KR20120066373A

KR20120066373A - Intelligent marketing expert system

Info

Publication number: KR20120066373A
Application number: KR1020100127684A
Authority: KR
Inventors: 김현수; 박주영
Original assignee: 국민대학교산학협력단
Priority date: 2010-12-14
Filing date: 2010-12-14
Publication date: 2012-06-22

Abstract

The proposed intelligent marketing support system includes a data control unit that collects and manages marketing related data, and a marketing modeling unit that calculates marketing issues by extracting data related to variables of a marketing model from a marketing database and applying the marketing model. In addition, a marketing model auto learning unit for updating a marketing model stored in a modeling database through learning is provided.
Marketing models related to various marketing issues are defined as variables and managed as a database to enable systematic and integrated automated data processing for various marketing issues. Furthermore, by automatically learning and updating the accumulated data, the marketing model can improve the performance of the system by itself and expand the function by generating a new marketing model.

Description

Intelligent marketing expert system {intelligent marketing expert system}

The present invention relates to computer-aided management support technology, and more particularly to computer-aided data processing technology for marketing support.

In order to enhance the competitiveness of the products or services that companies sell, a systematic understanding of customers and their activities and efficient and effective marketing activities are required. Marketing activities, however, are expensive, manpower, and time-consuming to collect and process data. Most data collection for marketing is done manually. The existing marketing support solution consists of simple modules such as questionnaire preparation and simple statistical analysis.

Advances in marketing theory segment the market into homogeneous market segments based on a number of criteria, and select the segment market to focus on among the segmented markets to position the product to differentiate the consumer. To determine the inputs to the elements of the marketing mix, including the product, place, price, and promotion. Many theories have been presented in the process. However, no system has been proposed to organically combine this series of marketing processes to support managers in making decisions.

The present invention aims to automate a series of marketing planning processes by computer.

Furthermore, an object of the present invention is to provide a computer-based marketing solution that can effectively solve various issues in all marketing stages, from product planning to market launch strategy and market management.

Furthermore, an object of the present invention is to enable a response to a new issue or an improved response to an existing issue by updating a marketing support process through automatic learning according to data accumulation.

In addition, it aims to automate the process of collecting customer data manually.

According to an aspect for achieving the above object, the intelligent marketing support system is a data control unit that collects and manages marketing-related data, and extracts data related to variables of the marketing model from the marketing database and applies it to the marketing model. It includes a marketing modeling unit for calculating the.

According to another aspect of the present invention, the intelligent marketing support system may further include a marketing model automatic learning unit for updating the marketing model stored in the modeling database through learning.

According to an aspect, the automatic learning unit may update the marketing model through a processing example of the system.

According to another aspect of the invention, the marketing modeling unit is a preference calculation unit for calculating the product preferences from the product specifications and panel data stored in the marketing database, and a market share calculation unit for calculating the market share from the product specifications and panel data stored in the marketing database It may include. This means that preference model and occupancy model are stored in the modeling database.

According to another aspect of the present invention, the marketing modeling unit may include an STP support unit that supports a market segmentation / target market / positioning process according to a segmentation / targeting / positioning (STP) procedure from marketing data stored in a marketing database.

In addition, the marketing modeling unit may include a marketing mix simulation unit for estimating the impact of marketing variables related to a product or service, a place, a price, and a promotion on sales. Can be.

Furthermore, according to another aspect of the present invention, the marketing modeling unit extracts data related to the variables of the concept-based marketing model defined in the modeling database from the marketing database and uses the concept-based marketing model to calculate the market demand at the product concept stage. It may include estimating concept-based demand estimation. Additionally, the marketing modeling unit may include a quality based demand estimator that extracts quality evaluation data related to variables of the quality based marketing model defined in the modeling database from the marketing database and estimates the quality based market demand through the quality based marketing model. In addition, the marketing modeling unit extracts the test market test data related to the variables of the test market test-based marketing model defined in the modeling database from the marketing database and estimates the test market test-based market demand through the test market test-based marketing model. Test-based demand estimation.

According to another aspect of the present invention, the intelligent marketing support system includes a data control unit that collects, stores and manages marketing related data in a marketing database. According to an aspect, the data controller includes a web information collection robot unit collecting product and customer information and data on a product life cycle from the web, and a tag that collects data on a product life cycle from a wireless RFID-based communication network. It may include an information collector.

According to another aspect of the present invention, the intelligent marketing support system may further include a questionnaire manager for sampling a questionnaire, generating a questionnaire, and conducting a questionnaire through the network and storing the result in a marketing database.

