Information processing device, information processing method, and information processing program

Abstract

This invention provides an information processing device, an information processing method, and an information processing program that can reduce the burden of obtaining information used in planning marketing strategies conducted using the internet. [Solution] The information processing device includes: an outcome information acquisition unit that acquires information regarding the results of the implementation of a first marketing measure as outcome information; an estimation unit that uses a learning model to estimate information used in planning the first marketing measure as planning information based on the outcome information; and a storage control unit that associates the outcome information and the planning information and stores them in a storage unit.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program, and more particularly to an information processing apparatus, an information processing method, and an information processing program suitable for supporting marketing measures using the Internet.

Background Art

[0002] For marketing measures using the Internet, it is important to measure and analyze the results of their implementation, and advertising effect measurement systems for measuring the results of marketing measures have emerged (see, for example, Patent Document 1). The information used in the planning of marketing measures and the information related to the results of the implementation of marketing measures are recorded in association with each other and continuously accumulated, enabling them to be utilized for review and improvement. Information related to the results of implementing marketing measures can be easily obtained by using an advertising effect measurement system disclosed in Patent Document 1 or the like.

[0003] Marketing measures are, for example, planned and designed by investigating and analyzing customer needs under the formulation of their strategies. The information used in the planning of marketing measures (hereinafter referred to as planning information) cannot be obtained as easily as the information related to the results of implementing marketing measures by using the advertising effect measurement system disclosed in Patent Document 1, and there are cases where it is difficult to obtain. This is because the planning of marketing measures is likely to depend on the experience and intuition of the persons in charge of each phase such as strategy formulation, investigation, analysis, and design, and thus it takes time to verbalize and document the planning information, and furthermore, it takes time to accumulate due to the lack of uniformity in the format of the planning information.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

[0005] Therefore, the present invention aims to provide an information processing device, an information processing method, and an information processing program that can reduce the burden of obtaining information used in planning marketing measures conducted using the Internet. [Means for solving the problem]

[0006] In other words, the information processing device according to the first embodiment includes: an outcome information acquisition unit that acquires information relating to the results of the implementation of the first marketing measure as outcome information; an estimation unit that estimates information used in planning the first marketing measure as planning information using a learning model based on the outcome information; and a storage control unit that stores the outcome information and the planning information in a storage unit in association with each other.

[0007] The second embodiment is an information processing device according to the first embodiment, comprising: a web information acquisition unit that acquires the content of a website used in the implementation of the first marketing measure as web information; a web image information extraction unit that extracts images contained in the website from the web information as web image information; a web text information extraction unit that extracts text contained in the website from the web information as web text information; and a web image feature information extraction unit that performs image analysis on the web image information to convert the features of the images contained in the website into text and extract them as web image feature information. The estimation unit may further take into account the web text information and web image feature information to estimate the information used in planning the first marketing measure as planning information.

[0008] A third embodiment is an information processing device according to the first embodiment, comprising: an advertising image information extraction unit that extracts images of advertisements used in the first marketing initiative from the results information as advertising image information; an advertising text information extraction unit that extracts the text of advertisements used in the first marketing initiative from the results information as advertising text information; and an advertising image feature information extraction unit that performs image analysis on the advertising image information to convert the features of the advertisement images into text and extract them as advertising image feature information. The estimation unit may further take into account the advertising text information and the advertising image feature information to estimate the information used in planning the first marketing initiative as planning information.

[0009] A fourth embodiment may be an information processing device according to the second embodiment, wherein the results information acquisition unit acquires the results of the implementation of the first marketing measure measured by an external tool as results information via a means of linking with the external tool provided by the external tool, and the web information acquisition unit accesses the website by referring to the URL of the website included in the results information and acquires web information.

[0010] The fifth aspect is an information processing device according to the third aspect, comprising: a reference result information acquisition unit that acquires information relating to a second marketing measure different from the first marketing measure, which is currently being implemented or has been implemented in the past, as reference result information; a reference plan information acquisition unit that acquires information used in the planning of the second marketing measure as reference plan information; a reference web information acquisition unit that acquires the content of a website used in the implementation of the second marketing measure as reference web information; a reference advertisement information acquisition unit that acquires information relating to an advertisement used in the second marketing measure as reference advertisement information; and the reference result The system further comprises: a reference information storage control unit that stores the information, the reference planning information, the reference advertising information, and the reference web information in a storage unit as reference information by relating them to each other; a similar advertising search unit that searches the reference advertising information relating to the reference advertising similar to the advertisement used in the first marketing measure from the reference information based on the advertising image information and the advertising text information; and a related information extraction unit that extracts the reference performance information, the reference planning information, and the reference web information associated with the reference advertising information found by the similar advertising search unit from the reference information. The estimation unit may further estimate the planning information by taking into account any or any combination of the reference performance information, the reference planning information, and the reference web information extracted by the related information extraction unit.

