KR20170037332A

KR20170037332A - System for user-centric market of applying the SCS

Info

Publication number: KR20170037332A
Application number: KR1020150136785A
Authority: KR
Inventors: 김진덕; 이현섭; 강효운; 박성훈
Original assignee: 동의대학교 산학협력단
Priority date: 2015-09-25
Filing date: 2015-09-25
Publication date: 2017-04-04

Abstract

The present invention overcomes the limitations of the existing Internet shopping mall and provides a fusion service. It provides the user with desired information through the needs analysis of the user and analyzes the user's behavior, preference, late period, SNS etc. for the provided data To provide a user-oriented market system using SCS that connects the business with the consumer in real-time, which can process the data, provide user-customized product information, and easily apply the OmniChannel system with simple membership. A needs analysis module that collects the results of analysis, internal SNS analysis, and external SNS analysis to recommend individual personalized products, a mobile shopping mall module that provides the user's product purchase function and review creation function, coupons A service that provides users with services such as There is composed, including a back module.

Description

A user-centric market system applying SCS (System for user-centric market of applying the SCS)

The present invention relates to a user-oriented market system using SCS, and more particularly, to a user-oriented market system that is different from a large market and is a user-oriented market system using SCS .

Recently, more targeted and tailored ads are emerging for individual consumers in new forms, formerly called so - called direct advertising. New initiatives have been developed to interact directly with consumers through pull and push campaigns and to provide advertisers with specific consumer data mining related to consumer buying habits, trends and forecasting of future habits. I am looking for a way to create more measurable ads.

Advances in technology tools combined with ingenious marketing extend existing direct mail marketing campaigns to new branches that include distribution and measurement through telemarketing, sales-to-point campaigns, computer platforms and the most recent telecommunications networks .

In addition, existing shopping malls can not analyze individual preferences by recommending products using popular preferences. Existing online shopping malls provide only general information such as low prices, processed images, and gift items in order to attract customers.

However, in recent years, shopping malls are interested in providing product information that reflects the needs of customers, but in general, the provided information only provides product information based on the user's simple search history, and the list of recommended products is provided reflecting the popularity of the public Therefore, there is a problem regardless of the preference of the individual. As a result, the need for customer needs analysis is emerging in order to meet customer requirements.

Patent Registration No. 10-1094337 (Registration date 2011.12.08) Patent Registration No. 10-1161084 (registered on June 22, 2012)

SUMMARY OF THE INVENTION Accordingly, the present invention has been made to solve the above problems, and it is an object of the present invention to overcome the limitations of the existing Internet shopping mall to provide a fusion service, to provide user with desired information through user's needs analysis, It is a user-centered market that uses SCS to connect users with consumers in real-time, which can process data, process customized product information by analyzing user behavior, preference, late, and SNS, The purpose of the system is to provide.

Other objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, a user-oriented market system using SCS according to the present invention is characterized by analyzing user behavior analysis, late analysis, internal SNS analysis, external SNS analysis, A mobile shopping mall module for providing a product purchase function and a review creation function of a user, and a payback module for providing a service to a user such as a coupon, a gift, and other convenience items that can be provided by a small business owner.

Preferably, the needs analysis module includes a behavior analysis module that stores all the log data of behavior such as category selection, product purchase, comment creation, etc. in the DB by a mobile shopping mall user, and a product analysis module based on a user- , An internal SNS module for reducing the functions of the existing social network method in which the user directly compares the two products and building it according to the shopping mall, And an external SNS module that utilizes a crawling bot that is manufactured by itself to collect the number of registered tweets.

The user-oriented market system using the SCS according to the present invention as described above has the following effects.

First, by solving the inconvenience of entering existing large markets through the recruitment of new entrants and acquiring users, it is possible to increase sales through online marketing and securing additional sales routes for small businesses by comparing differences with existing systems. .

Second, by supporting the product registration and customized marketing strategy of the seller according to the needs of consumers, it is possible to provide customized services to individual users and small business owners.

Third, there is no need for separate comment management, and it is possible to manage sales products effectively by understanding customer response to individual products through statistical data.

Fourth, systematic management and expansion of various small and midsize businesses can improve service reliability by enhancing product diversity such as existing large shopping malls and retail stores, and suggesting various ways of utilizing products.

