JP2000029931A - Marketing data gathering method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To acquire marketing data regarding whether information is selected or provided according to characteristics of individual users by gathering marketing data obtained by evaluating and analyzing operation that a user makes according to a rule used for the information selection and the contents of the operation. SOLUTION: A rule analyzing process part 204 analyzes and executes the description of a rule in page data, a condition retrieval part 206 retrieves whether or not there are data described in the condition part of the rule, and an executing process part 208 performs the process described in the execution part of the rule where the execution condition is established. A rule evaluation log generation part 212 records an analyzing process state in a rule evaluation log 800 and an action log 216 records the operation contents in an action log 1100. A log analyzing process part 220 generates various analytic data and presents them to a server manager. Thus, the marketing data obtained by evaluating and analyzing the operation that the user has done according to the rule used for the information selection and the operation contents are gathered.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention determines the characteristics of a user who provides information according to a preset rule, selects and outputs information, and based on the user's action on the output information, The present invention relates to a marketing data collection method for collecting market data obtained by evaluating and analyzing set rules, and more particularly to a technique effective when applied to a marketing data collection method using an interactive online information providing system.

[0002]

2. Description of the Related Art With the maturation of the consumer society, marketing methods are going to replace the traditional mass marketing with the personal marketing which grasps the needs of each customer and responds in detail. Behind the possibility of personal marketing is the development of information processing technology such as databases and communication technology. For example, the spread of computers and the Internet to the general public. From this, WWW (World Wide We
There is also a move to use the online information provision system represented by b) as a direct marketing channel with customers. As one of means for realizing personal marketing on the online information providing system, some rules are set in advance, and by using the rules, characteristics of the accessing user are determined and suitable for each user. There is a method of providing information (service).

For example, in a shopping site, rules such as “if the hobby is XX, display a newly published advertisement in the genre of XX”, “if the family's birthday is one week later, introduce a gift corner” Setting is conceivable. This allows the user to carefully select what they want instead of searching for what they want to buy, and the shop can also propose products that suit the user and increase the likelihood of purchase. Recommending products to high people can increase sales.

The characteristics of each user as input information to the rules include the user's own profile (age, gender, occupation, interest ...), access status (date / time, location ...), site There is a wide range of activities (such as what information you saw and what you saw ...). Also, for the rule that determines the output information by judging it, by combining simple rules, it is possible to set a complicated rule that can respond to various situations and select appropriate information. Such a combination of rules is called a rule set. The quality of a personal marketing system depends not only on the quality and quantity of information to be provided, but also on the accuracy of rules for selecting the information according to the characteristics of individual users.

[0005] When an online information providing system is taken as a tool for marketing activities, a process of collecting, analyzing, and feeding back a customer's reaction to the information provided therein is naturally required.

[0006] In a personal marketing system, the purpose is to provide information that fits each user, and thus the user's satisfaction with the provided information is one index. As a measure for measuring this, for example, in the WWW, the number of accesses (how many people have viewed the Web page) is widely used. For advertisements with small images placed on web pages called banner advertisements, a more rigorous measurement of the effect of the advertisement is required, and the number of clicks on the banner advertisement button (how many people were interested in the advertisement and asked for more information) ) And the ratio of the number of clicks to the number of accesses is also used. Also, as a measure for evaluating satisfaction with the entire site, a method of counting the number of repeaters instead of the total number of accesses is also used. These can be said to be data equivalent to audience ratings in broadcast media.

[0007] From the viewpoint of an intelligent search system based on rules, the above-described personal marketing system can be said to be a type of expert system. As a technique for improving the performance of the expert system, for example, as disclosed in Japanese Patent Application Laid-Open No. Hei 8-30456, the number of fires / inference executions of each rule is calculated as a hit ratio for each rule, and a high hit ratio is calculated. In order to improve the efficiency of the inference, there is a technique that changes the inference execution order so that the inference engine can be applied to the inference engine first. Here, the number of times of inference execution of the rule is the number of times that the input information has been evaluated as to whether the input information matches the execution condition of the rule, and the number of fires is the number of times that the input has matched the execution condition and the rule has been executed. is there.

