KR20160010756A - Online Shopping system having a shopping-analyzing function and method for shopping-analyzing - Google Patents

Online Shopping system having a shopping-analyzing function and method for shopping-analyzing Download PDF

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Publication number
KR20160010756A
KR20160010756A KR1020140090657A KR20140090657A KR20160010756A KR 20160010756 A KR20160010756 A KR 20160010756A KR 1020140090657 A KR1020140090657 A KR 1020140090657A KR 20140090657 A KR20140090657 A KR 20140090657A KR 20160010756 A KR20160010756 A KR 20160010756A
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South Korea
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shopping
consumer
product
purchase
service providing
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KR1020140090657A
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Korean (ko)
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김현석
김현호
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주식회사 넥스트웹
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Priority to KR1020140090657A priority Critical patent/KR20160010756A/en
Publication of KR20160010756A publication Critical patent/KR20160010756A/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions

Abstract

The present invention divides the online shopping stages of a consumer into a plurality of stages and determines the consumer's preference for each product based on the number of consumers according to the shopping stage after the product selection so as to provide analysis information based on objective product preference The present invention relates to an online shopping system and a shopping analysis method having a shopping analysis function.
An online shopping system and a shopping analysis method having a shopping analysis function according to the present invention are configured such that a service providing apparatus is combined with a plurality of consumer terminals through a communication network and the service providing apparatus includes a shopping web page The service providing device extracts the shopping related information from the BIG data including the consumer terminal information and the product code provided through the consumer terminal and generates the shopping analysis information, And a store comparison step, the consumer connected to the web page of the product comparison or store comparison step corresponding to the product selection after the product selection through the consumer terminal is set as the purchaser, and the product comparison or the shop comparison Calculate the purchase conversion rate for the number of consumers connected to the web page of the step , And provides the shopping analysis information generated by classifying the sales force state of the product based on the purchase conversion ratio with the product-specific purchaser.

Description

[0001] The present invention relates to an online shopping system and a shopping analysis method having a shopping analysis function,

The present invention divides the online shopping stages of a consumer into a plurality of stages and determines the consumer's preference for each product based on the number of consumers according to the shopping stage after the product selection so as to provide analysis information based on objective product preference The present invention relates to an online shopping system and a shopping analysis method having a shopping analysis function.

Currently, online shopping malls are activated due to the development of the Internet, and competition for online shopping consumers is intensifying.

However, since the online shopping mall can not be seen and viewed by the consumer, there are many cases where the product search is stopped.

Therefore, the online shopping mall is constantly making effort to increase the product purchase rate by providing the function of comparing each product and the price of each shopping mall.

In recent years, efforts have been made to provide customized product information to consumers based on consumer preferences. That is, the consumer's preference is important information as marketing data of the product.

However, in the conventional online shopping mall, the consumer's preference of the product is simply based on the purchase history information of the consumer. The judgment of the consumer's preference based on the sales amount of the product is not influenced by the degree of preference of the product, Since the price of the product is more influenced by the low cost of selling the product, there is a disadvantage that the reliability of the product as marketing data is rather weak.

[Related Bibliographic Information]

1. System and method for providing product recommendation service (Patent Application No.: 10-2008-0049457)

2. Purchasing Pattern-Based Shopping System and Method (Patent Application No. 10-2011-0005275)

Accordingly, the present invention has been made based on the above-mentioned points. The shopping step of a consumer using an online shopping mall is divided into an eye shopping, a product comparison, a store comparison, and a product purchase step. An online shopping system and a shopping analysis method having a shopping analysis function that provides more objective analysis information based on the consumer's preference by determining the preference state of the product based on the number of consumers accessing the website corresponding to the shopping site The technical purpose is to provide.

In order to achieve the above object, according to a first aspect of the present invention, there is provided an online shopping system having a shopping analysis function, wherein the service providing apparatus is configured by combining with a plurality of consumer terminals via a communication network, The service providing apparatus generates shopping analysis information by extracting shopping related information from the BIG data including the consumer terminal information and the product code provided through the consumer terminal, A shopping step, a product comparison step, and a store comparison step, the consumer connected to the web page of the product comparison or store comparison step after the product selection through the consumer terminal is set as the purchaser, and the consumer terminal Purchase for the number of consumers connected to the web page of the product comparison or store comparison phase Calculating a ring ratio, and further characterized in that is configured to provide the generated analysis information to group shopping selling power state for that product on the basis of the Product purchasing consumer purchase and conversion ratio.

