JP2012073880A - Investment amount determination support device, investment amount determination support method, and investment amount determination support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To support determination of an amount of money to be actually paid by presenting how much expense needs to be paid for an effective SEO measure for each search keyword used for a search through a search engine.SOLUTION: An evaluation value calculation part 151 calculates an evaluation value of effect of a SEO measure for all candidate search keywords, based upon values obtained by multiplying a charge set for each search keyword in the SEO measure by a weight coefficient of each search keyword, and an effect value obtained by digitizing effect obtained when a goal page is accessed. A weight coefficient adjustment part 152 adjusts the weight coefficient of each search keyword so that the evaluation value calculated by the evaluation value calculation part 151 is optimized. An investment amount output part 142 outputs values, obtained by multiplying charges stored in a keyword unit charge table 102 by the weight coefficients adjusted by the weight coefficient adjustment part 152, for each search keyword as optimum investment amounts of money for the SEO measure.

Description

  The present invention relates to an investment amount determination support apparatus, an investment amount determination support method, and an investment amount determination support program. The present invention particularly relates to a method for optimally distributing costs related to search keywords.

  There is web analysis software that collects traffic statistics data for search keywords used for search in a search engine (see, for example, Patent Document 1).

Special table 2009-514113 gazette

  When purchasing search keywords for SEO (search engine optimization) for a fee (using so-called keyword advertising), website operators use web analysis software, etc. To extract. However, even if a keyword is visited by a large number of visitors, if you do not know that the visitor has finally reached the goal page (the page that the website operator wants to visit), You can't know the cost and effectiveness of search keywords.

  In addition, it is difficult to determine the combination of the search keywords that are most effective in a limited budget by using conventional web analysis software.

  An object of the present invention is, for example, to show how much cost is effective for each search keyword and to support the determination of the amount actually paid.

An investment amount determination support apparatus according to an aspect of the present invention includes:
Each of a plurality of search keywords used for a search in the search engine is a measure to be performed so that a page (web page) in the website is displayed in a higher rank in the search engine search results. An investment amount determination support device that supports the determination of the investment amount for a measure for which a fee for carrying out is set in advance,
For each search keyword, a charge storage unit that stores the charge set in the countermeasure by a storage device;
An effect value storage unit that stores an effect value obtained by quantifying an effect obtained when a user who visits the website reaches a specific page within the website based on a search result of the search engine by a storage device; ,
The countermeasure for the plurality of search keywords based on a value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the charge storage unit and the effect value stored in the effect value storage unit An evaluation value calculation unit for calculating the evaluation value of the effect of the processing device,
A weighting factor adjusting unit that adjusts the weighting factor for each search keyword by a processing device so that the evaluation value calculated by the evaluation value calculating unit satisfies a predetermined condition;
An investment amount output unit that outputs a value obtained by multiplying the charge stored in the charge storage unit by the weight coefficient adjusted by the weight coefficient adjustment unit for each search keyword by the output device as an investment amount for the countermeasure. Prepare.

  The weighting factor adjusting unit adjusts the weighting factor for each search keyword so that the evaluation value calculated by the evaluation value calculating unit is optimized according to a heuristic algorithm.

The investment amount determination support device further includes:
Among the users who visited the website based on the search results of the search engine in the past, a number of users that have reached the specific page is stored as a number of arrivals by a storage device storage unit,
The evaluation value calculation unit uses the value obtained by multiplying the fee for each search keyword stored in the fee storage unit by the weighting factor for each search keyword, and the number of arrivals stored in the number-of-arrivals storage unit. The evaluation value is calculated based on a value obtained by multiplying the effect value stored in the value storage unit.

The investment amount determination support device further includes:
A threshold value input unit that receives an input of an upper limit value of the investment amount by an input device;
The weighting coefficient adjusting unit satisfies the evaluation value calculated by the evaluation value calculating unit with the predetermined condition, and sets the weighting factor for each search keyword to the fee for each search keyword stored in the fee storage unit. The weighting factor for each search keyword is adjusted so that the total value of the multiplied values is equal to or less than the upper limit value input to the threshold value input unit.

The investment amount determination support device further includes:
For each search keyword, a visitor number storage unit that stores, as a visitor number, the number of users who visited the website in the past based on the search results of the search engine,
The weight coefficient adjustment unit corresponds to a search keyword in which the evaluation value calculated by the evaluation value calculation unit satisfies the predetermined condition and the weight coefficient is greater than 0 among the plurality of search keywords. The weighting factor for each search keyword is adjusted so that the total number of visitors stored in the visitor number storage unit is equal to or greater than a predetermined lower limit value.

The investment amount determination support device further includes:
For each search keyword, a visitor number storage unit that stores, as a visitor number, the number of users who have visited the website in the past based on the search results of the search engine;
For each search keyword, among the users who visited the website in the past based on the search results of the search engine, the number of users who have reached the specific page is stored in the storage device as the number of arrivals. With
The weight coefficient adjustment unit corresponds to a search keyword in which the evaluation value calculated by the evaluation value calculation unit satisfies the predetermined condition and the weight coefficient is greater than 0 among the plurality of search keywords. For each search keyword, the ratio of the total number of visitors stored in the number of visitors storage unit and the total number of visitors stored in the number of arrivals storage unit is equal to or greater than a predetermined threshold. Adjust the weighting factor.