[0008]

SUMMARY OF THE INVENTION As a result of studying the above prior art, the present inventor has found the following problems.

[0009] The number of accesses and the number of clicks, which can be said to be a WWW audience rating, can only be data as to which information was presented to the user. There is a good possibility that the same information will be presented to the user through different backgrounds, and in the example of the rules described above, it was introduced in the new issue corner in "Hobby is an art appreciation person" A case can be considered in which the art book is proposed as a suitable gift for a "person whose family's birthday is near".

[0010] The purpose of the personal marketing system is to provide information in accordance with the characteristics of the user. In order to provide the same or more satisfactory information even when the same user accesses the next time, simply provide the information. It is necessary to grasp not only what information is provided, but also what attribute the user has selected. What is important in a personal marketing system is how much provided information (service) fits user needs. If you only provide the information requested by the user,
It can be considered that the number of accesses reflects the interest of the user to some extent. However, whether or not the user really feels satisfied, interesting, or helpful with the information automatically selected by the system based on the rules is not clear just by counting the number of times of access.

Data such as the number of clicks on banner advertisements and the number of repeaters on a site are effective measures when capturing users by mass, but the degree of satisfaction of each user cannot be determined from this. Furthermore, the degree of interest and depth of certain information varies depending on the user, and it cannot be estimated just by looking at the information or not.

While a general expert system is constructed in such a way that knowledge, experience, technology, and the like of a pre-existing expert are made into rules, a personal marketing system has a firm foundation for such rules. There is no such thing.

[0013] In a personal marketing system that deals with human sensibilities, it is impossible to design an optimal rule from the beginning. Even if an optimal rule is completed at a certain point in time, it is inevitable that the accessing user itself changes as a group or as an individual, so the rule must be changed with the passage of time. The cycle of setting hypotheses based on experience gained in the real world outside the network, designing rules based on them, and verifying and correcting the hypotheses based on the results of execution, always provides better information to users. It can be said that it is a shortcut for.

Therefore, it is not meaningful to apply a technique for improving performance on the premise that an optimum rule exists as disclosed in Japanese Patent Application Laid-Open No. Hei 8-30456 to a personal marketing system. In the personal marketing system, there is no guarantee that the executed rule is a good rule suitable for the situation, and it is impossible to compare the hit rates among the set rules.

What is needed is data that enables verification and analysis for rule improvement.

An object of the present invention is to specify the characteristics of individual users according to which information is selected and provided.
An object of the present invention is to provide a marketing data collection method capable of obtaining marketing data.

[0017] Another object of the present invention is to provide a marketing data collection method capable of obtaining marketing data regarding a user's interest in provided information.

Still another object of the present invention is to provide a marketing data collection method capable of comparing the execution status of set rules.

The above and other objects and novel features of the present invention will become apparent from the description of the present specification and the accompanying drawings.

[0020]

SUMMARY OF THE INVENTION Among the inventions disclosed in the present application, the outline of a representative one will be briefly described.
It is as follows. According to the rules set in advance, the characteristics of the user providing the information are determined, the information is selected and output, and the market data obtained by evaluating and analyzing the set rules based on the user's action on the output information is analyzed. A method for collecting market data, wherein data for evaluating the setting rule is recorded as rule evaluation log data, and the number of times of evaluation and execution of each rule are calculated from the rule evaluation log data, and the number of times of evaluation is calculated. When the user performs an operation on the information selected and output by the execution of the set rule, rule execution status information indicating the execution status of each rule in the group to which the rule belongs is created from the execution rule and the execution count. The data relating to the content of the operation, the data relating to the information targeted for the operation, and the information The data related to the rule used for the selection is recorded as action log information, and from the rule evaluation log data, rule execution status information and the action log information, the operation performed by the user is used for the information selection. rule,
And marketing data evaluated and analyzed according to the contents of the operation.

[0021]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of a personal marketing system to which the marketing data collecting method of the present invention is applied will be described below with reference to the drawings. In the present embodiment, a case will be described in which a product proposal and related information are provided according to the characteristics of a user, taking a membership-based online shop as an example.