According to the present invention, marketing models related to various marketing issues are defined as variables and managed as a database, thereby enabling systematic and integrated automated data processing for various marketing issues.

Furthermore, by automatically learning and updating the accumulated data, the marketing model can improve the performance of the system by itself and expand the function by generating a new marketing model.

In addition, cumbersome surveys are supported by computers, which can be semi-automated to save money and time.

1 illustrates a schematic configuration of an intelligent marketing support system according to an embodiment of the present invention implemented through a plurality of computers.
2 is a block diagram showing a schematic configuration of an intelligent marketing support system according to an embodiment of the present invention.

The foregoing and further aspects of the present invention will become more apparent through the preferred embodiments described with reference to the accompanying drawings. Hereinafter, the aspects of the present invention will be described in detail so that those skilled in the art can easily understand and reproduce.

1 illustrates a schematic configuration of an intelligent marketing support system according to an embodiment of the present invention implemented through a plurality of computers. An intelligent marketing support system is an organic integration of a series of software modules. These software modules may be executed on one or more computers. In one embodiment, this computer or computers may be connected to other computers via a network. 1 shows an embodiment in which the intelligent marketing support system according to the present invention is implemented in a plurality of servers, and the outside of the dotted line exemplarily shows external devices to which the system is connected.

As shown in FIG. 1, the database server 17 manages marketing data and marketing models. The modeling server 13 processes the marketing data using a marketing model corresponding to the marketing issue to calculate an answer to the issue. The auto learning server 15 updates or generates marketing models by analyzing and automatically learning the data. Users can use the intelligent marketing support system according to the present invention by connecting to the web server 11 through the respective computers 31, 33, 35.

The data collection server 19 connects to external computers via a network to collect marketing data. For example, the data collection server 19 may access the tag information management server 70. The tag information management server 70 collects PLM (Product Lifecycle Management) data by communicating with RF tags attached to a product. This enables real-time tracking and monitoring of the entire process, from production to shipment, shipment, delivery, sale and return, as well as simple transaction information.

In addition, the data collection server 19 collects marketing data by searching numerous web pages through the Internet network 50. This marketing data collection is done by software called web robots. However, the system shown in FIG. 1 is only an embodiment, and the intelligent marketing support system according to the present invention may be implemented in the form of a package program executed on a single personal computer based on several simple models.

2 is a block diagram showing a schematic configuration of an intelligent marketing support system according to an embodiment of the present invention. As shown, an intelligent marketing support system according to an embodiment stores a marketing issue, a marketing model related to the marketing issue, a modeling database 110 that defines and manages variables of the marketing model, and stores marketing related data. The marketing database 130, the data control unit 800 for collecting and managing marketing related data, and the marketing modeling unit for calculating marketing issues by extracting data related to variables of the marketing model from the marketing database and applying the marketing model to the marketing model. 300.

Marketing issues are issues that a company has in marketing. For example, 'Which level will your main customers be in the product concept stage?', 'What kind of marketing will you do to your specific customer base?' And 'What will your market share be?' Issues such as: Such marketing issues are structured by a structure such as a tree structure and managed in a database according to mutual relations, for example, so that systematic decision support is possible.

According to one aspect of the invention these marketing issues are computed by the computer by the marketing model. For example, a marketing model can be created by combining algebraically the variables that affect such an issue. However, the marketing model may be a model consisting of a series of procedures. Each procedure defines models that produce various variables related to marketing issues. The modeling database 110 stores marketing models for each marketing issue. Such a marketing model may be defined in the form of a data structure or a table.

According to another aspect of the present invention, the intelligent marketing support system includes a data control unit 800 that collects, stores and manages marketing related data in the marketing database 130. According to an aspect, the data controller 800 may include a data collection management unit 810 including a web information collection robot 811 and a tag information collection unit 813, and data for managing marketing data input through a terminal. The input module 830 is included.

In one embodiment, the web information collection robot unit 811 collects product and customer information and marketing data on a product life cycle from the web. For example, the web information collecting robot unit 811 automatically or semi-automatically collects user reviews on a new product, a user's opinion posted on a blog or a cafe, and stores it in the marketing database 130. As another example, the web information collecting robot unit 811 visits a competitor's website and automatically collects data such as specifications of a competitor's product, events such as discount sales, and delivery related information.