An investment amount determination support method according to an aspect of the present invention includes:
This is a measure implemented so that pages in a website are displayed in a higher rank in search engine search results, and is implemented for each of a plurality of search keywords used for search in the search engine. An investment amount determination support method for supporting the determination of the investment amount for a measure for which a charge for setting is set in advance,
For each search keyword, the computer stores the fee set for the countermeasure by a storage device,
The computer stores, by a storage device, an effect value obtained by quantifying an effect obtained when a user visiting the website reaches a specific page in the website based on a search result of the search engine,
Based on the value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the storage device and the effect value stored in the storage device, the computer executes the countermeasure for the plurality of search keywords. The evaluation value of the effect is calculated by the processing device,
The computer adjusts the weighting factor for each search keyword by a processing device so that the evaluation value to be calculated satisfies a predetermined condition,
The computer outputs, for each search keyword, a value obtained by multiplying the adjusted weighting factor by the charge stored in the storage device as an investment amount for the countermeasure by the output device.

An investment amount determination support program according to one aspect of the present invention includes:
This is a measure implemented so that pages in a website are displayed in a higher rank in search engine search results, and is implemented for each of a plurality of search keywords used for search in the search engine. An investment amount determination support program for supporting the determination of the investment amount for a measure for which a charge is set in advance,
For each search keyword, the charge set for the countermeasure is stored in the storage device, and the user who visited the website based on the search result of the search engine reaches a specific page in the website In a computer that stores the effect value obtained by quantifying the effect obtained in the storage device,
Based on the value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the storage device and the effect value stored in the storage device, the evaluation value of the effect of the countermeasure for the plurality of search keywords Evaluation value calculation processing for calculating
A weighting coefficient adjustment process for adjusting the weighting coefficient for each search keyword by a processing device so that the evaluation value calculated by the evaluation value calculation process satisfies a predetermined condition;
For each search keyword, an investment amount output process is performed in which a value obtained by multiplying the charge stored in the storage device by the weight coefficient adjusted by the weight coefficient adjustment processing is output by the output device as an investment amount for the countermeasure. .

  The weighting coefficient adjustment process adjusts the weighting coefficient for each search keyword so that the evaluation value calculated by the evaluation value calculation process is optimized according to a heuristic algorithm.

The computer further stores the number of users who have reached the specific page among the users who visited the website based on the search results of the search engine in the past as the number of arrivals by the storage device,
The evaluation value calculation process includes a value obtained by multiplying the weighting factor for each search keyword by a charge for each search keyword stored in the storage device, and an effect value stored in the storage device by the number of arrivals stored in the storage device. The evaluation value is calculated based on the value multiplied by.

The investment amount determination support program further includes:
Causing the computer to execute a threshold value input process for receiving an input of an upper limit value of the investment amount by an input device;
In the weight coefficient adjustment process, the evaluation value calculated by the evaluation value calculation process satisfies the predetermined condition, and the weight coefficient for each search keyword is multiplied by the charge for each search keyword stored in the storage device. The weighting factor for each search keyword is adjusted so that the total value is equal to or less than the upper limit value input in the threshold value input process.

The computer further stores, for each search keyword, the number of users who have visited the website in the past based on the search results of the search engine as the number of visitors by a storage device,
The weighting factor adjustment processing stores the evaluation value calculated by the evaluation value calculation processing that satisfies the predetermined condition and that corresponds to a search keyword having a weighting factor greater than 0 among the plurality of search keywords. The weighting factor for each search keyword is adjusted so that the total number of visitors stored in the apparatus is equal to or greater than a predetermined lower limit.

The computer further stores, for each search keyword, the number of users who have reached the specific page among the users who have visited the website based on the search results of the search engine in the past as the number of visitors. For each search keyword, the number of users who have reached the specific page among the users who visited the website based on the search results of the search engine in the past is stored as the number of arrivals by the storage device. ,
The weighting factor adjustment processing stores the evaluation value calculated by the evaluation value calculation processing that satisfies the predetermined condition and that corresponds to a search keyword having a weighting factor greater than 0 among the plurality of search keywords. The weighting factor for each search keyword is adjusted so that the ratio between the total number of visitors stored in the device and the total number of visitors stored in the storage device is equal to or greater than a predetermined threshold.

  According to one aspect of the present invention, the investment amount determination support apparatus causes a page in a website to be displayed higher in the search engine search results for each search keyword used for a search in the search engine. Therefore, it is possible to output the investment amount for the measures to be implemented to support the determination of the investment amount.

FIG. 3 is a diagram illustrating an example of a usage pattern of the investment amount determination support device according to the first embodiment. FIG. 2 is a block diagram showing a configuration of an investment amount determination support device according to the first embodiment. FIG. 3 is a diagram illustrating an example of a hardware configuration of an investment amount determination support device according to the first embodiment. The figure which shows an example of access history DB of the investment amount determination assistance apparatus which concerns on Embodiment 1. FIG. The figure which shows an example of the keyword unit charge table of the investment amount determination assistance apparatus which concerns on Embodiment 1, a goal page effect table, and a constraint condition table. 5 is a flowchart showing the operation of the investment amount determination support apparatus according to the first embodiment. The figure which shows an example of the calculation result of the investment amount determination assistance apparatus which concerns on Embodiment 1. FIG.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

Embodiment 1 FIG.
FIG. 1 is a diagram illustrating an example of a usage form of an investment amount determination support apparatus 100 according to the present embodiment.

  In FIG. 1, a user terminal 201 is a computer used by a visitor (user), and operates a web browser for the visitor to browse web pages. The user terminal 201 is, for example, a PC (personal computer).

  The web server 202 is a server computer that realizes a website composed of a plurality of web pages, and distributes the web pages to the user terminal 201.

