KR102801432B1 - Growing shopping mall analysis method, apparatus, and system - Google Patents

Abstract

One aspect of the present invention discloses a growth mall analysis method in a computing device. The method includes a step of obtaining information on sales amount according to a period for a plurality of shopping malls, a step of selecting N shopping malls as a comparison target group based on a sales ranking of a first period or a second period (wherein the second period represents a time after the first period, and N is an integer greater than or equal to 2), and a step of calculating a shopping mall growth index of each shopping mall based on a relative sales ranking of the second period compared to the first period for the N shopping malls.

Description

GROWING SHOPPING MALL ANALYSIS METHOD, APPARATUS, AND SYSTEM

The present invention relates to an analysis method, and more specifically, to a method and system for analyzing the growth potential of a shopping mall.

Recently, thanks to the development of computers and the Internet, most people have online stores. In particular, the number of self-employed people who only have their own online stores and conduct sales and distribution, etc., is steadily increasing, which is a recently emerging business model.

In such situations, operators of online shopping malls need to be aware of various factors that contribute to the decline in sales, such as lack of expertise in technology and design fields, lack of marketing capabilities of operators, or lack of experience in operating online shopping malls. However, most of them close down their businesses before they even recognize what the problem is.
Patent registration No. 1216692 describes a shopping mall management system that evaluates each performance evaluation item of shopping malls to which a classified group belongs, compares the evaluation average value for each performance evaluation item, and recommends a shopping mall management service related to a performance evaluation item with a low average value based on the evaluation average value.

However, although accurately identifying whether an online shopping mall is growing steadily over time, such as in the early or mid-term, or is in a decline, is a very important factor for quickly resolving the problem, conventional technologies do not support indicators or notices that can determine the growth and status of a shopping mall, making it difficult for online shopping mall operators to recognize this. In particular, identifying which shopping malls have good growth potential and which shopping malls have poor growth potential is very important information for business operators providing online shopping mall platform services. This is because the profitability of the platform business increases when shopping malls that are growing well are supported by a message to maintain their current status and grow even larger, and shopping malls that are not growing well are encouraged to quickly improve their current operational problems by providing information about them.

However, as explained above, the growth potential of these shopping malls is affected by the market, technology, and the individual capabilities of the operators, so although analysis of various indicators is necessary, there is no technology to professionally analyze and consult on this, which is emerging as a problem for everyone from platform service providers to shopping mall operators.

Registered Patent No. 1216692

An object of one aspect of the present invention for solving the above-described problem is to provide a method for analyzing the growth potential of a shopping mall based on sales indicators over time of the online shopping mall.

According to one aspect of the present invention for achieving the above object, a method for analyzing growth malls in a computing device may include a step of obtaining information on sales amount according to a period for a plurality of shopping malls, a step of selecting N shopping malls as a comparison target group based on a sales ranking of a first period or a second period (wherein the second period represents a time after the first period, and N is an integer greater than or equal to 2), and a step of calculating a shopping mall growth index of each shopping mall based on a relative sales ranking of the second period compared to the first period for the N shopping malls.

The above shopping mall growth index is calculated based on the relative difference value between the sales ranking within the second period and the sales ranking within the first period, and a smaller value of the shopping mall growth index may indicate higher growth, and a larger value may indicate lower growth.

The above method may further include a step of extracting a shopping mall having a shopping mall growth index smaller than a threshold value by using the shopping mall growth index.

The first period and the second period may be different points in time having the same length.

The above shopping mall growth indicator can be calculated based on at least one of a first indicator calculated based on the sales ranking of the second period and a second indicator calculated from the value obtained by subtracting the sales ranking of the first period from the sales ranking of the second period.

The above shopping mall growth index can be calculated by adding the values by assigning a first weight to the first index and a second weight to the second index.

When the second indicator is reflected in the shopping mall growth indicator, the value of the second indicator is, based on the relative sales ranking, given a value of 0 for the top shopping mall and a value of M for the bottom shopping mall (wherein M is a number greater than 0), but, for the shopping malls remaining among the N shopping malls excluding the top shopping mall and the bottom shopping mall, the value of the second indicator can be calculated as a value between 0 and M based on how close their relative sales rankings are to the top shopping mall or the bottom shopping mall.

