KR20150070481A - Marketing system and method using thereof - Google Patents

Marketing system and method using thereof Download PDF

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Publication number
KR20150070481A
KR20150070481A KR1020130156395A KR20130156395A KR20150070481A KR 20150070481 A KR20150070481 A KR 20150070481A KR 1020130156395 A KR1020130156395 A KR 1020130156395A KR 20130156395 A KR20130156395 A KR 20130156395A KR 20150070481 A KR20150070481 A KR 20150070481A
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KR
South Korea
Prior art keywords
data
user
behavior
product
message
Prior art date
Application number
KR1020130156395A
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Korean (ko)
Inventor
이상혁
이준호
노준환
박희성
정서원
오영재
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넷다이버(주)
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Priority to KR1020130156395A priority Critical patent/KR20150070481A/en
Publication of KR20150070481A publication Critical patent/KR20150070481A/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
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0213—Consumer transaction fees
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0255—Targeted advertisements based on user history
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement
    • 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
    • G06Q30/0282—Rating or review of business operators or products

Abstract

A marketing system according to an aspect of the present invention includes a user database for storing past behavior data for a plurality of users; A behavior prediction unit for extracting behavior data associated with the behavior event from the past behavior data and analyzing the extracted behavior data to generate prediction data for the next behavior of the specific user when the behavior event of the specific user is received, ; A product extracting unit for extracting a recommended product matching with the generated predictive data from product data registered by a plurality of seller members; A message generating unit for generating a product message including at least one of an advertisement related to the extracted recommended product, a discount coupon, and a mobile product that can be exchanged for the recommended product in a store; And a communication unit for transmitting the generated product message to the terminal of the specific user. The past behavior data and action event may include at least one of user identification information, time, location, date, and consumption information.

Description

[0001] MARKETING SYSTEM AND METHOD USING THEREOF [0002]

The present invention relates to a marketing system, and more particularly, to a marketing system using a mobile terminal and a marketing method using the same.

Recently, as the spread of smart phones and the use of mobile internet have increased, advertisement or marketing service using mobile terminals has been increasing. Mobile terminals can communicate in real time without restriction of time and place, and it is easy to share information through social networks.

However, since the advertisement service using the mobile terminal is provided indiscriminately regardless of the place and the place, most of the users are treated as unnecessary information.

Accordingly, in recent years, a technique for providing a customized advertisement according to the location of a user has been developed. Korean Patent Laid-Open Publication No. 10-2006-0119476 (hereinafter referred to as "prior art"), if the registered advertisement category is designated from the mobile terminal after determining the location of the mobile terminal, A technique for transmitting a coupon to a mobile terminal is disclosed.

However, the prior art has the inconvenience that the user has to designate the desired advertisement category in advance and provides the advertisement or the discount coupon based only on the location information of the user, so that various needs of the user that may occur in the same place are satisfied There is a problem that it can not give.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide an advertisement system capable of providing an advertisement that can expect a maximum advertising effect to a specific user and an advertisement method using the advertisement system.

It is another object of the present invention to provide an advertisement system in which a small business owner can share a behavior data for a user and create a profit in exchange for sharing the advertisement data and an advertisement method using the advertisement system .

It is another object of the present invention to provide an advertisement system capable of providing a small amount of data for a small business person with a weak capital and collecting personal data, Another technical challenge is to provide.

According to an aspect of the present invention, there is provided a marketing system including a user database for storing past behavior data for a plurality of users; A behavior prediction unit for extracting behavior data associated with the behavior event from the past behavior data and analyzing the extracted behavior data to generate prediction data for the next behavior of the specific user when the behavior event of the specific user is received, ; A product extracting unit for extracting a recommended product matching with the generated predictive data from product data registered by a plurality of seller members; A message generating unit for generating a product message including at least one of an advertisement related to the extracted recommended product, a discount coupon, and a mobile product that can be exchanged for the recommended product in a store; And a communication unit for transmitting the generated product message to the terminal of the specific user. The past behavior data and action event may include at least one of user identification information, time, location, date, and consumption information.