[0011] The sixth aspect is an information processing device according to the first aspect, wherein the results information acquisition unit acquires information regarding the results of the first marketing measure before its implementation as pre-implementation results information, and the estimation unit further estimates the planning information by taking into account the comparison results obtained by comparing the results information and the pre-implementation results information.

[0012] The seventh embodiment is an information processing device according to the first embodiment, further comprising an IR information acquisition unit that acquires IR information of a business operator implementing the first marketing measure, and the estimation unit further takes the IR information into consideration to estimate the planning information.

[0013] The eighth aspect is an information processing device according to the first aspect, further comprising a competitor information acquisition unit that acquires information about other businesses that compete with the business implementing the first marketing measure as competitor information, and the estimation unit may further take the competitor information into consideration when estimating planning information.

[0014] The ninth embodiment is an information processing device according to the first embodiment, further comprising an industry analysis information acquisition unit that acquires information relating to the analysis of the industry in which the first marketing measure is implemented as industry analysis information, and the estimation unit may further take the industry analysis information into consideration to estimate planning information.

[0015] The tenth aspect is an information processing device according to the fourth aspect, in which the results of the implementation of the first marketing measure are measured using multiple external tools, the results information acquisition unit cooperates with each of the multiple external tools via the means of cooperation with the external tools provided by each of the multiple external tools and acquires results information from each of the multiple external tools, the storage control unit integrates the multiple results information acquired from each of the multiple external tools into items that are categorized according to the content of the results information and stores them in the storage unit, and the estimation unit uses a learning model to estimate the information used in planning the first marketing measure as planning information based on the integrated multiple results information.

[0016] The information processing method according to the 11th aspect involves causing a computer to perform the following steps: an outcome information acquisition step of acquiring information relating to the results of the implementation of the first marketing measure as outcome information; an estimation step of using a learning model to estimate information used in planning the first marketing measure as planning information based on the outcome information; and a storage control step of associating the outcome information and the planning information and storing them in a storage unit.

[0017] The information processing program according to the twelfth aspect causes a computer to implement an achievement information acquisition function that acquires information regarding the results of implementing the first marketing measure as achievement information, an estimation function that estimates, based on the achievement information, information used in planning the first marketing measure as planning information using a learning model. Further, the information processing program according to the twelfth aspect causes a computer to implement a storage control function that associates the achievement information and the planning information and stores them in a storage unit.

Effect of the Invention

[0018] The information processing apparatus according to the present invention includes an achievement information acquisition unit that acquires information regarding the results of implementing the first marketing measure as achievement information, an estimation unit that estimates, based on the achievement information, information used in planning the first marketing measure as planning information using a learning model, and a storage control unit that associates the achievement information and the planning information and stores them in a storage unit. Therefore, it is possible to reduce the burden of obtaining information used in planning marketing measures carried out using the Internet.

[0019] In addition, the information processing method and the information processing program according to the present invention can reduce the burden of obtaining information used in planning marketing measures carried out using the Internet, similar to the information processing apparatus according to the present invention.

Brief Description of the Drawings

[0020] [Figure 1] FIG. 1 is a network configuration diagram showing the usage environment of the information processing apparatus according to the present embodiment. [Figure 2] FIG. 2 is a diagram for explaining the outline of the information processing apparatus according to the present embodiment. [Figure 3] FIG. 3 is a flowchart for explaining the outline of the information processing apparatus according to the present embodiment. [Figure 4] FIG. 4 is a block diagram for explaining an example of the hardware configuration of the information processing apparatus according to the present embodiment. [Figure 5] FIG. 5 is a block diagram for explaining an example of the functional configuration of the information processing apparatus according to the present embodiment. [Figure 6] FIG. 6 is a diagram for explaining the assignment of the item names of the data of the information processing apparatus to the item names of the data of external tools, Tool A, Tool B, and Tool C. [Figure 7] FIG. 7 is a diagram for explaining the aggregation of data aggregated from a plurality of external tools, Tool A, Tool B, and Tool C, as the same items for the information processing apparatus. [Figure 8] FIG. 8 is a diagram for explaining an example of a list of marketing measures in which planning information is estimated for the information processing apparatus according to the present embodiment. [Figure 9] FIG. 9 is a diagram for explaining a detailed display of a marketing measure, which is an example of a report output by the information processing apparatus according to the present embodiment. [Figure 10] FIG. 10 is a diagram for explaining a report including a time-series line graph, which is an example of a report of the information processing apparatus according to the present embodiment. [Figure 11] FIG. 11 is an example of a flowchart of an information processing program according to the present embodiment. [Figure 12] FIG. 12 is an example of a flowchart of an information processing program according to another embodiment.