The shopping mall growth index can be calculated by further considering the difference between the sales amount of the first period and the sales amount of the second period as a third index.

When the values of the shopping mall growth indicators calculated using at least one of the first indicator and the second indicator for a plurality of shopping malls among the above N shopping malls are the same, the shopping mall growth indicator can be calculated by additionally reflecting the third indicator.

The above N shopping malls can be selected based on competing shopping malls that can be grouped into the same category.

The above N shopping malls may be among the top N shopping malls based on the sales ranking of the second period.

The above method further includes a step of visualizing the shopping mall growth index, wherein the shopping mall growth index can be visualized using the shopping mall growth index as a first axis and the sales amount of some of the N selected shopping malls as a second axis.

When visualizing the above shopping mall growth indicator, one of the N selected shopping malls is displayed as one point, and the displayed point can be displayed in different colors depending on the period.

A shopping mall that exists in a first region located closer to 0 than the first threshold value of the first axis and corresponds to a point where the value of the second axis becomes greater than the third threshold value over time can be defined as a growth mall.

A shopping mall corresponding to a point where the change value of the value of the second axis over time is less than the fourth threshold value and the change value of the value of the first axis has a value greater than the fifth threshold value in the direction away from 0 can be defined as a declining mall.

At least one shopping mall can be selected and displayed as a first visual representation based on a threshold value for at least one of the first axis and the second axis, and shopping malls that are not selected among the N shopping malls can be displayed as a second visual representation.

In another aspect of the present invention for achieving the above object, a growth mall analysis device comprises: a storage unit for acquiring and storing information on sales amount according to a period for a plurality of shopping malls; a processor for selecting N shopping malls as a comparison target group based on sales rankings of a first period and a second period; and calculating a shopping mall growth index of each shopping mall based on a relative sales ranking of the second period compared to the first period for the N shopping malls, wherein the second period represents a time after the first period, and N may be an integer greater than or equal to 2.

In accordance with another aspect of the present invention for achieving the above-described purpose, a system for analyzing a growth mall in an e-commerce system includes a shopping mall operation server which provides information related to sales amount over a period of a plurality of shopping malls associated with e-commerce, and a growth mall analysis device which obtains information on sales amount over a period of time for the plurality of shopping malls from the shopping mall operation server, selects N shopping malls as a comparison target group based on a sales ranking of a first period or a second period, and calculates a shopping mall growth index of an individual shopping mall based on a relative sales ranking of the second period compared to the first period for the N shopping malls, wherein the second period represents a time after the first period, and N may be an integer greater than or equal to 2.

In another aspect of the present invention for achieving the above object, a computer-readable storage medium including instructions executable by a processor, the instructions, when executed by the processor, may be configured to obtain information on sales amounts over a period for a plurality of shopping malls, select N shopping malls as a comparison target group based on a sales ranking of a first period or a second period (wherein the second period represents a time after the first period, and N is an integer greater than or equal to 2), and calculate a shopping mall growth index of each shopping mall based on a relative sales ranking of the second period compared to the first period for the N shopping malls.

According to the growth mall analysis method and system of the present invention, by quantitatively and accurately analyzing the growth potential of each online shopping mall, it helps shopping mall operators in store operation and also helps shopping mall platform operators in growth management of their shopping malls, thereby increasing the overall sales of shopping malls and preventing sales decline.

Figure 1 is a block diagram schematically showing a system to which a growth mole analysis method according to one embodiment of the present invention is applied.
Figure 2 is a flow chart specifically illustrating a growth mole analysis method according to one embodiment of the present invention.
Figure 3 is a conceptual diagram explaining the process of estimating a high growth potential neck according to a growth mole analysis method according to one embodiment of the present invention.
Figure 4 is a conceptual diagram for explaining a method of analyzing the growth potential of a shopping mall based on a graph of correlation between a shopping mall growth indicator and sales revenue calculated by a growth mall analysis method according to one embodiment of the present invention.
Figure 5 is a graph comparing the growth indices of the top 100 shopping malls in terms of sales in 2016 and 2019.
Figure 6 is a graph showing the growth indicator trend over time from 2015 to 2019 for the top 500 shopping malls in terms of sales.
Figure 7 is a conceptual diagram to explain the growth potential according to the relationship between the current growth indicator and sales compared to the past.
Figure 8 is a graph showing the flow of growth indicators over time from 2016 to 2019 for the top 500 shopping malls with high growth potential.
FIG. 9 is a block diagram schematically illustrating a growth mole analysis device according to one embodiment of the present invention.

