WO2020116985A1

WO2020116985A1 - E-commerce platform evaluation system and method

Info

Publication number: WO2020116985A1
Application number: PCT/KR2019/017183
Authority: WO
Inventors: 윤석인
Original assignee: 주식회사 인스브룩크
Priority date: 2018-12-06
Filing date: 2019-12-06
Publication date: 2020-06-11
Also published as: KR102045477B1

Abstract

The present invention relates to an e-commerce platform evaluation system. The e-commerce platform evaluation system comprises: a server (3) capable of collecting, recording, evaluating, and comparing sales data about sites or deals of e-commerce companies on a network; a terminal (5) capable of transmitting/receiving data to/from the server (3) by being connected to the server (3) through the network (N); and a database (15) provided to the server (3) so as to store the collected, recorded, evaluated, and compared data, wherein the server (3) evaluates sales sites and deals on the network by comprising: a collection unit (7) for collecting sales data about a site or deal to be evaluated; an evaluation unit (9) for analyzing and evaluating the collected sales-related sales data; a rank calculation unit (11), which calculates an ECDI point for the site to be evaluated on the basis of the analyzed data so as to calculate rank and evaluate the merits and demerits of the site, and changes, deletes, or adds the site or deal to be evaluated according to an evaluation result; and an output unit (13) for outputting the evaluation result.

Description

E-commerce platform evaluation system and method

The present invention relates to an e-commerce platform evaluation system and method, and more specifically, to calculate an evaluation element capable of measuring the sales activities and sales conditions of operating entities of an electronic commerce (EC) site by formula It is about technology that can provide objective criteria by calculating ECDI (Electronic Commerce Deal Index) points, and efficiently perform comparison and analysis of each platform.

In general, e-commerce stands for electronic commerce, which means buying and selling goods and services through an online network. With the widespread use of smartphones in recent years, the proportion of mobile shopping has skyrocketed, and the proportion of e-commerce is increasing.

And, on this e-commerce, “Dillot” was introduced to social commerce, which selects one product per day for each online shopping channel such as an open market and exposes it at a special price on a daily basis. Therefore, after the emergence of such deals, the sales channel stimulated consumers' purchasing needs with only a large number of deals curated by the sales channel, and as the proportion of mobile sales increased, it spread to the open market.

In addition, the sales strategy of online channels, which ranks their sales platform customers according to the purchase price and issues discount coupons differentially, induces purchases to consumers by competing for sellers listed by search term.

At this time, the sales channels are composed of deals with important exposure accounts in addition to the search window, enabling sales of products in the initial stage, as well as leading sales by search results.

However, the deal exposure between manufacturers and sellers is difficult for most manufacturers and sellers, with the exception of a few large corporate brands with specialized online sales and marketing organizations.

And because competition between sales channels is intensified, some sales channels give up their sales margins or use huge marketing costs for product discounts, so they want manufacturers and sellers to consider their sales channels as the most important. It is a conflict factor that exists in the liver.

On the other hand, from the manufacturer's point of view, even if you want to continuously expose the deals by differently configuring the product as their demands in various major channels, once the unreasonable product composition is exposed in the deal of a specific sales channel, other sales channels require better configuration. Therefore, the reality is that we have no choice but to hesitate to operate aggressive deals.

In this regard, a variety of technical methods are provided to determine how a specific seller is conducting sales activities on a major online sales channel. The most common method is to determine the sales status of each online seller who is exposed to a specific keyword or product category by manufacturer. It is understood by the crawling technique.

Keywords, manufacturers (brands), share of each sales channel, growth rate, price fluctuations, etc. can be checked through these crawling techniques, and these analysis results are used for strategic purposes such as identifying competitor trends, marketing, and establishing price strategies.

In addition, it is a general trend in the current online market competition that the seller, or the online sales channel that the seller enters, links the results of the sales analysis to its marketing activities. In other words, major online sales channels seek to attract consumers' attention by recruiting sellers who can compose more attractive products and exposing them at major deals to increase their control over consumers.

In addition, sellers also seek to increase market share through deals with their product composition specialized in the channel, and manufacturers strategically select sales channels that are popular or relevant to sales activities by exposing the sellers' deals. The effort is to increase the effectiveness of online promotions by providing marketing costs.

After all, in order to activate online sales, attractive deals must be established and continuously exposed to each channel, and a virtuous cycle in which the added value such as sales revenue from the overall and continuous online promotion activities is set again as the marketing budget of each manufacturer's online channel 'S online business structure is established.

However, in reality, one of the big problems facing manufacturers and sellers is that it is difficult to predict which deal of which channel is best for the product being handled, and there is a lack of strategic basis for which deal portfolio to operate in the entire channel.

In the case of major channels' major deals, there is some consensus on the degree of sales advantage, but the newly created deals are relatively difficult to grasp the information, competitor trends, and sales trends of the product groups for all deals.

In addition, the sales channel may request the manufacturer for additional DC and stable inventory required to construct the deal along with marketing costs based on the metrics advantageously processed by the company, which makes it difficult for manufacturers to operate sound deals in other sales channels. do.

Because of this, if the seller has been burdened by excessive marketing costs, excessively low product composition, or excessive inventory preparation, if the sales channel leads to poor sales performance, the manufacturer operates additional deals with the sales channel. Not only will this be difficult, but it can also lose momentum in the online business as a whole.

Even in this case, it is difficult for the manufacturer to protest against excessive configuration and inventory preparation for the channel, and it is difficult to continuously expose additional poor brands even in deals constructed with the channel.

This problem can spread to other sales channels. In fact, a brand that did not perform well in other sales channels is requesting a good account with a more unreasonable structure to enter a new sales channel, but it is difficult to expose.