Embodiment for Implementing the Invention

[0021] The information processing apparatus 10 according to an embodiment of the present disclosure will be described with reference to FIGS. 1 to 10. In these drawings, the same or corresponding parts are denoted by the same reference numerals, and redundant descriptions are omitted. Also, in all the drawings, the components for explaining the present disclosure are illustrated in an extract, and other components may be omitted from the illustration. Furthermore, the present disclosure is not limited to the embodiments described below. The information processing apparatus 10 according to the present embodiment is a so-called computer, and may be, for example, a workstation, a server, a personal computer (hereinafter referred to as a PC), a notebook PC, a tablet PC, a smartphone, or the like.

[0022] (Overview of the information processing device 10) First, an overview of the information processing device 10 will be described with reference to Figures 1 to 3. Figures 1 and 2 are diagrams illustrating the overview of the information processing device 10 according to this embodiment, and Figure 3 is a flowchart illustrating the overview of the information processing device 10 according to this embodiment from a functional perspective. The information processing device 10 is connected to the information communication network 11. Information and communication network 11 refers to communication infrastructure including the Internet and local area networks (LANs). The information processing device 10 connects to external tools 12, user terminals 13, and websites 15 via an information and communication network 11. In Figure 1, one external tool 12, one user terminal 13, and one website 15 are shown, but they are not limited to a specific number. The information processing device 10 can connect to multiple external tools 12, user terminals 13, and websites 15, as long as it does not exceed its own processing capacity. Furthermore, the information processing device 10 may connect directly to the external tool 12 and the user terminal 13 without going through the information and communication network 11.

[0023] (External tool 12) The external tool 12 is a device for measuring the results of the implementation of marketing measures, and like the information processing device 10, it may be a so-called computer, such as a workstation, server, personal computer (hereinafter referred to as PC), notebook PC, tablet PC, or smartphone. External tool 12 may be, for example, an advertising effectiveness measurement tool. Furthermore, the external tool 12 may be used in conjunction with the advertising effectiveness measurement tool, or in place of the advertising effectiveness measurement tool, and may include customer relationship management (CRM) tools, marketing automation (MA) tools, and order management tools. In addition, in the description of the information processing device 10 according to this embodiment, marketing measures may be described separately as the first marketing measure and the second marketing measure. In this case, the first marketing measure is the marketing measure that is the subject of estimation of the planning information 20, and the second marketing measure is a different marketing measure from the first marketing measure, for example, the second marketing measure may be a marketing measure that is currently being implemented or has been implemented in the past.

[0024] An advertising effectiveness measurement tool is a tool used to measure the effectiveness of an advertisement and its results. Metrics for measuring advertising effectiveness include brand awareness, website visits, click-through rates, and inquiries. Examples of advertising effectiveness measurement tools include AD Ebis (registered trademark) and GA4 (Google Analytics 4, registered trademark). Methods for measuring advertising effectiveness include, for example, measuring the number of ad impressions, click-through rates, and conversion rates as evaluation metrics. Customer Relationship Management (CRM) tools are used to collect and manage customer information. Specifically, they record and manage customer information such as company details, department name, job title, contact information, and purchase / behavioral history. Zoho CRM (registered trademark) is an example of a CRM tool. Marketing automation (MA) tools are designed to automate marketing activities, specifically by automating and streamlining tasks such as customer information management and lead nurturing. Examples of MA tools include Salesforce®. An order management tool is a system that efficiently manages a series of transactions and tasks that occur in order processing. Specifically, it is a tool that allows for centralized management of order processing operations in the cloud. An example of an order management tool is the shopping cart system of an e-commerce (EC) website.