The present invention can have various modifications and embodiments, and specific embodiments are illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to specific embodiments, but should be understood to include all modifications, equivalents, or substitutes included in the spirit and technical scope of the present invention.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are only used to distinguish one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component. The term and/or includes any combination of a plurality of related described items or any item among a plurality of related described items.

When it is said that a component is "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components in between. On the other hand, when it is said that a component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprises" or "has" and the like are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms defined in commonly used dictionaries, such as those defined in common dictionaries, should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and will not be interpreted in an idealized or overly formal sense unless expressly defined in this application.

Hereinafter, with reference to the attached drawings, a preferred embodiment of the present invention will be described in more detail. In order to facilitate an overall understanding in describing the present invention, the same reference numerals are used for the same components in the drawings, and redundant descriptions of the same components are omitted.

Fig. 1 is a block diagram schematically showing a system to which a growth mall analysis method according to one embodiment of the present invention is applied. As shown in Fig. 1, a growth mall analysis system according to one embodiment of the present invention may include a shopping mall (110-1 to 110-N) and a growth mall analysis device (120).

Referring to Figure 1, a shopping mall (110-1 to 110 to N) may refer to a shopping mall that sells products and/or services via the Internet, and includes a server that handles information related to the shopping mall or a terminal of an operator of the shopping mall. The terminal may include a computing device such as a smartphone or a PC.

According to an embodiment of the present invention, a plurality of shopping malls (110-1 to 110-N) can be linked to a growth mall analysis device (120) via a communication network (not shown). The shopping malls (110-1 to 110-N) can provide information related to the operation (including sales) of the shopping mall to the growth mall analysis device (120). At this time, periodic sales information such as daily sales information and monthly sales information can be provided. In addition, data related to the sales status of each product can also be provided. The plurality of shopping malls (110-1 to 110-N) can include a device that requests growth analysis of the shopping mall.

The growth mall analysis device (120) may be a device that receives sales information according to a period from at least one of the shopping malls (110-1 to 110-N), analyzes the information, and provides the analysis results to the shopping mall (110-1 to 110-N). The growth mall analysis device (120) may be implemented as a computing device. The computer device referred to here may be implemented as a server-class computer terminal. The computer device may be equipped with an input device, a display device, a networking device, a hard disk, a memory for storing a program, and a processor for executing the program stored in the memory, which are typical computer terminals. However, it is not necessarily required to be implemented as a server-class computer terminal. The growth mall analysis device (120) may be a server of a business operator that operates an Internet shopping mall platform. It may be implemented as the same device as the platform operation server (not shown), or it may be implemented as a separate server device that is linked with the platform operation server. The growth mall analysis device (120) may be called a "server" or a "server device."

The growth mall analysis device (120) selects N comparison groups (N is a natural number) based on sales rankings within a specific period, calculates sales rankings for the selected shopping malls within the next period, and calculates a growth index of the shopping mall (which may be called a "shopping mall growth index") by using the change in sales ranking between the two periods as basic information for the growth ranking. Then, the individually calculated growth indexes are returned to the shopping malls (110-1 to 110-N), so that the current shopping mall's growth is judged, and accordingly, the shopping malls (110-1 to 110-N) are supported to continue to develop. The shopping mall growth index value can be calculated by setting it so that a smaller value indicates higher growth, and a larger value indicates lower growth. In other words, a value closer to 0 indicates higher growth. Accordingly, shopping malls with relatively high shopping mall growth index values can be judged to be currently experiencing growth delays, and it is necessary to consider the cause of the growth delays themselves. Until now, such analysis indicators were not supported, so it was not easy for shopping mall operators to find a way to determine whether their shopping malls were currently growing well. However, by using a growth analysis method according to one embodiment of the present invention, the degree of growth can be easily determined, and thus the shopping malls can be operated efficiently.

According to another embodiment of the present invention, a terminal associated with a shopping mall (110-1 to 110-N) can be controlled to download an application provided by a growth mall analysis device (120) and perform a process of calculating a growth index based on sales information performed by the growth mall analysis device (120) within the terminal. In other words, a processor (not shown) of the terminal can be enabled to calculate and provide a growth index by executing the downloaded application without a separate server device.