As such, the difficulty in understanding the exact status of the sales channel or predicting the performance of the company creates anxiety for strategic investment in the online market, and as a result, commercial activities may be reduced. Is being done.

For example, in order to make it difficult to compare prices for each sales channel, provide a separate product with a separate package or capacity, or configure a high basic price exposed from a deal, but provide a separate discount coupon, or provide an offline delivery to the sales channel in the first place. There are ways to supply large quantities. Alternatively, the method of always controlling the sales price of all sales channels is also applied.

However, the exclusive products for each sales channel are limited to brand products that are already selling well, and the method of giving the discount coupon separately after raising the base price has recently been exposed with the coupon price.

In addition, when directly supplying to a sales channel, the manufacturer or seller cannot manage the selling price because the method of automatically tracking the lowest price of the stock purchased by the channel is used.

And, if the exposure prices of all sales channels are managed identically, it is difficult to secure exposure opportunities from all channels because the sales channels do not feel the advantage for the corresponding brand.

First of all, since the sales channel does not disclose the internal data sold, the seller or manufacturer has no choice but to continue sales and marketing for the channel based on an analysis that is advantageous to the sales channel. Since it is managed not to be exposed simultaneously, it is difficult for manufacturers or sellers to effectively implement an exposure strategy.

In the end, even if the consumer's demand for a branded product of a manufacturer increases and the market grows, competition between the sales channels intensifies, so the manufacturer and the seller need to request a lower price.

On the contrary, in the case of sales channels that have achieved customer satisfaction with the company, it is difficult to find a manufacturer or seller to enter and pay a fair marketing cost to the company despite their high marketing cost and competitive advantage. As the competition intensifies, the profit structure deteriorates, leading to a high level of dissatisfaction with manufacturers or sellers.

For example, despite the fact that there is data that a certain brand has a certain product composition at a certain time and is sold at a specific price in a specific sales channel, such data is not transparently disclosed because it is tied to a competitive environment between sales channels. It is difficult for the seller to make a reasonable decision and the consumer has difficulty in experiencing the manufacturer's product by configuring the product according to channel characteristics.

In particular, the objective basis for the competitor's activities in the marketing strategy of the manufacturer is not only for its product planning, but also for cost reduction and increase in advertising and advertising costs, as well as for sales channels and consumers. It is also necessary.

Therefore, it is possible to more clearly check the attractiveness of deals for each sales channel and to enable objective management of the competing deals to grasp the current status of competitors' deals, while enabling sustainable management of the indicators as well as the sales channels' There is an urgent need for a new type of index system that can guarantee a reasonable profit for enhancing attractiveness.

An object of the present invention is an electronic commerce (ECDI) index by calculating an evaluation factor capable of measuring the sales activities and sales conditions of the electronic commerce (EC) site operators who participate in the online distribution market. It is to provide objective criteria by calculating points, and to provide technologies that can efficiently compare and analyze each platform.

Another object of the present invention, by providing transparent information about the domestic e-commerce platform evaluation system competition market, induces fair competition between EC/channel sites, through which the ultimate reduction through cost reduction and efficient resource allocation of manufacturers/sellers It is to provide technology to connect to the utility of buyers and to lay the foundation for sustainable and healthy growth in the E-Maker market through active consumption.

The present invention has been proposed to achieve the above object, the e-commerce platform evaluation system (1) according to an embodiment of the present invention,

A server 3 capable of collecting, recording, evaluating and comparing sales data of e-commerce companies' sites or deals on the network;

A terminal 5 capable of transmitting and receiving data by being connected to the server 3 through a network (N) network; And

Included in the server (3) includes a database 15 for storing the collected, recorded, evaluated, compared data,

The server 3 includes: a collection unit 7 for collecting sales data for an evaluation target site or deal;

An evaluation unit 9 for analyzing and evaluating the collected sales-related sales data;

A ranking calculation unit 11 for calculating a ranking by calculating ECDI points for the site to be evaluated based on the analyzed data, evaluating superiority, and changing, deleting, or adding the site or deal to be evaluated according to the evaluation result; And

By including the output unit 13 for outputting the evaluation results, sales sites and deals are evaluated on the network.

E-commerce platform evaluation method according to another embodiment of the present invention,

Selecting a site to be evaluated on the network by the collection unit 7 of the server 3 (S10);

Selecting a specific deal in the site to be evaluated by the collection unit 7 (S12);

Collecting sales-related data by the collection unit 7 from the selected evaluation target site or deal (S14);

Analyzing and evaluating the collected sales-related data by the evaluation unit 9 (S16);

A step (S20) of calculating the ranking of the evaluation target site or deal by processing the analyzed data by the ranking calculating unit 11; And

And outputting the result calculated by the output unit 13 (S22).

As described above, the e-commerce evaluation system and method according to an embodiment of the present invention has the following effects.

First, it provides objective criteria by calculating the Electronic Commerce Deal Index (ECDI) points by calculating the evaluation factors that can measure the sales activities and sales conditions of the operators of electronic commerce (EC) sites by formulas. Each deal can be objectively evaluated by measuring the exposure level by period, but calculating it daily and comparing it with yesterday's index. As such, the ECDI indicator is presented as a leading indicator of online sales, and the deal preference indicators of major online channels are presented to the online market. It can be used as an index of activity of participating manufacturers or sellers, and as a result, it is possible to identify the characteristics of each online sales channel in the online market that changes every time, and further provide a basis for establishing an accurate marketing strategy.

Second, by applying ECDI points, it is first quantified to what extent the site has been exposed to the outside, and secondly, it is quantified whether it is easy to access specific deals on the site, Thirdly, it is possible to evaluate the sales performance of a specific product on the site.