1 is a block diagram illustrating a configuration of a user-centered market system using SCS according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a state in which a behavior type is stored in a database table in the needs analysis module of FIG. 1; FIG.
FIG. 3 is a block diagram illustrating a stored procedure and a procedure for behavior analysis in the needs analysis module of FIG. 1; FIG.
FIG. 4 is a diagram for explaining a process of building a database dictionary through the late analysis module in the needs analysis module of FIG. 1; FIG.
FIG. 5 is a diagram for explaining a process of digitizing through the preference quantification algorithm in the needs analysis module of FIG. 1
FIG. 6 is a diagram for explaining the verification process of the preference quantization algorithm in the needs analysis module of FIG. 1;
FIG. 7 is a diagram showing a personalized recommendation recommendation page through a needs analysis result in the need analysis module of FIG. 1; FIG.
8 is a diagram showing a page provided by the mobile shopping mall module of Fig. 1 to a user for login; Fig.
9 is a block diagram showing a page provided to the user after login by the mobile shopping mall module of Fig.
FIG. 10 is a diagram for explaining a method of paying a point to a user by the payback module of FIG. 1

Other objects, features and advantages of the present invention will become apparent from the detailed description of the embodiments with reference to the accompanying drawings.

A preferred embodiment of a user-centered market system using SCS according to the present invention will be described with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. It is provided to let you know. Therefore, the embodiments described in the present specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and are not intended to represent all of the technical ideas of the present invention. Therefore, various equivalents It should be understood that water and variations may be present.

FIG. 1 is a configuration diagram illustrating a configuration of a user-centered market system using SCS according to an embodiment of the present invention.

As shown in FIG. 1, a needs analysis module for collecting the results of user behavior analysis, a later analysis, an internal SNS analysis, and an external SNS analysis to recommend individual personalized products, A mobile shopping mall module provided, and a payback module that provides services such as coupons, prizes and other convenience items that can be provided by a small business owner to the user.

The needs analysis module of FIG. 1 includes a behavior analysis module in which a mobile shopping mall user stores all log data of an action such as category selection, product purchase, and comment creation in a DB, and a product analysis module based on a user- , An internal SNS module for reducing the functions of the existing social network method in which the user directly compares the two products and building it according to the shopping mall, And an external SNS module that utilizes a crawling bot that is built to collect the tweet of the tweet.

Hereinafter, the behavior analysis module will be described in detail.

(1) Function of behavior analysis module

The behavior analysis module is a module for analyzing the behavior of the user. The behavior analysis module analyzes the behavior pattern of the user of the shopping mall and uses it to grasp the preference of the individual on the product, and performs individual product recommendation through the analyzed preference.

(2) Data storage by pattern

The selection of categories, product selection, and purchase of goods, which are behaviors that mobile shopping mall users can take, are stored in a database table as a behavior type. As shown in Fig. 2, each table is weighted by 10, 20, 70% To be applied. In this case, the user's simple repetitive behavior is also reflected in the behavior analysis result which has an absolute influence on the preference such as the purchase of the product, thereby providing a reliable needs analysis result.

The behavior analysis is a module that stores and utilizes information on user behavior (category click, product selection, product purchase), and outputs the analysis result as shown in Table 1 below.

(3) Stored procedures and procedures for behavioral analysis

First, behavior analysis uses MS-SQL stored procedures to analyze the needs of individuals and groups.

Then, the result of the behavior analysis of the user is derived by integrating the output results through the temporary table. In this case, the table used for the behavior analysis result by individual, group, and period (month) is as shown in FIG.

Input_all (action analysis result gathering procedure) shown in FIG. 3 is a procedure collecting behavior analysis results finally performed in each procedure. Each executed procedure includes input_userstat (user's personal behavior analysis), Input_userGstat (Behavioral analysis by group), Input_monthlystat_all (behavioral analysis by specific period), and the lower table is shown in FIG.

Next, the late analysis module will be described in detail.

(1) Functions of Later Analysis Module

The Late Analysis Module is a module for analyzing the user's written testimonial. It analyzes the personal preference of the product through the analyzed period and grasps the preference of the individual through the analyzed preference. The product of the negative preference is the product It is excluded from recommendation.

(2) Database dictionary construction

A database dictionary is needed to analyze the written reviews and to analyze the preferences of the products. At this time, as shown in FIG. 4, the database dictionary collects, analyzes, and builds up a later date that eight scribes will be added to the database dictionary through 8 weeks.

The collected data are analyzed to generate two databases.

Next, we will prioritize the equalization of categories of products targeting the large Internet shopping malls Auction, Gmarket, 11th, and Interpark. In other words, the categories are mismatched, and the products are fragmented. In order to solve the problem, the categories are uniformized and 34 categories including upper, middle, and lower structures are established. In this process, two databases are additionally created by discovering that there are postings used only in certain categories.

The constructed database dictionary consists of a total of four tables: a positive word table, a negative word table, a frequently used positive word table (added table), and a frequently used negative word table (added table). As a result of this configuration, we construct a database dictionary consisting of frequently mentioned words and basic words.

On the other hand, analysis of user 's testimony is done through the algorithm of preference quantification, which is designed to grasp the preference of the individual using the written testimony. At this time, the written review is used as a set of strings, which is converted into a number that can be actually verified by matching with a database dictionary.

The preference quantification algorithm will be described in detail as follows.