Figure 2 is a flow chart specifically illustrating a growth mole analysis method according to one embodiment of the present invention.

Referring to Fig. 2, the server first obtains product sales-related information (e.g., sales information by period) of individual shopping malls. Then, it selects a comparison target group based on this (S210). Basically, the server selects the top N in sales ranking based on the sales amount during a specific period. At this time, N can be any number such as 50, 100, 200, or 500. And, the period can be a specific year. For example, N can be selected based on the current time. That is, the top 500 in sales ranking for this year, 2021, can be selected as the comparison target group. Or, the top 500 in sales ranking for the past year, 2015, can be selected as the comparison target group. The bottom 500 can also be the target. In another example, considering the category of the shopping mall, shopping malls in the same category can be selected. For example, after filtering clothing-related shopping malls, you can select the top N shopping malls based on sales over a certain period as a comparison group. In another example, you can select a total of 500 competitive shopping malls by combining the top 250 and the bottom 250 in sales rankings as a comparison group.

After selecting the comparison target group, the server obtains sales ranking information for different time intervals (e.g., the following year) for shopping malls in the comparison target group (S220). That is, sales ranking information for a specific year, sales ranking information after the specific year, and sales ranking information for at least two or more time intervals are obtained. For example, sales ranking information for 2015 can be obtained and then sales ranking information for 2016 can be obtained. At this time, it is desirable to obtain sales information together. In addition, it is not necessary to select one year as the unit for obtaining sales and/or sales ranking information. One month, quarter, and/or half-year can also be the unit period for obtaining sales and/or sales ranking information and calculating growth indicators accordingly.

After acquiring the sales ranking information, the server calculates the relative growth ranking of at least two time intervals acquired in step (S220) as a growth index (S230). That is, the server calculates the growth index based on the difference value of the sales ranking of the previous ranking compared to the recent ranking in two or more time intervals. For example, if Company A, which was ranked 10th in 2015, became 1st in 2016, the difference value of the sales ranking between the two would be 9 (above). In the case of Company B, which was ranked 4th in 2015 but became 10th in 2016, the difference value of the sales ranking between the two would be -6 (above). In this way, after calculating the sales ranking difference values above for all shopping malls included in the comparison target group, the calculated values can be relatively compared to set the shopping mall with the highest value as the growth index 0, and the shopping mall with the lowest value as the growth index 1. For shopping malls with values between the highest and lowest points, the growth index of the shopping mall is calculated based on where the value is located between the highest and lowest points.

After calculating the growth index by year, M companies with small values (M is a natural number smaller than N) are extracted (S240). The extracted companies are companies with high growth potential, and related information can be provided to the companies. In addition, M companies with large values can be extracted, judged as companies with low growth potential, and growth index information can be provided to the related companies. Alternatively, a separate high-growth mall management process can be established for the M companies with high growth potential so that they can be managed separately on the shopping mall service platform server. Conversely, companies with low growth potential can be managed separately by executing a low-growth mall management process.

FIG. 3 is a conceptual diagram for explaining a process of estimating a high-growth potential neck according to a growth mole analysis method according to one embodiment of the present invention.

Referring to Fig. 3, the growth index may be calculated solely based on the difference value of the relative sales ranking, but may also be calculated by a combination of the difference value of the current ranking (which may be interpreted as a ranking of the latest time interval) and the relative sales ranking. In other words, the growth index value may be calculated by assigning a weight to the value of the current ranking and a weight to the difference value of the relative sales ranking. It is desirable that the weights add up to 1. For example, they may be assigned 0.5 each. Alternatively, they may be set to various values such as 0.6 and 0.4, 0.7 and 0.3. When converting the value of the current ranking to the value of the growth index, the first place is converted to the value of 0, and the last place is converted to the value of 1, and the growth index values based on the current ranking of shopping malls with rankings in between have values between 0 and 1 depending on how close they are to either the first place or the last place. When 100 shopping malls are compared, 1st place can have a value of 0, 2nd place can have a value of 0.01, 3rd place can have a value of 0.02, 99th place can have a value of 0.99, and 100th place can have a value of 1.