[0025] (User terminal 13) The user terminal 13 is an information processing terminal used by the user of the information processing device 10, and like the information processing device 10, it is a so-called computer, and may be, for example, a workstation, server, personal computer (hereinafter referred to as PC), notebook PC, tablet PC, or smartphone.

[0026] (Website 15) Website 15 refers to a website used to implement the first marketing initiative, and for example, it contains information about the products and services targeted by the first marketing initiative. Website 15 may also include a landing page. A landing page is the first webpage a user encounters, typically accessed through an advertisement. The goal of a landing page is to provide in-depth information about a product or service, encouraging visitors to take action (convert). The web information acquisition unit 28 of the information processing device 10, as described below, accesses the website 15 using a URL (Uniform Resource Locator) registered in the external tool 12 or included in the results information described below, and acquires the content of the website 15 as web information 19. The URLs registered in the external tool 12 or included in the results information described below may be URLs of the website 15 or URLs of landing pages. The content of website 15 refers to all or part of the information posted on website 15. Web information 19 is data in a format that can be processed by the information processing device 10, representing the content of the website 15, and includes web image information and web text information as described below. Web image information refers to images contained in website 15, and web text information refers to text contained in website 15. A URL is a formal sequence of symbols used to identify a resource on a website 15 or a landing page contained within a website 15, and is equivalent to the address of a website 15 or landing page on the internet.

[0027] (Overview of the processing flow up to the estimation of project information 20) Referring to Figure 2, an overview of the processing flow up to the estimation of planning information 20 in the information processing device 10 will be explained. Figure 2 is a diagram illustrating the overview of the information processing device 10 according to this embodiment. The information processing device 10 estimates the planning information 20 by following the processing steps (A, B, C) shown in Figure 2. In the processing steps shown in Figure 2, the steps are performed in the order of Step A (obtaining result information), Step B (obtaining web information), and Step C (estimation). However, the order of Step A (obtaining result information) and Step B (obtaining web information) may be reversed. The information processing device 10 obtains performance information 18 from an external tool 12 (Step A) and web information 19 from a website 15 (Step B) as performance information 17 of the marketing measures. Next, the information processing device 10 estimates the planning information 20 of the marketing measures based on the performance information 17 of the marketing measures (Step C). Performance information 17 refers to information that shows the results and performance of marketing measures, and includes, but is not limited to, performance information 18 and web information 19, and may also include various other types of information, such as pre-implementation performance information, IR information, competitor information, and industry analysis information, as described below. Furthermore, pre-implementation results information refers to information about the results before the implementation of marketing measures; IR (Investor Relations) information refers to information about the public relations activities of the business implementing the marketing measures toward investors, such as information that a company provides to investors regarding its management, financial status, and performance outlook; competitor information refers to information about other businesses that compete with the business implementing the marketing measures; and industry analysis information refers to information about the analysis of the industry in which the marketing measures are implemented.

[0028] (Results Information 18) Performance information 18 refers to information regarding the results of implementing marketing measures. Performance information 18 includes, for example, the ad name / group name / campaign name, destination URL, delivery settings, ad text, ad image, and measurement data. An advertisement name refers to the name given to an advertisement used in the implementation of the marketing initiative being investigated. A group name refers to the name given to the group to which the marketing initiative being estimated belongs. A campaign name refers to the name given to an activity or event carried out as part of a marketing initiative that is being investigated. The destination URL refers to the URL of a website or landing page, which is the dedicated webpage that a target customer (potential customer, prospect, etc.) "first arrives" when they click on an advertisement, email, social networking service (SNS), search results, etc. Delivery settings refer to the system for managing the conditions and timing of delivering messages and advertisements used in marketing initiatives to target customers. For example, the role of delivery settings is to decide who (target customers), what type of advertisement (ad type), what bid price, and when (timing) to send it. The term "advertising text" refers to the text used in advertisements, such as product names, service names, taglines, slogans, product / service descriptions, product / service providers, and descriptions of those providers. The advertising text is extracted as advertising text information from the results information 18 by the web text information extraction unit 30 described later.