Third, ECDI points can effectively evaluate the performance of a site by reflecting external information such as exposure and access to the site and deals, as well as internal information such as the sales amount and quantity of products sold at each site.

Fourth, it is possible to evaluate by various factors by collecting sales performance according to the location of deals in each site.

Fifth, by objectively evaluating the activity status of each site or deal by ECDI points, change, delete, and add deals to be evaluated according to the results, and also create, delete, change, and add categories. By managing history based on ECDI points, there is an advantage in that an objective index can be set and fair evaluation can be conducted continuously.

Sixth, by providing transparent information on the e-commerce platform evaluation system competition market, it is possible to induce fair competition between EC/channel sites, and through this, through the cost reduction of manufacturers/sellers and efficient resource allocation, it is the ultimate buyer's utility. I can connect.

Seventh, there is an advantage in improving the efficiency and speed of work by processing the online market research process from one program to one screen.

1 is a diagram schematically showing an e-commerce platform evaluation system according to an embodiment of the present invention.

FIG. 2 is a diagram showing a management server structure of the e-commerce platform evaluation system shown in FIG. 1.

FIG. 3 is a diagram schematically showing an operation structure of the e-commerce platform evaluation system illustrated in FIG. 1.

4 is a diagram schematically showing the structure of the evaluation unit shown in FIG. 2.

5 is a diagram schematically showing the structure of the ranking calculator shown in FIG. 2.

6 is a flowchart illustrating an e-commerce platform evaluation system method according to another embodiment of the present invention.

7 is a flowchart showing the ranking calculation step illustrated in FIG. 6 in more detail.

8 is a flowchart showing the evaluation steps shown in FIG. 6 in order.

FIG. 9 is a diagram schematically showing a relationship of collecting data from deals of each site and calculating a ranking by the e-commerce platform evaluation system shown in FIG. 1.

Hereinafter, an e-commerce platform evaluation system according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

As illustrated in FIGS. 1 to 9, the e-commerce platform evaluation system 1 proposed by the present invention is based on an e-commerce platform evaluation index (ECDI) method, which is an online sales strategy index based on deals. It evaluates commerce sites, compares superiority, and changes, deletes, or adds deals or categories depending on the results.

That is, online market share by manufacturer, brand, sales channel, growth rate, price fluctuation, public transaction amount, channel influence, number of visitors, product name, product code, category, basic price and selling price, product review number, sales volume, purchase satisfaction, payment terms, Provides objective evaluation indicators for each site or deal by collecting data such as delivery conditions, site and account names, and sales hours (hereinafter, sales data), calculating and comparing ECDI (Electronic Commerce Deal Index) points .

Therefore, by applying these ECDI points, it is first quantified to what extent the site has been exposed to the outside, and secondly, it is quantified whether it is easy to access specific deals on the site, Thirdly, it is possible to evaluate the sales performance of a specific product on the site.

In more detail, the e-commerce platform evaluation system 1 is a server capable of collecting, recording, and evaluating and comparing sales data of sites and deals of companies participating in the online distribution market on the network, and evaluating and comparing according to the condition data. )Wow; A terminal 5 capable of transmitting and receiving data by being connected to the server 3 through a network (N) network; It is provided in the server 3, and includes a database (Data base) 15 for storing the collected, recorded, evaluated, and compared data.

The e-commerce platform evaluation system 1 will be described in more detail.

The server 3 refers to a typical server 3, and is a computer hardware on which the server 3 program is executed. It monitors and controls the entire network N, such as printer control and file management, or mainframe or public network. Connection to other networks (N) through a network, software resources such as data, programs, files, or modem, fax, and printer sharing. It supports sharing of hardware resources such as other equipment.

The server 3 is equipped with an e-commerce platform evaluation system 1 related app to be connected by the terminal 5 and the network N to manage the Internet homepage corresponding to the URL, and according to the request of the terminal 5 It prints the internet homepage and evaluation points and results linked to the URL.

In more detail, the server 3, as shown in Figure 2, the member authentication unit 4 and; A collection unit (7) for collecting sales data for the site or deal to be evaluated; An evaluation unit 9 for analyzing and evaluating the collected sales-related sales data; A ranking calculation unit 11 for calculating an ECDI point and calculating a ranking for the site to be evaluated based on the analyzed data; And it includes an output unit 13 for outputting the calculated results.

In the server 3, the member authentication unit 4, the collection unit 7, the evaluation unit 9, the ranking calculation unit 11, and the output unit 13 interpret the input data and execute the command Means a microprocessor that performs operations, etc.

The member authentication unit 4 checks whether the member is authenticated based on the ID and password of the accessing member and the information stored in the member information DB. At this time, the member includes a member who has requested the evaluation of the e-commerce platform evaluation system (1), is a seller or a brand company, or has applied for membership on other relevant websites.

The collection unit 7 grasps a situation in which a specific seller is active on a main online sales channel. The collection unit 7 first selects a site to be evaluated. In other words, a plurality of companies that do distribution business online are selected, and they are selected in consideration of each company's public transaction amount, channel influence, and the number of visitors.

Then, the deals in the selected site are collected for evaluation.

At this time, the criteria of the core deal can be selected in various ways, for example, whether it is listed on the main page of the site or on the top, whether it is major in generating sales, or in an independent configuration to search through a separate menu. It is selected based on criteria such as whether it is organized and whether it has been continuously exposed in a certain area for a certain period of time.

And, as a collection method, sales data by manufacturer of online sellers exposed to a specific keyword or product category is searched and collected.

At this time, there are various ways to search for data on a site online, and as an example, a method of searching web data using a crawler or scraping.