The service providing apparatus may further include a popular sale product having a purchase demand of a certain level or higher and a purchase conversion ratio of a certain level or higher for each product, a promotional recommendation product having a purchase demand of a certain level or higher but a purchase conversion ratio lower than a certain level, Wherein the product status is classified into one of a demand increase product whose demand is below a certain level but whose purchase conversion rate is equal to or higher than a certain level, and a sales promotion enhancement product whose purchase demand is lower than a certain level and the purchase conversion rate is lower than a certain level.

In addition, the service providing apparatus determines the proceeding state of the shopping step of the eye shopping step, the goods comparing step, and the shop comparing step for the consumer connected to the shopping web server through the consumer terminal, The merchandise comparison step is a step in which at least two goods codes are extracted from the BIG data, and the store comparison step is set to extract one product code and at least two store codes from the BIG data .

In addition, the merchandise comparison step and the store comparison step may set the selected merchandise as a purchase interest item, and provide the shopping history information for each consumer including the shopping stage for each shopping date and the information about the shopping target item .

The service providing apparatus may be configured to provide popular product information to a consumer terminal in an eye shopping stage and provide coupon information related to a product selected in the consumer terminal to a consumer terminal in a product selection or a shop selection step .

In addition, the service providing apparatus may further include a number of visiting consumers who access the shopping web server through a consumer terminal, a number of eye shopping consumers who performed the eye shopping step of the visiting consumers, A first conversion ratio from the visiting consumer to an eye shopping consumer, and a second conversion ratio from the eye shopping consumer to the goods or the shop shopping consumer, , A third conversion rate from a product or store consumer to a purchasing consumer, a fourth conversion rate from a visiting consumer to a goods or store shopping consumer, and a fifth conversion rate from the visiting consumer to the purchasing consumer.

Also, the service providing apparatus may be configured to calculate a purchase time of each product by a consumer from a goods or shop comparison step to a purchase point, calculate an average purchase time of the consumer by each product, A consumer who is shopping before the current average purchase time is set as a key marketing consumer and a consumer who is currently shopping after the current average purchase time is set as a prospective consumer.

In addition, the service providing apparatus sets up a point-of-sale marketing consumer and a departure expected consumer on the basis of an average purchase time of a customer corresponding to the top 90% of the purchasers of the product.

In addition, the service providing apparatus is configured to provide a shopping step of a consumer who is going through shopping through a web server, in the form of an image.

According to a second aspect of the present invention, there is provided a shopping analysis method for an online shopping system, wherein the service providing apparatus is configured by combining with a plurality of consumer terminals through a communication network, Shopping information of the online shopping system that generates shopping analysis information by extracting shopping related information from the BIG data including the consumer terminal information provided through the consumer terminal and the product code and the store code, The method comprising: dividing a shopping step in the service providing apparatus into an eye shopping step, a goods comparison step, and a store comparison step, and accessing a web page corresponding to the goods comparison or store comparison step after the product selection through the consumer terminal A first step of setting a consumer as a purchaser; A second step of calculating a purchase conversion ratio with respect to the number of consumers connected to the web page from the product comparison or the store comparison step to the product purchase step with respect to the merchandise through the consumer terminal; And a third step of classifying the sales force state with respect to the product based on the purchase conversion ratio of the product purchaser by the product in the second stage.

In the third step, the popular sale product having a purchase demand of a certain level or higher and a purchase conversion ratio of a certain level or higher, and a promotional recommendation product having a purchase demand of a certain level or higher but lower than a certain level, Is classified into one of a demand-expanding product whose purchase conversion rate is less than a certain level but a purchase conversion rate of which is equal to or higher than a certain level, and a sales promotion promotion product whose purchase demand is lower than a certain level and the purchase conversion rate is lower than a certain level.

In the first step, the item shopping step is a step in which no item code is extracted from the BIG data. In the item comparing step, at least two item codes are extracted from the BIG data. And at least two shop codes are extracted.

In addition, in the first step, the service providing apparatus determines the proceeding state of the shopping step of the eye shopping step, the goods comparing step, and the shop comparing step for the consumer connected to the shopping web server through the consumer terminal, And setting the selected merchandise as a desired item of interest by the consumer in the shop comparison step, and providing the shopping history information for each consumer including the shopping stage for each shopping date and the information about the interested merchandise.

Also, in the first step, the service providing apparatus may provide popular product information to the consumer terminal of the eye shopping step, and provide coupon information related to the selected product to the consumer terminal of the product selection or store selection step .