  The user terminal 201 and the web server 202 perform IP (Internet Protocol) communication via the Internet 203. In IP communication, the user terminal 201 transmits an IP packet to the web server 202 via the Internet 203. Similarly, the web server 202 transmits an IP packet to the user terminal 201 via the Internet 203. A web browser operating on the user terminal 201 and the web server 202 perform HTTP (hypertext transfer protocol) communication using IP communication. In HTTP communication, a web browser operating on the user terminal 201 transmits an HTTP request to the web server 202. The web server 202 returns an HTTP response to the HTTP request. For example, when a visitor selects a link displayed in a search result or the like of a search engine, the web browser transmits an HTTP request specifying a URL (Uniform, Resource, Locator) of the link destination. When receiving the HTTP request, the web server 202 returns an HTTP response including an HTML (Hyper Text Markup Language) file of the web page corresponding to the designated URL.

  The investment amount determination support apparatus 100 is a computer including an access history DB 101, a keyword unit charge table 102, a goal page effect table 103, an interface unit 104, and an analysis unit 105. The investment amount determination support apparatus 100 analyzes the visitor's behavior in the website (that is, performs access analysis or web analysis) using these components, and the goal page (an example of a specific page) of the website. And the search keyword when the website is visited, and the optimal (preferably most effective) keyword / cost sharing combination within the budget is calculated. The investment amount determination support apparatus 100 may be configured by a single computer or may be configured by a plurality of computers (for example, each component may be realized by a separate computer). ).

There are mainly three types of access analysis methods as follows.
(1) Access log type: Each time the web server 202 accesses a web page, the HTTP request or a part of the HTTP response is recorded in an access log in a predetermined format. Access analysis is performed by analyzing the access log.
(2) Beacon (tag) type: A small program (tag) is attached to each web page, and when the visitor's web browser reads the web page, the information acquired by the program is different from the web server 202. Sent to analysis server. The analysis server records this information in a database (DB). Access analysis is performed by analyzing information on this database.
(3) Packet capture type: The packet capture device captures all packets (packets including an HTTP request or an HTTP response) flowing on a network connecting the web server 202 to the outside (Internet 203). The access analysis is performed by restoring the HTTP communication from the captured packet and analyzing the HTTP communication.

  In this embodiment, it is assumed that the investment amount determination support apparatus 100 performs a packet capture type access analysis. The investment amount determination support apparatus 100 may perform access analysis of other methods.

In the present embodiment, visitor access analysis is performed in the following flow.
(1) A visitor searches with a search keyword and visits a website.
(2) The analysis unit 105 writes the number of visitors with the search keyword in the access history DB 101.
(3) A visitor reaches the goal page of the website.
(4) The analysis unit 105 writes the number of goal page arrivals for the search keyword in the access history DB 101.

In the present embodiment, keyword analysis is performed in the following flow.
(5) The person in charge holds in advance the unit fee for each keyword in the keyword unit fee table 102 and the effect for each goal page (converted into the amount) in the goal page effect table 103.
(6) Using the interface unit 104, the person in charge designates the budget (upper limit of cost), the lower limit value of the total number of visitors for each keyword summation, and the lower limit value of the arrival rate.
(7) The analysis unit 105 optimizes the budget within the budget from the search keyword and the number of visitors in the access history DB 101, the number of goal page visitors, the keyword unit charge table 102, and the goal page effect table 103 (desirably, the most effective (High) Search keyword and cost sharing combination.

  As described above, according to the present embodiment, it is possible to present to the person in charge a guideline regarding an optimal (preferably the most effective) combination of search keyword and cost sharing based on the data of web analysis. .

  FIG. 2 is a block diagram illustrating a configuration of the investment amount determination support apparatus 100.

  In FIG. 2, the investment amount determination support apparatus 100 includes the access history DB 101, the keyword unit fee table 102, the goal page effect table 103, the interface unit 104, and the analysis unit 105 as well as the constraint condition table 106 as described above. . The investment amount determination support apparatus 100 includes hardware such as a processing device 191, a storage device 192, an input device 193, and an output device 194. The hardware is used by each unit of the investment amount determination support apparatus 100. For example, the processing device 191 is used to perform calculation, processing, reading, writing, and the like of data and information in each unit of the investment amount determination support device 100. The storage device 192 is used to store the data and information. The input device 193 is used to input the data and information, and the output device 194 is used to output the data and information.

  The access history DB 101 includes a visitor number storage unit 111 and an arrival number storage unit 112. The visitor number storage unit 111 stores, for each search keyword, the number of users who have visited the website in the past based on the search results of the search engine (that is, the number of visitors) by the storage device 192. The number-of-arrivals storage unit 112 stores, for each search keyword, the number of users who have reached the goal page among the users who have visited the website based on the search results of the search engine in the past (that is, the number of arrivals). 192 for storing.

  The keyword unit fee table 102 is an example of a fee storage unit, and the storage device 192 stores a fee set for SEO countermeasures for each search keyword. In this embodiment, as an SEO countermeasure, any countermeasure should be used as long as it is implemented so that pages in the website are displayed higher in search engine search results. Can do. Here, it is assumed that, as a measure for SEO, a charge for implementing each of a plurality of search keywords used for search by a search engine is set in advance. For example, if a keyword advertisement is used as an SEO measure, the keyword unit fee table 102 stores an advertisement fee set by the search engine for each search keyword.

  The goal page effect table 103 is an example of an effect value storage unit, and an effect obtained by quantifying an effect obtained when a user who visits a website reaches the goal page in the website based on a search result of a search engine. The value is stored in the storage device 192. For example, the goal page effect table 103 stores, for each goal page, a value obtained by converting an effect such as a profit obtained by a website operating company by a visitor accessing the goal page into an amount of money.

  The interface unit 104 includes a threshold value input unit 141 and an investment amount output unit 142. The threshold value input unit 141 receives an input of an upper limit value (that is, a budget) of the investment amount for SEO measures from the person in charge of the website management company or the like by the input device 193. Further, the threshold value input unit 141 receives a lower limit of the total number of visitors (that is, the total number of visitors) from the person in charge or the like, and a ratio between the total value of the number of visitors and the total value of the number of visitors (that is, , Arrival rate) and a lower limit value (threshold value). The investment amount output unit 142 outputs the optimum investment amount for the SEO countermeasure calculated by the analysis unit 105 by the output device 194 for each search keyword. Thereby, the person in charge or the like supports the determination of the investment amount for SEO measures.