According to another aspect of the present invention, there is provided a marketing method comprising: receiving an action event for a specific user from a first seller member terminal; Extracting behavior data associated with the behavior event from past behavior data of the specific user and analyzing the extracted behavior data to generate prediction data for the next behavior of the specific user; The method of claim 1, further comprising: extracting a recommendation product matched with the generated predictive data from the product data registered by the plurality of seller members, and extracting an advertisement, a discount coupon and a coupon from the extracted recommendation product, Generating a merchandise message including at least one merchandise; Transmitting the generated product message to the terminal of the specific user; And receiving a response message for the merchandise message from the terminal of the specific user, a first fee to be obtained by the first seller member who provided the action event and a second fee to be paid by the second seller member And calculating a fee. The past behavior data and action event may include at least one of user identification information, time, location, date, and consumption information.

According to another aspect of the present invention, there is provided a marketing method for extracting behavior data associated with an action event from a past behavior data of a user corresponding to the user terminal, Analyzing the extracted behavior data to generate predictive data for the next behavior of the user; The method of claim 1, further comprising: extracting a recommendation product matched with the generated predictive data from the product data registered by the plurality of seller members, and extracting an advertisement, a discount coupon and a coupon from the extracted recommendation product, Generating a merchandise message including at least one merchandise; And transmitting the generated merchandise message to the user terminal. The past behavior data and action event may include at least one of user identification information, time, location, date, and consumption information.

According to the present invention, it is possible to appropriately satisfy the user's various needs according to time, place, date, etc. by predicting the next behavior based on past behavior data for the user and providing the advertisement considering the next action of the user , It is possible to maximize the advertising effect.

In addition, according to the present invention, since the small business owners provide the action data for the user and use it to advertise the goods of other small business owners, it is possible to share the marketing information among the small business owners, There are other effects that can be expected.

Further, according to the present invention, when the behavior data is used for advertisement of the commodity, there is another effect that the commission is paid to the small business owner who provided the action data, thereby enhancing the competitiveness of profit creation and voluntary participation of small business owners .

1 is a schematic view illustrating a marketing system according to an embodiment of the present invention.
2 is a block diagram illustrating the marketing server of FIG.
3 is a diagram for explaining an example of predicting the next behavior of a specific user.
4 is a diagram for explaining another example of predicting the next behavior of a specific user.
5 is a flowchart illustrating a marketing method according to an embodiment of the present invention.
6 is a flowchart illustrating a marketing method according to another embodiment of the present invention.

The description of the disclosed technique is merely an example for structural or functional explanation and the scope of the disclosed technology should not be construed as being limited by the embodiments described in the text. That is, the embodiments are to be construed as being variously embodied and having various forms, so that the scope of the disclosed technology should be understood to include equivalents capable of realizing technical ideas.

Meanwhile, the meaning of the terms described in the present application should be understood as follows.

The terms "first "," second ", and the like are intended to distinguish one element from another, and the scope of the right should not be limited by these terms. For example, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

It is to be understood that when an element is referred to as being "connected" to another element, it may be directly connected to the other element, but there may be other elements in between. On the other hand, when an element is referred to as being "directly connected" to another element, it should be understood that there are no other elements in between. On the other hand, other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

It should be understood that the singular " include "or" have "are to be construed as including a stated feature, number, step, operation, component, It is to be understood that the combination is intended to specify that it does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

In each step, the identification code (e.g., a, b, c, etc.) is used for convenience of explanation, the identification code does not describe the order of each step, Unless otherwise stated, it may occur differently from the stated order. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed technology belongs, unless otherwise defined. Commonly used predefined terms should be interpreted to be consistent with the meanings in the context of the related art and can not be interpreted as having ideal or overly formal meaning unless explicitly defined in the present application.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a schematic view illustrating a marketing system according to an embodiment of the present invention.

The marketing system 100 according to an embodiment of the present invention includes a merchant member terminal 110, a user terminal 120, and a marketing server 130, as shown in FIG.

First, the seller member terminal 110 is a terminal installed in a place where a seller member sells a product, and is connected to the marketing server 130 via a wireless or wired Internet.

The merchant member terminal 110 generates an action event for the current action of the user and provides the action event to the marketing server 130 when the user visits the selling place and purchases the commodity. At this time, the action event includes at least one of the identification information of the user, the time, the location, the date, and the consumption information.

In one embodiment, the consumption information may include membership card usage details of a user managed by membership, such as a business name, a trade name, a price, a date, a time, and the like.

Meanwhile, the merchant member corresponds to the merchandise registration and member registration to be advertised in the marketing server 130.

