JP2001236405A - Device and method for supporting sales promotion, and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To properly recommend an article or service matching the taste of a user flexibly, in compliance with the sales strategy, etc., of the seller side. SOLUTION: This device is equipped with a personal characteristics acquisition part 17, which acquires personal characteristics information on the user, an action characteristics acquisition part 19 which finds action characteristics of the user by utiliging the history of net surfing, and rule-setting part 21 which sets recommended rules, and a recommendation engine 23 determines whether the personal property information and action characteristics information obtained as to a user having visited a web page meet satisfies the conditions of the rule. When the conditions are satisfied, the target determined by the rule is recommended for recommending an article matching the taste to the user and the personal characteristics and action characteristics are optionally combined, to flexibly generate various rules which match the sales strategy.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for supporting sales promotion, and a recording medium, and more particularly to an apparatus and method for recommending goods and services suitable for individual users on a network such as the Internet. It is suitable.

[0002]

2. Description of the Related Art Conventionally, advertising has been widely used as a means for promoting sales of goods or services. Advertisement methods include flyers distributed in stores and on the street, insert advertisements such as newspapers and magazines, advertisements using mass media such as television and radio, and direct mail targeting specific users. .

[0003] In recent years, in particular, various advertisements have been performed using web pages opened on the Internet with the widespread use of computers and the development of network technology. In addition to online advertisements, online shopping or online sales for buying and selling products published on web pages on the Internet are also becoming popular.

By the way, most of the conventional advertisements are transmitted from a seller to an unspecified number of people. In this case, advertising is not performed only for those who are interested in the product or service, so that there is much waste and the advertising effect is not sufficient. Therefore, in order to enhance the advertising effect, a method of accumulating personal attributes and past purchase histories of each person and performing advertisements according to the personal attributes and purchase histories to individual persons has been used. Was.

[0005] An advertisement using such a purchase history, etc.
It is also used in recent systems for advertising on web pages. That is, the personal attribute (name, gender, date of birth, etc.) and purchase history (purchased product, purchase time, price, quantity, etc.) obtained when a user visits a web page such as online sales and purchases a product are stored in a database. Some methods have been proposed in which the advertisement is accumulated according to the personal attribute and the purchase history.

[0006]

However, in the conventional advertisement system performed on the Internet, even if the advertisement uses personal attributes or purchase histories, the contents of the advertisement to be provided are not provided by the seller (such as the Internet sales or the like). Page administrator).

[0007] That is, in the case of advertising using personal attributes, for example, individual users are divided into classes according to age or gender, and the seller appropriately decides according to which class the user simply belongs to. Only selectively offered ads. Also, in the case where an advertisement is performed using the purchase history, for example, an advertisement such as a predetermined upgraded product or a related product is merely provided for the purchased product.

As described above, in the conventional advertisement system, the advertisement is merely provided in accordance with the mere classification result of the user based on the personal attribute or the purchase history, and the content of the advertisement to be provided is fixed. Therefore, from the viewpoint of the seller, it is difficult to provide an advertisement reflecting various sales strategies at that time, and from the viewpoint of the user, the provided advertisement is not necessarily a preference of the user. However, there is a problem that the advertising effect is not improved more than expected because it is not always suitable for the application.

The present invention has been made to solve such a problem, and flexibly responds to a sales strategy of a seller and recommends an appropriate product or service suitable for a user's preference. The purpose is to be able to achieve higher advertising effectiveness by being able to do so.

[0010]

SUMMARY OF THE INVENTION A sales promotion support apparatus of the present invention is a sales promotion support apparatus for recommending a product that meets a user's preference on a network, and acquires information relating to a user's personal attribute. Personal attribute acquiring means, behavior characteristic acquiring means for obtaining the user's behavior characteristic using the access history of the user on the network, and a condition using at least one of the user's personal attribute and behavior characteristic; A rule setting means for setting a rule including a target to be recommended when the condition is satisfied;
The personal attribute information acquired by the personal attribute acquisition unit and the behavior characteristic information acquired by the behavior characteristic acquisition unit determine whether or not a condition in a rule set by the rule setting unit is satisfied. Recommendation execution means for recommending a target in the rule.