As another example, the web information collection robot unit 811 collects panel data through an online survey. It is based on a voluntary network of consumers. To this end, in order to induce the participation of consumers, by establishing a standard (Norm) on relevant product evaluation information, it provides useful information to consumers, providing their own evaluation information voluntarily, and in various forms such as personal feedback on the information provided. It provides a place to create and share information. Such survey data can be collected in the form of continuous planning data or through one-time surveys. Such data may also be collected via the web and / or via a mobile device.

In one embodiment, quality assessment attributes are defined for each product production consumption cycle ranging from product planning, commercialization, distribution, purchasing, and consumption. Measurement indicators according to physical / functional quality, distribution / service quality, consumer perception quality (initial purchase evaluation, brand evaluation, etc.) are collected through producers, distribution networks or the web, and from these information, physical quality index, distribution quality index, perception Quality indices are derived and combined to define a comprehensive quality index.

According to another aspect of the present invention, the intelligent marketing support system may further include a questionnaire manager 900 for sampling a questionnaire, generating a questionnaire, and conducting a questionnaire through the network to store the result in the marketing database 130. have. According to an embodiment, the survey management unit 900 writes a questionnaire using a sampling module, a list maker, and an existing questionnaire DB to sample a questionnaire, selects a layout, and registers a questionnaire online. Includes a survey auto-generating module. In addition, the survey management unit 900 provides a survey tracking module that tracks the response history through the survey's existing response history information and compares the responses of the progress survey with the past responses, and provides survey-specific incentives according to the respondent's application and accumulates incentives. Incentive management module to manage the.

According to another aspect of the present invention, the data collection management unit 810 may include a tag information collection unit 813 for collecting data on the product life cycle from a wireless tag-based communication network. In one embodiment, the tag information collection unit 813 collects PLM (product lifecycle management) data obtained through the product lifecycle management. PLM Data contains detailed information about the product and information about events and decisions made during the product's life. In one embodiment, this data collection is based on the Electronic Product Code Information Service (EPCIS) standard technology.

The EPCglobal network provides a standard for how to assign electronic product codes (EPCs) to logistics objects for logistics information exchange based on EPC codes and RFID technology. With this technology, companies can track goods along the supply chain to pursue object visibility, traceability, automation and security, minimizing inventory, minimizing product loss, handling orders quickly, and responding to changes in consumer preferences. Improvement and the like can be achieved.

The architecture of the EPCIS technology is to clean and collect, remove duplicate information, and group information by tagging the RFID tags recorded by EPC, the reader which is the device that reads the information of the tags, and the tag information read by the reader. Application Layer Event (ALE), EPC Information Service (EIS) that stores and provides refined EPC information, ONS (Object Name Service) that provides global search service on EPCglobal Network, and logistics information in EPCglobal Network. It consists of EPCIS DS (Discovery System), which plays a role in finding and accessing.

EPCIS receives tag event information from middleware and uses it to generate product status and tracking information, which is then stored and managed in a local repository for future use. In addition, it plays a role of hub of information gathering about EPC, and EPC middleware is a software component that plays a role of connecting software and hardware in EPCglobal Network structure and manages EPC real-time event information while interworking with EPCIS and existing system. Manage information.

EPCIS data occurs during the execution of business processes and has a four-dimensional structure: what, when, where, and why. Using this four-dimensional structure of EPCIS technology, the information and customer information that accompanies the entire product lifecycle, from product planning, production distribution, and consumption, can be managed as a unified information system and used for marketing information extraction.

In accordance with a further aspect of the present invention, each of the functional units 811, 813, 830 of the data control unit 800 according to an embodiment semantic input data to facilitate the collection of atypical information in collecting data. See Data Semantic Standard 850 for coordination. Through this, unstructured information is linked to enable automatic / semi-automatic input.

According to another aspect of the present invention, the marketing modeling unit 300 includes a preference calculator 331 for calculating a product preference from product specifications and panel data stored in the marketing database 130, and a product stored in the marketing database 130. It may include a share calculator 333 for calculating the market share from the specifications and panel data. This means that the preference model and the occupancy model are stored in the modeling database 110.

Product preferences related to individual consumers' choice of product purchase with respect to specifications defined as level values for each product attribute such as product function and quality, and input variables such as distribution methods, advertising costs, and panel data collected from individual consumers. The market share that can be calculated from age can be estimated by various mathematical models. The marketing modeling unit 300 according to the present invention is a dynamic sales diffusion process (prediction of new product sales trend over time from the beginning of the market entry) series model and static equilibrium market share (new product to the market). Market share forecast when settling) Share is calculated by combining series models.