In addition, in the first step, the service providing apparatus determines whether the number of visited consumers who access the shopping web server through the consumer terminal, the number of eye shopping consumers who performed the eye shopping step of the visiting consumers, The number of shoppers and the number of purchasers who have performed the purchasing process among the visiting consumers are analyzed and the first conversion ratio from the visiting consumer to the eye shopping consumer and the first conversion ratio from the eye shopping consumer to the goods or the shop shopping consumer A third conversion rate from the merchant or store consumer to the purchaser consumer, a fourth conversion rate from the visiting consumer to the merchandise or store shopping consumer, a fifth conversion rate from the visiting consumer to the purchaser consumer, .

In the first step, the service providing apparatus calculates a purchase time for each consumer from a product comparison or a store comparison step to a purchase point, calculates an average purchase time for each product, The consumer who is in the shopping before the current average purchase time is set as the primary marketing consumer for the product and the consumer who is currently shopping after the current average purchase time is set as the consumer who is leaving the product.

Also, in the first step, the service providing apparatus sets a point-of-sale marketing consumer and a departure expected consumer on the basis of an average purchase time of a customer corresponding to the top 90% of the purchasers of the product, which are late.

Also, in the first step, the service providing apparatus provides a shopping step of a consumer who is going through shopping through a web server, in the form of an image.

In another aspect of the present invention,

The service providing apparatus is configured by combining with a plurality of consumer terminals through a communication network. The service providing apparatus provides a shopping web page for providing a product to a consumer terminal, and also stores the consumer terminal information and the product code Related information from the included BIG data to generate shopping analysis information,

The service providing apparatus is configured to display current consumer shopping steps by using data collected from a plurality of consumer terminals by dividing them into an eye shopping step, a product comparison step, and a store comparison step,

The eye shopping step is a step in which a product code is not extracted from the BIG data,

The product comparison step is a step of extracting at least two product codes from the BIG data,

The shop comparison step is set to a step of extracting one product code and at least two shop codes from the BIG data.

According to the present invention, a shopping pattern of a consumer using an online shopping mall is classified into a plurality of stages, and the preference degree of a product is determined based on the number of consumer connections at the shopping stage after the product selection, .

In addition, according to the present invention, it is possible to objectively determine shopping activity information of a consumer using a shopping stage of a consumer using an online shopping mall, By providing consumers classified as anticipated consumers and anticipated departure consumers, it is possible to provide customized services to consumers by objectively grasping the shopping patterns of consumers who are online shopping in real time.

In addition, according to the present invention, it is possible to extract shopping target information while minimizing the system load by extracting only desired information in real time from a large-capacity BIG data including information for shopping analysis and then performing garbage processing, It is possible to secure the reliability of the analysis information by preventing the operation of the analysis information.

In addition, according to the present invention, the shopping analysis information on a product is basically calculated based on the consumer terminal information connected to the web site, thereby eliminating the risk of the leakage of the personal information of the consumer.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic view of an online shopping system having a shopping analysis function according to a first embodiment of the present invention; FIG.
FIG. 2 is a block diagram showing the internal configuration of the service providing apparatus 100 shown in FIG. 1 functionally separated. FIG.
FIG. 3 is a view showing a shopping analysis process of the shopping analysis processing unit 130 shown in FIG. 2. FIG.
FIG. 4 illustrates an information processing procedure for the shopping pattern analysis 131 of the shopping analysis processing unit 130 shown in FIG.
5 to 8 are views illustrating an output screen of shopping analysis information provided through the operator terminal 150 shown in FIG.

Hereinafter, embodiments according to the present invention will be described with reference to the drawings. However, the embodiments described below are illustrative of one preferred embodiment of the present invention, and examples of such embodiments are not intended to limit the scope of the present invention. The present invention can be variously modified without departing from the technical idea thereof.

FIG. 1 is a diagram showing a schematic configuration of an online shopping system having a shopping analysis function according to a first embodiment of the present invention.

As shown in FIG. 1, an online shopping system having a shopping analysis function is configured by combining a service providing apparatus 100 with a plurality of consumer terminals 200 through a communication network 1.

The consumer terminal 200 may be a wired terminal such as a computer or a wireless terminal such as a smart phone.

In addition, the service providing apparatus 100 manages a shopping mall that provides shopping services for various goods, and also checks a shopping pattern of a consumer through the consumer terminal 200 connected to a web server for shopping mall operation, Shopping pattern information for the customer, and analysis information about the consumer status information and the merchandise sales force state.