FIG. 1 is a block diagram showing an example of a system configuration of a personal marketing system to which the marketing data collecting method of the present embodiment is applied.

As shown in FIG. 1, in the personal marketing system of the present embodiment, a personal marketing system client 100 (hereinafter simply referred to as a client) and a personal marketing system server 130 (hereinafter simply referred to as a server) are connected to a network line 120. The configuration of the client server system connected via the.

In FIG. 1, reference numeral 100 denotes a client used by a user, which displays an online shop screen on a display device 102, and a keyboard 104 and a mouse 106.
Use to interact with the shop. The speaker 108 is used for outputting information including sound. The communication device 114 communicates with the server 13 via the network line 120.
0 is accessed. Reference numeral 112 denotes a main memory, and 116 denotes a storage device, which stores programs and various types of information for extracting information on products and performing shopping. A control device 110 controls all of them.

In the server 130, the display device 1
The server administrator uses the keyboard 32, the keyboard 134, and the mouse 136 for input and output for replacing various data and generating analysis data from log information. 142 is the main memory, 150 is the product DB, 152 is the content D
B and 154 are member DBs and 156 is a log information DB that stores various programs and data used for providing information to the user. The product DB 150 stores various data for managing products handled in the online shop of the present embodiment, and includes a product information table 1000. The content DB 152 stores data used to generate information provided to the user, and includes page data 300, a rule set table 500, a rule table 600, and a content table 700. The member DB 154 stores information on the accessing user, and includes a member information table 400. Also, the log information DB 15
6 stores data generated in association with the provision of information to the user, and includes a rule evaluation log 800, an action log 11
00 is included. The communication device 144 is a client 100
The controller 140 controls all of these.

The configuration of the system shown in FIG. 1 is merely an example, and other configurations are possible, for example, by distributing information about users in the storage device 116 on the client side.

FIG. 2 is a diagram showing an example of a software configuration of the personal marketing system of the present embodiment. In FIG. 2, reference numeral 200 denotes a server program of the personal marketing system. A communication control unit 218 performs a process of receiving a request from the client 100 via the OS 222 and returning data according to the request. When a personal marketing system is constructed using WWW functions, this part is realized by a WWW server. 202 is a page generation processing unit, and a page (screen data) provided to the user in accordance with the request received by the communication control unit 218
Is generated. When generating a new page, the page analysis processing unit 210 analyzes the description of the page, calls up necessary data, and generates screen data for displaying the page on the display device 102 of the user. The rule analysis processing unit 204 analyzes and executes the description of the rule included in the page data, and the condition search unit 206 performs a search to determine whether or not the data described in the condition part of the rule exists. The unit 208 performs the processing described in the execution unit of the rule for which the execution condition is satisfied. Reference numeral 212 denotes a log generation processing unit. The rule evaluation log generation unit 212 reports the status of the processing performed by the rule analysis processing unit 204 to the rule evaluation log 80.
0, and the action log 216 records the contents of the operation performed by the user in the action log 1100. Further, the log analysis processing unit 220 generates various analysis data based on the log information and presents the generated analysis data to the server administrator.

FIG. 3 shows page data 3 using rules.
00 and an example of a screen displayed by the execution. FIG. 3A is a diagram schematically illustrating a method of describing page data 300.
Reference numerals 2 and 304 are descriptions of rules included therein. FIG.
(B) and FIG. 3 (C) are examples of screens generated as a result of interpretation of the page description, and 310 and 320 are screens presented to different users, respectively. The interpretation of this page description is based on a member information table that stores data about the user. Here, the personal computer counter of the online shop of the present embodiment is displayed, and the information of the new product information 312 and the information of the advertisement 314 are selected and displayed according to the characteristics of each user by using the rules 302 and 304. I have.

FIG. 4 is a diagram showing an example of the member information table 400. In FIG. 4, 402 is a user ID registered in the online shop, and 404 is a password used for user authentication in the online shop. Also, name 406, address 408, age 410, gender 412, occupation 414, occupation type 416, personal computer history 418,
The hobby 420 and the like are registered when becoming a member of the online shop, and are appropriately changed in accordance with changes in the contents. In the present embodiment, a rule processing procedure using data registered in the member information table 400 in advance will be described.
Of course, there is other data that reflects the characteristics of the user. For example, each time a user accesses, information on the time, access location (work or home, etc.), and client environment (desktop or mobile, etc.) It is also possible to acquire and record the information and provide information according to the information.

