KR20010100702A - Method for providing purchase information on goods - Google Patents

Abstract

The present invention relates to a product purchase information service method for assisting users who are connected to a product purchase information service system through the Internet to purchase a product. According to the method of the present invention, the expert evaluation data collection step of data-saving the expert's evaluation on a plurality of evaluation items of a specific product is stored in a database, and the consumer's preference for the plurality of evaluation items of the specific product by data, The ranking of each medium for a product is made into data, and data obtained by comprehensively evaluating the data of expert data, consumer preference data, and ranking data for each medium are generated and stored in a database. Expert evaluation data, consumer preference data, media ranking data, and comprehensive evaluation data stored in the database are provided to the user at the request of a user connected to the service system. As such, by providing the objective purchase information to assist the consumer in making a decision to purchase a product, the consumer can make a clearer decision to purchase a product without being disadvantaged in purchasing the product.

Description

Product purchase information service method {METHOD FOR PROVIDING PURCHASE INFORMATION ON GOODS}

The present invention relates to a method of providing product purchase information to help general consumers to make a purchase decision of a product to purchase on the Internet.

Launched from ARPNET, which began to share information related to military research by the US Department of Defense, the Internet is now becoming a huge and efficient media and media of information connected to numerous computer networks around the world. The Internet is used for various purposes such as e-mail, newsgroups, file transfers, information transmission and retrieval, and the number of networks connected to the Internet and the number of Internet users are rapidly increasing. The utilization of information transfer of goods is also rapidly spreading.

Currently, comprehensive information such as Déza (www.deja.com), Epinion (www.epinions.co.kr), Entalk (www.entalk.co.kr) is a model of information delivery of products through the Internet. There are delivery media, and there are companies that deliver information by specialty such as Kbench (www.kbench.co.kr), but all of them collect opinions only from the dissatisfied factors and subjective viewpoints of general consumers, In addition, companies that provide a search service also provide only information or search results unilaterally made by the company through the Internet.

Therefore, a new model of information collection is required to provide consumers with objective and credible product information just before the decision to purchase a product to satisfy their needs for information.

SUMMARY OF THE INVENTION The present invention has been made to meet the above-described needs, and an object of the present invention is to provide a system for providing product purchase information to general consumers to help in making a product purchase decision.

According to the present invention for achieving the above object, a product purchase information service method for helping users connected to the product purchase information service system through the Internet to make a decision to purchase a product: a plurality of evaluation items of a specific product An expert evaluation data collection step of data-saving the expert's ratings in a database, a consumer preference data collection step of data-saving the user's preferences for a plurality of evaluation items of the specific product and a database for the specific product Comprehensive ranking data collection step for collecting media ranking data stored in the database, and comprehensive evaluation data for generating and storing data comprehensively evaluating expert evaluation data, consumer preference data, and ranking data for each media stored in the database. student And providing the user with expert evaluation data, consumer preference data, ranking data for each medium, and comprehensive evaluation data stored in the database at the request of the user connected to the service system.

1 is a schematic block diagram of a product purchase information service system according to the present invention;

2 is a flowchart illustrating a process of using a service performed in a product purchase information service system of the present invention;

3A to 3D are flowcharts illustrating a process of collecting evaluation data for product purchase in the product purchase information service system of the present invention, respectively;

4 is a diagram illustrating an initial screen for user input in a product purchase information service system of the present invention.

<Description of the symbols for the main parts of the drawings>

100: product purchase information processing server 200: Internet

300: user computer 500: database group

Hereinafter, with reference to the accompanying drawings will be described in detail the operation of the preferred embodiment according to the present invention.

Referring to FIG. 1, a schematic block diagram of a product purchase information service system constructed in accordance with the present invention is connected to a product purchase information processing server 100, a database (DB) group 500, and the Internet 200. It includes a number of users 300.

The server 100 presents a plurality of users 300 with products displayed in a shopping mall, collects various evaluation data to help determine objective purchase intentions for the presented products, and collects the collected evaluation data to the user. to provide. Various evaluation data includes media-specific evaluation data of a specific product, expert evaluation data of an expert evaluating a specific product, consumer preference data of a specific product, and detailed description in a medium such as a research institution, a bookstore or a daily newspaper. Comprehensive evaluation data including aggregated evaluation data of one medium, expert evaluation data, and consumer preference data are included.