[0029] Advertisement images refer to images used in advertisements, and include, for example, cover images, thumbnail images, eye-catching images, banner images, profile images, and product / service introduction images. Advertisement images are extracted as advertisement image information from the results information 18 by the web image information extraction unit 29 described later. Measurement data refers to data obtained by an external tool 12 that measures the results of the implementation of marketing measures, and includes, for example, advertising expenditure, number of impressions, number of clicks, click-through rate, cost per click, number of conversions, conversion rate, and cost per conversion. The advertising fee refers to the actual cost paid to the media when placing an advertisement. An impression count refers to the number of times an advertisement was displayed, and is also simply called the number of impressions. The impression count can also be written as "imp count". Click count refers to the number of times a link on a website or advertisement has been clicked, and can also be abbreviated as CT (Click Through). Click-through rate (CTR) is the percentage of people who clicked on an ad after seeing it. Cost per click (CPC) refers to the cost paid for each click, and is also known as CPC. The number of conversions refers to the number of times a specific goal set in an ad or website is achieved, and may also be written as CV (Conversion). The conversion rate is an indicator that shows what percentage of website visits or advertisement visits resulted in a conversion (a target action), and is also called CVR (Conversion Rate). Cost per conversion (CPA) refers to the cost incurred to acquire one conversion through advertising.

[0030] (Web information 19) Web information 19 refers to the content of websites 15 used in implementing marketing strategies. Web information 19 may refer to all or part of the information posted on the website 15, which is obtained from the website 15 by the web information acquisition unit 28. For example, web information 19 refers to image information and text information included in the website 15. Furthermore, this image and text information may include descriptions of the products and services targeted by the first marketing initiative, their selling points, and usage scenarios, as well as information about the target customers of the first marketing initiative.

[0031] (Project Information 20) Planning information 20 refers to information used in planning marketing strategies, such as basic information about the marketing strategy and information related to the plan of the marketing strategy, such as information related to the implementation conditions. Basic information about a marketing initiative includes, for example, the outline of the marketing initiative (title), the target period (implementation period), the status, the initiative group, the person in charge, and the budget amount. The marketing initiative summary (title) is a concise summary of the title and content assigned to the marketing initiative. The target period (implementation period) refers to the period during which marketing measures were implemented. Status refers to information that indicates the current stage, progress, and state of a marketing initiative. A "strategy group" refers to the title and overview of a group of marketing strategies to which the target marketing strategy belongs. The term "person in charge" refers to the name of the person responsible for marketing initiatives. The budget amount refers to the amount of money allocated to marketing initiatives. Information regarding the planning of marketing initiatives includes information on the timing of implementation, the media to be used, the type of advertising, the amount of advertising expenditure, and hypotheses and background information obtained from research and analysis of customer needs.

[0032] (MCM(Marketing campaign management)16) Planning information 20 and performance information 17 are used in the main steps of MCM16. MCM (Marketing Campaign Management)16 refers to the process of comprehensively managing marketing campaigns, from planning and execution to performance measurement, review, and improvement, in order to achieve specific goals. Specifically, it is the process of effectively delivering appropriate messages to target customers such as potential and prospective customers, with the aim of increasing sales and improving customer loyalty. MCM16 is offered as SaaS (Software as a Service), and examples include AD EBiS Campaign Manager (registered trademark) from Ilgurum (registered trademark). The information processing device 10 reduces the burden of acquiring planning information 20, which is the first step in MCM 16, by estimating planning information 20 based on performance information 17. The MCM16 process includes steps such as planning the marketing initiative, executing the marketing initiative, measuring the effectiveness of the marketing initiative, and improving the marketing initiative.

[0033] (Planning steps for marketing strategies) In this step, for example, goals may be set, target customers may be selected, messages may be created, and implementation methods may be decided. The information processing device 10 estimates the planning information 20, which is the information decided and used in this step. Goal setting refers to establishing specific goals to be achieved through marketing initiatives (such as increased sales or improved brand awareness), and may be KPIs (Key Performance Indicators) or KGIs (Key Goal Indicators). Selecting target customers means clearly defining which customers you will approach. Message creation refers to creating a message that resonates with the target customer. Determining the implementation method means deciding which media, channels (advertising, email, social media, etc.), and methods (discounts, gifts, point rewards, etc.) to use.

[0034] (Steps for executing marketing strategies) In this step, marketing measures may be implemented based on the plan that has been developed.

[0035] (Steps for measuring the effectiveness of marketing initiatives) In this step, you may use external tools such as 12 to measure the effectiveness of marketing measures in real time, check KPIs (Key Performance Indicators), and measure progress toward the achievement goals.

[0036] (Steps to improve marketing strategies) In this step, you may analyze the data obtained in the effectiveness measurement step, evaluate the effectiveness of the marketing measures, identify areas for improvement, implement the PDCA cycle, and optimize the marketing measures.

