so the value of k is set to 2 based on the two aspects; Taking the adjustment price of 20 yuan, the unit test price of 4 yuan, and the reference average price of 140 yuan as an example, the final calculated value range is [148, 132]. The 5 test prices are 148, 144, 140, 136, and 132 respectively; the difference between two adjacent test prices is 4.

[0021] The operation test module is used to operate at different test prices in multiple operating cycles based on multiple test prices, and record the actual sales volume of each operating cycle; The operation test module is configured with an operation test strategy, which includes: Set the operating cycle with the first day as the unit, set the selling price of the test product to CZ3 before the start of the first operating cycle, and execute the sales statistics cycle; The sales statistics cycle includes: putting the test product on the e-commerce platform for sale; recording the sales volume of the test product in the current operating cycle on the last day of the operating cycle and marking it as the actual sales volume; then removing the test product from the shelves, adjusting the selling price to CZ3 minus TJ, and repeating the sales statistics cycle; In specific implementation, the value of the first day, i.e. the operating cycle, is set by the merchant. In theory, the larger the value of the first day, the more meaningful the final sales volume will be. For example, if the operating cycle is set to 7 days, the data for 30 days will be more meaningful than if the operating cycle is set to 30 days.

[0022] The price analysis module is used to establish a scatter plot based on actual sales volume and test price, calculate and analyze the data in the scatter plot, and output the change slope; calculate and analyze the change slope and the data in the scatter plot, and output operation reference information based on the calculation and analysis results; The price analysis module is configured with quantity-price analysis strategies, which include: Create a scatter plot with the selling price of the test product as the horizontal axis and the actual sales volume as the vertical axis; Calculate the absolute value of the slope of the line segment connecting two adjacent points, marked as the change slope; The maximum change slope is marked as the analysis slope, the line segment corresponding to the analysis slope in the scatter plot is marked as the dividing line segment, the change slope corresponding to the line segment on the left side of the dividing line segment is divided into the first slope group, and the change slope corresponding to the line segment on the right side of the dividing line segment is divided into the second slope group; Calculate the average of the change slopes in the first slope group, marked as the left slope; Calculate the average of the change slopes in the second slope grouping, labeled as the right slope; Calculate the ratio of the left slope to the right slope, labeled as the slope analysis ratio; If the slope analysis ratio is greater than or equal to the slope change threshold, a first prediction process is performed; In the specific implementation, the slope change threshold is set to 1.2. When the slope analysis ratio is greater than or equal to the slope change threshold, it means that when the price is lowered and below a certain value, the sales volume will not change significantly. At this time, only the selling price and sales volume before this value need to be analyzed, that is, the first prediction processing, which can reduce the amount of calculation of the system; If the slope analysis value is less than the slope change threshold, it means that the sales volume changes significantly each time the price changes, and it is necessary to analyze the sales volume at each price, that is, the second prediction process; Analyze the slope analysis ratio, and if the slope analysis ratio is less than the slope change threshold, perform a second prediction process; See also Figure 3 as well as Figure 4 As shown, Figure 3 as well as Figure 4 The scatter plots after connection are obtained in two possible cases, and T1 and T2 are both analytical slopes; Figure 3 The data in is shown in Table 1-1; Figure 4 The data in are shown in Table 1-2; Table 1-1 Figure 3 Data display table Table 1-2 Figure 4 Data display table It should be noted that when operating cross-border e-commerce, low prices do not necessarily mean high sales; for example Figure 3 As shown in the figure, when the price of the product is lower than 140 yuan, the sales volume does not increase significantly compared with the previous price. Figure 4 The corresponding situation is that every time the price is reduced, the sales volume will increase significantly, so it is necessary to further analyze the relationship between price and sales volume, that is, to divide the slope of change into two groups for analysis; The first prediction process includes: Mark the points on the line segment corresponding to the analysis slope and on the right side of the line segment as predicted points; Get the actual sales volume and test price of the forecast point, calculate the product of the actual sales volume and the test price at each forecast point, and mark it as the forecast income; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

[0023] The second prediction process includes: Calculate the product of actual sales volume and test price for each point in the scatter plot and mark it as predicted revenue; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