The evaluation data of the media independently evaluates specific products in each medium and reflects the ranking according to the evaluation. Items to be considered are product design, robustness, performance, after-sales service, convenience, function, and stability. It may be evaluated or may be evaluated according to a unique evaluation item for each medium, and simply refers to the ranked rank of a specific product in each medium.

The expert evaluation data and the consumer preference data are included in the scoring criteria for each evaluation item of the specific product according to the present invention, for example, the evaluation items such as design, robustness, performance, after-sales service, convenience, function, and safety. According to the rating means the data evaluated by assigning a score. According to the present invention, instead of applying the same scoring to each evaluation item, the final consumer preference data is generated by using the importance recommended by the consumer for each evaluation item of the specific product as a weight. For example, when the product is a car, the safety item recommended by the consumer is evaluated by applying different addition rates for each item in a manner of giving a higher weight to other items.

Meanwhile, the comprehensive evaluation data refers to data that is comprehensively evaluated by applying different ratios to each of the expert evaluation data and the consumer preference data. According to the present invention, the expert evaluation data has a higher application cost than the consumer preference data. Apply. For example, 20% of the total is applied to the consumer preference data, 30% of the total is applied to the expert evaluation data and the ranking data by media, and the rest is applied to the product sales ranking and expert opinion. By assigning weights in such a way that the ratios are appropriately distributed according to the number of recommendations, the overall objective evaluation is made.

The database group 500 includes a shopping mall database 510 in which various products presented for shopping to consumers are sorted and organized by items, and a user log database in which the tracks of consumers who search for products presented in the shopping mall database 510 are recorded. 520, a user profile database 530 recording personal details of a consumer registered in the system of the present invention, an expert opinion database 540 for storing the above-mentioned expert opinion, and the above-described consumer preference data The consumer preference database 550 includes a product database 560 in which expert evaluation data, consumer preference evaluation data, and comprehensive evaluation data are stored, along with details of various products presented to consumers. In response to a request of a consumer connected to the server 100 through the Internet 200, the server 100 searches the database group 500 to display evaluation data corresponding to the user request or a screen associated with the user 300. To provide.

Each user 300 is a generic meaning including the general consumer, the expert, and the operator for inputting the ranking data shown in the offline medium in the present invention, and accesses the server 100 of the present invention through the Internet 200. It can be understood that it refers to a computer that can enter the item you want, your opinions or ratings that can help users or consumers to make purchase decisions on various screens provided from the connected server 100 Should.

Hereinafter, the system of the present invention having the above-described configuration will be described in detail as follows with reference to the accompanying drawings.

Referring to FIG. 2, a process of providing a user with evaluation data, expert evaluation data, consumer preference data, and comprehensive evaluation data collected for a specific product in the product purchase information providing service system according to the present invention.

In step 210, an initial page as shown in FIG. 4 is presented to a user 300 connected through the Internet 200. The left side of this page lists the product folders (410, 420, 430, 440, ...), which categorizes various products by item, and at the top of this page, the consumer preferences folder 450 and the media ranking folder 460. ), Various menu folders such as expert evaluation folder 470, comprehensive evaluation folder 480, bulletin board folder 490, and the like are listed.

When the user 300 selects a product classification to which a specific product belongs in the initial page and selects the detailed product (step 212), the server 100 may apply to the product classification in the shopping mall database 510 in the database group 500. The grades and scores of the competitive products, including the specific product to which they belong, are retrieved and provided on the screen of the user computer 300 (step 214). Each product registered in the shopping mall database 510 may be assigned with a unique product code, thereby providing identification power with other products. In this state, the user 300 clicks on a particular product he wants to see on the screen and selects one of the selection folders listed at the top of the initial page of FIG. 4 (step 216), so that the user himself purchases. Proceed to the evaluation data folder of the product you wish to

When the user 300 clicks on the consumer preferences folder 450, the result of converting the opinions that other users have evaluated on a specific product as a score is read from the product database 560 and presented to the user 300 (step 218). When clicking the ranking folder 460 for each medium, the ranking evaluated for each of the various media is read from the product database 560 and presented to the user 300 (step 220), and if the expert rating folder 470 is clicked, Data converted from the opinions evaluated by the experts on the specific product as scores are read from the product database 560 and presented to the user 300 (step 222), and when the comprehensive evaluation folder 480 is clicked, the consumer preference data and The score data that comprehensively evaluated the expert rating data is read from the product database 560 and presented to the user 300 (step 224). When the bulletin board folder 490 is clicked on, the user can input his / her opinion, and a bulletin board for referencing another user's opinion is displayed (step 226).