Also, the service providing apparatus 100 receives a large amount of BIG data including web page contents, a product code, a store code, and consumer terminal information according to a shopping mall usage from a web server for the shopping mall operation, After extracting necessary information, for example, product code, shop code and consumer terminal information, the BIG data is garbage-processed. That is, the service providing apparatus 100 classifies the shopping stages of the consumers into a plurality of stages based on the data extracted in real time from the large-capacity BIG data, and analyzes the consumer activity patterns in each shopping stage to determine the preference.

FIG. 2 is a block diagram showing the internal configuration of the service providing apparatus 100 shown in FIG. 1 functionally separated.

2, the service providing apparatus 100 includes a web server 110, a service control unit 120, a shopping analysis processing unit 130, a database 140, and an operator terminal 150. At this time, each of the elements may be configured as a separate server and may be coupled through the LAN 160. It is also possible to configure the elements in one server.

The web server 110 provides various web pages for shopping mall operation to the consumer terminal 200. For example, the web server 110 provides various web pages related to merchandise shopping to the consumer terminal 200. At this time, the shopping mall provided by the web server 110 may be configured to provide various online services such as providing a sound source, providing news, and the like, as well as selling merchandise.

The service control unit 120 controls the overall operation according to the present invention. That is, the service control unit 120 basically performs various procedure processes including shopping for goods and purchasing goods based on the information provided through the web server 110. At this time, the service control unit 120 transmits a large-capacity BIG data including the selected / input merchandise code and the store code through the consumer terminal 200 provided from the web server 110, To the shopping analysis processing unit (130).

The shopping analysis processing unit 130 extracts shopping analysis related information from the large-capacity BIG data provided in real time through the service control unit 120, analyzes the shopping pattern of the corresponding consumer, And generates analysis information for the analysis. That is, the shopping analysis processing unit 130 extracts the consumer terminal information, the product code, the shop code, the shopping date / time, and the web page index information from the large-capacity BIG data provided in real time and performs a garbage process. In addition, the shopping analysis processing unit 130 registers and stores the information extracted from the BIG data in the database 140.

The database 140 is used for storing various shopping related information. The database 140 stores consumer personal information including consumer terminal information, consumer shopping information in which a shopping access date, a shopping product code, Merchandise registration information for storing the product name, price, image, feature, registration date, etc. for each code, store registration information for storing the store name for each store code, merchandise shopping information for storing the date of purchase for each product,

The operator terminal 150 performs communication with a service operator. That is, an input means for inputting information required by the operator and a display means for outputting information are provided, and the operator can register and inquire various information related to the shopping service, will be. In particular, the operator terminal 150 displays shopping pattern analysis information, consumer state analysis information, and product sales force analysis information as shown in FIGS. 5 to 8.

FIG. 3 shows a shopping analysis process of the shopping analysis processing unit 130 shown in FIG. 2, and is classified into a shopping pattern analysis 131, a consumer status analysis 132, and a product sales force analysis 133 process.

1. Shopping Pattern Analysis (131)

The shopping pattern analysis is aimed at visually and clearly observing the shopping pattern of the irregular and complicated consumer in objectively measuring and grasping.

The shopping pattern analysis 131 includes a shopping progress state analysis step 131a and a purchase interest product setting step 131b as shown in FIG.

Herein, the shopping progress state analysis step 131a is a step in which the shopping analysis unit 130 determines whether the current shopping step of the consumer is an eye shopping step after visiting connection based on the BIG data provided through the web server 110, Whether it is a store comparison step or a product purchase step. At this time, the shopping analyzing unit 130 classifies the state in which the product code is not extracted in the BIG data as the eye shopping step, and the state in which the product code is extracted from the BIG data, , A state in which at least two store codes are extracted from the BIG data and only one product code is extracted is classified as a store comparison step, and when the web page index is information corresponding to "purchase" do. That is, the eye shopping step is a step for the consumer to look through various goods provided in the shopping mall. In the product comparison step, the consumer compares the items of interest in detail and selects one purchase item. Comparing the shopping malls according to the sales conditions of the selected goods, and selecting the shopping malls from which the goods are to be purchased.

This is because the shopping stage is divided into multiple stages according to the decision stage of the consumer who carries out the shopping activity, and it is possible to objectively determine the possibility of purchasing the product based on the progress of the shopping of the consumer. Here, the order of each shopping step can be changed according to the shopping pattern of the consumer.