Here, the rule setting means may be able to set a recommended product itself or a group of products belonging to the same category as the target. Further, the rule setting means may be capable of setting a plurality of rules.

Further, the sales promotion support method of the present invention is a sales promotion support method for recommending a product that matches a user's preference on a network, wherein at least one of a user's personal attribute and behavioral characteristics is used. A condition and a rule comprising a target to be recommended when the condition is satisfied are set, and personal attribute information obtained for a certain user and behavior characteristic information obtained by using the access history on the network for the certain user Determines whether the conditions set as the rules above are met,
When satisfying, the target in the rule is recommended.

Here, as a target of the above rule,
The recommended product itself or a group of products belonging to the same category may be settable. Further, a plurality of the above rules may be set.

Further, a computer-readable recording medium according to the present invention is characterized in that a program for causing a computer to function as each means according to any one of claims 1 to 3 is recorded. According to another aspect of the present invention, a program for causing a computer to execute the processing procedure of the sales promotion support method according to any one of claims 4 to 6 is recorded.

[0015]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram illustrating an example of the overall configuration of a network system to which a sales promotion support device according to the present embodiment is applied. As shown in FIG. 1, in the network system of the present embodiment, a seller terminal 1 and a user terminal 2 ₋₁ ,
_Are connected to each other.

The seller terminal 1 has a web page related to online sales at a shopping mall or the like, and sells various products (in the present specification, the term “product” includes services) online. The user terminals 2 _-1 , 2 _-2 ,... Can access the web page of the seller terminal 1 via the network 3 and purchase desired products. At this time, the user terminals 2 _-1 , 2 _-2 ,... _Can receive a recommendation of a product matching the user's preference from the seller terminal 1 as needed.

In FIG. 1, one seller terminal 1 is used.
Although two user terminals 2 _-1 , 2 _-2 ,... _Are shown, the number is not limited to this. In FIG. 1, the network 3
Although only the seller terminal 1 and the user terminals 2 _-1 , 2 _-2 ,... Are connected above, a dedicated advertisement server device for executing the function of the sales promotion support device according to the present embodiment is connected to the seller terminal 1. May be provided separately. In this case, the subject of the seller terminal 1 and the advertisement server device may be different.

FIG. 2 is a block diagram showing a main functional configuration of the seller terminal 1 according to the present embodiment. Each block shown here constitutes a sales promotion support device of the present embodiment.

In FIG. 2, a product information storage section 11 stores information on various products including products posted on a web page of the seller terminal 1. The product information stored here is, for example, product master information as shown in FIG. The merchandise master information stores the category, type, genre, maker (artist name), merchandise name, and the like to which the merchandise belongs, arranged hierarchically as shown in FIG. In this embodiment, link information (item-genre) between a product name and a genre and link information (item-maker) between a product name and a maker are also stored.

The URL information storage unit 12 stores information on a tree structure of various pages including a web page of the seller terminal 1. UR stored here
The L (Uniform Resource Locator) information is, for example, URL master information as shown in FIG. The URL master information organizes and stores a URL tree structure from a top page constituting the home page to a lowermost sub page, and image information and other related information on a product posted on each page.

The link unit 13 is connected to the URL information storage unit 1
UR specified from URL information stored in 2
For each of the web pages under L and the resources such as image information contained therein, the product information stored in the product information storage unit 11 and the URL stored in the URL information storage unit 12 are stored. Make an association.

For example, the URL of a product 1 shown in FIG. 3 and an image X.GIF representing the product 1 shown in FIG.
BC.co.jp/xxx/yyy/X.GIF). Product 1
Is linked to the corresponding genre by link information (item-genre), and link information (item-m
aker) is associated with the corresponding maker, and as a result, the relationship between the URL and the corresponding product genre and maker is recognized.
Note that the URL may be directly associated with the genre, and the URL may be directly associated with the manufacturer.

Also, the product category of music shown in FIG. 3 may be associated with the URL (www.ABC.co.jp/xxx/yyy/) of the web page introducing the music shown in FIG. good. In addition, the video shown in FIG.
D, DVD, and the URL of a web page (www.ABC.co.jp/
xxx / yyy /).

The point is that the URL of each page shown in the tree structure as shown in FIG. 4 is linked to which category, type, genre, maker, and product in the product master shown in FIG. Define. This link part 1
3 are stored in the related data storage unit 14.