A user who accesses the server 100 of the present invention checks the various evaluation data described above, and purchases a product he / she wants to purchase through a shopping mall provided by the system of the present invention or another shopping mall linked to the system of the present invention. The user can search and purchase from the database 510 and perform shopping in which payment for the purchased goods is collectively performed (step 230). This shopping process is conventional and a detailed description thereof will be omitted.

At this time, the user accesses the server 100 of the present invention while surfing the various products of the shopping mall provided by the present invention, the consumer's behavior, behavior or traces for the goods, for example, the user's access to the surfing products The time and the like are registered in the user log database 520, and the online ranking processing method for the sales ranking for a specific product selected and purchased by a consumer connected to the server 100 of the present invention and sold. Processing is converted into scores. The user log data stored in the user log database 520 may be later analyzed and referred to the evaluation of consumer preferences by gender, gender consumer preferences, and regional consumer preferences classified by age, gender, region, education, income distribution, and purchase amount. have.

Referring now to FIG. 3, there is shown a flow chart for generating various evaluation data in accordance with the present invention.

3A illustrates a process of generating consumer preference data, wherein a user 300 connected to the system of the present invention searches for information of each product from the initial screen shown in FIG. Starts with the step of selecting (step 310).

In operation 312, the server 100 searches the shopping mall database 510 for details of the product selected by the user 300, and inputs an evaluation opinion of the user 300 for the product together with the retrieved details. To the computer of the user 300. The evaluation opinion input screen includes items that are considered to reflect consumer preferences, for example, evaluation items such as product design, robustness, performance, after-sales service, convenience, function, and stability, and their respective items. Two criteria are presented to reflect consumer preferences. The first criterion is the consumer's own opinion on each of the items described above, for example, "very bad", "bad", "just so", "good", "very good", etc. For each item described above, opinions of the degree of importance to the consumer, for example, "not important", "moderate", "important", "very important", and the like.

In the next step 314, the user 300 checks the details of the selected specific product, and with reference to the two criteria mentioned above for his opinion or evaluation in a voting manner for each evaluation item for the product. Enter each one. The evaluation opinions of the consumers entered in this way are stored in the consumer preference database 550, and the consumer evaluation opinions registered in the consumer preference database 550 are retrieved at the request of the user 300 as described with reference to FIG. 2. It may be provided to the user 300.

In a next step 316, the evaluation of the product voted by the user 300 is converted into a score in the following manner. In other words, for evaluation opinions such as "very bad", "bad", "just so", "good", "very good", and the like, points of different levels, for example, "0-4" points, Similarly, scores of different levels, for example, "0-3" points, are converted to importance opinions such as "not important", "moderate", "important", and "very important".

Table 1 shows the results of generating the final consumer preferences by evaluating the evaluation criteria of the specific product selected by the consumers based on the two criteria described above by 50 consumers and converting the evaluation opinions into scores.

In the above Table 1, the importance average value calculated for each item is calculated as a percentage (%) as in the following equation, and is used as the weight of each corresponding evaluation opinion item.

Weight = {(totality of importance-mean of importance) / totality of importance} × 100

The weight of each item calculated in Equation 1 above is multiplied by the average value of the evaluation opinions of the corresponding items as shown in Equation 2, and then averaged to generate the above-described consumer preference value.

Consumer preference = Σ (mean rating × weight) / number of items

Similarly, for other products, the consumer preference value is generated through the above-described method, and the calculated consumer preference plays a role in ranking the products that consumers think, and the consumer preference value is determined by the code of the product. Associated together and registered with the database 550 as consumer preference data (step 318).

Referring to FIG. 3B, a process of generating as data reflecting the ranking of products evaluated by media such as various newspapers, broadcasts, magazines, etc., wherein the user 300 accesses the system of the present invention either online or offline. In the state, the process starts with selecting a product for ranking input from the initial screen shown in FIG. 4 (step 320).

The user 300 inputs a ranking determined for each product by a calculation method as described with reference to FIG. 3A for items such as design, robustness, performance, after-sales service, convenience, function, and stability for each product, or each medium. The ranking data determined by using a specific method performed in step is input for each product (step 322). The ranking thus entered is registered in the media ranking database 570 (step 324). The media ranking data registered in the media ranking database 570 may be searched and provided to the user 300 according to the request of the user 300 as described with reference to FIG. 2.

FIG. 3C is a process of generating expert evaluation data, which begins with the step of an expert who is connected to the system of the present invention on-line or off-line to select a product to be evaluated by the initial screen shown in FIG. 4 (step 330).