FIGS. 5 and 6 show examples of a rule set table 500 and a rule table 600 used for rule definition. In FIG. 5, an area 502 stores a rule set ID, and an area 504 stores a rule set name, which is used for identifying a rule set. An area 506 stores a rule ID of a rule group forming a rule set.

In FIG. 6, reference numeral 602 denotes an area for storing a rule ID used for identifying a rule; 604, a condition part of the rule; and 606, an area for storing a description of a rule execution part. In the present embodiment, for the sake of simplicity, as shown in FIG. 6, it is assumed that the rules constituting the rule set have an or relationship, and are evaluated from the top in the order in which they are stored in the rule ID 506. When one rule hits, the following rules shall not be processed.

FIG. 7 is a diagram showing an example of a contents table 700 for storing data relating to contents constituting information provided to a user. In FIG. 7, 70
An area 2 stores a content ID used for identifying the content. Target ID stored in 704
Is data for identifying what information the content is, and is used for tracing operations performed by the user and information provided to the user. Reference numeral 706 denotes an area for storing data indicating the type of the content. Reference numeral 708 stores data relating to a page linked from the content, here, a page ID used for identifying the page in the server. An area 710 stores a keyword related to the content. 712 stores the data type of the content to be provided to the user. According to the content, the data itself is stored if the data type is text, and the data itself is stored if the data type is large-capacity media data such as an image. File name is 71
4 is stored.

FIG. 8 is a diagram showing an example of items recorded in a rule evaluation log 800 for recording the status of rule evaluation and execution. In FIG. 8, reference numeral 802 denotes data relating to a user who provides information; 804, data relating to the time at which the evaluation is performed; 806, data relating to a page on which rules are set; 808, a rule set to which the evaluated rule belongs. Data 810 is an item in which data on the evaluated rule is recorded. Also, 8
Reference numeral 12 denotes a result of the evaluation of the rule, which records whether or not the execution condition of the rule is satisfied. Reference numeral 814 denotes an item in which data relating to the content to be displayed as a result of execution of the rule condition is recorded.

Next, the page data 300 as shown in FIG. 3A is interpreted to output screen data as shown in FIGS. 3B and 3C, and a rule evaluation log 800 is generated. The procedure will be described with reference to the flowchart in FIG. The data referred at this time are the above-described member information table 400, rule set table 500, rule table 600, and content table 700.

The procedure for generating the rule evaluation log 800 is as follows.
As shown in FIG. 9, first, when a new page is generated in response to a request from the user received by the communication control unit 218, the page generation processing unit 202 searches for the corresponding page data 300, and the page analysis processing unit 210
Reads the description for each unit of interpretation (step 90
0). In the present embodiment, the description is processed for each line. It is determined whether the read description is a rule (step 902). If not, the page description is interpreted (step 904), and display data is generated accordingly (step 906).

If the description is a rule, the rule analysis processing unit 204 searches the rule set table 500 for a corresponding rule set from the described rule set ID (step 908), and constructs the rule set. A rule is searched from the rule ID 506 (step 9)
10). In the case of the rule description 302 shown in FIG.
Since “rule set ID = 3”, a rule set named “PC new product information” is set on the page, and this rule set has rule ID = 31, 32, 3
It is composed of 3, 50, 51 and 55 rules.

The contents of the first rule found by the search are searched from the rule table 600 (step 91).
2) It is determined whether the contents described in the condition part 604 are satisfied (step 914). The first rule that composes the rule set "PC New Product Guide" is Rule I
This rule is D = 31, and the rule is to be executed when the “occupation” of the user is “sales”.
The condition search unit 206 interprets this, searches the member information table 400 for the data of the user who has requested this page, and checks whether the job type 416 is “sales”.