[0037] (Specific example of the processing flow up to the estimation of planning information 20) Referring to Figure 3, a specific example of the processing flow up to the estimation of planning information 20 by the information processing device 10 will be explained. As shown in Figure 3, the processing flow of the information processing device 10 includes a performance information step S21, a data merging and filtering step S22, an image text conversion step S23, a similar advertisement search step S24, a background information collection and organization step S25, and a planning information estimation step S26.

[0038] (Performance Information Step S21) In the performance information step S21, the information processing device 10 acquires the results information 18 and web information 19 as performance information 17. The results information acquisition unit 27, described later, acquires information regarding the results of the implementation of marketing measures as results information 18, and the web information acquisition unit 28, described later, acquires the content of the website 15 used in the implementation of the marketing measures as web information 19.

[0039] (Data merging and filtering step S22) In the data merging and filtering step S22, the information processing device 10 merges the performance information 17 obtained from different data sources with the same items and stores them in the storage unit 10d, and removes the performance information 17 with a small amount of data from among the acquired performance information 17. Performance Information 17 shall be categorized according to the content of Performance Information 17. Different data sources refer to different external tools12 and different websites15. The data merging and filtering step S22 may be performed by the results information acquisition unit 27 and the web information acquisition unit 28, which will be described later.

[0040] (Image text conversion step S23) In the image text conversion step S23, the information processing device 10 extracts the image features contained in the website 15 as text-based web image feature information from the web information 19 obtained from the website 15, and extracts the characters contained in the website 15 as web character information. The information processing device 10 acquires the content of the website 15 used for implementing marketing measures as web information 19, and based on the web information 19, extracts images contained in the website 15 as web image information and text contained in the website 15 as web text information. Furthermore, the information processing device 10 performs image analysis on the web image information to convert the features of the images contained in the website 15 into text and extracts it as web image feature information. The image text conversion step S23 may be performed by the web text information extraction unit 30 and the web image feature information extraction unit 31, which will be described later.

[0041] Furthermore, in the image text conversion step S23, the information processing device 10 extracts images of advertisements used in the first marketing initiative as advertising image information from the results information 18, and extracts the text of advertisements used in the first marketing initiative as advertising text information. Furthermore, the information processing device 10 performs image analysis on the advertising image information, converts the characteristics of the advertisement images into text, and extracts it as advertising image characteristic information. The image text conversion step S23 may be performed by the web text information extraction unit 30 and the web image feature information extraction unit 31, which will be described later.

[0042] (Search step S24 for similar ads) In the similar ad search step S24, the information processing device 10 searches for similar ads from ads used in past or concurrently implemented second marketing initiatives to ads used in the first marketing initiative that is the target of estimation in the planning information 20. The similar ad search step S24 may be performed by the similar ad search unit 40.

[0043] (Background information collection and organization step S25) In the background information collection and organization step S25, the information processing device 10 collects and organizes background information for the first marketing measures that will be used to estimate the planning information 20 by collecting planning information for similar advertisements that have been searched, comparing it with the results of similar advertisements, or comparing the results before and after the implementation of the first marketing measures. The background information collection and organization step S25 may be performed by the related information extraction unit 41 described later.

[0044] (Estimated step S26 of project information 20) In the estimation step S26 of the planning information 20, the information processing device 10 estimates the planning information 20 of the first marketing measure. The estimation step S26 of the planning information 20 may be performed by the estimation unit 45 described later.

[0045] (Hardware configuration of the information processing device 10) The hardware configuration of the information processing device 10 according to this embodiment will be described with reference to Figure 4. Figure 4 is a diagram illustrating an example of the hardware configuration of the information processing device 10 according to this embodiment. The information processing device 10 includes a communication interface 10a, a ROM (Read Only Memory) 10b, a RAM (Random Access Memory) 10c, a storage unit 10d, an arithmetic unit 10e, and an input / output interface 10f, etc.

[0046] The communication interface 10a has the function of sending and receiving data handled by the information processing device 10 to and from other devices via the information communication network 11. The information processing device 10 connects to external tools 12, user terminals 13, and websites 15 connected to the information communication network 11 using a communication interface 10a.