According to another aspect of the present invention, the marketing modeling unit 300 is an STP support unit that supports the market segmentation / target market / positioning process according to the segmentation / targeting / positioning (STP) procedure from the marketing data stored in the marketing database 130 And may include 371. The marketing management process presented by Philip Kotler includes research (R), segmentation (S), targeting (T), positioning (P) and marketing mix (market research). This is a process of control (C) that includes determining the MM, determining the implementation of the marketing mix (I), obtaining feedback, evaluating the results, and modifying or improving the STP strategy or marketing mix tactics.

According to Kotler, effective marketing starts with research. A survey of the market reveals different market segments (S) made up of consumers with different needs. It is wise for firms to establish a segment market that they can better meet than their competitors. Firms should position their products in each target market to indicate how their products differ from the competition. STP represents a company's strategic marketing thinking. Based on STP, companies develop MM, a tactical marketing mix consisting of a mix of product, price, distribution and promotion decisions. The company then executes the marketing mix (I). Finally, control measures (C) are used to monitor and evaluate results and to improve STP strategy and MM tactics.

The STP support unit 371 supports market segments based on the data collected by the survey management unit 900 or the data control unit 800 and collected in the marketing database 130, and compares the characteristics of each segment market. Assist in establishing a market by presenting a market that matches For this set target market, the STP support unit 371 provides a simulation to determine the best marketing mix by inputting product specifications and characteristics of the target market. In addition, these simulation results estimate the consumer preference and market share. Marketing mix simulation is explained in full detail below.

According to another aspect of the invention, the marketing modeling unit 300 from the marketing data stored in the marketing database 130 to the product or service (product), place (place), price (price), promotion (promotion) A marketing mix simulation unit 373 that estimates the impact of related marketing variables on sales may be included. The marketing mix simulation unit 373 may be a module called by the STP support unit 371.

A marketing mix is a marketing tactic that combines all the inputs that a company uses to focus marketing activities in a target market set according to its marketing goals, according to the environment and situation of the company and maximizes the marketing effect. The elements of the marketing mix are often referred to as 4Ps in terms of product or service, place, price, and promotion. The components of the marketing mix also vary from product planning, sales channels, advertising, storage, packaging, and display. In order to increase the effectiveness of marketing, all these factors must be combined with the strategic goals of marketing, organically combining the functions of each sector based on those goals, and carrying out the overall marketing activities. The marketing mix first forms the bottom mix such as the product mix, the promotion mix, the sales route mix, and then integrates and configures the bottom mixes to realize the most efficient marketing mix. In practice, this mix is strategically changed according to the type and status of the company, and is formed differently according to the market target.

The marketing mix simulation unit 373 supports identifying the maximum point of demand based on the marketing mix optimization model that simulates the distribution network based on the test market evaluation result, the survey result, and the set product specification.

Furthermore, according to another aspect of the present invention, the marketing modeling unit 300 extracts data related to the variables of the concept-based marketing model defined in the modeling database 110 in the marketing database 130 to extract the concept-based marketing model. It may include a concept based demand estimation (391) for estimating the market demand in the product concept stage through. By doing so, we can avoid the existing decision-making process of identifying success after launching new products, and improve the success rate and minimize the opportunity cost by judging marketability in advance in the concept stage.

The concept-based demand estimator 391 models the advertising cost input effect by predicting the recognition rate according to the advertising cost input, and earlyly identifies the market through modeling to predict the penetration rate of new concept products. Support decision making. In addition, the main variables involved in the modeling are automatically databased so that the decision norm can be established.

This process begins with collecting information on the evaluation of the new product concept and the new product prototype (purchasing intention, overall evaluation, specific attribute evaluation, etc.) from the consumer through the questionnaire management unit 900. Afterwards, the purchase intention data is used to predict the probability of trial purchase and repurchase of consumers. The probability matrix is then constructed to predict whether a favor for a product concept leads to a prototype or a test purchase leads to a repurchase. Next, the probability of purchase is calculated through a mathematical model that estimates how the overall evaluation and specific attribute evaluation of the concept and product of the consumer are related to the probability of purchase. Finally, we predict the possibility of trial purchase and repurchase of concept and prototype based on probability matrix and mathematical model.

In this regard, the Dynamic Sales Diffusion Process Model uses a repair model for each of the Awareness Module, Trial Module, Repeat Module, and Total Volume Module. We forecast new product sales trends from time to time. At the same time, the Static Equilibrium Market Share Model includes Trial and Repeat Modules, Market Share Estimation Modules, Preference Modules, and Source of Sales Modules. We estimate the market share of a new product when it enters the market through the repair model.