If the job type 416 of the user in the member information table 400 shown in FIG. 4 is "sales" and the condition is satisfied, then the content to be presented to the user is searched according to the description of the rule execution unit 606. (Step 9
16), display data is generated using the corresponding content (step 918). Rule ID = 3 shown in FIG.
Rule 1 describes that contents having a keyword of note type are displayed from contents of the type "PC new product".
Searching from 0, content ID = 70658, 7924
0 is detected, and the data is used to calculate 31 in FIG.
2 is generated. At this time, by adding data relating to the used rule to each content, when recording an operation on the content in the action log 1100, data relating to the rule can be added as one of the recording items.

When the execution of the rule is completed, the execution status is recorded in the rule evaluation log 800 (step 92).
0). In the case of FIG. 3B, with respect to the rule with the rule ID = 31, the evaluation result 812 is “True”, and it is recorded that the contents with the content IDs = 70658 and 79240 are displayed.

If the execution condition of the rule is not satisfied, the rule is not hit in the rule evaluation log, that is, the fact that the evaluation result 812 is “False” is recorded (step 922), and the rule 506 of the rule set is recorded. Is checked (step 924), and if there are still rules to be evaluated, the process returns to step 910 to determine the rule I of the next rule.
Read D602.

For example, when the job type 416 of the user is a technical job, the second rule of the rule set “PC new product guide”, the rule with the rule ID = 32, is executed, and FIG.
A screen like (C) is output, and the rule evaluation log 800
Records the evaluation status of two rules of rule ID = 31 and 32.

In this embodiment, as described above, since all the rule groups constituting the rule set are in the relation of or, the execution of the rule set ends when one rule hits. Even if no rule is hit, the execution of the rule set ends when all rules in the rule set have been evaluated.

Page data 30 according to the page description
When the generation of 0 or the execution of the rule set specified by the rule description is completed, it is checked whether or not a description remains in the page data 300 (step 926). Interpret and execute

FIG. 10 is a diagram showing an example of the product information table 1000, which is used for shopping processing and product management in the online shop of this embodiment.
In FIG. 10, 1002 is a product number used for product management, 1004 is a target ID of the product, 1006 is a product name, 1008 is a product classification, 1010 is a price, 101
An area 2 stores the specifications of the product.

The target ID stored in 1004 is
It is provided for each product, and is stored in the target ID 704 when content related to the product is registered in the content table 700. By using the same target ID commonly for advertisements, product catalogs, order forms, etc., it is possible to determine how information about the same subject is provided to the user and how the user responded to it. Can be traced.

In this embodiment, an example of a product is described. However, the present invention is not limited to a product, but can be applied to general information provided to a user. For example, it is conceivable to assign a target ID to various types of event information and discount coupons. It is not always necessary to assign one target ID to one content.
There may also be content with.

FIG. 11 is a diagram showing an example of items recorded in the action log 1100 for recording an operation performed by the user on the personal marketing system. FIG.
, 1102 is data relating to the user who performed the operation, 1104 is data relating to the time at which the operation was performed, 1
Reference numeral 106 denotes an operated page, and reference numeral 1108 denotes an item for recording data relating to the content subjected to the operation.

Data related to the operation performed by the user is recorded in 1110. In the case of the present embodiment, regarding the products sold in the online shop, "look" (whether or not information on the products has been seen) or "choice" ( There are actions such as whether the product was added to the purchase list) and "order" (whether the product was actually ordered). For example, if a user clicks on an ad for a product to view its detailed information,
The action "look" is recorded. Also, 111
2 is the target ID of the content that was operated
Is recorded, and a rule ID 1114 is recorded as data relating to a rule used for selecting the content.

FIG. 12, FIG. 13, FIG. 14 and FIG.
FIG. 9 is a diagram showing an example of analysis tables 1200 to 1500 generated by a log analysis processing unit 220 using data of a rule evaluation log 800 and an action log 1100.

In FIG. 12, an analysis 1200 extracts, from the rule evaluation log 800, the number of times of evaluation and the number of times of execution of each rule constituting a rule set within a certain period, and calculates and displays a hit ratio, an execution share, and an evaluation ratio. It is a table.