The access history acquisition unit 15 acquires a URL history when a user accesses a web page, that is, a so-called net surfing history. This U
The RL history information is information indicating which user has accessed which URL. The URL history information acquired by the access history acquisition unit 15 is stored in the access history information storage unit 16.

The URL history information acquired here includes:
For example, only the web surfing history for the web page of the seller terminal 1 itself may be used. In addition, the history of the web surfing for a plurality of registered web pages existing on the network 3 is stored at a place other than the seller terminal 1 and accessed via a network such as a paper medium, a removable storage medium, or the Internet. The history acquisition unit 15 may acquire it.

The personal attribute acquiring section 17 acquires information on personal attributes of each user and stores the information in the personal attribute information storage section 1.
8 is stored as a database. The personal attribute information includes, for example, name, zip code, address (prefecture, city, town, town, etc.), telephone number, e-mail address, nationality, age, gender, occupation, educational background, computer OS used, ISP ( Information such as Instruction Set Processor, browser, credit card (company, number, etc.), condominium / owned house, married / unmarried, family structure, etc. can be considered.

The personal attribute acquiring section 17 sequentially acquires, for example, personal attribute information input when a user accesses a web page of the seller terminal 1 and purchases a product. In addition, the personal attribute input when accessing the web page opened by another seller, not limited to the personal attribute input when accessing the own web page, is stored in a place other than the seller terminal 1. It is also possible for the personal attribute acquiring unit 17 to acquire the same via a paper medium, a removable storage medium, or the network 3 such as the Internet. Further, instead of waiting for the user to visit the web page and input the personal attribute information, a questionnaire or the like may be performed to actively acquire the personal attribute information.

The behavior characteristic acquiring unit 19 uses the association information stored in the associated data storage unit 14 and the URL history information of surfing stored in the access history information storage unit 16 to request a user to recommend a product. Find favorite genre, maker, product type, category, etc. That is, the behavior characteristic acquiring unit 19 regards the genre, maker, type, category, and the like of the product associated with the web page accessed more or longer for a user's favorite genre. Acquired as behavioral characteristic information of net surfing. The behavior characteristic information acquired by the behavior characteristic acquisition unit 19 is stored in the behavior characteristic information storage unit 20.

The rule setting section 21 is for the seller to arbitrarily set rules used when recommending a product by the recommendation engine 23 described later, and includes input devices such as a keyboard and a mouse. The rule set here is composed of a condition part defined using at least one of the personal attribute information and the behavior characteristic information, and a target part defining a recommended product or the like. That is, the rule setting unit 21 creates a condition from a combination of the personal attribute information and the behavior characteristic information, and recommends a product or the like recommended when the condition is satisfied in the product information stored in the product information storage unit 11. Register as a target from.

The condition part can be created by arbitrarily combining one or more pieces of personal attribute information and one or more pieces of behavior characteristic information. It is also possible to create the condition part using only one of the personal attribute information and the behavior characteristic information. Regarding the target section, it is possible to register not only the recommended product itself but also the genre, maker, type, category, and the like of the product as a target.

For example, as an example of a rule, when setting a rule that “recommend a woman who loves skiing in her 20s in Tokyo to the Naeba Prince Hotel”, “Tokyo”,
“Twenties” and “female” are conditions created for personal attribute information, and “I like skiing” is a condition created for behavior characteristic information. “Naeba Prince Hotel” is a target recommended when all of the above conditions are satisfied.

The “Naeba Prince Hotel” shown here
The target may be a final product, or may correspond to a genre, a maker, or the like.
For example, if "Naeba Prince Hotel" is considered a genre, the final product can be "Suite Room", "Semi-Suite Room", "Twin Room", "Single Room", etc. Will recommend these final products. Whether the “Naeba Prince Hotel” is the final product or the genre or maker may be set when the seller creates a rule.

Note that the rules set by the rule setting unit 21 are actually SQL (Structured Query Language).
e) Described by a statement or the like. This rule setting unit 21
Are set in the rule information storage unit 22. At this time, the seller can create a plurality of different rules in accordance with various sales strategies and register them in the rule information storage unit 22.