In the embodiment of FIG. 3, the shopping mall (310) is ranked 1st in sales ranking as of 2018 (or the latest time section), and the increase in sales ranking in 2018 compared to 2017 is +25, so the growth index is 0. On the other hand, the shopping mall (312) is ranked 500th, the lowest among 500 comparison groups, in sales ranking as of 2018, and the increase in sales ranking is -20, so the growth index is 1. However, at this time, if the growth index is calculated using only the relative ranking index, the shopping mall (316) with a sales ranking difference of -25 may have a higher growth index than the shopping mall (312) with a sales ranking difference of -20. For example, it may have a value of 1.

Meanwhile, when the difference in sales rank is reflected in the growth index, for example, when the highest point is +25 and the lowest point is -25, the growth index value based on the sales rank difference of the shopping mall (314) with the growth index of +5 can be calculated based on how close it is to which point between the highest point and the lowest point. In the example above, +5/(25-(-25)) can be calculated to be a value of 0.1. In particular, at this time, it is desirable that the shopping mall with the + relative rank value and the shopping mall with the - relative rank value are calculated based on different criteria. It is desirable that the shopping mall with the + relative rank value is calculated by adding from 0 based on how far it is from the highest point, and the shopping mall with the - relative rank value is calculated by subtracting from 1 based on how far it is from the lowest point.

In another example, not only the current rank and the increase in sales rank, but also the sales in the recent time period can affect the growth index. This can also be considered as a factor in calculating the growth index with a certain weight.

Meanwhile, the server can calculate the growth index every year through the growth index calculation method as above, and extract the top M shopping malls. Looking at the table on the right side of Fig. 3, the top 3 shopping malls with the lowest growth index values from 2016 to 2019 are displayed, and conversely, the bottom 3 shopping malls with the highest growth index values from 2016 to 2019 are displayed. The server can also calculate the average of the growth indexes for a recent certain year based on the annual growth index values, and then extract the shopping mall with the lower average as the top growth shopping mall. Alternatively, the server can extract the top growth shopping malls by focusing on the trend of the growth index decreasing among the shopping malls with the growth index lower than the threshold based on the annual growth index values. This can be arbitrarily set through user settings and can be changed later.

FIG. 4 is a conceptual diagram for explaining a method of analyzing the growth potential of a shopping mall based on a correlation graph between a shopping mall growth index and sales generated by a growth mall analysis method according to one embodiment of the present invention.

Referring to FIG. 4, the server can generate and visualize a two-dimensional graph based on the shopping mall growth index and sales amount calculated by the methods of FIGS. 2 and 3. At this time, the server can display the time sections on the two-dimensional graph by dividing them into different visual expressions. The different visual expressions can be achieved by different shapes, colors, sizes, saturation, brightness, etc. For example, the point corresponding to 2016 of a specific shopping mall can be displayed in red, in blue for 2017, in yellow for 2018, and in black for 2019, thereby visualizing the growth index value and sales amount according to the year of the shopping mall.

According to the embodiment of FIG. 4, the server can set a threshold value (420) for the growth index and set a growth mall area (hatched area) having a growth index value smaller than the threshold value. It is preferable that the threshold value (420) be 0.2. In some cases, it may be set differently, such as 0.25, 0.15, 0.125, 0.1, or 0.05. All (or at least some) shopping malls included in the area can be interpreted as growth malls. At this time, a shopping mall that is included in the growth mall area and has sales exceeding the critical difference (Δs) for a certain period of time (for example, from 2016 to 2019) can be analyzed as a top growth mall. The critical difference value (Δs) can be set to approximately 500 million. However, it does not have to be limited to this, and it can be set to a relative sales growth value of 100 million, 200 million, 300 million, 1 billion, 2 billion, or 10%, 20%, 30%, 50%, ... 100%.