Reference numeral 1202 denotes a rule set ID, 1204 denotes a rule ID of a rule constituting the rule set, 1206
Indicates the execution condition part of each rule. 12
08 is the number of times of evaluation of each rule, and 1210 is the number of times of execution. In this embodiment, the number of times of evaluation of the first rule of the rule set is the number of times of evaluation of the rule set. 1
Reference numeral 212 denotes a rule hit ratio, which is obtained by calculating the number of times of execution of each rule / the number of times of evaluation of each rule. 1
Reference numeral 214 denotes a rule execution share, which is obtained by calculating the number of executions of each rule / the number of evaluations of the rule set. Reference numeral 1216 denotes a rule evaluation rate, which is obtained by calculating the number of evaluations of each rule / the number of evaluations of the rule set.

From these data, the following information can be read. That is, the rule execution share 1214 indicates the ratio of the user who has viewed the content displayed by executing the rule set for each attribute. In the example of FIG. 12, the user who saw the PC new product information has many sales, technical, and design occupations, and it is effective to carry out product development and promotion activities in the shop for these occupations. Conceivable. There are extremely few office and sales users, and measures to attract these users to the shop should be implemented. Alternatively, since there are quite a lot of users who hit “condition = default” which is not any job type, there is a possibility that there is a problem in setting job types such as office work and sales. In this regard, the registered contents of the rule set and the member information table 400 By reviewing it, it is possible to improve the situation to reflect the actual situation of the user.

The data of the hit rate and the evaluation rate are as follows:
It is a character that depends on the order of evaluation of each rule constituting the rule set and its result, the evaluation rate gradually decreases according to the order in which the rules are evaluated, and the hit rate is conversely the same number of hits The higher the rating, the higher it will be. Therefore, it is meaningless to simply compare the numerical values themselves, but by looking at how the rules change, for example, focusing on extremely large fluctuations or prominent parts, the order of rule evaluation, Problems related to the settings can be found.

The rule ID = none at the bottom is
It shows the number of times no rule in the rule set was hit. In this example, since there is a rule of “condition = default”, the data is all 0. If not, however, no rule is hit and the data indicates the rate at which the rule set did not occur. For example, if the rule execution condition is not satisfied, record the data indicating the status of the user who is the target of the determination if the rule execution condition is not satisfied. From the above, it is possible to clarify the types of rules to be added to the rule set and the improvements of the current rules.

As described above, by analyzing the content provided to the user by using the rule element used for selecting the content, the relationship between the characteristics of the user and the content is clarified, and a better service is provided. For marketing activities.
At the same time, it is possible to read the presence of inappropriate rule settings from these data, and the improvement of such rules can improve the quality of the personal marketing system itself.

In FIG. 13, reference numeral 1300 denotes a reaction extracted by the user from the rule evaluation log 800 and the action log 1100 with respect to the product represented by the content displayed by each rule, and indicates the ratio of the reaction according to the stage. It is an analysis table. 1302 is a rule ID of each rule, 1304 is an execution condition part of the rule, 13
Reference numeral 06 indicates the type of rule. 1308
Is the number of times each rule has been executed during the period of the analysis target, 1310 is the number of times that the content displayed by executing the rule has been selected and the item information has been viewed, and 1314 is the number of times that the same item has been put into a shopping basket and added to the purchase list. , 13
Numeral 18 is the number of times the product was actually purchased. These data are extracted using the target ID of each content as a key. Also, 1312 is the ratio of the number of times the product information was viewed 1310 to the number of times the rule was executed (the number of times the content was displayed) 1308, and 1316 is the number of times 1314 that the product was placed in the basket relative to the number of times the product information was viewed 1310 The ratio, 1320, is the number of times 1318 that the product was actually purchased compared to the number of times 1314 that the product was put in the basket.
The percentage of each is displayed.

The data shown in the analysis table 1300 is
It is a tracing of how deeply the user was involved in the content expressed in each content, not just whether the content was viewed individually. You can read the degree. In other words, it indicates how much each piece of information fits the user's interest and how much power he had to appeal to the user.