In step 332, the server 100 retrieves the details of the product selected by the expert from the product database 560 with reference to the product code and evaluates the user 300 for the product along with the retrieved details. A screen for inputting an opinion is provided to the computer of the user 300.

At this time, the items considered in reflecting the opinions of experts are the design, robustness, performance, after-sales service, convenience, function, stability, and the like of the consumer preference described in FIG. 3A. The importance opinion is also calculated in the same manner as the calculation method of consumer preference described in FIG. 3A and associated with the code data of the corresponding product and registered in the expert opinion database 540 (step 338).

When reflecting the opinions of the experts, the expert group has expertise unlike the consumer group, so that when entering the importance opinion, the rating or ranking can be directly assigned.

Meanwhile, the expert opinions registered in the expert opinion database 540 are listed in the bulletin board established in the present invention, and the expert opinions listed in the bulletin board can be confirmed by the user 300 connected to the system of the present invention. After the procedure, it is possible to record the recommendation of the user 300 who agrees with the expert opinion. Recommendations that agree with expert opinion can be used as an addition point for expert evaluation data, as described later.

Referring to FIG. 3D, a flowchart for generating comprehensive evaluation data obtained by comprehensively evaluating the consumer preference data and the expert evaluation data generated in FIGS. 3A, 3B, and 3C is illustrated.

In step 350, a shopping mall database, along with expert rating data values, consumer preference data, and media ranking data for a particular product stored in the expert opinion database 540, consumer preference database 550, and media ranking database 570. The product sales rank is read from 510.

Next, in step 352, different weights are assigned to various data read from each database to generate comprehensive evaluation data. For example, we assign 10% to product sales ranking data, 20% to consumer preference data, 30% to expert rating data, and 30% to media ranking data. At this time, as can be seen from the assigned weight, the weighting of the expert rating data and the ranking data for each medium is higher than that of the consumer preference data, and as described above, the number of recommendations for the expert opinion posted on the bulletin board is 10. By assigning percentage points, a comprehensive evaluation data is generated. As a result, a comprehensive evaluation of each product is performed and the corresponding score is calculated.

In step 354, the server 100 selectively finds products whose calculated scores fall below a predetermined reference score and then removes them from the product database 560.

Thereafter, the comprehensive evaluation data of the product generated through the above-described process in step 356 and satisfying the reference score or more is stored in the product database 560 in association with the corresponding product code (step 356). The comprehensive evaluation data registered in the product database 560 may be searched and provided to the user 300 at the request of the user 300 as described with reference to FIG. 2.

As described above, in the product purchase information service system of the present invention, by providing the purchase information to the user to help in making a product purchase decision, it is possible to prevent the product purchase from being disadvantaged and to determine a more clear product purchase intention. There will be.

Claims (5)

In the product purchase information service method for helping users who are connected to the product purchase information service system through the Internet to make a decision to purchase a product, Expert evaluation data generation step of storing the data of the evaluation of the expert group for a plurality of evaluation items of a specific product in a database; A consumer preference data generation step of storing user preferences of a plurality of evaluation items of the specific product in a database; Generating rank data for each medium by storing the rank of each medium for the specific product in a database; A comprehensive evaluation data generation step of generating data comprehensively evaluating expert evaluation data, consumer preference data, and ranking data for each medium stored in the database and storing the data in a database; And providing the expert evaluation data, consumer preference data, ranking data for each medium, and comprehensive evaluation data stored in the database to the user according to a request of a user connected to the service system. . The method of claim 1, wherein each of said expert rating data and consumer preference data generation steps are: Setting a plurality of evaluation items for the product; Receiving evaluation opinions of respective expert groups and consumer groups for each of the evaluation items; Converting evaluation opinions for each of the evaluation items into respective scores; Obtaining a weight for each of the evaluation items; And generating the consumer preference data by weighting a weight of each evaluation item to a score for the corresponding evaluation item, respectively. The method of claim 2, wherein the weight is: The method of claim 1, characterized in that the importance calculated by each consumer group and expert group for each of the evaluation items is calculated as a percentage. The method of claim 1, wherein the comprehensive evaluation data generation step: And generating the comprehensive evaluation data by applying different weights to the expert evaluation data, consumer preference data, and ranking data for each medium. 5. The method of claim 4, wherein the highest weight is applied to the expert evaluation data in the comprehensive evaluation data generation step.