When using a crawler, data can be automatically retrieved from the web using Python.

For example, it searches the URL of the site, collects an XML-based format such as RSS (Really Simple Syndication), and converts the collected information into an XML format to process the data.

In addition, various types of data can be collected, and for example, data such as the above sales data, market share, growth rate, price fluctuation, disclosure transaction amount, channel influence, and visitor number by manufacturer, brand, and sales channel are collected. Is done. In addition, the collected data can be used for purposes such as understanding competitor trends, marketing, and establishing price strategies.

It is preferable to collect this data multiple times per day, for example, at least three times.

In addition, when collecting data, a snapshot of an evaluation target deal and information on a product detail page are also collected.

In addition, the collected data will collect data such as product name, product code, category classification, basic price and selling price, number of product reviews, manufacturer and brand, sales volume, purchase satisfaction, payment terms, shipping terms, site and account name, and time. .

In addition, the collection unit 7 also collects data on a predetermined deal in the site.

Deal means an area for posting an advertising banner, etc. on an Internet website, and can be analyzed by a cookie or the like that is stored when a user visits the advertising banner of the site.

For example, the criteria for selection deals can be selected in a variety of ways, for example, whether they are listed on the main page or top of the site, whether they are major in generating sales, and can be searched in a separate menu. It is selected based on the criteria such as whether it is organized as a composition and whether it is continuously exposed in a certain area for a certain period.

And, the collected information is stored in the database 15, and can be withdrawn if necessary.

The evaluation unit 9 analyzes the collected sales-related data. That is, the sales status is grasped by analyzing data such as sales performance of a site or deal.

For such data analysis, in-depth analysis technology using statistical processing, data mining, graph mining, machine learning, and artificial intelligence can be applied.

Data analysis is possible in various ways, for example, KDD (Knowledge Discovery in Database), SEMMA (Sampling Exploration Modification Modeling Assessment), and CRISP-DM (CRoss Industry Standard Process for Data Mining).

KDD is a data mining process that is logically organized to find statistical patterns from data based on profiling technology.

The evaluation unit 9 for performing such data analysis includes: a selection module 20 for selecting target sales data among sales data, as shown in FIG. 4; A pre-processing module 22 for processing the selected sales data in a constant format; A conversion module 24 for processing the formatted sales data according to an analysis purpose; And a mining module 26 that performs analysis by processing the converted sales data by an algorithm.

The selection module 20 selects target data. For example, target data such as keyword, manufacturer, brand, market share, growth rate, price fluctuation, disclosed transaction amount, channel influence, and visitor number are selected as sales data.

The pre-processing module 22 identifies noise, outliers, and missing values included in the data, removes them if necessary, or reprocesses them as meaningful data to purify them into a data set.

For example, the outliers, missing values, etc. included in the data related to the sales company, sales record, share, etc. collected in the selection step (S18) are reprocessed in a format suitable for analysis to be purified to an analytical state.

The conversion module 24 converts variables to be generated and selected according to the purpose of analysis, and reduced dimensions to be analyzed efficiently.

For example, in the conversion step (S23), data such as data keywords, manufacturers, brands, and market share, growth rate, and price fluctuations are converted to suit the purpose of analysis. If it is, the time and sales quantity are set as variables, and the sales data of last year is excluded, so that it is converted to be suitable for analysis.

The mining module 26 selects a technique suitable for the purpose of data analysis and applies an appropriate algorithm to execute the operation.

For example, the sales data processed in the conversion step S23 is calculated using an algorithm to calculate the actual sales amount this year.

As described above, when the data analysis is completed by the evaluation unit 9, the evaluation target site is evaluated based on the data analyzed by the ranking calculation unit 11 of the server 3 to calculate ranking.

In other words, the ranking is calculated based on the deal data collected from a plurality of sites.Each deal's Electronic Commerce Deal Index (ECDI) points, sales index, accessibility, number of sales, estimated transaction amount, total number of accounts, Depth, etc. It is evaluated based on criteria.

5, the ranking calculating unit 11 includes a point calculating module 30 for calculating ECDI points; A comparison module 32 for comparing the superiority of the activity of the manufacturer or the seller by the ECDI point; A change module 34 for changing, deleting, or adding the target site or deal by the ECDI point; A category module 36 for creating, changing, deleting, and adding categories by ECDI points; And a point management module 38 for managing ECDI points.

First, in the point calculation module 30 for calculating ECDI points,

The ECDI points refer to data processed by the evaluation unit 9, that is, points obtained by synthesizing sales-related data generated at each site or deal for each day. In other words,

ECDI Points=Exposure Degree*Approach Degree*Sales Amount*Sales Quantity ------------------ Formula 1

(Exposure: the number of traffic on the site, access: the number of steps to access the deal)

The exposure degree means the number of deals or traffics of a site, and indicates the amount of data transmitted to a specific inquiry server, and is expressed in bps units.

The access degree (Depth) is a number indicating the number of steps for accessing a corresponding deal on the Internet, and is a number indicating how many steps are performed in stepwise access by category to a product category textually stored in each server.

For example, when approaching the deal immediately, the number of steps is assigned as 3, and if the deal is accessed through several steps, the number of steps is assigned as 1, and in the middle, 2 is assigned. .

In Equation 1, the sales quantity and the sales amount are applied as variables, but the present invention is not limited thereto, and the items of sales data listed above are also applicable.

That is, the sales quantity is applicable to sales data 1, and the sales amount is applicable to sales data 2.

In addition, in calculating the ECDI points, the daily sales may be pointed or the share of deals may be pointed and evaluated. The daily points of each account are calculated by dividing the daily points of each deal by the number of accounts. In addition, each day's ECDI points are composed independently, and the previous day's performance does not affect the day's ECDI points.