Through this, the concept-based demand estimate (391) supports management decision-making by showing not only the expected sales of new products, but also the time it takes to settle in the market, and identifying the source of sales of new products.

Additionally, the marketing modeling unit 300 extracts quality evaluation data related to variables of the quality-based marketing model defined in the modeling database 110 from the marketing database 130 to estimate quality-based market demand through the quality-based marketing model. And may include a quality based demand estimate 393.

In order to minimize the probability of failure due to the quality of prototypes being released without evaluation based on customer value and to identify the point of improvement, quality based demand estimation (393) uses the quality evaluation to determine the long-term demand of consumers (repetitive purchasing power). Modeling and repetitive purchase quantity estimation to estimate whether the finished product is manufactured and demand based on quality. Furthermore, the key parameters involved in this model are automatically databased to accumulate criteria.

In addition, the marketing modeling unit 300 extracts the test market test data related to the variables of the test market test-based marketing model defined in the modeling database 110 from the marketing database 130 and tests the test market test-based marketing model. It may include a test market test based demand estimator 395 that estimates market test based market demand.

After the launch of new products, the decision-making system according to market reactions involves a lot of costs and management risks. It can be minimized. Test market test-based demand estimator (395) estimates total demand and market share based on a model that estimates the concept that is closest to the release state and product quality at the same time based on customer value. In addition, it automatically accumulates the criteria by automatically database the main variables involved in this model.

The quality-based demand estimate 393 and the test market test-based demand estimate 395 have different data based on the concept-based demand estimate 391 described above. .

According to another aspect of the present invention, the intelligent marketing support system may further include a marketing model automatic learning unit 500 for updating the marketing model stored in the modeling database 110 through learning. In one embodiment, the marketing model auto learning unit 500 is presented corresponding to various models used in the marketing modeling unit 300. FIG. 2 shows auto-learning units 510, 531, 533, 571, 573, 591, 593, and 595 for these various models.

The modeling database 110 automatically accumulates and accumulates models related to each marketing issue and key variables related to the models. Since key variables are automatically databased, it is possible to add new variables or evaluate their impact on marketing issues. Artificial intelligence technology can be applied for pattern recognition according to time flow of marketing decision result. The marketing model auto learning unit 500 verifies the significance of the marketing model and the variables or parameters related thereto, for example, through R-square of regression analysis. In any situation defined by marketing data, the decision-making process of marketing issues is analyzed to derive meaningful decision information by means of accurate quantitative models rather than inertia or intuition and to feed back periodically. Through this, it is possible to maximize the effect of accumulating knowledge and accumulate and reproduce the accumulated knowledge.

According to an aspect, the automatic learning unit 500 may include a case automatic learning unit 510 for updating a marketing model through a processing example of the system. Case automatic learning unit 510 according to an embodiment is to change the model used in the marketing decision and the variables or parameters involved in the model through the analysis cases accumulated in the process of using the intelligent marketing support system according to the present invention It automatically updates according to the environment so that decision-making system can be established according to systematic and scientific marketing issues.

110 Modeling Database 130 Marketing Database
300 Marketing Modeling Unit 331 Preference Calculator
333 Share Calculator 371 STP Support
373 Marketing Mix Simulation
391 Concept-Based Demand Estimation 393 Quality-Based Demand Estimation
395 Test Market Test-Based Demand Estimator
500 Marketing Model Auto Learning Department
510 Case Study
533 Share Model Auto Learning Division 571 STP Model Auto Learning Division
573 Marketing Mix Model
591 Concept-Based Model
593 Quality-based model automatic learning department
595 Test Market Test-based Model
700 User Interface 800 Data Controls
810 Data Collection Management Unit 811 Web Information Collection Robot Unit
813 Tag information collector 830 Data input module
850 Data Semantic Standard 900 Survey

Claims

A modeling database for defining and managing marketing issues, marketing models associated with the marketing issues, and variables of the marketing models;
A marketing database for storing marketing related data;
A data control unit for collecting and storing marketing related data in the marketing database;
A marketing modeling unit extracting data related to variables of a marketing model defined in the modeling database from the marketing database to calculate a marketing issue through the marketing model;
Intelligent marketing support system comprising a; user interface for processing input and output between the user and the processing block of the entire system.

The system of claim 1 wherein the intelligent marketing support system is:
And a marketing model auto learning unit for updating the marketing model stored in the modeling database through learning.