  The constraint condition table 106 is an example of a condition storage unit, and the storage device 192 stores values (upper limit value, lower limit value, threshold value) input to the threshold value input unit 141.

  The analysis unit 105 includes an evaluation value calculation unit 151 and a weight coefficient adjustment unit 152. The evaluation value calculator 151 multiplies the weighting factor for each search keyword by the charge for each search keyword stored in the keyword unit charge table 102 and the effect value stored in the goal page effect table 103. The processing device 191 calculates an evaluation value (that is, an overall evaluation value) of the effect of SEO countermeasures for all candidate search keywords. The weight coefficient adjustment unit 152 adjusts the weight coefficient for each search keyword by the processing device 191 so that the evaluation value calculated by the evaluation value calculation unit 151 satisfies a predetermined condition. Specifically, the weight coefficient adjustment unit 152 refers to the value stored in the constraint condition table 106 according to the heuristic algorithm so that the evaluation value calculated by the evaluation value calculation unit 151 is optimized (that is, The weighting coefficient for each search keyword is adjusted so that the condition that the evaluation value is the optimum value is satisfied. The analysis unit 105 calculates, for each search keyword, a value obtained by multiplying the charge stored in the keyword unit charge table 102 by the weighting coefficient adjusted by the weighting coefficient adjustment unit 152 by the processing device 191, and uses the calculation result as an SEO measure. To the investment amount output unit 142 as the optimal investment amount.

  FIG. 3 is a diagram illustrating an example of a hardware configuration of the investment amount determination support apparatus 100.

  In FIG. 3, the investment amount determination support apparatus 100 is a computer, and includes an LCD 901 (Liquid / Crystal / Display), a keyboard 902 (K / B), a mouse 903, an FDD 904 (Flexible Disk / Drive), and a CDD 905 (Compact / Disc). Drive) and hardware devices such as a printer 906 are provided. These hardware devices are connected by cables and signal lines. Instead of the LCD 901, a CRT (Cathode / Ray / Tube) or other display device may be used. Instead of the mouse 903, a touch panel, a touch pad, a trackball, a pen tablet, or other pointing devices may be used.

  The investment amount determination support apparatus 100 includes a CPU 911 (Central Processing Unit) that executes a program. The CPU 911 is an example of a processing device 191. The CPU 911 includes a ROM 913 (Read / Only / Memory), a RAM 914 (Random / Access / Memory), a communication board 915, an LCD 901, a keyboard 902, a mouse 903, an FDD 904, a CDD 905, a printer 906, and an HDD 920 (Hard / Disk) via a bus 912. Connected with Drive) to control these hardware devices. Instead of the HDD 920, a flash memory, an optical disk device, a memory card reader / writer, or other storage medium may be used.

  The RAM 914 is an example of a volatile memory. The ROM 913, the FDD 904, the CDD 905, and the HDD 920 are examples of nonvolatile memories. These are examples of the storage device 192. The communication board 915, the keyboard 902, the mouse 903, the FDD 904, and the CDD 905 are examples of the input device 193. The communication board 915, the LCD 901, and the printer 906 are examples of the output device 194.

  The communication board 915 is connected to a LAN (Local / Area / Network) or the like. The communication board 915 is not limited to a LAN, but is an IP-VPN (Internet, Protocol, Private, Network), a wide area LAN, an ATM (Asynchronous / Transfer / Mode) network, WAN (Wide / Area / Network), or the Internet. 203 may be connected. LAN, WAN, and the Internet 203 are examples of networks.

  The HDD 920 stores an operating system 921 (OS), a window system 922, a program group 923, and a file group 924. The programs in the program group 923 are executed by the CPU 911, the operating system 921, and the window system 922. The program group 923 includes programs that execute the functions described as “˜units” in the description of the present embodiment. The program is read and executed by the CPU 911. The file group 924 includes data, information, and signal values described as “˜data”, “˜information”, “˜ID (identifier)”, “˜flag”, and “˜result” in the description of this embodiment. And variable values and parameters are included as items of “˜file”, “˜database”, and “˜table”. The “˜file”, “˜database”, and “˜table” are stored in a storage medium such as the RAM 914 or the HDD 920. Data, information, signal values, variable values, and parameters stored in a storage medium such as the RAM 914 and the HDD 920 are read out to the main memory and the cache memory by the CPU 911 via a read / write circuit, and extracted, searched, referenced, compared, and calculated. It is used for processing (operation) of the CPU 911 such as calculation, control, output, printing, and display. During the processing of the CPU 911 such as extraction, search, reference, comparison, calculation, calculation, control, output, printing, and display, data, information, signal values, variable values, and parameters are temporarily stored in the main memory, cache memory, and buffer memory. Remembered.

  The arrows in the block diagrams and flowcharts used in the description of this embodiment mainly indicate input / output of data and signals. Data and signals are recorded in memory such as RAM 914, FDD904 flexible disk (FD), CDD905 compact disk (CD), HDD920 magnetic disk, optical disk, DVD (Digital Versatile Disc), or other recording media Is done. Data and signals are transmitted by a bus 912, a signal line, a cable, or other transmission media.