First, the Late Analysis module is based on a late review consisting of two pages (rating, image, and review).

First, it uses the rating function.

That is, to prevent the reliability of the result of the late analysis by simple word matching, the following four factors that greatly affect the customer satisfaction are selected through the preliminary investigation.

(1) Packing status - Wrapped properly?

(2) Speed of courier - Was courier delivered quickly?

(3) Size of product - Did the order size come in properly?

(4) State of the product - Is the product defective?

Next, use the image and review page. At this time, the image and the late writing page can attach up to 4 images, and the latter can write up to 500 characters.

5, and the rating score and the late ratio are constructed as shown in Table 2 below in order to select a user's unfounded later periods.

Then, the verification is performed as shown in FIG. 6 through a preference quantization algorithm. At this time, the verification selects three items for the same product, and obtains results similar to human judgment.

The results of the obtained Later Analysis Module are reflected in the analysis of individual preference. At this time, when the score is less than 20 points based on 20 points, the product is excluded from the needs analysis result as shown in FIG.

Next, the internal SNS module will be described in detail.

(1) Functions of internal SNS module

The internal SNS module allows users to select two products directly to support community functions (writing a topical review, writing a review), and collecting the preferences of community users through "Topic".

(2) Service routine

First, the social network service compares two products directly by the user.

At this time, as a comparison method, the content of the product registration area is displayed in a list format on the screen of the registered internal social network. The basic information can be obtained through the product name and the product image, and the area made of the circle can be configured to vote on the product that it likes by touching. You can use the review button located at the bottom of each product to create a product review and review others' reviews. You can also collect user log records of the process of selecting product category as a core function and personal preference And analyze the product preference of the individual.

The preference of the collected community users is used to calibrate behavioral and late analysis results. The generated topic is used to analyze the preference of the individual by gathering basic data necessary for behavioral analysis and later analysis, and one of the two products Is used to analyze the preferences of the group and others.

Next, the external SNS module will be described in detail.

(1) Function of external SNS module

The external SNS module collects Twitter 's Favorite and Retweet numbers and uses the collected data to analyze groups and others' needs. And it supports to collect Twitter data through crawling bot. Since it does not provide information in its own API, it collects Favoir and Retweet information through its own crawling bot. Because it does not use its own API, it does not matter if the structure of the tweeter changes, and it is possible to collect desired data with only the operation of the data filter.

The collected external SNS data is used to analyze popular preferences, and the product recommendation rank is determined when recommending products by group using accumulated TWpoints in the database.

The mobile shopping mall module of FIG. 1 allows a user to log in to analyze the needs of the user, stores all the behavior information of the user after logging in, and enables the user to make a product purchase function and review in using the shopping mall.

In one embodiment, as shown in FIG. 8, first, an application initial screen is provided to provide membership and login. This allows the user to enter personal information (ID, password, mobile phone number), and enter information for needs analysis (Twitter nickname, sex, date of birth, region).

As shown in FIG. 9, two pages (top and top views) are displayed as a top screen (an internal SNS function for analyzing user-defined preferences) and a top view (a function for purchasing products or confirming needs analysis results) .

The payback module shown in FIG. 1 is a service that can be provided by a small business owner, such as a coupon, a prize, or other convenience items, and provides points through community activities (new subscription, login, review, etc.). In this case, the points to be paid include a customized coupon, a prize cycle, and other convenience items.

A method of paying a point to the user will be described with reference to FIG.

First, the user is paid points for five items: new member registration, login (attendance check), topic writing, written writing, and late sharing.

At this time, the new member subscription is one-time point payment, which induces the first 100 points and the user's usage, checks it once, and gives the user application usage continuously by giving 10 points at the login.

In addition, the user can share information with 20 points by creating a Topic and share information among multiple users.

In addition, the user can acquire 20 points and accumulate points continuously in the latter period. In addition, 10 points will be paid to share the latter period.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It will be apparent to those skilled in the art that various modifications may be made without departing from the scope of the present invention. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

Claims

A needs analysis module that collects the results of user behavior analysis, late analysis, internal SNS analysis, and external SNS analysis to recommend individual personalized products,
A mobile shopping mall module for providing a product purchase function and a review creating function of the user,
And a pay-back module for providing services such as coupons, prizes and other convenience items that can be provided by the small business owner to the user.

The method of claim 1, wherein the needs analysis module
A behavior analysis module for storing all log data of behavior such as category selection, purchase of goods, comment creation, etc. in the DB by the mobile shopping mall user,
A late analysis module for digitizing the user's reaction to the product based on the user's written review (rating and registration content)
An internal SNS module for reducing the functions of the existing social network system in which the user directly compares the two products,
And an external SNS module that utilizes a self-manufactured crawling bot to collect retweets related to tweets, thereby fetching the number of retries of the tweets.