[0024] Example 2 Secondly, the difference between this embodiment and embodiment 1 is that, with respect to object differentiation processing, when the data implementation object of the historical test data is 0 during the object differentiation processing, no further division can be performed. Therefore, this embodiment further explains when the data implementation object of the historical test data is 0 during the object differentiation processing: When the data implementation object of the historical test data is 0, where B1 is the beginning of the test flowchart, B3 is the end of the test flowchart, B2 is the judgment symbol of the test flowchart, B4 is the first main processing area, and B5 is the second main processing area, the test flowchart of the historical test data is obtained, and the test flowchart based on the historical test data records the process line that can complete the test in the shortest time as the main line of the test flowchart, and records the test flowchart other than the main line as the auxiliary line of the test flowchart; When establishing a test BPMN diagram, the main line of the test diagram is placed in the first main processing area, the auxiliary line of the test diagram is placed in the second auxiliary processing area, and the lines are connected according to the relationship in the test flow chart.

[0025] Example 3 Second, see Figure 2 As shown, the present invention also provides a cross-border e-commerce operation method based on SaaS, including: Step S1, obtaining a first quantity of commodity sales information, performing calculation and analysis on the commodity sales information, and outputting a commodity reference value based on the calculation and analysis results; Step S1 also includes the following sub-steps: Step S101, marking the goods that the cross-border e-commerce needs to sell as test goods, and obtaining the first quantity of goods with the highest sales volume and the same as the test goods on the cross-border e-commerce platform through network communication, the goods sales information including the price and sales volume of the goods; Step S102, calculating the average of all commodity prices, marked as average selling price; calculating the average of all sales volumes, marked as average sales volume; Step S103, for each commodity sales information, using the commodity reference formula to calculate the commodity selling price, sales volume, average selling price and average sales volume, and outputting the commodity reference value of each commodity sales information; The reference formula configuration for commodities is: ; Where SZ is the reference value of the product, SJ is the selling price of the product, PJ is the average selling price, SL is the sales volume, and PL is the average sales volume.

[0026] Step S2, calculating the reference value of the commodity, outputting the reference average value and the adjusted price; calculating the reference average value based on the adjusted price, and outputting a plurality of test prices; Step S2 also includes the following sub-steps: Step S201, sorting the commodity reference values ​​in ascending order to obtain a reference sequence; Step S202, calculating the median of the reference sequence, and deleting the commodity reference values ​​that are less than the median of the sequence; Step S203, obtaining the commodity prices corresponding to the reference values ​​of the remaining commodities, calculating the average of the commodity prices, and marking it as the reference average; Step S204, calculating the difference between the largest commodity price and the smallest commodity price among the commodity prices, and marking it as an adjustment price; Step S205, calculating the adjusted price using the selling price adjustment formula and outputting the unit test price; The price adjustment formula is configured as: ; Where TJ is the unit test price, CJ is the adjusted price, and D is the first quantity; Step S206, setting the reference average value as the median of the interval, using the left interval calculation formula to calculate the left endpoint of the interval; using the right interval calculation formula to calculate the right endpoint of the interval; The left interval calculation formula is configured as: CZ2=CZ1-k×TJ; where CZ1 is the reference average value and CZ2 is the left endpoint of the interval; The right interval calculation formula is configured as CZ3=CZ1+k×TJ; where CZ3 is the right endpoint of the interval; The test price value interval [CZ3, CZ2] is obtained, where the difference between two adjacent test prices is TJ, and there are 2k+1 test prices in the interval.

[0027] Step S3, based on the multiple test prices, operating at different test prices in multiple operating cycles, and recording the actual sales volume of each operating cycle; Step S3 also includes the following sub-steps: Step S301, setting the operating cycle with the first day as the unit, setting the selling price of the test product to CZ3 before the start of the first operating cycle, and executing the sales statistics cycle; Step S302, the sales statistics cycle includes: putting the test product on the e-commerce platform for sale; recording the sales volume of the test product in the current operating cycle on the last day of the operating cycle, marking it as the actual sales volume; then removing the test product from the shelf, adjusting the selling price to CZ3 minus TJ, and repeating the sales statistics cycle.