  In the description of the present embodiment, what is described as “to part” may be “to circuit”, “to device”, “to device”, and “to step”, “to process”, “to” ~ Procedure "," ~ process ". That is, what is described as “˜unit” may be realized by firmware stored in the ROM 913. Alternatively, what is described as “˜unit” may be realized only by software, or only by hardware such as an element, a device, a board, and wiring. Alternatively, what is described as “to part” may be realized by a combination of software and hardware, or a combination of software, hardware and firmware. Firmware and software are stored as programs in a recording medium such as a flexible disk, a compact disk, a magnetic disk, an optical disk, and a DVD. The program is read by the CPU 911 and executed by the CPU 911. That is, the program causes the computer to function as “to part” described in the description of the present embodiment. Or a program makes a computer perform the procedure and method of "-part" described by description of this Embodiment.

  Hereinafter, the operation of the investment amount determination support apparatus 100 (investment amount determination support method according to the present embodiment, processing procedure of the investment amount determination support program according to the present embodiment) will be described.

  FIG. 4 is a diagram illustrating an example of the access history DB 101.

  In FIG. 4, the access history DB 101 stores data that records which page the site visitor actually saw and what keyword visited. In other words, the access history DB 101 is a DB of the history of visitors to the website in the past (positioned as a result value for each keyword). The access history DB 101 is defined by various schemes such as an access log, a flow line analysis result, and a log shaping result depending on the purpose of use.

  In this example, the access history DB 101 includes the number of visitors (the number of visitors who have reached a page other than the goal page) for each combination of the search keyword and the page in order to hold information necessary for the optimization calculation. ) Is remembered. For example, the access history DB 101 has “30” visitors who have reached the “top page” of the website based on a search result using “XYZ software”, which is the name of the website operating company, as a search keyword. I remember that. Further, for example, the access history DB 101 changes to an “ABC program material request completion page” which is one of goal pages based on a search result using “ABC program” which is one of XYZ software products as a search keyword. Remember that there are “8” visitors.

  In this example, the total number of visitors of all pages corresponding to one search keyword is treated as the number of visitors corresponding to the search keyword. For example, the number of visitors corresponding to “ABC program” is “13” + “47” + “8” + “8” + “1” + “4” = 81 people. Of these, 13 persons who have reached the goal page “ABC program material request completion page”, “DEF program material request completion page”, and “email inquiry completion screen” are the number of arrivals (goal page arrival number). Note that the access history DB 101 may explicitly store the number of visitors and the number of visitors. In this case, the access history DB 101 stores, for each search keyword, the number of visitors (it is not necessary to distinguish which page is visited) and for each combination of the search keyword and the page (only the goal page may be used). The number of arrivals (the number of goal page arrivals) is stored.

  FIG. 5 is a diagram showing an example of the keyword unit fee table 102, the goal page effect table 103, and the constraint condition table 106.

  In FIG. 5, the keyword unit fee table 102 stores data indicating how much investment is made. Specifically, the keyword unit fee table 102 is a fee table that is paid to display a page higher in the search results when a search is performed with a certain keyword in the search engine. In general, the larger the granularity, the larger the amount of money. Therefore, it is necessary to invest a larger amount of money in order to display high-level search results with abstract keywords such as “product”.

  For example, the keyword unit charge table 102 stores that the charge for implementing the SEO countermeasure (specifically, the charge for the keyword advertisement) for the search keyword “XYZ software” is “1000” yen. Yes.

  The goal page effect table 103 stores data indicating how much profit is assumed when a certain page is viewed. That is, the goal page effect table 103 is a table in which the effect is converted into an amount for each goal page defined in the website. For example, it is decided how many yen you will earn if you see a certain page. How much value is defined for each page depends on the purpose of the site and the industry of the company. In general, a goal page is a document request screen, a shopping completion screen, an order completion screen, a member registration screen, a financial application completion screen, or the like.

  For example, the goal page effect table 103 stores that the effect when the “ABC program material request completion page” is accessed is “1500” yen. The goal page effect table 103 also stores the URL of “ABC program material request completion page”.

  The constraint condition table 106 defines conditions that are necessary for calculation and meaningful in marketing. In other words, the constraint condition table 106 is a definition table of constraint conditions required for performing optimization calculation. For example, when the evaluation function for calculating the evaluation value is defined as the sum of the effects, the effect increases as the budget is applied, and the calculation becomes divergent. Therefore, it is necessary to set a constraint condition.

  In this example, the constraint condition table 106 does not greatly deviate from general site operation conditions, and is a basic index in web analysis. The lower limit value is stored. For example, in the constraint table 106, it is assumed that the keyword having the greatest effect is based on the assumption that the budget is “10000” yen, at least “100” people visit the website, and “5”% of people reach the goal page. Constraints are stored such that the proposition is to obtain a combination and cost distribution.

  FIG. 6 is a flowchart showing the operation of the investment amount determination support apparatus 100.

  In step S101 (threshold input processing), the threshold value input unit 141 receives input of budget, the lower limit value of the total number of visitors, and the lower limit value of the arrival rate from the person in charge or the like by the input device 193. The threshold value input unit 141 stores the input budget, the lower limit value of the total number of visitors, and the lower limit value of the arrival rate in the constraint condition table 106. In the example of FIG. 5, “10000” yen as the budget, “100” people as the lower limit value of the total number of visitors, and “5”% as the lower limit value of the arrival rate are input to the threshold value input unit 141. 106. Each value is an arbitrary value of 0 or more.

  In step S102 (weighting factor adjustment processing), in the case of the first processing, the weighting factor adjustment unit 152 generates a random number as a weighting factor for each search keyword by the processing device 191. The second and subsequent processes will be described later.

  In step S103 (evaluation value calculation processing), the evaluation value calculation unit 151 uses the evaluation function to evaluate the effect of the SEO countermeasure when the weighting factor adjusted (or generated) by the weighting factor adjustment unit 152 is applied. Is calculated by the processing device 191. Here, the evaluation function is a function that determines what is used as an evaluation index. In the present embodiment, since it is aimed at an optimal distribution of costs (cost-effectiveness), it is the ratio of the investment amount to the profit. For example, the following evaluation function can be defined.