And, in order to calculate the importance of each variable differently, a constant may be applied to each variable as shown in the following equation. In other words,

ECDI point=(exposure rate*n1)*(approach rate*n2)*(sales amount*n3)*(sales quantity*n4)--Equation 2

At this time, n1...n4 represent importance, and the importance of each variable such as exposure, accessibility, sales amount, and sales quantity can be differentially applied.

In other words, because there are products whose exposure is important depending on the product category, and products whose sales amount is important, variables suitable for evaluating the product can be calculated more heavily.

For example, if exposure is important, n1 is set to 4, and the other variable is a method of applying a smaller number such as 1,2,3.

The ECDI point is calculated according to Equation 1 above. In other words,

For example, assuming that the amount of traffic per day of B deal on site A is 1 million bps, and access to the deal is possible directly, the access level is assumed to be 100 sold for 3 or 1 day and the sales amount is 10 million won. , ECDI points may be calculated as follows by Equation 1.

ECDI points=1,000,000*3*100*10,000,000=3,000,000,000,000,000

On the other hand, D traffic on the C site has 100,000 traffic per day, and in order to access the deal, it has to go through three levels of categories, so accessibility is 10 sold per day, and the amount sold is 1 million won. Assuming in the case, ECDI points may be calculated by Equation 1 below.

ECDI points=100,000*1*10*1,000,000=3,000,000,000,000

That is, it can be evaluated that the B deal of the A site is more efficient than the D deal of the C site.

The evaluation range of the above-mentioned ECDI points can be analyzed by category or by period. Then, the ranking of each analyzed site is calculated. That is, the rank is calculated in order of efficiency in a plurality of deals.

In addition, in calculating the ranking, the comparison module 32 compares the superiority by the ECDI point and calculates the ranking.

That is, in comparing and evaluating the activity of each manufacturer or seller, it is based on an arbitrary number that is qualitatively and quantitatively evaluated and summed up in the activities of major e-commerce sites.

For example, it is a method in which the activities of the quantity, product type, return quantity, promotion, event, etc. sold by the manufacturer A on the B site are numerically calculated according to Equation 1 above, calculated as ECDI points, and ranked and evaluated.

At this time, if there are multiple sellers of one manufacturer, the sum of ECDI points acquired by each seller through the manufacturer's product is used as a criterion for evaluating the activity of the manufacturer's EC site.

Through this, it is possible to mutually compare activities of each manufacturer/seller by period, site, category, and other detailed criteria.

Basically, ECDI logic gives daily points to every individual account at each site, so it is technically possible to perform cross-comparison, cross-comparison, and sub-comparison.

In the above, the ranking was calculated by ECDI points, but the present invention is not limited to this, and it can also be calculated by the share occupied by deals for a certain period of time on the site.

In other words, share = number of days occupied by accounts in a given period/(total number of accounts per day* days)

Alternatively, it can be calculated as the estimated daily sales of the deal.

That is, it is a method of ranking according to sales sold through a specific deal for a predetermined period. For example, daily sales are calculated by calculating data such as the site, account name, brand, product, price, daily sales quantity, and date of a specific deal.

Then, the ranking of each site is evaluated according to the calculated sales.

At this time, it is calculated as the ranking of sales, but it is calculated based on the ranking with the highest sales, or reflects the ranking with the lowest sales.

Therefore, when evaluating a plurality of sites, the top 10 sites in the sales ranking may be selected, or the bottom 5 sites may be selected.

Or it can be calculated for each type of product sold. That is, by grasping the types of products preferred by the buyer among the products sold, the ranking is calculated according to the product type by preference.

Or you can also calculate by period. That is, it is possible to calculate a period of high efficiency by analyzing the sales efficiency on a monthly, daily, or quarterly basis.

On the other hand, after the evaluation as described above, the change module 34 may change, add, or delete the ECDI target site or deal according to the evaluation result.

In other words, ECDI evaluates the major deal activities of sites with a certain level of influence in the e-commerce market. These rankings are driven by various external factors such as changes in market environment, changes in competitors' competition, and changes in consumer preferences. Changes are possible.

For example, if the ECDI points of certain sites or deals of company A decreases compared to the previous period, or the market environment for the product changes, delete company A from the site or deal, change to company B, or , Company C can be added.

In order to reflect these changes smoothly, ECDI will continuously monitor the market environment. In addition, deals that occur frequently and intermittently to achieve business objectives at each site are calculated in addition to ECDI when the need is recognized. At this time, adding, deleting, or changing deals does not affect the ECDI evaluation of other deals.

On the other hand, it is possible to change, delete, and add these ECDI evaluation rankings to sites and deals as well as categories.

That is, the category module 36 selects an internal product category standard for detailed market information collection and performs detailed comparison. This can be created, added, or deleted by a specific category according to the internal and external environment, such as changes in consumption patterns and market environment. This is an occasional activity, and is performed when the need for category modification is sufficiently recognized internally and externally.

For example, as a result of monitoring and comparing ECDI points for a specific category of B, C, and D sellers of A site in real time, if the ECDI points of B sellers are low, the method of deleting the B sellers category is a method.

In addition, in the evaluation method described above, the point management module 38 manages ECDI points, and these ECDI points are based on ECDI points on a specific day, and historically manage points by date after that date.

Therefore, ECDI points of each day can be patterned by cumulative management, and by monitoring these patterns, it is possible to grasp the fluctuations of sites or deals to be evaluated. At this time, if the sum of total points changes due to the addition or deletion of a site or deal, the numerical change is reflected as it is.