Further, by analyzing each rule, it is possible to find out a difference in behavior depending on the characteristics of the user. In the example of FIG. 13, the user of “job type = sales” and the user of “job type = technical” have almost the same number of orders, but the user of “sales” has been carefully added to the purchase list until the decision is made. There is a different user profile that always buys things, in contrast, users of "technology" feel free to add to and delete from the purchase list, and change the method of providing information according to each characteristic, This can help to provide services more suited to the user.

In FIG. 14, reference numeral 1400 denotes an analysis table in which rules having the same condition are extracted from the rule evaluation log 800, the rule table 600, and the content table 700, and the content displayed by each rule is compared and displayed. 1402 is a description of the condition used for extracting the rule, 1404 is the extracted rule, 1406 is the execution count of each rule, 1408 is the content ID of the content displayed by each rule, 1410 is the type of content, and 1412 is the target of the content. The ID is displayed.

A rule executed under the same condition;
By displaying a list of contents selected by the rule, it is possible to grasp the type, content, and amount of information presented to a user having certain characteristics. As a result, it is possible to consider the distribution of the balance of information that the user sees, so that strategies such as avoiding monotonous provision of only the same quality of information, and conversely, applying a quantitative offensive to the information to be focused on are considered. Can stand.

In FIG. 15, reference numeral 1500 denotes the number of evaluations of each rule set extracted from the rule evaluation log 800, displayed in comparison with a preset target value, and also shows a change along the time axis. It is an analysis table. 1502 is a rule set to be analyzed, 1504 is a target value of each rule set, 1506, 1508, 151
0 and 1512 indicate the number of evaluations of the rule set in each period.

By setting a target value for each rule set, if the actual condition is lower than the target value, the cause is investigated and countermeasures are performed. If the actual condition is significantly higher than the target value, rules are set in detail and contents are set. Services such as frequent replacement of services can be provided.
Here, the comparison is performed on a rule set basis, but of course, an execution target value may be set on a rule basis and compared with the actual situation.

Further, by observing the change along the time axis, it is possible to grasp a tendency such as a change in the characteristics of the user or a change in interest over time, and to change the service accordingly. .

In addition to the analysis tables shown in FIGS. 12, 13, 14 and 15, information on rules, information on contents, and data recorded in the rule evaluation log and the action log are used as keys, and Extraction and analysis are possible, and a combination of the axes shown so far can be used. Which item is designated as the key of the analysis depends on which viewpoint needs to be analyzed, that is, it is determined by the purpose and operation policy of each personal marketing system. In this embodiment, an example of application to an online shop has been described.
For example, when the personal marketing system of the present invention is applied to an education system, a target ID is set for each field of each teaching material, and analysis is performed using the target ID as a key, whereby the type of the teaching material and the distribution of contents can be viewed.

As described above, according to the present embodiment,
Since it is possible to know the evaluation status of the rule used for providing information to the user, it is possible to grasp what characteristics of each user the information has been selected and provided.

Further, by tracing the action performed by the user on the provided information, it is possible to grasp how the user is interested in the information.

Furthermore, data is accumulated not only for the executed rules but also for the rules that have been evaluated but not executed, and the rules for which the evaluation itself has not been performed are included in the rules for each user and rule set. Can provide marketing data that can be compared and analyzed from other aspects, such as the execution status of the data and changes over time.

As described above, the invention made by the present inventor is:
Although specifically described based on the embodiment, the present invention
It is needless to say that the present invention is not limited to the above-described embodiment, but can be variously modified without departing from the scope of the invention.

[0069]

The effects obtained by the representative ones of the inventions disclosed in the present application will be briefly described as follows. Since it is possible to know the evaluation status of the rule used for providing information to the user, it is possible to grasp what characteristics of each user the information has been selected and provided. Further, by tracing the action performed by the user on the provided information, it is possible to grasp how the user is interested in the information. Furthermore, data is accumulated not only for executed rules but also for rules that have been evaluated but not executed, and for rules that have not been evaluated, the execution status of rules for each user and rule set. And marketing data that can be compared and analyzed in other aspects, such as changes over time.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating an example of a system configuration of a personal marketing system to which a marketing data collection method according to an embodiment of the present invention is applied.

FIG. 2 is a diagram illustrating an example of a software configuration of the personal marketing system according to the embodiment.