[0028] Step S4, establishing a scatter plot based on the actual sales volume and the test price, calculating and analyzing the data in the scatter plot, and outputting the change slope; calculating and analyzing the change slope and the data in the scatter plot, and outputting the operation reference information based on the calculation and analysis results; Step S4 also includes the following sub-steps: Step S4011, creating a scatter plot with the selling price of the test product as the horizontal axis and the actual sales volume as the vertical axis; Step S4012, calculating the absolute value of the slope of the line segment connecting two adjacent points, and marking it as the change slope; Step S4013, marking the maximum change slope as the analysis slope, marking the line segment corresponding to the analysis slope in the scatter plot as the dividing line segment, dividing the change slope corresponding to the line segment on the left side of the dividing line segment into the first slope group, and dividing the change slope corresponding to the line segment on the right side of the dividing line segment into the second slope group; Step S402, calculating the average value of the change slopes in the first slope group, marked as the left slope; calculating the average value of the change slopes in the second slope group, marked as the right slope; Step S403, calculating the ratio of the left slope to the right slope, marked as the slope analysis ratio; Step S404, analyzing the slope analysis ratio, if the slope analysis ratio is greater than or equal to the slope change threshold, performing a first prediction process; The first prediction process includes: Mark the points on the line segment corresponding to the analysis slope and on the right side of the line segment as predicted points; Get the actual sales volume and test price of the forecast point, calculate the product of the actual sales volume and the test price at each forecast point, and mark it as the forecast income; Mark the test price as a reference price, combine the reference price with the corresponding predicted income to obtain operation reference information; output the operation reference information; The second prediction process includes: Calculate the product of actual sales volume and test price for each point in the scatter plot and mark it as predicted revenue; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

[0029] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0030] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0031] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

Claims

1. A cross-border e-commerce operation method based on SaaS, It is characterized in that include: Acquire a first amount of commodity sales information, perform calculation and analysis on the commodity sales information, and output a commodity reference value based on the calculation and analysis result; Calculate the reference value of the product, output the reference average value and adjust the price; Calculate the reference average based on the adjusted price and output multiple test prices; Based on multiple test prices, operate at different test prices in multiple operating cycles and record the actual sales volume in each operating cycle; Create a scatter plot based on actual sales volume and test price, calculate and analyze the data in the scatter plot, and output the change slope; Perform calculation and analysis on the change slope and data in the scatter plot, and output operational reference information based on the calculation and analysis results.

2. According to the SaaS-based cross-border e-commerce operation method of claim 1, It is characterized in that Acquiring a first amount of commodity sales information, performing calculation and analysis on the commodity sales information, and outputting a commodity reference value based on the calculation and analysis result includes: Mark the goods that the cross-border e-commerce needs to sell as test goods, and obtain the first quantity of goods with the highest sales volume and the same as the test goods on the cross-border e-commerce platform through network communication, wherein the goods sales information includes the price and sales volume of the goods; Calculate the average of all product prices and mark it as average selling price; Calculate the average of all sales and label it as average sales; For each commodity sales information, the commodity price, sales volume, average selling price and average sales volume are calculated using the commodity reference formula, and the commodity reference value of each commodity sales information is output; The reference formula configuration of the product is: Among them, SZ is the reference value of the product, SJ is the selling price of the product, PJ is the average selling price, SL is the sales volume, and PL is the average sales volume.

3. According to the SaaS-based cross-border e-commerce operation method of claim 2, It is characterized in that Calculate the reference value of the product, output the reference average value and adjust the price including: Sort the commodity reference values ​​in ascending order to obtain a reference sequence; Calculate the median of the reference sequence and delete the reference values ​​of commodities that are smaller than the median. Get the commodity prices corresponding to the reference values ​​of the remaining commodities, calculate the average of the commodity prices, and mark it as the reference average; Calculate the difference between the largest and smallest commodity prices among the commodity prices, and mark it as the adjusted price; Use the selling price adjustment formula to calculate the adjusted price and output the unit test price; The selling price adjustment formula is configured as: Where TJ is the unit test price, CJ is the adjusted price, and D is the first quantity.