The method of claim 2, wherein the automatic learning unit:
Intelligent marketing support system comprising; case automatic learning unit for updating the marketing models stored in the modeling database through the processing case of the system.

The method of claim 1, wherein the marketing modeling unit:
A preference calculator for calculating a product preference from product specifications and panel data stored in the marketing database;
And a market share calculator for calculating market share from product specifications and panel data stored in the marketing database.

The method of claim 1, wherein the marketing modeling unit:
Intelligent marketing support system including; STP support for supporting the market segmentation / target market / positioning process according to the segmentation / targeting / positioning (STP) procedure from the marketing data stored in the marketing database.

The method of claim 1, wherein the marketing modeling unit:
Intelligent marketing support including; marketing mix simulation unit for estimating the impact of marketing variables related to products, services, places, prices, and promotions on sales system.

The method of claim 1, wherein the marketing modeling unit:
A concept based demand estimator for extracting data related to variables of a concept based marketing model defined in the modeling database from the marketing database and estimating market demand in a product concept stage through the concept based marketing model;
A quality based demand estimator for estimating quality based market demand through a quality based marketing model by extracting quality evaluation data related to variables of the quality based marketing model defined in the modeling database;
Number of test market test bases that extracts test market test data related to variables of the test market test-based marketing model defined in the modeling database from the marketing database and estimates the test market test-based market demand through the test market test-based marketing model Intelligent marketing support system, including; lumbar government.

The method of claim 1, wherein the data control unit:
A web information collection robot unit collecting product and customer information and data on a product life cycle from the web;
An intelligent marketing support system comprising; a data collection management unit comprising; tag information collection unit for collecting data on the product life cycle from a wireless tag-based communication network.

The system of claim 1 wherein the intelligent marketing support system is:
And a questionnaire management unit for sampling a questionnaire, generating a questionnaire, and conducting a questionnaire through a network to store a result in the marketing database.

A data collection step of collecting marketing-related data and storing it in a marketing database;
A marketing modeling step of extracting data related to variables of a marketing model defined in a modeling database from the marketing database to calculate a marketing issue through the marketing model;
And an output step of outputting the calculated marketing issue to a user.

The method of claim 10, wherein the intelligent marketing processing method is:
And a marketing model auto-learning step of updating the marketing model stored in the modeling database through learning.

The method of claim 11, wherein the automatic learning step is:
And an automatic learning step of updating marketing models stored in the modeling database through the cases processed in the marketing modeling step.

The method of claim 10, wherein the marketing modeling step is:
A preference calculation step of calculating product preferences from product specifications and panel data stored in the marketing database;
And a market share calculation step of calculating a market share from product specifications and panel data stored in the marketing database.

The method of claim 10, wherein the marketing modeling step is:
STP support step of supporting a market segmentation / target market / positioning process according to the segmentation / targeting / positioning (STP) procedure from the marketing data stored in the marketing database.

The method of claim 10, wherein the marketing modeling step is:
Intelligent marketing processing, comprising: a marketing mix simulation step of estimating the impact of marketing variables related to products, services, places, prices, and promotions on sales. Way.

The method of claim 10, wherein the marketing modeling step is:
A concept-based demand estimation step of estimating market demand at a product concept stage through the concept-based marketing model by extracting data related to variables of the concept-based marketing model defined in the modeling database; Marketing treatment method.

The method of claim 10, wherein the marketing modeling step is:
A quality based demand estimation step of estimating quality based market demand through a quality based marketing model by extracting quality evaluation data relating to variables of the quality based marketing model defined in the modeling database from the marketing database; Way.

The method of claim 10, wherein the marketing modeling step is:
Test market test based demand for extracting test market test data related to the variables of the test market test based marketing model defined in the modeling database from the marketing database and estimating the test market test based market demand through the test market test based marketing model Intelligent marketing processing method comprising a; estimating step.

The method of claim 10, wherein said data collection step is:
Web information collection step of collecting product and customer information, data on the product life cycle (product life cycle) on the web; intelligent marketing processing method comprising a.

The method of claim 10, wherein said data collection step is:
And tag information collecting step of collecting data on a product life cycle from a wireless RFID tag based communication network.

The method of claim 10, wherein the intelligent marketing processing method is:
And a questionnaire management step of sampling a questionnaire subject, generating a questionnaire, and conducting a questionnaire through a network, and storing the result in the marketing database.

A computer-readable recording medium storing a program in which the intelligent marketing processing method according to claim 10 is implemented.