Evaluation function: Evaluation value I = Σ (goal page effect × number of visitors) / Σ (search keyword unit charge × investment specific gravity) = (page 1 effect × number of visitors + page 2 effect × number of visitors +...) / (Keyword 1 fee x Keyword 1 investment specific gravity + Keyword 2 fee x Keyword 2 investment specific gravity + ...)
Goal page effect (page x effect): an effect value (effect value of page x) for each goal page stored in the goal page effect table 103 (x = 1, 2,...). In the example of FIG. 5, “1500” yen is added as the effect value of “ABC program material request completion page”.
Number of arrivals: Number of arrivals for each goal page stored in the number of arrivals storage unit 112 of the access history DB 101. In the example of FIG. 4, “8” is added as the number of arrivals of the “ABC program material request completion page”.
Search keyword unit charge (keyword y charge): A charge for each search keyword stored in the keyword unit charge table 102 (a charge for keyword y) (y = 1, 2,...). In the example of FIG. 5, “500” yen is added as the fee for the “ABC program”.
Investment specific gravity (keyword y investment specific gravity): A weighting factor adjusted (or generated) by the weighting factor adjusting unit 152. The investment specific gravity is a practical design variable (value randomly given by a random number) in the calculation of the evaluation value. The investment specific gravity is a weight indicating how many times an investment is made for a certain keyword, and is set, for example, in the range of 0.0 to 10.0 (times).

  In the present embodiment, it is assumed that the number of visitors to each goal page has a positive linear correlation with respect to the keyword investment amount.

  As described above, the evaluation value calculation unit 151 stores the value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the keyword unit charge table 102 and the number of arrivals storage unit 112 of the access history DB 101. Based on a value obtained by multiplying the stored number of arrivals for each goal page by the effect value for each goal page stored in the goal page effect table 103, the evaluation value I is calculated by the processing device 191. Accordingly, the most effective keyword and its cost distribution are derived from the past access logs visiting the site with various search keywords. Since past data is used, keywords that have never been accessed in the past are inevitably excluded from optimization.

  If the optimization calculation is completed, the process proceeds to step S104, and if not completed, the process returns to step S102. The analysis unit 105 (the evaluation value calculation unit 151 or the weight coefficient adjustment unit 152) determines whether or not the optimization calculation has been completed. For example, the analysis unit 105 determines that the optimization calculation has been completed when an evaluation value I equal to or greater than a preset threshold value appears in the evaluation value I calculated by the evaluation value calculation unit 151. Alternatively, when GA (genetic algorithm) is used as the optimization method, for example, the analysis unit 105 determines that the optimization calculation is completed when the calculation for the maximum number of generations is completed. Note that the analysis unit 105 may determine whether or not the optimization calculation has been completed according to a determination criterion other than those exemplified here.

  In step S102 (weight coefficient adjustment processing), in the case of the second and subsequent processes, the weight coefficient adjustment unit 152 refers to the value stored in the constraint condition table 106 according to the heuristic algorithm, and is calculated by the evaluation value calculation unit 151. The processing device 191 adjusts the weighting factor for each search keyword so that the evaluation value is optimized. The weight coefficient adjusting unit 152 can use the above-described GA, neural network, or the like as a heuristic algorithm. The present embodiment relates to a multivariable optimization problem (depending on the investment performance, there are many types of search keywords), and the cost-benefit correlation is not always linear (in general, nonlinear optimization) Therefore, it is desirable for the weight coefficient adjustment unit 152 to use the above-described GA as a heuristic algorithm, for example. This makes it possible to provide a highly expandable device. In GA, an evaluation value I that finally survives is obtained as an optimal (highly cost-effective) solution by a method that simulates biological evolution in nature, such as selection, crossover, and mutation. Note that the weight coefficient adjustment unit 152 may use another heuristic algorithm such as a neural network, or may use a mathematical algorithm such as linear programming or nonlinear programming.

  Here, FIG. 7 shows an example of the calculation result.

  In one process of step S102, the weight coefficient adjustment unit 152 uses the processing device 191 to calculate a total value T1 obtained by multiplying the weight coefficient for each search keyword by the charge for each search keyword stored in the keyword unit charge table 102. calculate. Further, the weight coefficient adjustment unit 152 stores the number of visitors in the access history DB 101 corresponding to a search keyword having a weight coefficient larger than 0 (that is, a search keyword to be invested) among a plurality of candidate search keywords. The processing device 191 calculates a total value T2 of the number of visitors stored in the unit 111 and a total value T3 of the number of arrivals stored in the number of arrivals storage unit 112 of the access history DB 101. Then, the weight coefficient adjustment unit 152 has the total value T1 (that is, the total investment amount) equal to or less than the budget stored in the constraint table 106, and the total value T2 (that is, the total number of visitors) is stored in the constraint table 106. Search so that the stored total number of visitors is equal to or higher than the lower limit value and the ratio of the total value T3 to the total value T2 (that is, the reaching rate) is equal to or higher than the lower limit value of the reaching rate stored in the constraint condition table 106. Set the weighting factor for each keyword. For example, in “Solution 2” in FIG. 7, for each search keyword, “1”, “0.9”, “1.1”, “11.6”, “1.4”, “0.3” "," 0 ", etc., weighting factors are set. At this time, the total investment amount is “9980” yen less than or equal to the upper limit value “10000” yen, the total number of visitors is “132” people with a lower limit value “100” or more, and the arrival rate is “ 5.31 "%. “Solution 2” corresponds to one individual in GA. Therefore, there are “solution z” corresponding to the number of individuals set in GA (z = 1, 2,...).