In addition, the calculated ranking of each site may be output by the output unit 13. For example, it may be output in various ways, such as sales rank by period, sales rank by product, and sales rank by brand.

In addition, these results can be output in the form of a monitor screen or a report.

Meanwhile, as another embodiment of the present invention, a trend prediction unit 27 may be additionally included.

That is, in the future, products, periods, brands, periods, sales, and sales will be predicted, and competitor activities and future sales forecasts can be predicted.

In other words, it is a method of predicting future performance by processing and patterning existing ECDI performance data and processing this pattern functionally.

In this case, when the trend prediction unit 27 predicts the performance, the function formula for the past performance may be applied in the same way in the future period, or may be predicted by applying the inflation rate to the function formula.

In this process, the future performance can be forecast by applying the artificial neural network method.

That is, using an artificial neural network, various classifications and clustering are possible, so that a desired operation is performed by putting various layers on the data for classification or clustering.

The artificial neural network is composed of a multi-layered structure, each layer is composed of several nodes, and each node actually operates, and the calculation process is similar to the neurons that make up the human neural network.

The node reacts when it receives a stimulus of a certain size or more, and the magnitude of the reaction is approximately proportional to the value multiplied by the input value and the node's coefficient (or weights). In general, a node receives multiple inputs and has coefficients equal to the number of inputs. Therefore, different weights can be assigned to various inputs by adjusting this coefficient.

Finally, the multiplied values are all added up and the sum goes into the input of the activation function. The result of the active function corresponds to the output of the node, and this output is ultimately used for classification or regression analysis.

At this time, the input data becomes the input of the first layer, after which the output of each layer becomes the input of the next layer again.

Through this process, the output value is fed back to the initial input value and continuously updated by appropriate correction, and learning is performed by repeating this process.

In the present invention, when forecasting future performance, input the past product, period, brand, period, and sales data into the artificial neural network as input values, calculate and output them through a multi-layer structure, and then output the output values again With iterative calculation, it is possible to forecast future earnings.

Then, after the trend prediction is completed, the sales strategy may be established by the sales strategy establishment unit 28.

In other words, when a new product is sold online, the trend prediction unit 27 establishes a strategy by reflecting the predicted result. If a future product price, brand, or buyer trend is predicted, the new product is reflected by this trend This is how it applies to strategy.

For example, the ECDI point calculated by Equation 1 above can be patterned to set a function expression, and the same expression can be applied to the future period to predict future performance. It is a way to apply.

In addition, the sales strategy establishing unit 28 may provide insights of overall market, product group, and platform trend change, tracking, and sales strategy. In addition, it is possible to provide integrated market analysis and efficient sales strategies through accumulated insights.

Hereinafter, an e-commerce platform evaluation system method according to a preferred embodiment of the present invention will be described in more detail with reference to the accompanying drawings.

As shown in Figure 6, the e-commerce platform evaluation method proposed by the present invention is a member authentication step (S5); Selecting a site to be evaluated on the network by the collection unit 7 of the server 3 (S10); Selecting a specific deal in the site to be evaluated by the collection unit 7 (S12); Collecting sales-related data by the collection unit 7 from the selected evaluation target site or deal (S14); Analyzing and evaluating the collected sales-related data by the evaluation unit 9 (S16); Calculating the ECDI points of the evaluation target site or deal by calculating the ranking by processing the analyzed data by the ranking calculating unit 11 (S20); And it includes the step of outputting the result calculated by the output unit 13 (S22).

In this e-commerce platform evaluation system (1) method,

First, the client accesses the e-commerce platform evaluation system (1) evaluation agency's Internet homepage and authenticates the member. That is, member authentication is performed by entering the member's ID and password. Of course, if you are not a member, you can omit this authentication process.

When the member authentication is completed, a step (S10) of selecting a site to be evaluated proceeds.

In this step S10, a plurality of sites are searched online by the collection unit 7 of the server 3 to select a site to be evaluated.

At this time, the site search includes a variety of data, for example, a web (WEB) such as TEXT, Twitter, blog, e-mail, documents, newspaper articles, and a wide range of searches.

There are various ways to search for a site online, and as an example, a method of searching web data using a crawler or scraping.

Then, the target site is selected based on the processing result of the collected data.

At this time, it is selected in consideration of the public transaction amount, channel influence, number of visitors, etc. of companies operating each site.

In the step of selecting a specific deal in the site (S12), a deal for posting an advertising banner, etc. on a predetermined Internet website is selected. At this time, in selecting a deal, first, the data of the deal is analyzed. When a user visits an advertisement banner of the corresponding site, it may be analyzed by a cookie or the like stored.

Then, a step (S14) of collecting sales-related data from the deal of the site to be evaluated proceeds.

When collecting the deal data at this stage, the deal data is retrieved by using a crawler or scraping.

At this time, various types of data are available for the collected deals, for example, the sales data, share of each manufacturer, brand, sales channel, growth rate, price fluctuation, public transaction amount, channel influence, number of visitors, etc. Data will be collected.

In addition, the data includes data such as product name, product code, category classification, basic price and selling price, product review number, manufacturer and brand, sales volume, purchase satisfaction, payment terms, shipping terms, site and account name, and time. In addition, when collecting data, a snapshot of an evaluation target deal and information on a product detail page are also collected.

The collected data can be used for purposes such as understanding competitor trends, marketing, and establishing price strategies. In addition, data collection is preferably performed multiple times per day, for example, at least three times.

When the data collection step S14 is completed as described above, a step S16 of analyzing and evaluating the collected sales-related data proceeds.

In this step (S16), in-depth analysis technology using statistical processing, data mining, graph mining, machine learning, and artificial intelligence can be applied to extract values such as sales performance of deals.