In addition, in the present invention, marketing information for each shopping step can be set and provided differently by dividing shopping steps according to a consumer's decision step. For example, the consumer terminal of the eye shopping stage may provide popular product information in the form of a pop-up window on one side of the consumer terminal, and coupon information related to the product selected by the consumer terminal may be provided to the consumer terminal It can be configured to be provided as a pop-up window on one side.

The purchase interest product setting step 131b is a step for setting a purchase interest product 131a in the product comparison step 131a of the shopping progress state analysis step 131a, Is set as a purchase interest item. That is, the purchased interest goods analyzed in the purchase interest product setting step 131b serve as a basis for determining the preference of the consumer and are provided to the corresponding consumer terminal 200 through the web server 110, . ≪ / RTI >

5 and 6 are views illustrating information analyzed through the shopping pattern analysis 131 in the shopping analysis processing unit 130 provided through the operator terminal 150. FIG.

5, the shopping analysis processing unit 130 stores shopping dates including date and time information, behavior patterns corresponding to shopping stages such as "product comparison" and "order & And stores the shopping pattern history information of the consumer, which is composed of the product name and the product code (ID) set as the shopping pattern history information, through the operator terminal 150. At this time, the first to third priorities are set to rank for products having a high detection frequency based on the number of product IDs extracted from the BIG data in the product comparison step and the store comparison step. Accordingly, the purchase-desired product information can be changed in real time.

That is, the operator performed the commodity comparison step on December 17, 2008 for the first rank purchase intention product "In-Balloon-Santa's Gift Snow Bear" through the analysis information shown in FIG. 5, 2008 It is easy to confirm that the order step of December 18 is carried out. In addition, for the purchase of the above-mentioned "doll in the balloon - Santa's gift snow bear", attention is paid to the item "Sky Heart Bear-Sky in the Balloon" and "Gift Allerview" .

Accordingly, the shopping analysis processing unit 130 provides the shopping history information of the consumer, which is the sectional area analyzed at the specific shopping activity progress time, together with the shopping history information of the consumer, It is possible to more accurately grasp the purchase status of the consumer through the comprehensive understanding of the process of the shopping activity.

Also, as shown in FIG. 6, the shopping analysis processing unit 130 displays the shopping pattern conversion ratio information of the consumer through the operator terminal 150. In other words, the shopping pattern conversion ratio information indicates the total number of visiting consumers (A) who visited the web site at least once on the statistical day and the total visiting consumer who visited the website on the statistical day, (B), the total number of visiting consumers who have visited the website on the statistical day, (C) the number of visiting consumers whose behavior pattern is determined more than once, B, C-, C-> D, and A-, D-, D-, D-, and D- > C, A-> D, and provides the calculation result information. The percentage of consumers who are converted to each shopping stage, including the purchase of such products, can be used as objective data to grasp the level and appropriateness of the shopping functions and products required for the operator to perform consumer decision-making activities at each stage.

2. Consumer Status Analysis (132)

The shopping analysis processing unit 130 generates analysis information on the anticipated purchaser and the anticipated anticipating consumer in the consumer state analysis 132.

That is, the shopping analysis processing unit 130 determines whether or not the consumer is satisfied with the product comparison step of the shopping pattern analysis step 131 and the shop comparison step consumer, that is, And calculates the purchase time required by the consumer who is set as the expected consumer for each product. In this case, the purchase time is the time taken from the merchandise comparison stage or the store comparison stage to the purchase of the merchandise.

In addition, the shopping analysis processing unit 130 may calculate the average time of the purchase time for the entire 90% of the entire purchaser or some of all the purchasers, for example, The estimated purchase time is divided into the purchase completed consumer, the main marketing consumer, and the expected leaving consumer by comparing the current shopping pattern step of the expected purchaser with the estimated time of departure. Herein, the purchasing completed consumer is a consumer who has completed purchasing goods within the estimated departure time, the key marketing consumer is a consumer whose shopping activity is ongoing within a departure prediction reference time after being set as a purchasing expected consumer, It is a consumer who has not purchased a commodity since it was set as an anticipated consumer but has exceeded the anticipated departure time. In other words, the key marketing consumer is a prospective purchaser who has a high possibility of purchasing the product through the promotion due to the high possibility of purchase in the corresponding shopping mall. If the prospective consumer desires to leave the shopping mall or purchases the product, It can be judged.

FIG. 7 illustrates an output screen in which information generated through the consumer status analysis 132 in the shopping analysis processing unit 130 is provided through the operator terminal 150. FIG.