The recommendation engine 23 includes personal attribute information on various users stored in the personal attribute information storage unit 18;
By performing a predetermined process based on the behavior characteristic information relating to various users stored in the behavior characteristic information storage unit 20 and the rule information stored in the rule information storage unit 22, the user seeking a product recommendation can be A product matching the user's preference is taken out of the product information storage unit 11 and recommended.

That is, the recommendation engine 23 first uses the identification information (for example, cookie information) of the user who has visited the web page of the seller terminal 1 to obtain personal attribute information and behavior related to the user. The characteristic information is read from the personal attribute information storage unit 18 and the behavior characteristic information storage unit 20, respectively.

Next, the read personal attribute information and behavior characteristic information are compared with various rules stored in the rule information storage unit 22 to check whether there is a rule satisfying the condition. If there is a rule that satisfies the condition, the product indicated by the rule is recommended. When a genre, a maker, a product type, a category, or the like is set as a target instead of the product itself, one or more products belonging to the genre, the maker, or the like are extracted from the product information storage unit 11, and are extracted. Output as a product list recommended to the user.

The recommendation engine 23 may be activated when a user visiting the web page of the seller terminal 1 requests a recommendation of a product. It may be started automatically when accessed.

Note that the product information storage unit 11 shown in FIG.
The URL information storage unit 12, the related data storage unit 14, the access history information storage unit 16, the personal attribute information storage unit 18, the behavior characteristic information storage unit 20, and the rule information storage unit 22 are configured by a storage device such as a hard disk. .
Further, the link unit 13, the access history acquisition unit 15, the personal attribute acquisition unit 17, the behavior characteristic acquisition unit 19, and the rule setting unit 21
And the recommendation engine 23 are, for example, CPU, ROM, R
It is composed of a microcomputer having an AM or the like.

As described above, in the present embodiment, a recommendation rule is set by arbitrarily combining personal attribute information and behavior characteristic information, and when a condition specified by the rule is satisfied, a product designated as a target is selected. Since the recommendation is made, it is possible to recommend a product that better suits the taste for each user. Moreover, the content of the recommended product dynamically changes in conjunction with the behavior characteristic information based on daily surfing by the user himself. As a result, the advertisement effect can be significantly improved as compared with one-way advertisement based on the seller's own judgment.

The present embodiment also has the advantage that the seller can flexibly create a sales plan by arbitrarily setting various rules instead of one fixed simple rule. Have. Regarding the setting of this rule, not only can the condition part be set by arbitrarily combining personal attribute information and behavior characteristic information, the target to be recommended can specify not only the product itself but also the genre and manufacturer, etc. Recommendation rules can be made more flexibly according to the seller's sales strategy. In addition, it is possible to specify a user who matches the seller's sales strategy and recommend products, and recommend products only to interested users.

It should be noted that the sales promotion support apparatus of the present embodiment described above includes a computer CPU or MPU,
It is configured by a RAM, a ROM, and the like, and can be realized by operating a program stored in the RAM or the ROM. Therefore, a program that causes a computer to operate to perform the above functions is stored in, for example, a CD-RO.
This can be realized by recording the data on a recording medium such as M and reading it into a computer. As a recording medium for recording the above program, in addition to the CD-ROM,
Floppy disks, hard disks, magnetic tapes, magneto-optical disks, nonvolatile memory cards, and the like can be used.

The functions of the above-described embodiments are implemented when the computer executes the supplied program, and the OS (operating system) or other application software running on the computer in the computer. When the functions of the above-described embodiment are realized in cooperation with the computer, or when all or a part of the processing of the supplied program is performed by a function expansion board or a function expansion unit of a computer, the functions of the above-described embodiment are realized. Such a program is also included in the embodiment of the present invention.

Further, in order to use the present invention in a network environment, all or some of the programs may be executed by another computer.

The above-described embodiment is merely an example of an embodiment for carrying out the present invention, and the technical scope of the present invention should not be interpreted in a limited manner. It is. That is, the present invention can be embodied in various forms without departing from the spirit or main features thereof.

For example, in the above embodiment, as a prerequisite for the processing by the recommendation engine 23, the personal attribute information of each user is acquired in advance and the personal attribute information storage unit 1 is obtained.
8, when a user visiting the web page of the seller terminal 1 requests a recommendation of a product,
The user himself / herself may input necessary personal attribute information.