[0047] The storage unit 10d can be used as a storage device for the information processing device 10 and can be composed of, for example, a hard disk drive, a solid state drive, and flash memory. Furthermore, the storage unit 10d can also be configured using cloud storage. The storage unit 10d of the information processing device 10 may also be configured as a database. By configuring the storage unit 10d as a database, vast amounts of data can be managed efficiently, providing excellent searchability and accessibility, and enabling quick retrieval of necessary information. Furthermore, the storage unit 10d of the information processing device 10 stores information processing programs (described later), an OS (Operating System) necessary for the operation of the information processing device 10, various other applications, and various data used by those applications. The OS is a type of application built into the information processing device 10 and has the function of controlling the basic functions of the information processing device 10.

[0048] The arithmetic unit 10e may include a central processing unit (CPU), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), etc., and may be realized by logic circuits (hardware) formed on an integrated circuit (IC) chip, an LSI (Large Scale Integration), etc., or by dedicated circuits.

[0049] The input / output interface 10f transmits and receives data to and from external devices of the information processing device 10. External devices refer to input devices 10g and output devices 10h that input and output data to and from the information processing device 10. Input devices 10g are, for example, keyboards and mice, and output devices 10h are, for example, monitors, printers, and speakers.

[0050] The information processing device 10 stores the information processing program in the ROM 10b or storage unit 10d and loads the information processing program into the main memory, which is composed of RAM 10c or the like. The arithmetic unit 10e accesses the main memory into which the information processing program has been loaded and executes the information processing program.

[0051] (Functional configuration of the information processing device 10) Referring to Figure 5, an example of the functional configuration of the information processing device 10 will be described. Figure 5 is a block diagram illustrating an example of the functional configuration of the information processing device according to this embodiment. The information processing device 10, by executing the information processing program described later, has a calculation unit 10e equipped with functional units such as a results information acquisition unit 27, a web information acquisition unit 28, a web image information extraction unit 29, a web text information extraction unit 30, a web image feature information extraction unit 31, an advertisement image information extraction unit 32, an advertisement text information extraction unit 33, an advertisement image feature information extraction unit 34, a reference results information acquisition unit 35, a reference project information acquisition unit 36, a reference web information acquisition unit 37, a reference advertisement information acquisition unit 38, a reference information storage control unit 39, a similar advertisement search unit 40, a related information extraction unit 41, an IR information acquisition unit 42, a competitor information acquisition unit 43, an industry analysis information acquisition unit 44, an estimation unit 45, and a storage control unit 46.

[0052] (Result Information Acquisition Department 27) The results information acquisition unit 27 acquires information regarding the results of the implementation of the first marketing measure as results information 18. The first marketing initiative refers to the marketing initiative that is the subject of estimation for the planning information 20. The results information acquisition unit 27 acquires the results of the implementation of the first marketing measure, as measured by the external tool 12, as results information 18 through the means of linking with the external tool 12 provided by the external tool 12. The means of linking with the external tool 12 refers to a means of linking data with the external tool 12. Specifically, it refers to an interface that allows the external tool 12 to obtain measurement data from other systems, applications, devices, websites, etc. (hereinafter referred to as "systems, etc.") or to obtain data and requests from other systems, etc. Examples of means of linking with the external tool 12 include APIs (Application Programming Interfaces), file linking, and web service linking. An API (Application Programming Interface) is an interface that connects application software to each other, serving as a gateway for utilizing the resources of applications and software through a program. File linking refers to a method of linking systems and other components via files, rather than directly connecting them. This file linking can be automated or performed manually. Web service integration refers to a method of exchanging data between multiple systems using web services, and may also involve using APIs provided by web services. The results information acquisition unit 27 may acquire information regarding the results obtained from the implementation of the first marketing measure, specifically the results obtained before the implementation of the first marketing measure, as pre-implementation results information. Furthermore, if the performance information acquisition unit 27 acquires measurement data as performance information 18, it may remove advertisements with a small amount of data. For example, the performance information acquisition unit 27 may remove advertisements with low advertising expenditures, or with low numbers of impressions or clicks.

[0053] When measuring the results of the first marketing initiative using multiple external tools 12, the results information acquisition unit 27 may cooperate with each of the multiple external tools 12 through the means of cooperation with the external tools 12 provided by each of the multiple external tools 12, and acquire results information 18 from each of the multiple external tools 12.

[0054] Referring to Figures 6 and 7, a method for integrating and grouping data in the information processing device 10 when the data structure granularity of measurement data from external tools 12, namely tools A, B, and C, differs from that of the information processing device 10 will be explained. Figure 6 is a diagram illustrating the assignment of data item names in the information processing device 10 to the data item names of external tools 12, namely tools A, B, and C, and Figure 7 is a diagram illustrating how data aggregated from multiple external tools 12, namely tools A, B, and C, is aggregated as the same item in the information processing device 10.