Various methods of data analysis are possible, for example, KDD, SEMMA, and CRISP-DM.

The evaluation step (S16), as shown in Figure 8, the selection step (S18) for selecting the target data from the sales data; A pre-processing step (S21) of processing the selected sales data in a constant format; A conversion step (S23) of processing the formatted sales data according to the analysis purpose; It includes a data mining step (S24) for performing analysis by processing the converted sales data by an algorithm.

In the selection step S18, target data is selected. For example, target data such as keyword, manufacturer, brand, share by sales channel, growth rate, price fluctuation, disclosed transaction amount, channel influence, and visitor number are selected.

In the pre-processing step (S21 ), noise, outliers, and missing values included in the data are identified and removed if necessary, or reprocessed as meaningful data to be purified into a data set.

For example, the outliers, missing values, etc. included in the data related to the sales company, sales record, share, etc. collected in the selection step (S18) are reprocessed into a format suitable for analysis to be purified to an analytical state.

In the conversion step (S23), variables are generated and selected according to the purpose of analysis, and the dimensions are reduced to convert them for efficient analysis.

In the data mining step (S24), a technique suitable for the purpose of analysis is selected and an appropriate algorithm is applied to execute the operation.

When the data analysis step (S16) is completed, a step (S20) of evaluating a site to be evaluated based on the analyzed data and calculating the ranking proceeds.

In this step (S20), the ranking is calculated by the ranking calculating unit 11 based on the analyzed data.

That is, bar data collected from a plurality of sites is analyzed and compared with each other. As a result, each deal is compared based on sales index, accessibility, number of sales, estimated transaction amount, total number of accounts, and depth. At this time, the comparison range can be analyzed by category or by period.

In more detail, as shown in FIG. 7, in the ranking calculation step (S20 ), the ECDI point is calculated by the point calculation module 30 (S30 ), and the comparison module 32 is used by the ECDI point. Compare the superiority and rank of the activity of the manufacturer or the seller (S32), and the change module 34 changes, deletes, or adds the target site or deal based on the evaluation result of the ECDI point, and the category module 36 According to this ECDI point, the category is created, changed, deleted, and added (S34), and the point management module 38 manages the ECDI point (S36).

First, in the point calculation step (S30) for calculating the ECDI point,

ECDI points are points measured by synthesizing sales-related information from each site or deal for each day. In other words,

For example, if the corresponding deal is directly approached, the number of steps is assigned as 3, and if the deal is accessed only after several steps, the number of steps is assigned as 1 and the middle is assigned.

And, in order to calculate the importance of each variable differently, a constant may be applied to each variable.

ECDI point=(exposure rate*n1)*(approach rate*n2)*(sales amount*n3)*(sales quantity*n4)-equation 2

The evaluation method using these ECDI points is as follows. In other words,

ECDI points=1,000,000*3*100*10,000,000=3,000,000,000,000,000

ECDI points=100,000*1*10*1,000,000=3,000,000,000,000

In calculating the ECDI points, the daily sales may be pointed or the share of deals may be pointed and evaluated.

At this time, the evaluation scope can be analyzed by category or by period. Then, the ranking of each analyzed site is calculated. That is, the rank is calculated in order of efficiency in a plurality of deals.

When the point calculating step (S30) is completed, a step (S32) in which the comparison module 32 compares the superiority by the ECDI point and calculates the ranking.

That is, in comparing and evaluating the activities of each manufacturer or seller, it is based on an arbitrary number that is qualitatively and quantitatively evaluated and added to the activities of major EC sites.

For example, it is a method of evaluating and evaluating activities such as quantity, product type, quantity of returns, promotions, events, etc. sold by the manufacturer A on the site B as points.

In the above, the ranking was calculated by ECDI points, but the present invention is not limited to this, and can be calculated by the share occupied by deals for a certain period of time on the site.

Alternatively, it can be calculated as a daily estimate of the deal.

Then, the ranking of each site is evaluated according to the calculated sales.

Meanwhile, a step (S34) of changing, adding, or deleting the ECDI target site or deal may be performed.

In other words, ECDI evaluates the major deal activities of sites that have a certain level of influence over the EC market.These rankings are changed by various external factors such as changes in the market environment, changes in competition among competitors, and changes in consumer preferences. This is possible.

In order to smoothly reflect these changes, ECDI will monitor the continuous market environment between operations. In addition, deals that occur frequently and intermittently to achieve business objectives at each site are calculated in addition to ECDI when the need is recognized. At this time, adding, deleting, or changing deals does not affect the ECDI evaluation of other deals.

Meanwhile, a step S34 in which a category can be changed, deleted, or added may be performed.

That is, in this step, the internal product category criteria are selected for detailed market information collection by the category module 36 and detailed comparison is performed. This can be created, added, or deleted in a specific category by internal and external environments such as changes in consumption patterns and market conditions. This is an occasional activity, and is performed when the need for category modification is sufficiently recognized internally and externally.

For example, as a result of real-time monitoring and comparison of ECDI points for specific categories of B, C, and D sellers of the A site, if the ECDI points of the B sellers are low, the method of deleting the B sellers category is a method.

Then, in the ECDI point management step (S36), the point management module 38 manages the ECDI points.

These ECDI points are based on ECDI points on a specific day, and historical points are managed by date after that date. These ECDI points reflect the numerical changes as they are when the sum of total points changes due to the addition or deletion of sites or deals.

As described above, when the step S20 of calculating the ranking for the deals of each site is completed, the step S22 of outputting the calculated results proceeds.

In this step, the ranking for each site calculated together may be output by the output unit 13.

At this time, the result can be output in a variety of topics, for example, it can be output in a variety of ways, such as sales ranking by period, sales ranking by product, sales ranking by brand.