In the above embodiment, the user's behavior characteristic information is also obtained in advance and stored in the behavior characteristic information storage unit 20. However, when the processing by the recommendation engine 23 is performed, the seller terminal 1 The behavior characteristic information may be obtained in real time from the URL history information of the user who has visited the web page.

[0048]

As described above, according to the present invention, a rule using at least one of a user's personal attribute and behavioral characteristics and a target recommended when the condition is satisfied is set and obtained for a certain user. It is determined whether or not the behavior characteristic information obtained by using the obtained personal attribute information and the access history on the network satisfies the condition set as the rule, and when it satisfies the condition, recommends a target in the rule. So,
It is possible to recommend a product that better suits the taste for each user. In addition, the content of the recommended product can be dynamically changed in conjunction with the behavior characteristic information based on daily surfing by the user himself. As a result, the advertising effect can be significantly improved as compared with a one-way advertisement based on the seller's own judgment.
In addition, it is possible to specify a user who matches the seller's sales strategy and recommend products, and recommend products only to interested users.

Further, according to the present invention, the conditions of the rules can be set by arbitrarily combining the user's personal attributes and behavioral characteristics, and the recommended target is not limited to the product itself, but also to the genre and manufacturer. And so on can be specified as a group in which similar products are grouped, so that rules can be made more flexibly according to the seller's sales strategy. In addition, by setting and registering a plurality of rules, a specified target can be recommended if any of the rules is satisfied, so that the sales promotion plan can have variations.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating an example of the overall configuration of a network system to which a sales promotion support device according to an embodiment is applied.

FIG. 2 is a block diagram illustrating a configuration example of a seller terminal (sales promotion support device) according to the embodiment;

FIG. 3 is a diagram showing an example of product master information.

FIG. 4 is a diagram illustrating an example of URL master information.

[Explanation of symbols]

_{DESCRIPTION OF SYMBOLS} 1 Seller terminal _2-1 and _2-2 user terminal 3 Network 11 Merchandise information storage unit 12 URL information storage unit 13 Link unit 14 Related data storage unit 15 Access history acquisition unit 16 Access history information storage unit 17 Personal attribute acquisition unit 18 Personal attribute information storage unit 19 Behavior characteristic acquisition unit 20 Behavior characteristic information storage unit 21 Rule setting unit 22 Rule information storage unit 23 Recommendation engine

Claims (8)

[Claims] 1. A sales promotion support device for recommending a product that matches a user's preference on a network, comprising: a personal attribute acquisition unit for acquiring information on a user's personal attribute; A behavior characteristic acquisition unit that obtains the behavior characteristic of the user by using the access history; a condition using at least one of the personal attribute and the behavior characteristic of the user; and a rule including a target recommended when the condition is satisfied. A rule setting unit to be set, and whether the personal attribute information acquired by the personal attribute acquisition unit and the behavior characteristic information acquired by the behavior characteristic acquisition unit satisfy a condition in a rule set by the rule setting unit. And recommendation execution means for recommending a target in the above rule when the condition is satisfied. Sales promotion support device to. 2. The sales promotion support apparatus according to claim 1, wherein the rule setting means can set, as the target, a recommended product itself or a group in which products belonging to the same category are put together. 3. The sales promotion support device according to claim 1, wherein the rule setting means can set a plurality of rules. 4. A sales promotion support method for recommending a product that matches a user's preference on a network, comprising: a condition using at least one of a user's personal attribute and a behavioral characteristic; A rule consisting of a target to be recommended is set, and the personal attribute information obtained for a certain user and the behavior characteristic information obtained by using the access history on the network for the certain user are the conditions set as the rule. A sales promotion support method characterized in that it is determined whether or not a target is satisfied, and when the target is satisfied, a target in the above rule is recommended. 5. The sales promotion support method according to claim 4, wherein the target of the rule can be a recommended product itself or a group of products belonging to the same category. 6. The sales promotion support method according to claim 4, wherein a plurality of the rules can be set. 7. A computer-readable recording medium on which a program for causing a computer to function as each of the means according to claim 1 is recorded. 8. A computer-readable recording medium on which a program for causing a computer to execute the processing procedure of the sales promotion support method according to claim 4 is recorded.