Next, the user terminal 120 is connected to the marketing server 130 based on wireless communication such as a cellular network or a wireless LAN (WLAN) as a computing means capable of wireless communication and portable. For example, the user terminal 120 may be a smart phone having a predetermined application installed therein.

In one embodiment, the user terminal 120 may download and install an application from the marketing server 130 that can send a product recommendation message to the user. Hereinafter, for convenience of explanation, it is assumed that the user terminal 120 is a smart phone in which an application is installed. However, it will be appreciated that the user terminal 120 in accordance with the disclosed technology includes all portable devices capable of wireless communication.

The user terminal 120 generates an action event for the user's current action through the application, and provides the generated action event to the marketing server 130. At this time, the action event includes at least one of identification information of the user (e.g., user terminal identification number), time, location, date, and consumption information.

In one embodiment, the application may generate an action event that includes a location acquired through a Global Positioning System (GPS) module and a time at which the location was acquired.

In another embodiment, the application may obtain the consumption information of the user from the credit card details received at the user terminal 120 or the usage history of the mobile wallet installed in the user terminal 120, An event can be generated.

Meanwhile, the user terminal 120 receives an advertisement for a specific commodity or a commodity message including a discount coupon from the marketing server 130 through an application.

Next, when an action event for a specific user is received, the marketing server 130 predicts the next action of the specific user and recommends the product based on the predicted next action. Hereinafter, the marketing server 130 will be described in more detail with reference to FIG.

2 is a block diagram illustrating the marketing server of FIG.

2, a marketing server 130 according to an exemplary embodiment of the present invention includes a user database 220, an action event receiving unit 230, a next action predicting unit 240, a seller database 250, (260), a message generating unit (270), and a message transmitting / receiving unit (280).

In one embodiment, the marketing server 130 may further include at least one of a regional database 210 and a fee estimator 290.

First, the local database 210 stores commercial data of a region classified by the administrative unit or by lot number.

At this time, the local commercial data includes at least one of the business name, the business type, and the business attribute of the business located in each region. The property of the business includes at least one of the customer's major customer, average daily sales, . In addition, the regional trade data may further include the density index of each industry in each region.

Such local commercial data can be obtained by analyzing commercial analysis data provided by a public institution or a credit card company.

Next, the user database 220 stores past behavior data for a plurality of users. At this time, the past behavior data includes at least one of user identification information, time, location, weather, date, and consumption information. The consumption information includes at least one of a business name, a product name, a price, a date, and a time.

Such behavior data may be periodically collected from the merchant member terminal 110 or the user terminal 120, collected every time an action event occurs, and stored in the user database 220.

For example, the marketing server 130 may periodically request action data including time and location to the user terminal 120, receive action data according to the request, and store the action data in the user database 220. [

Alternatively, if the merchant member terminal 110 generates an action event according to the purchase action of the user, the marketing server 130 receives the generated action event from the seller member terminal 110 and transmits the generated action event to the user database 220 ). ≪ / RTI >

Meanwhile, the user database 220 may further include user data for a plurality of users. At this time, the user data may include at least one of user identification information, acquaintance information, residence information, workplace information, and anniversary information that the user provides while registering with the marketing server 130.

Next, the action event receiving unit 230 receives an action event for a specific user from the seller's member terminal 110 or the user terminal 120. [

At this time, the received action event is information on the current action, and may be used as a reference action for predicting the next action, and may be stored in the user database 220 as past action data.

Next, the next behavior predicting unit 240 generates predictive data by predicting the next behavior of a specific user based on an action event.

More specifically, the next behavior prediction unit 240 may extract behavior data associated with an action event received from the seller terminal 110 or the user terminal 120 in the user database 220.

At this time, the next behavior predicting unit 240 may extract behavior data that matches at least one of a time range, a location, a date, and consumption information including the time of the behavior event among the past behavior data of the specific user.

Here, the time range means a time range including a specific time included in an action event. For example, if the time included in the action event is 9:10 AM, the time range may be from 8:10 AM to 10:10 AM.

The next behavior predicting unit 240 analyzes the extracted behavior data to infer the consumption pattern in chronological order, and generates the prediction data by predicting the next behavior according to the inferred consumption pattern.

At this time, the generated prediction data includes user identification information and next consumption behavior information. The user identification information includes a user terminal identification number, and the next consumption behavior information may include at least one of a business type, a business name, a product, a location, and a point identification information.