Meanwhile, in the case of the points of shopping mall A, if the 2016 point (410) and the 2019 point (412) have growth index values less than the threshold value (420), and exist in the growth mall area, and the difference between the two points (410, 412) is greater than the threshold difference (Δs), the server can analyze shopping mall A as the top growth mall. In this embodiment, the top growth mall was selected on the premise that both points (410, 412) exist in the growth mall area, but this is not necessarily the case. In another example, only the point (412) of the latest section exists in the growth mall area, and the top growth mall can be determined based on the sales amount being greater than the threshold difference (Δs). However, if both points (410, 412) exist in the growth mall area, it can be determined to be a more stable growth mall than a shopping mall in which only one point (412) exists in the growth mall area. That is, based on whether the current point (412) or two points (410, 412) exist in the growth mall area, it is possible to predict that the shopping mall will have high growth potential in the future, and based on whether the difference in sales is greater than the critical difference value (Δs), a high-growth shopping mall can be extracted more clearly. In another example, a shopping mall whose sales exceed the critical value (not shown) can be extracted as a high-growth shopping mall. A shopping mall with high growth potential extracted based on various criteria such as the above can be provided with growth-related indicators and sales-related analysis results to the shopping mall, thereby supporting the realization of high growth with the current operation in the future.

Figure 5 is a graph comparing the 2016 and 2019 growth indices of the top 100 shopping malls in terms of sales.

Referring to FIG. 5, the server can select the shopping malls with the top 100 sales based on the current sales as the comparison target group. Then, for the shopping malls, the growth index in 2016 and the growth index in 2019 are visualized and compared according to the method of FIG. 4. The server can express the 2016 points (based on the sales amount + growth index value) in red and the 2019 points in blue. As shown in the graph of FIG. 5, it can be confirmed that shopping malls with low sales in 2016 points but low growth index values are achieving high sales above the reference value in 2019.

Figure 6 is a graph showing the flow of growth indicators over time for the top 500 shopping malls in terms of sales from 2015 to 2019.

Referring to Figure 6 (a), when looking at the sales and growth indicators of the top 500 shopping malls with the highest sales as of the present (2019), it can be seen that they are concentrated in places where sales are low but the growth indicator values are low.

And, referring to (b) of Fig. 6, it can be confirmed that sales and growth indicators have increased together since 2017, and referring to (c) of Fig. 6, sales in 2019 exceeded 950 million, but it can be confirmed that the growth indicator is evenly distributed from 0 to 1. In other words, it is analyzed that as sales increase, the growth indicator shows a trend of decreasing.

In this way, the server can visualize the concept of time in the form of a visual representation of points and a video on a two-dimensional graph of sales and growth indicators. It can be created in a video file format (gif, mp4, webm, mkv, flv, avi, wmv asf, mpg, mpeg, m4v) according to the form of a video that moves over time. Since it is a video, the file can be played in a form in which the visual representation of a point corresponding to a shopping mall (e.g., color) changes over time, and when its position changes and reaches the point of the final time section, it stops and is completed.

Meanwhile, the server can select shopping malls to be displayed as points with a prominent visual expression (e.g., changing to sky blue, blue, or navy). For example, the entire comparison target group can be set to 1,500, and only the top 100, 200, ... 500 can be selected as the targets of prominent visual expression based on the values of sales or growth indicators to generate a video for visualization. At this time, points corresponding to shopping malls that are not selected but belong to the comparison target group can be displayed in an inconspicuous color such as gray so that they stand out against the background. Through this, it is possible to compare how the points of the entire comparison target group are positioned and the formation of the selected target.

Figure 7 is a conceptual diagram to explain growth potential according to the relationship between current growth indicators and sales compared to the past.

In the graphs of Figs. 5 and 6, it can be seen that the changes in points on the two-dimensional graphs of shopping malls whose growth rate decreases over time are patterned. If this is explained based on the graph of Fig. 7, it can be confirmed that in the early period (e.g., 2016) (indicated by the dotted line), there are low sales and low growth indicator values, and later, while the sales increase slightly, the growth indicator value increases. Then, at the current (e.g., 2019) starting point (indicated by the solid line), points can be formed in a form where the sales are fixed to a certain extent and the growth indicator increases from 0.25 to 1. The server analyzes this phenomenon by generating a file in a video format with the concept of time added.

Meanwhile, at this time, if there is a large change in the sales value, it can be seen as a mall that has grown significantly, but the amount of change is also less than the first threshold value (not shown), and the growth index of the current starting point (2019) compared to the growth index of the early period (2016) has a value greater than the second threshold value (not shown) in the direction away from 0, then the shopping malls can be defined as malls with slow sales growth and low future growth potential (declining malls). In particular, it is desirable to define a shopping mall that has changed from 2016 (the hatched area) to 2019 (the hatched area) as a declining mall with particularly low growth potential. At this time, the first threshold value can be set to 100 million, and the second threshold value can be set to 0.75. This is not necessarily limited to this, and the first threshold can be set to 50 million won, 200 million won, 300 million won, etc., and the second threshold can also be set to other values such as 0.5, 0.6, 0.7, 0.8, 0.9, etc. The first threshold can be set to the absolute sales amount at the current point in time, not the difference between the initial period and the current point in time, and the second threshold can be set to the absolute growth index value at the current point in time, or the difference between the initial period and the current point in time.