  In step S103, the evaluation value calculation unit 151 inputs the weighting factor for each search keyword set in step S102 as described above to the evaluation function, and calculates the evaluation value I. At this point (that is, the current generation), if the calculation for the maximum number of generations set in the GA has been completed, the optimization calculation has been completed. Otherwise, the optimization calculation has been completed. There will be no. If the optimization calculation is completed, the evaluation value calculation unit 151 selects the maximum value from the calculated evaluation values I. The evaluation value I selected here becomes the optimal solution, and the process proceeds to step S104. For example, in “Solution 2” in FIG. 7, “1.26” is calculated as the evaluation value I, and this is the optimal solution. On the other hand, if the optimization calculation is not completed, the process of step S102 is executed again.

  In step S104 (investment amount output processing), the analysis unit 105 calculates, for each search keyword, a value obtained by multiplying the charge stored in the keyword unit charge table 102 by the weighting coefficient adjusted by the weighting coefficient adjustment unit 152. Calculate according to Then, the analysis unit 105 passes the calculation result to the investment amount output unit 142 as an optimal investment amount for SEO countermeasures. The investment amount output unit 142 outputs the optimum investment amount for the SEO countermeasure calculated by the analysis unit 105 by the output device 194 for each search keyword.

  In step S103, when the optimization calculation is completed, the evaluation value calculation unit 151 may select two or more optimal solutions. For example, the evaluation value calculation unit 151 may select not only the first largest value among the evaluation values I calculated in the last generation but also the second largest value as the optimal solution. Alternatively, the evaluation value calculation unit 151 may select all evaluation values I that are equal to or higher than a preset threshold value as the optimum solution from the evaluation values I calculated in the last generation. In this case, in step S <b> 104, the analysis unit 105 calculates an investment amount for each search keyword for each optimal solution, and passes the calculation result to the investment amount output unit 142. The investment amount output unit 142 outputs the investment amount for each search keyword for each optimum solution. As a result, the person in charge or the like can compare a plurality of search keywords and cost distribution patterns and finally select the one he / she thinks is appropriate.

  As described above, according to the present embodiment, it is understood how much focus should be applied to which keyword based on past data / actual value. For example, is it better to attract a lot of customers to low-value goal pages? You can see if is better. That is, it is possible to take measures in consideration of human judgment and mechanical optimization results. In addition, the range of marketing measures and measures will expand.

  DESCRIPTION OF SYMBOLS 100 Investment amount determination support apparatus, 101 Access history DB, 102 Keyword unit charge table, 103 Goal page effect table, 104 interface part, 105 analysis part, 106 Restriction condition table, 111 Visitor number storage part, 112 Reaching number memory part , 141 Threshold input unit, 142 Investment amount output unit, 151 Evaluation value calculation unit, 152 Weight coefficient adjustment unit, 191 Processing device, 192 Storage device, 193 input device, 194 output device, 201 User terminal, 202 Web server, 203 Internet 901 LCD, 902 keyboard, 903 mouse, 904 FDD, 905 CDD, 906 printer, 911 CPU, 912 bus, 913 ROM, 914 RAM, 915 communication board, 920 HDD, 921 Packaging system, 922 window system, 923 programs, 924 files.

Claims (13)