Hereinafter, with reference to FIGS. 3 and 4, a method of predicting the next behavior using past behavior data of a specific user will be described.

3 is a diagram for explaining an example of predicting the next behavior of a specific user.

It is assumed that the user database 220 stores past behavior data for the first user, as shown in FIG.

The action event receiving unit 230 receives the action event 320 including the date, time, and location (2013/12/12, 8:00 am, Gangnam Station) from the first user terminal 120, 320 to the next behavior predicting unit 240.

The next behavior predicting unit 240 may extract past behavior data 310 associated with the action event 320 for the first user in the user database 220. At this time, the next behavior predicting unit 240 may extract only past activity data for the first user within the time range from 7:00 am to 9:00 am, or only the data whose location is in the Gangnam area.

Then, the next behavior predicting unit 240 can infer the consumption pattern by analyzing the past behavior data 310 extracted. 3, if the action data 310 for the first user is extracted, the action predicting unit 240 analyzes the extracted action data 310 to determine whether the first user is present at 8:00 am to 8:00 am If you are located in Gangnam Station within 30 minutes, you can deduce the consumption patterns of purchasing coffee by visiting A coffee shop.

According to the action event 320, since the first user is located in Gangnam Station at 8:00 am, the next behavior predicting unit 240 visits the A coffee specialty store by the next action of the first user according to the inferred consumption pattern, You can expect to buy.

Accordingly, the next behavior predicting unit 240 can generate the prediction data including the identification number of the first user terminal 120 and the next consumption behavior information of the first user (A coffee shop, coffee shop, and Gangnam store).

4 is a diagram for explaining another example of predicting the next behavior of a specific user.

It is assumed that the user database 220 stores past behavior data for the second user, as shown in FIG.

The action event receiving unit 230 receives an action event 420 including date, time, location, consumption information (2013/12/13, 7:50 pm, Gangnam Station, E restaurant) from the second user terminal 120 And may forward the received action event 420 to the next action predicting unit 240. [

The next behavior predicting unit 240 may extract past behavior data 410 associated with the action event 320 for the second user in the user database 220. At this time, the next behavior predicting unit 240 may extract only past activity data for the first user within the time range from 7:00 PM to 9:00 PM, or only the data whose location is in the Gangnam area.

Then, the next behavior predicting unit 240 can infer the consumption pattern by analyzing the past behavior data 410 extracted. 4, if the behavior data 410 for the second user is extracted, the next behavior prediction unit 240 analyzes the extracted behavior data 410, and the second user analyzes the extracted behavior data 410, After visiting a restaurant where it is located, you can deduce the consumption pattern of visiting a coffee shop afterwards.

According to the action event 420, the second user visits the E-restaurant located at Gangnam Station at 7:50 pm, and the next behavior predicting unit 240 predicts the next behavior of the second user according to the inferred consumption pattern, Visits can be predicted.

Accordingly, the next behavior prediction unit 240 can generate prediction data including the identification number of the second user terminal 120 and the next consumption behavior information of the second user (coffee shop, Kangnam station).

3 and FIG. 4, the following behavior predicting unit 240 predicts the next behavior using only past behavior data. However, in another embodiment, the past behavior data, the user data, Data may be used to predict the next behavior.

For example, it is assumed that the action event received from the third user terminal 120 includes the red-entry zone at 12:00 pm, 12:00 pm, and 7:00 pm. The next behavior predicting unit 240 may extract topical area data near the red-orange area from the regional topical area data, and extract the third-user user's residence information and the working area information from the user data.

When the merchandise data for the Hong-ad-dressing area shows a high density index in the restaurants and bars, the residence of the third user is the shrine, and the workplace is Gangnam, the next behavior predicting unit 240 determines that the third user meets the acquaintance And can predict a restaurant or pub visit with the next action of the third user.

Next, the seller database 250 stores seller information and product information registered by a plurality of seller members at the time of member registration. The merchant information includes at least one of a business type, a business name, a location, and a point identification information, and the merchandise information includes at least one of a merchandise name, a price, an advertisement, a discount coupon, and a mobile merchandise that can be exchanged for a merchandise at a store.

Next, the product extracting unit 260 extracts a recommendation product matching with the predicted data generated by the next behavior predicting unit 240 in the seller database 250.