As shown in FIG. 7, the shopping analysis processing unit 130 displays and outputs the dropout possibility analysis information for each shopping consumer through the operator terminal 150. That is, the shopping analysis processing unit 130 stores a shopping date including date and time information, a purchase progress step such as "product comparison ", a first to third ranking purchase pricing product including a product name and a product ID, In addition to displaying and outputting the information in the form of a chart, displays the information on the probability of purchasability analysis for the first and second rank purchase potential products. At this time, the first rank order and second rank purchase purchase merchandise purchase / detachability analysis information includes information on a product name ("iRiver Necklace type MP3 N10 (256M) + Funcake coupon + our hard use ticket 15,000 won ", iRiver MP3 IFP- + Funcake coupon + our hard voucher 15,000 won ") is displayed in the form of an arrow in the direction of the arrow in the horizontal direction, and the estimated time of purchase release is displayed on the progress bar And the current purchase activity progress point is indicated by a triangle (?) Shape identification mark on the progress bar of the arrow shape. In addition, the date and time information of the purchase intention estimation time and the date and time of the purchase intention estimation time are displayed on the progress status bar of the arrow form, and the time information .

Accordingly, the shopping analysis unit 130 schematically displays the shopping progress point and the out-of-purchase estimated time for the interested goods set at the stage after the product selection, and displays the image in the form of an image, It is possible to visually check the information on the shopping progress step and the goods of interest for purchase.

3. Analysis of product sales force (133)

The shopping analysis processing unit 130 calculates a commodity purchase conversion ratio based on the commodity purchase demand in the commodity sales force analysis process 133 and classifies the commodity status according to the commodity purchase demand and the commodity purchase conversion ratio.

That is, the shopping analysis processing unit 130 determines the preference for the corresponding product based on the estimated purchase consumer information for the specific product in step 133, and also grasps the purchase demand for the corresponding product.

In addition, the shopping analysis processing unit 130 calculates the ratio of consumers who actually purchased the corresponding product among the estimated purchasing consumers in the product selling power analysis step 133, as the purchase conversion ratio of the corresponding product.

Then, the shopping analysis processing unit 130 analyzes the popular sales product, the sales promotion promotion product, the demand expansion product, and the sales promotion promotion product based on the product-specific purchase demand and the purchase conversion ratio in the product sales force analysis step (133).

FIG. 8 shows an example of a screen in which analysis information generated through the merchandise sales force analysis 132 in the shopping analysis processing unit 130 is output through the operator terminal 150. FIG.

As shown in FIG. 8, the shopping analysis processing unit 130 analyzes the goods classification state based on the product sales force based on the consumer preference and the purchase conversion ratio in four steps. 8A is a popular selling product, FIG. 8B is a promotional recommended product, FIG. 8C is a demand increasing product, and FIG. 8D is a sales promotion product. Here, the popular sale product A is a product whose purchase demand for the product is at a certain level or higher and whose purchase conversion is at a certain level or higher, which is expected to increase the sales further due to increase in exposure of the product. Demand is higher than a certain level, but the purchase conversion is below a certain level. It is a product that is expected to improve sales by offering promotions to improve price competitiveness. Demand increase goods (C) are products whose purchase demand is below a certain level, (D) is an unprofitable product whose purchasing demand and purchase conversion ratio are below a certain level. This is because the sales increase enhancement product (D) is expected to increase sales by selective improvement of purchase demand or price competitiveness. Of the product. This makes it possible to more objectively judge product preference for a specific product through the scale of the prospective buyer, and to grasp the level of competitiveness of the product selling unit price through the purchase conversion ratio.

That is, according to the embodiment, a shopping pattern of a consumer using an online shopping mall is divided into a plurality of stages, a preference degree of a product is determined based on the number of consumer connections in a product comparison and a store comparison step, Thus, it is possible to precisely and efficiently grasp the level of selling unit price for each product without performing a direct confirmation operation of the competitive price for each product, which requires much time and effort, The sales force of the product can be further strengthened.

In the above-described embodiment, the online shopping system for providing a shopping mall for purchasing goods has been described. However, the present invention can also be applied to an online system for providing various information services such as providing sound sources and providing news .

100: service providing apparatus, 110: web server,
120: service control unit, 130: shopping analysis processing unit,
140: database, 150: operator terminal,
160: LAN, 200: Consumer terminal,
1: Network.