This is a measure implemented so that pages in a website are displayed in a higher rank in search engine search results, and is implemented for each of a plurality of search keywords used for search in the search engine. An investment amount determination support device that supports the determination of the investment amount for a measure for which a charge is set in advance,
For each search keyword, a charge storage unit that stores the charge set in the countermeasure by a storage device;
An effect value storage unit that stores an effect value obtained by quantifying an effect obtained when a user who visits the website reaches a specific page within the website based on a search result of the search engine by a storage device; ,
The countermeasure for the plurality of search keywords based on a value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the charge storage unit and the effect value stored in the effect value storage unit An evaluation value calculation unit for calculating the evaluation value of the effect of the processing device,
A weighting factor adjusting unit that adjusts the weighting factor for each search keyword by a processing device so that the evaluation value calculated by the evaluation value calculating unit satisfies a predetermined condition;
An investment amount output unit that outputs a value obtained by multiplying the charge stored in the charge storage unit by the weight coefficient adjusted by the weight coefficient adjustment unit for each search keyword by the output device as an investment amount for the countermeasure. An investment amount determination support apparatus characterized by comprising:   The investment according to claim 1, wherein the weighting factor adjustment unit adjusts the weighting factor for each search keyword so that the evaluation value calculated by the evaluation value calculation unit is optimized according to a heuristic algorithm. Amount determination support device. The investment amount determination support device further includes:
Among the users who visited the website based on the search results of the search engine in the past, a number of users that have reached the specific page is stored as a number of arrivals by a storage device storage unit,
The evaluation value calculation unit uses the value obtained by multiplying the fee for each search keyword stored in the fee storage unit by the weighting factor for each search keyword, and the number of arrivals stored in the number-of-arrivals storage unit. 3. The investment amount determination support apparatus according to claim 1, wherein the evaluation value is calculated based on a value obtained by multiplying the effect value stored in the value storage unit. The investment amount determination support device further includes:
A threshold value input unit that receives an input of an upper limit value of the investment amount by an input device;
The weighting coefficient adjusting unit satisfies the evaluation value calculated by the evaluation value calculating unit with the predetermined condition, and sets the weighting factor for each search keyword to the fee for each search keyword stored in the fee storage unit. 4. The investment amount determination according to claim 1, wherein the weighting factor for each search keyword is adjusted so that a total value of the multiplied values is equal to or less than an upper limit value input to the threshold value input unit. Support device. The investment amount determination support device further includes:
For each search keyword, a visitor number storage unit that stores, as a visitor number, the number of users who visited the website in the past based on the search results of the search engine,
The weight coefficient adjustment unit corresponds to a search keyword in which the evaluation value calculated by the evaluation value calculation unit satisfies the predetermined condition and the weight coefficient is greater than 0 among the plurality of search keywords. The investment amount determination according to claim 1 or 2, wherein the weighting factor for each search keyword is adjusted so that the total number of visitors stored in the visitor number storage unit is equal to or greater than a predetermined lower limit value. Support device. The investment amount determination support device further includes:
For each search keyword, a visitor number storage unit that stores, as a visitor number, the number of users who have visited the website in the past based on the search results of the search engine;
For each search keyword, among the users who visited the website in the past based on the search results of the search engine, the number of users who have reached the specific page is stored in the storage device as the number of arrivals. With
The weight coefficient adjustment unit corresponds to a search keyword in which the evaluation value calculated by the evaluation value calculation unit satisfies the predetermined condition and the weight coefficient is greater than 0 among the plurality of search keywords. For each search keyword, the ratio of the total number of visitors stored in the number of visitors storage unit and the total number of visitors stored in the number of arrivals storage unit is equal to or greater than a predetermined threshold. 3. The investment amount determination support apparatus according to claim 1, wherein a weighting coefficient is adjusted. This is a measure implemented so that pages in a website are displayed in a higher rank in search engine search results, and is implemented for each of a plurality of search keywords used for search in the search engine. An investment amount determination support method for supporting the determination of the investment amount for a measure for which a charge for setting is set in advance,
For each search keyword, the computer stores the fee set for the countermeasure by a storage device,
The computer stores, by a storage device, an effect value obtained by quantifying an effect obtained when a user visiting the website reaches a specific page in the website based on a search result of the search engine,
Based on the value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the storage device and the effect value stored in the storage device, the computer executes the countermeasure for the plurality of search keywords. The evaluation value of the effect is calculated by the processing device,
The computer adjusts the weighting factor for each search keyword by a processing device so that the evaluation value to be calculated satisfies a predetermined condition,
An investment amount determination support method, wherein the computer outputs a value obtained by multiplying a charge stored in a storage device by an adjusted weighting factor for each search keyword as an investment amount for the countermeasure by an output device. This is a measure implemented so that pages in a website are displayed in a higher rank in search engine search results, and is implemented for each of a plurality of search keywords used for search in the search engine. An investment amount determination support program for supporting the determination of the investment amount for a measure for which a charge is set in advance,
For each search keyword, the charge set for the countermeasure is stored in the storage device, and the user who visited the website based on the search result of the search engine reaches a specific page in the website In a computer that stores the effect value obtained by quantifying the effect obtained in the storage device,
Based on the value obtained by multiplying the weighting factor for each search keyword by the charge for each search keyword stored in the storage device and the effect value stored in the storage device, the evaluation value of the effect of the countermeasure for the plurality of search keywords Evaluation value calculation processing for calculating
A weighting coefficient adjustment process for adjusting the weighting coefficient for each search keyword by a processing device so that the evaluation value calculated by the evaluation value calculation process satisfies a predetermined condition;
For each search keyword, an investment amount output process is performed in which a value obtained by multiplying the charge stored in the storage device by the weight coefficient adjusted by the weight coefficient adjustment processing is output by the output device as an investment amount for the countermeasure. Investment amount decision support program characterized by this.   9. The investment according to claim 8, wherein the weighting factor adjustment processing adjusts the weighting factor for each search keyword so that the evaluation value calculated by the evaluation value calculation processing is optimized according to a heuristic algorithm. Amount decision support program. The computer further stores the number of users who have reached the specific page among the users who visited the website based on the search results of the search engine in the past as the number of arrivals by the storage device,
The evaluation value calculation process includes a value obtained by multiplying the weighting factor for each search keyword by a charge for each search keyword stored in the storage device, and an effect value stored in the storage device by the number of arrivals stored in the storage device. 10. The investment amount determination support program according to claim 8, wherein the evaluation value is calculated based on a value multiplied by. The investment amount determination support program further includes:
Causing the computer to execute a threshold value input process for receiving an input of an upper limit value of the investment amount by an input device;
In the weight coefficient adjustment process, the evaluation value calculated by the evaluation value calculation process satisfies the predetermined condition, and the weight coefficient for each search keyword is multiplied by the charge for each search keyword stored in the storage device. The investment amount determination support program according to any one of claims 8 to 10, wherein the weighting factor for each search keyword is adjusted so that a total value of the values is equal to or less than an upper limit value input in the threshold value input process. . The computer further stores, for each search keyword, the number of users who have visited the website in the past based on the search results of the search engine as the number of visitors by a storage device,
The weighting factor adjustment processing stores the evaluation value calculated by the evaluation value calculation processing that satisfies the predetermined condition and that corresponds to a search keyword having a weighting factor greater than 0 among the plurality of search keywords. 10. The investment amount determination support program according to claim 8, wherein the weighting factor for each search keyword is adjusted so that the total number of visitors stored in the apparatus is equal to or greater than a predetermined lower limit value. The computer further stores, for each search keyword, the number of users who have reached the specific page among the users who have visited the website based on the search results of the search engine in the past as the number of visitors. For each search keyword, the number of users who have reached the specific page among the users who visited the website based on the search results of the search engine in the past is stored as the number of arrivals by the storage device. ,
The weighting factor adjustment processing stores the evaluation value calculated by the evaluation value calculation processing that satisfies the predetermined condition and that corresponds to a search keyword having a weighting factor greater than 0 among the plurality of search keywords. Adjusting the weighting factor for each search keyword so that the ratio of the total number of visitors stored in the device and the total number of visitors stored in the storage device is equal to or greater than a predetermined threshold. The investment amount determination support program according to claim 8 or 9.

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