4. According to the SaaS-based cross-border e-commerce operation method of claim 3, It is characterized in that The reference average is calculated based on the adjusted price, and multiple test prices are output including: Set the reference mean value to the median of the interval, and use the left interval calculation formula to calculate the left endpoint of the interval; use the right interval calculation formula to calculate the right endpoint of the interval; The left interval calculation formula is configured as: CZ2 = CZ1-k×TJ; wherein CZ1 is the reference average value, and CZ2 is the left endpoint of the interval; The right interval calculation formula is configured as CZ3=CZ1+k×TJ; wherein CZ3 is the right endpoint of the interval; The test price value interval [CZ3, CZ2] is obtained, where the difference between two adjacent test prices is TJ, and there are 2k+1 test prices in the interval.

5. According to the SaaS-based cross-border e-commerce operation method of claim 4, It is characterized in that Based on multiple test prices, operate at different test prices in multiple operating cycles, and record the actual sales volume of each operating cycle including: Set the operating cycle with the first day as the unit, set the selling price of the test product to CZ3 before the start of the first operating cycle, and execute the sales statistics cycle; The sales statistics cycle includes: putting the test product on the e-commerce platform for sale; recording the sales volume of the test product in the current operating cycle on the last day of the operating cycle and marking it as the actual sales volume; then removing the test product from the shelf, adjusting the selling price to CZ3 minus TJ, and repeating the sales statistics cycle.

6. According to the SaaS-based cross-border e-commerce operation method of claim 5, It is characterized in that A scatter plot is created based on actual sales volume and test price, and the data in the scatter plot is calculated and analyzed. The output change slope includes: Create a scatter plot with the selling price of the test product as the horizontal axis and the actual sales volume as the vertical axis; Calculate the absolute value of the slope of the line segment connecting two adjacent points, marked as the change slope; The largest change slope is marked as the analysis slope, the line segment corresponding to the analysis slope in the scatter plot is marked as the dividing line segment, the change slope corresponding to the line segment on the left side of the dividing line segment is divided into the first slope group, and the change slope corresponding to the line segment on the right side of the dividing line segment is divided into the second slope group.

7. According to the SaaS-based cross-border e-commerce operation method of claim 6, It is characterized in that Perform calculation and analysis on the change slope and the data in the scatter plot, and output operational reference information based on the calculation and analysis results, including: Calculate the average of the change slopes in the first slope group, marked as the left slope; Calculate the average of the change slopes in the second slope grouping, labeled as the right slope; Calculate the ratio of the left slope to the right slope, labeled as the slope analysis ratio; If the slope analysis ratio is greater than or equal to the slope change threshold, a first prediction process is performed; The slope analysis ratio is analyzed, and if the slope analysis ratio is less than the slope change threshold, a second prediction process is performed.

8. According to the SaaS-based cross-border e-commerce operation method of claim 7, It is characterized in that The first prediction process includes: Mark the points on the line segment corresponding to the analysis slope and on the right side of the line segment as predicted points; Get the actual sales volume and test price of the forecast point, calculate the product of the actual sales volume and the test price at each forecast point, and mark it as the forecast income; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

9. According to the SaaS-based cross-border e-commerce operation method of claim 8, It is characterized in that The second prediction process includes: Calculate the product of actual sales volume and test price for each point in the scatter plot and mark it as predicted revenue; The test price is marked as a reference price, and the reference price is combined with the corresponding predicted income to obtain operation reference information; and the operation reference information is output.

10. A SaaS-based cross-border e-commerce operation system, used to implement a SaaS-based cross-border e-commerce operation method according to any one of claims 1 to 9, It is characterized in that Including market analysis module, operation test module and price analysis module; The market analysis module is used to obtain a first amount of commodity sales information, perform calculation and analysis on the commodity sales information, and output a commodity reference value based on the calculation and analysis result; The market analysis module is also used to calculate the reference value of the commodity, output the reference average value and adjust the price; Calculate the reference average based on the adjusted price and output multiple test prices; The operation test module is used to operate at different test prices in multiple operation cycles based on multiple test prices, and record the actual sales volume of each operation cycle; The selling price analysis module is used to establish a scatter plot based on actual sales volume and test price, calculate and analyze the data in the scatter plot, and output the change slope; Perform calculation and analysis on the change slope and data in the scatter plot, and output operational reference information based on the calculation and analysis results.

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