Claims (19)

The service providing apparatus is configured by combining with a plurality of consumer terminals through a communication network. The service providing apparatus provides a shopping web page for providing a product to a consumer terminal, and also stores the consumer terminal information and the product code Related information from the included BIG data to generate shopping analysis information,
The service providing apparatus divides the shopping step into an eye shopping step, a goods comparison step, and a store comparison step, and sets the consumer connected to the web page of the goods comparison or store comparison step after the product selection through the consumer terminal as the purchaser And calculating a purchase conversion ratio for the number of consumers connected to the web page of the product comparison or store comparison stage through the consumer terminal with respect to the product, And provides the shopping analysis information generated by classifying the shopping analysis information. The method according to claim 1,
Wherein the service providing apparatus includes a popular sales product having a purchase demand of a certain level or higher and a purchase conversion ratio of a certain level or higher,
Promotion recommendation products whose purchase demand is above a certain level but whose purchase conversion ratio is below a certain level,
Demand increase products whose purchase demand is below a certain level but whose purchase conversion rate is above a certain level,
Wherein the product status is classified into one of sales promotion promotion products whose purchase demand is lower than a predetermined level and purchase conversion rate is lower than a certain level. The method according to claim 1,
Wherein the service providing apparatus determines a proceeding state of a shopping step of an eye shopping step, a goods comparison step, and a shop comparison step for a consumer connected to a shopping web server through a consumer terminal,
The eye shopping step is a step in which a product code is not extracted from the BIG data,
The product comparison step is a step of extracting at least two product codes from the BIG data,
Wherein the store comparison step is set to a step of extracting one product code and at least two store codes from the BIG data. The method of claim 3,
Wherein the service providing apparatus sets a product selected by a consumer as a desired item of interest in a product comparison step and a store comparison step,
The shopping history information for each customer including the shopping stage by shopping date and the shopping target information of the shopping target. The method according to claim 1,
Wherein the service providing apparatus provides the popular goods information to the consumer terminal of the eye shopping step and provides the coupon information related to the selected commodity to the consumer terminal of the product selection or store selection step Online shopping system with shopping analysis function. The method according to claim 1,
The service providing apparatus may further include a number of visiting consumers who have accessed the shopping web server through the consumer terminal, a number of eye shopping consumers who performed the eye shopping step of the visiting consumers, The number of consumers and the number of purchasers who made purchase processing among visiting consumers are analyzed,
A first conversion rate from the visiting consumer to an eye shopping consumer, a second conversion rate from the eye shopping consumer to the goods or store shopping consumer, a third conversion rate from the goods or store consumer to the purchaser consumer, A fourth conversion rate to a shop shopping consumer, and a fifth conversion rate information from a visiting consumer to a purchasing consumer. The method according to claim 1,
The service providing apparatus calculates a purchase time for each product from a product or shop comparison step to a purchase point,
The average purchase time of each product is calculated,
A consumer who is shopping before the current average purchase time is set as a primary marketing consumer for the product based on the average purchase time of the product by the product and a consumer who is shopping after the current average purchase time is set as the consumer Of-line shopping system having a shopping analysis function. 8. The method of claim 7,
Wherein the service providing device sets a point-of-sale marketing consumer and a departure expected consumer based on an average purchase time of a customer corresponding to the top 90% of the purchasers of the product. 8. The method of claim 7,
Wherein the service providing apparatus is configured to provide a shopping progressing step of a consumer who is going to shop through a web server in the form of an image. The service providing apparatus is configured by combining with a plurality of consumer terminals through a communication network. The service providing apparatus provides a shopping web page for providing a product to a consumer terminal, A shopping analysis method of an online shopping system for extracting shopping related information from BIG data including a store code to generate shopping analysis information,
The shopping step in the service providing apparatus is divided into an eye shopping step, a goods comparison step, and a store comparison step, and a consumer connected to a web page corresponding to a goods comparison or store comparison step corresponding to goods after the goods selection through the consumer terminal A first step of setting the user as a consumer,
A second step of calculating a purchase conversion ratio with respect to the number of consumers connected to a web page from a goods comparison or a store comparison step to a product purchase step,
And a third step of classifying the sales force state for the corresponding product based on the purchase conversion ratio with the product specific purchaser in the first and second steps in the service providing apparatus. Way. 11. The method of claim 10,