FIG. 3 is a diagram showing an example of page data 300 using a rule and a screen displayed by execution thereof.

FIG. 4 is a diagram showing an example of a member information table 400.

FIG. 5 is a rule set table 5 used for rule definition
It is the figure which showed the example of 00.

FIG. 6 is a diagram showing an example of a rule table 600.

FIG. 7 is a diagram showing an example of a content table 700 for storing data relating to content constituting information provided to a user.

FIG. 8 is a diagram showing an example of items recorded in a rule evaluation log 800 that records the status of rule evaluation and execution.

FIG. 9 is a flowchart illustrating a procedure for generating a rule evaluation log 800.

FIG. 10 is a diagram showing an example of a product information table 1000.

FIG. 11 is an action log 1100 that records an operation performed on a personal marketing system by a user.
It is a figure showing an example of a record item.

FIG. 12 is a diagram illustrating an example of an analysis table generated by a log analysis processing unit 220;

FIG. 13 is a diagram illustrating an example of an analysis table generated by a log analysis processing unit 220;

FIG. 14 is a diagram illustrating an example of an analysis table generated by a log analysis processing unit 220;

FIG. 15 is a diagram illustrating an example of an analysis table generated by a log analysis processing unit 220;

[Explanation of symbols]

100: Personal marketing system client, 102, 132: Display device, 104, 134: Keyboard, 106, 136: Mouse, 108: Speaker, 110, 140: Control device, 112, 142: Main memory, 116: Storage device, 130 … Personal marketing system server, 150… Product DB, 152
... Content DB, 154 ... Member DB, 156 ... Log Information DB, 200 ... Server Program, 300 ... Page Data, 400 ... Member Information Table, 500 ... Rule Set Table, 600 ... Rule Table, 700 ... Content Table, 800 ... Rule Evaluation log, 1000: Product information table, 1100: Action log, 1200
~ 1500 ... Analysis table.

 ──────────────────────────────────────────────────の Continuing on the front page (72) Inventor Manabu Ishikawa 2-2-2 Kagahara, Tsuzuki-ku, Yokohama-shi, Kanagawa Prefecture Within the System Development Division, Hitachi, Ltd. Chome No.2 F-term in Hitachi, Ltd. System Development Division (Reference) 5B049 BB11 CC00 EE05 FF07 GG02 GG09

Claims (6)

[Claims] 1. According to a preset rule,
A market data collection method that determines the characteristics of a user who provides information, selects and outputs information, and collects market data that evaluates and analyzes the set rules based on a user's action on the output information. The data for evaluating the setting rule is recorded as rule evaluation log data, the number of evaluations and the number of executions of each rule are calculated from the rule evaluation log data, and the number of evaluations and the number of executions of each rule are calculated. Creates rule execution status information indicating the execution status of each rule in the group to which it belongs, and when the user performs an operation on the information selected and output by the execution of the set rule, Data, data about the information that was the subject of the operation, and rules used to select the information. Data as action log information, and from the rule evaluation log data, rule execution status information and the action log information, the operation performed by the user, the rule used for the information selection, and the content of the operation. A marketing data collection method characterized by collecting marketing data evaluated and analyzed according to the conditions. 2. The rule evaluation log data includes data on an evaluated rule, data on a rule evaluation result, data on the accessed user, data on a rule evaluation time, data on a content selected by execution, and a rule. 2. The marketing data collecting method according to claim 1, wherein data on a group to which the group belongs and data on a page on which rule evaluation has been performed are recorded. 3. The marketing data according to claim 1, wherein, as the information indicating the execution status of the rule, data indicating a ratio of the number of executions of each rule to the number of evaluations of each rule is created. Collection method. 4. The information indicating the execution status of the rule is defined as:
2. The marketing data collection method according to claim 1, wherein data indicating a ratio of the number of executions of the rule is created. 5. The information indicating the execution status of the rule includes:
2. The marketing data collection method according to claim 1, wherein data indicating a ratio of the number of evaluations of each rule is created. 6. The method according to claim 1, wherein the rule evaluation log data is divided in units of a predetermined time, and information indicating an execution status of the rule is created and output for each period. Marketing data collection method described in.