Next, the message generating unit 270 generates a product message including at least one of an advertisement of a recommended product extracted by the product extracting unit 260, a discount coupon, and a mobile product.

Next, the message transmission / reception unit 280 transmits the product message generated by the message generation unit 270 to the user terminal 120. Meanwhile, the message transmission / reception unit 280 receives a response message from the user terminal 120.

The response message includes one of whether a product message is delivered, a response to the product message, and whether the product message is consumed.

For example, an application installed in the user terminal 120 can generate a response message and transmit it to the marketing server 130 when the received product message is selected by the user.

In another example, an application installed in the user terminal 120 may display a window for inputting an evaluation index for a product along with a received product message when a product message received by the user is selected. When the user inputs the evaluation index, the response message including the evaluation index input by the user can be generated and transmitted to the marketing server 130.

In another example, an application installed in the user terminal 120 may generate a response message and transmit the response message to the marketing server 130 when downloading a discount coupon included in a product message or purchasing a mobile product by a user.

Next, when the fee calculation unit 290 receives the response message from the user terminal 120, the fee calculation unit 290 determines whether the first seller member providing the action event of the specific user and the second seller member selling the product recommended to the specific user Calculate the commission.

More specifically, the marketing server 130 receives information on an action of a specific user, that is, an action event, from the first seller member, and based on the information, In the case of providing, the second seller member receives the fee from the second seller member, and a part thereof is paid to the first seller member.

In order to carry out the above, the fee calculation unit 290, when receiving the response message from the user terminal 120, calculates a first fee to be paid by the first seller member and a second fee to be paid by the second seller member. The first fee and the second fee may be the same, and the second fee may be higher than the first fee.

On the other hand, the fee calculation unit 290 can calculate the fee when the evaluation index of the user is included in the response message and the evaluation index is a predetermined value or more.

5 is a flowchart illustrating a marketing method using a marketing system according to an embodiment of the present invention.

5, when the marketing server 130 receives an action event for a specific user from the first seller member terminal 112, the marketing server 130 extracts past action data associated with the received action event (S501 and S502) .

The action event is data on a current behavior of a specific user, and includes at least one of a user's identification information, a time, a location, a date, and consumption information. The consumption information may include a membership card usage history of a user managed by membership, for example, a business name, a product name, a price, a date, a time, and the like.

On the other hand, past behavior data is data on past behavior of a specific user and is stored and managed in a separate database. The past behavior data includes at least one of user identification information, time, location, weather, date, and consumption information, and the consumption information includes at least one of a business name, a product name, a price, a date, and a time.

Such past behavior data may be periodically collected from the seller terminal 110 or the user terminal 120, collected every time an action event occurs, and stored in a separate database.

When an action event for a specific user is received from the first seller member terminal 112, the marketing server 130 extracts action data associated with the action event from past action data for the specific user.

More specifically, the marketing server 130 may extract behavior data that matches at least one of a time range, a location, a date, and consumption information including a time of an activity event among the past activity data of the specific user.

Next, the marketing server 130 analyzes the extracted behavior data to infer the consumption patterns of the specific users in chronological order, and generates prediction data by predicting the next behavior of the specific user according to the inferred consumption patterns (S503 and S504) .

At this time, the generated prediction data includes user identification information and next consumption behavior information. The user identification information includes a user terminal identification number, and the next consumption behavior information may include at least one of a business type, a business name, a product, a location, and a point identification information.

Next, the marketing server 130 extracts a recommended product matching with the predicted data, and generates a product message including at least one of the extracted recommended product advertisement, discount coupon, and mobile product (S505 and S506).

Next, the marketing server 130 transmits the goods message to the user terminal 120 (S507).

On the other hand, when the marketing server 130 receives the response message to the product message from the user terminal 120, the marketing server 130 stores the action event as past behavior data, and stores the first seller member and the second seller member (S508, S509 and S510).

When the marketing server 130 recommends or provides the information on the goods of the second seller member to the specific user based on the action event provided by the first seller member, the marketing server 130 receives a fee from the second seller member, To the first seller member.

Accordingly, the marketing server 130 calculates a first fee to be paid by the first seller member and a second fee to be paid by the second seller member. At this time, the calculated first fee and the second fee may be the same, and the second fee may be higher than the first fee.

Next, the marketing server 130 transmits the first commission payment information to the first merchant member terminal 112 and receives the second commission payment request to the second merchant member terminal 114 (S511).