In addition, these results can be output in the form of a monitor screen or report.

Meanwhile, as another embodiment of the present invention, after the step of calculating and comparing rankings is completed, a trend prediction step (S25) may be additionally performed.

In this step, the prediction unit 27 of the server 3 may predict future products, periods, brands, periods, sales, sales of competitors, and future sales forecasts.

That is, it is a method of predicting future performance by processing and patterning existing performance data and processing the pattern in a functional manner.

In this process, it is possible to forecast the future performance by applying the artificial neural network method.

Then, after the trend prediction step (S25) is completed, a step (S27) of establishing a sales strategy based on the predicted trend data may be further performed.

In other words, when a new product is sold online, a strategy is established by reflecting the results predicted in the prediction step (S25). If a trend for a future price, brand, or buyer of a given product is predicted, the new product sales strategy is reflected by this trend It is a way to apply.

The e-commerce platform evaluation system 1 described above is implemented in the form of program instructions that can be executed through various computer components and can be recorded in a computer-readable recording medium. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. medium), and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.

The present invention relates to an e-commerce platform evaluation system and method, and more specifically, to provide an objective criterion for measuring the sales activity and sales situation of manufacturers and sellers in the EC channel site participating in the online distribution market, and each platform. It can be used in the field of e-commerce as a technology that can efficiently perform comparison and analysis.

Claims

A server 3 capable of collecting, recording, evaluating and comparing sales data of e-commerce companies' sites or deals on the network;

A terminal 5 capable of transmitting and receiving data by being connected to the server 3 through a network (N) network; And

Included in the server (3) includes a database 15 for storing the collected, recorded, evaluated, compared data,

The server 3 includes: a collection unit 7 for collecting sales data for an evaluation target site or deal;

An evaluation unit 9 for analyzing and evaluating the collected sales-related sales data;

A ranking calculation unit 11 for calculating a ranking by calculating ECDI points for the site to be evaluated based on the analyzed data, evaluating superiority, and changing, deleting, or adding the site or deal to be evaluated according to the evaluation result; And

E-commerce platform evaluation system (1) for evaluating sales sites and deals on the network by including an output unit (13) for outputting evaluation results.
According to claim 1,

The evaluation unit 9 includes a selection module 20 for selecting target data among sales data; A pre-processing module 22 for processing the selected sales data in a constant format; A conversion module 24 for processing the formatted sales data according to an analysis purpose; An e-commerce platform evaluation system (1) comprising a mining module (26) that performs analysis by processing the converted sales data by an algorithm.
According to claim 1,

The ranking calculator 11 includes a point calculating module 30 for calculating ECDI points; A comparison module 32 for comparing the superiority of the activity of the manufacturer or the seller by the ECDI point; A change module 34 for changing, deleting, or adding the target site or deal according to the comparison result of the ECDI points; A category module 36 for creating, changing, deleting, and adding categories according to a comparison result of ECDI points; E-commerce platform evaluation system (1) comprising a point management module (38) for managing ECDI points.
According to claim 3,

The ranking calculation unit 11 is an e-commerce platform evaluation system (1) that calculates a ranking by calculating ECDI points of each site or deal according to Equation 1 below.

ECDI Points=Exposure Level*Access Rate*Sales Amount*Sales Quantity ---------------------Formula 1

(Exposure: the number of traffic on the site, access: the number of steps to access the deal)
The method of claim 4,

When the importance of each variable in Equation 1 is to be calculated differently, an e-commerce platform evaluation system (1) for calculating by applying a importance index to each variable.

ECDI point=(exposure rate*n1)*(approach rate*n2)*(sales amount*n3)*(sales quantity*n4)--Equation 2

(Exposure: the number of traffic on the site, access: the number of steps to access the deal

n1...n4: importance)
Selecting a site to be evaluated on the network by the collection unit 7 of the server 3 (S10);

Selecting a specific deal in the site to be evaluated by the collection unit 7 (S12);

Collecting sales-related data by the collection unit 7 from the selected evaluation target site or deal (S14);

Analyzing and evaluating the collected sales-related data by the evaluation unit 9 (S16);

By processing the analyzed data by the ranking calculation unit 11, the ECDI points of the evaluation target site or deal are calculated and the ranking is calculated to evaluate the superiority, and the evaluation target site or deal can be changed, deleted, or added according to the evaluation result. A step of performing (S20); And

E-commerce platform evaluation method comprising the step of outputting the result calculated by the output unit (S22).
The method of claim 6,

In the evaluation step (S16), a selection step (S18) of selecting target data from the sales data; A pre-processing step (S21) of processing the selected sales data in a constant format; A conversion step (S23) of processing the formatted sales data according to the analysis purpose; E-commerce platform evaluation method comprising a data mining step (S24) for performing analysis by processing the converted sales data by an algorithm.
The method of claim 6,

In the ranking calculation step (S20), the ECDI points are calculated by the point calculation module 30, and the comparison module 32 compares the superiority of the activity of the manufacturer or the seller by the ECDI points, and the change module 34 According to the comparison result of the ECDI point, the target site or deal is changed, deleted, and added, and the category module 36 creates, changes, deletes, and adds a category based on the comparison result of the ECDI point, and the point management module ( 38) E-commerce platform evaluation method comprising managing ECDI points.
The method of claim 8,

In the ranking calculation step (S20), the e-commerce platform evaluation system (1) for calculating the ranking by calculating the ECDI points of each site or deal according to Equation 1 below.

ECDI Points=Exposure Degree*Approach Degree*Sales Amount*Sales Quantity ------------------ Formula 1

(Exposure: the number of traffic on the site, access: the number of steps to access the deal)