Figure 8 is a graph showing the flow of growth indicators over time from 2016 to 2019 for the top 500 high-growth shopping malls.

Referring to Figure 8 (a), it can be seen that all points on the 2016 sales and growth indicator graph of 500 shopping malls with high growth potential have growth indicators close to 0, but the number is not large.

Referring to Figure 8 (b), it can be seen that all of the shopping malls in 2019 have growth indices close to 0 and have been replaced with a large number of points.

In this way, when analyzing the growth rate of shopping malls based on the growth index, it can be confirmed that only some of them continue to grow, and it can be confirmed that shopping malls with high initial growth potential are likely to maintain growth in the future. There may be cases where shopping malls with low growth potential suddenly rebound, but it can be seen that such cases rarely occur in actual analysis. Therefore, comparing the growth potential index calculated by the growth mall analysis method according to one embodiment of the present invention with sales revenue and steadily observing over time to extract shopping malls that maintain their growth can be an important function in operating a shopping mall platform. In addition, it is desirable to execute a slightly different process management for the extracted shopping malls as growth malls. It may be desirable to manage them as a separate process that supports shopping malls with high growth potential in various ways, such as providing discounts on paid items related to the shopping mall or providing shopping mall promotion methods tailored only to the shopping mall.

Fig. 9 is a block diagram schematically illustrating a growth mole analysis device according to one embodiment of the present invention. As illustrated in Fig. 9, the device may include a communication unit (910), a processor (920), a memory (930), and an input/output module (940).

Referring to FIG. 9, the communication unit (910) is a component for exchanging information with other devices, such as a shopping mall operator terminal or a shopping mall platform server, via a wireless and/or wired network. The communication unit (910) includes a communication device associated with a wired or wireless network. The communication unit (910) may include an antenna.

The processor (920) obtains the sales information, sales information, etc. of the shopping mall received through the communication unit (910), selects a comparison target group based on the shopping mall's category, sales amount/sales ranking for a specific period, etc. according to the programming, calculates a growth index based on the sales ranking and/or sales amount changes of the selected comparison target group over time, and visualizes the same. In addition, based on the changes in sales amount and/or growth index over time in the visualized graph, a high-growth shopping mall or a declining shopping mall, etc., can be inferred, and the inference result can be provided to the corresponding shopping mall operator or shopping mall platform operator (manager).

The memory (930) is a storage device that stores commands related to programs to be executed by the processor (920) and stores various data required by the processor (920). The memory (930) may store sales information of a shopping mall, sales information, sales rankings, information on members who purchased products at a specific shopping mall, and information on the product purchase history of shopping mall members. The memory (930) may be implemented as a local memory within the device or as an external large-capacity database.

The input/output module (940) includes information input means such as a keyboard and mouse, and information output means such as a monitor, TV, and touch screen. The input/output module (940) is used to change various setting values (threshold value, threshold difference, etc.) related to the calculation of growth indicators (weight, calculation method, etc.) and the analysis method of the calculated growth indicators, and to display related contents.

The systems or devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the systems, devices, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the OS. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone, but those skilled in the art will appreciate that the processing device may include multiple processing elements and/or multiple types of processing elements. For example, the processing device may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible.

The software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device. The software and/or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal waves, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over network-connected computer systems, and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

The method according to the embodiments may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiments or may be those known to and usable by those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands such as ROMs, RAMs, flash memories, etc. Examples of the program commands include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described above by way of limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, appropriate results can be achieved even if the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or are replaced or substituted by other components or equivalents.

Therefore, other implementations, other embodiments, and equivalents to the claims are also included in the scope of the claims described below.