Wherein the third step is a step of selecting a popular sale item having a purchase demand of a certain level or higher and a purchase conversion ratio of a certain level or higher,
Promotion recommendation products whose purchase demand is above a certain level but whose purchase conversion ratio is below a certain level,
Demand increase products whose purchase demand is below a certain level but whose purchase conversion rate is above a certain level,
Wherein the sales promotion status of the online shopping system is classified as one of the sales promotion promotion products whose purchase demand is lower than the predetermined level and the purchase conversion rate is lower than the predetermined level. 11. The method of claim 10,
In the first step, the eye shopping step is a step in which a product code is not extracted from the BIG data,
The product comparison step is a step of extracting at least two product codes from the BIG data,
Wherein the store comparison step is set to a step of extracting one product code and at least two store codes from the BIG data. 11. The method of claim 10,
In the first step, the service providing apparatus determines a proceeding state of a shopping step of an eye shopping step, a goods comparison step, and a store comparison step for a consumer connected to a shopping web server through a consumer terminal,
The merchandise selected by the consumer in the merchandise comparison step and the store comparison step is set as a purchase interest item,
A shopping step for each shopping date, and shopping target information for each customer including the shopping target information of the purchase target. 11. The method of claim 10,
In the first step, the service providing apparatus provides the popular goods information to the consumer terminal in the eye shopping step, and provides the coupon information related to the selected product to the consumer terminal in the product selection or store selection step Of the shopping mall. 11. The method of claim 10,
In the first step, the service providing apparatus performs a comparison between the number of visiting consumers who access the shopping web server through the consumer terminal, the number of i-shopping consumers who performed the i-shopping step of the visiting consumers, The number of shoppers and the number of shoppers who have performed purchase processing among the shoppers are analyzed,
A first conversion rate from the visiting consumer to an eye shopping consumer, a second conversion rate from the eye shopping consumer to the goods or store shopping consumer, a third conversion rate from the goods or store consumer to the purchaser consumer, A shopping conversion method of the online shopping system characterized by providing a fourth conversion rate as a shop shopping consumer, a fifth conversion rate information from a visiting consumer to a purchasing consumer. 11. The method of claim 10,
In the first step, the service providing apparatus calculates the commodity purchase time for each consumer from the commodity comparison or store comparison step to the purchase point,
The average purchase time of each product is calculated,
A consumer who is shopping before the current average purchase time is set as a primary marketing consumer for the product based on the average purchase time of the product by the product and a consumer who is shopping after the current average purchase time is set as the consumer A shopping analysis method of an online shopping system. 17. The method of claim 16,
Wherein in the first step, the service providing apparatus sets up a point-of-sale marketing consumer and a departure expected consumer on the basis of an average purchase time of a customer corresponding to the top 90% Shopping analysis method. 17. The method of claim 16,
Wherein in the first step, the service providing apparatus schematically provides a shopping step of a consumer who is going to shop through a web server, in an image form. The service providing apparatus is configured by combining with a plurality of consumer terminals through a communication network. The service providing apparatus provides a shopping web page for providing a product to a consumer terminal, and also stores the consumer terminal information and the product code Related information from the included BIG data to generate shopping analysis information,
The service providing apparatus is configured to display current consumer shopping steps by using data collected from a plurality of consumer terminals by dividing them into an eye shopping step, a product comparison step, and a store comparison step,
The eye shopping step is a step in which a product code is not extracted from the BIG data,
The product comparison step is a step of extracting at least two product codes from the BIG data,
Wherein the store comparison step is set to a step of extracting one product code and at least two store codes from the BIG data.
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KR20210016747A (en) * 2019-08-05 2021-02-17 카페24 주식회사 Method, Apparatus and System for Recommending Promotion Time
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101959808B1 (en) * 2017-10-30 2019-03-19 씨케이브릿지 주식회사 On-line Integrated Management System
WO2019088316A1 (en) * 2017-10-30 2019-05-09 씨케이브릿지 주식회사 Online integrated management system
KR20190086173A (en) 2018-01-12 2019-07-22 고현규 Sale product analysis and promotion system of on-line shopping mall
KR20200054353A (en) * 2018-11-02 2020-05-20 포에스비 주식회사 A Customized Campaign Management System And Method Using customer segmentation
KR20200060140A (en) * 2018-11-22 2020-05-29 주식회사 넥슨코리아 Method for providing payment analysis information in game providing apparatus and game providing apparatus
KR20210016747A (en) * 2019-08-05 2021-02-17 카페24 주식회사 Method, Apparatus and System for Recommending Promotion Time
KR20210016748A (en) * 2019-08-05 2021-02-17 카페24 주식회사 Apparatus, Method and System for Display Promotion Time
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