Although the marketing method according to an embodiment of the present invention illustrated in FIG. 5 is described as receiving an action event of a specific user from a seller's member terminal, in another embodiment, the action event may be directly provided from the user terminal .

This will be described in more detail with reference to FIG.

6 is a flowchart illustrating a marketing method according to another embodiment of the present invention.

Referring to FIG. 6, the marketing server 130 receives an action event from the user terminal 120 and extracts past behavior data associated with the received action event (S601 and S602).

The user terminal 120 may transmit an action event to the marketing server 130 through an application downloaded in advance. At this time, the user terminal 120 periodically generates an action event and transmits it to the marketing server 130. Alternatively, when the use of a credit card or mobile wallet is detected, an action event may be generated and transmitted to the marketing server 130.

Hereinafter, S602 to S609 are similar to S502 to S509 of FIG. 5, so that a description thereof will be omitted.

However, since the marketing method according to another embodiment of the present invention is not provided with the action information from the seller member, the step of calculating the fee to be paid to the seller member is omitted.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit and scope of the present invention as set forth in the following claims It can be understood that

Claims (8)

A user database for storing past behavior data for a plurality of users;
A behavior prediction unit for extracting behavior data associated with the behavior event from the past behavior data and analyzing the extracted behavior data to generate prediction data for the next behavior of the specific user when the behavior event of the specific user is received, ;
A product extracting unit for extracting a recommended product matching with the generated predictive data from product data registered by a plurality of seller members;
A message generating unit for generating a product message including at least one of an advertisement related to the extracted recommended product, a discount coupon, and a mobile product that can be exchanged for the recommended product in a store; And
And a communication unit for transmitting the generated product message to the terminal of the specific user,
Wherein the past behavior data and the action event include at least one of user identification information, time, location, date, and consumption information. 2. The apparatus of claim 1,
Wherein the action data extracting unit extracts action data matched with at least one of a predetermined time range including a time of the action event, a position, a date, and consumption information among past action data for the specific user. 2. The apparatus of claim 1,
Analyzes the extracted behavior data to infer the consumption patterns of the specific user in chronological order, and generates the prediction data by predicting the next behavior according to the inferred pattern. The method according to claim 1,
Wherein the user database further stores user data for the plurality of users and the user data includes at least one of user identification information, acquaintance information, residence information, workplace information, and anniversary information,
Wherein the next behavior predicting unit generates prediction data for a next action of the specific user by analyzing the extracted behavior data and user data for the specific user. The method according to claim 1,
An action event receiving unit for receiving the action event from the terminal of the first seller member; And
When a response message to the goods message is received from the terminal of the specific user, a first fee to be obtained by the first seller member who provided the action event and a second fee to be paid by the second seller member who sells the recommended goods And a fee calculation unit for calculating a fee based on the fee. 6. The method of claim 5,
Wherein the response message includes one of whether or not to deliver the product message, an evaluation index for the product message, and whether to consume the product included in the product message. Receiving an action event for a particular user from a first merchant member terminal;
Extracting behavior data associated with the behavior event from past behavior data of the specific user and analyzing the extracted behavior data to generate prediction data for the next behavior of the specific user;
The method of claim 1, further comprising: extracting a recommendation product matched with the generated predictive data from the product data registered by the plurality of seller members, and extracting an advertisement, a discount coupon and a coupon from the extracted recommendation product, Generating a merchandise message including at least one merchandise; And
Transmitting the generated product message to the terminal of the specific user; And
When a response message to the goods message is received from the terminal of the specific user, a first fee to be obtained by the first seller member who provided the action event and a second fee to be paid by the second seller member who sells the recommended goods ≪ / RTI >
Wherein the past behavior data and the action event include at least one of user identification information, time, location, date, and consumption information. Extracting behavior data associated with the behavior event from the past behavior data of the user corresponding to the user terminal when the behavior event is received from the user terminal and analyzing the extracted behavior data to predict the next behavior of the user ;
The method of claim 1, further comprising: extracting a recommendation product matched with the generated predictive data from the product data registered by the plurality of seller members, and extracting an advertisement, a discount coupon and a coupon from the extracted recommendation product, Generating a merchandise message including at least one merchandise; And
And transmitting the generated merchandise message to the user terminal,
Wherein the past behavior data and the action event include at least one of user identification information, time, location, date, and consumption information.
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