Claims (19)

In a method of analyzing growth in computing devices,
A step of obtaining information on sales amount over a period of time for multiple shopping malls;
A step of selecting N shopping malls as a comparison target group based on the sales ranking of the first period or the second period, wherein the second period represents a time after the first period, and N is an integer greater than or equal to 2; and
For the above N shopping malls, a step of calculating a shopping mall growth index of each shopping mall is included based on the relative sales ranking of the second period compared to the first period.
The above shopping mall growth indicator is calculated based on at least one of a first indicator calculated based on the sales ranking of the second period and a second indicator calculated from the value obtained by subtracting the sales ranking of the first period from the sales ranking of the second period.
A growth mall analysis method in which the above shopping mall growth indicator is calculated by adding a value by assigning a first weight to the first indicator and a second weight to the second indicator. In paragraph 1,
The above shopping mall growth indicator is calculated based on the relative difference value between the sales ranking within the second period and the sales ranking within the first period.
A growth mall analysis method in which a smaller value of the above shopping mall growth index indicates higher growth potential, and a larger value indicates lower growth potential. In paragraph 1,
A growth mall analysis method further comprising a step of extracting shopping malls having a shopping mall growth index lower than a threshold value using the above shopping mall growth index. In paragraph 1,
A growth mole analysis method, wherein the first period and the second period are different points in time having the same length. delete delete In paragraph 1,
When the above second indicator is reflected in the above shopping mall growth indicator, the value of the above second indicator is, based on the relative sales ranking, given a value of 0 for the top shopping mall and a value of M for the bottom shopping mall (where M is a number greater than 0).
A growth mall analysis method, wherein, for the shopping malls other than the top shopping mall and the bottom shopping mall among the N shopping malls, the value of the second indicator is calculated as a value between 0 and M based on how close their relative sales rankings are to the top shopping mall or the bottom shopping mall. In paragraph 1,
A growth mall analysis method for calculating the shopping mall growth index by further considering the difference between the sales amount of the first period and the sales amount of the second period as a third index. In Article 8,
A growth mall analysis method, wherein, when the values of the shopping mall growth indicators calculated using at least one of the first indicator and the second indicator for a plurality of shopping malls among the N shopping malls are the same, the shopping mall growth indicator is calculated by additionally reflecting the third indicator. In paragraph 1,
The above N shopping malls are selected based on competitive shopping malls that can be grouped into the same category, which is a growth mall analysis method. In paragraph 1,
A growth mall analysis method in which the above N shopping malls are among the top N shopping malls based on the sales ranking of the above second period. In paragraph 1,
Further comprising a step of visualizing the above shopping mall growth indicator,
A growth mall analysis method for visualizing the shopping mall growth index by using the above shopping mall growth index as the first axis and the sales amount of some of the N selected shopping malls as the second axis. In Article 12,
When visualizing the above shopping mall growth indicator, one of the N selected shopping malls is displayed as one point,
The points displayed above are displayed in different colors depending on the period, a growth rate analysis method. In Article 12,
A growth mall analysis method, which defines a shopping mall corresponding to a point where the value of the second axis becomes greater than the third threshold value over time while existing in a first region located closer to 0 than the first threshold value of the first axis, as a growth mall. In Article 12,
A growth mall analysis method, which defines a shopping mall corresponding to a point where the change value of the value of the second axis over time is less than the fourth threshold value and the change value of the value of the first axis has a value greater than the fifth threshold value in a direction away from 0 as a declining mall. In Article 12,
A growth mall analysis method, wherein at least one shopping mall is selected based on a threshold value for at least one of the first axis and the second axis and displayed as a first visual representation, and shopping malls that are not selected among the N shopping malls are displayed as a second visual representation. In the growth mall analysis device,
A storage unit that obtains and stores information on sales over a period of time for multiple shopping malls;
A processor is included that selects N shopping malls as a comparison group based on the sales rankings of the first period and the second period, and calculates a shopping mall growth index of each shopping mall based on the relative sales rankings of the second period compared to the first period for the N shopping malls.
The above second period represents the time after the above first period,
N is an integer greater than or equal to 2,
The above shopping mall growth indicator is calculated based on at least one of a first indicator calculated based on the sales ranking of the second period and a second indicator calculated from the value obtained by subtracting the sales ranking of the first period from the sales ranking of the second period.
A growth mall analysis device in which the above shopping mall growth indicator is calculated by adding a value obtained by giving a first weight to the first indicator and a second weight to the second indicator. delete delete

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