[0055] The corresponding item setting screen 62 shown in Figure 6 is a screen for setting the item names of the measurement data of tools A, B, and C acquired by the information processing device 10, and the item names that are assigned when the data is acquired by the information processing device 10. The corresponding item setting screen 62 includes the item name column 62a for tool A, the item name column 62b for tool B, the item name column 62c for tool C, and the item name column 62d for the information processing device 10. In item 1, column 62e, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C represent the negotiation date, closing date, and purchase date, respectively. In item 2, column 62f, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C are the trading partner name, customer name, and client name, respectively. In item 3, column 62g, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C are the names of the person in charge, staff members, and personnel, respectively. In item 4, column 62h, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C are the campaign name, promotion name, and initiative name, respectively. In item 5, column 62i, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C represent the number of conversions, the number of CVs, and the number of Conversions, respectively. In item 62j, the item name columns 62a for tool A, 62b for tool B, and 62c for tool C are area, region, and district, respectively.

[0056] As described above, the item names for items 1 through 6 in Tool A, Tool B, and Tool C are all different. In response to this, when the information processing device 10 acquires measurement data from tool A, measurement data from tool B, and measurement data from tool C, it assigns the item names of the first to sixth items of the information processing device 10 as follows. In item 62e, the measurement data for tool A, tool B, and tool C are acquired and aggregated by the information processing device 10, with the aggregation date (year, month, day) assigned as the item name for aggregation. In item 62f of the second section, the measurement data for tools A, B, and C are acquired and aggregated by the information processing device 10, with the customer name assigned as the item name for aggregation. In item 3, 62g, the measurement data for tool A, tool B, and tool C are not acquired by the information processing device 10. In item 4, 62h, the measurement data for tools A, B, and C are acquired and aggregated by the information processing device 10, with "campaign" assigned as the item name for aggregation. In item 5, 62i, the measurement data for tools A, B, and C are acquired and aggregated by the information processing device 10, with the number of web applications assigned as the item name for aggregation. In item 62j, the measurement data for tools A, B, and C are acquired and aggregated by the information processing device 10, with the area assigned as the item name during aggregation.

[0057] Figure 7 shows the aggregated results 68, which are displayed after the measurement data from tool A, tool B, and tool C have been integrated and grouped by the information processing device 10. The aggregated results 68 include the marketing initiative name list 68a, the external tool name list 68b, and the number of web applications list 68c. The marketing initiative listed in the column 68a, "○○ initiative," is displayed as an external tool 12, with measurement data from Tool A, Tool B, and Tool C aggregated and grouped. The sum of the measured values ​​from tool A (68e), tool B (68f), and tool C (68g) is displayed as a total value of 68d. In this way, even if the item names and indicators, which are the data composition granularity of the measurement data from tool A, tool B, and tool C, differ from the item names and indicators of the information processing device 10, the information processing device 10 can still aggregate the data.

[0058] (Web Information Acquisition Section 28) The web information acquisition unit 28 acquires the content of the website 15 used in the implementation of the first marketing measure as web information 19. The web information acquisition unit 28 accesses the website 15 by referring to the URL of the website 15 included in the result information 18 and acquires the web information 19. The web information acquisition unit 28 may acquire web information 19 using a learning model, or it may acquire web information 19 using a rule-based system. The learning model used by the web information acquisition unit 28 is a model that has been trained to acquire web information 19 from the website 15, or a model in which an algorithm or mathematical formula has been constructed. Learning models may include algorithms, mathematical models, statistical models, machine learning models, generative AI (Artificial Intelligence), and large language models (LLMs). The same applies hereafter.

[0059] (Web image information extraction unit 29) The web image information extraction unit 29 extracts images contained in the website 15 from the web information 19 as web image information. The web image information extraction unit 29 extracts images contained in the content of the website 15 as web image information from the web information 19 acquired by the web information acquisition unit 28. The web image information extraction unit 29 may input the web information 19 into a learning model capable of recognizing images and extract images contained in the web information 19 (i.e., the content of the website 15) as web image information, or it may use a rule-based system to extract image information contained in the web information 19. The learning model used by the web image information extraction unit 29 is a model that has been trained to recognize and extract images contained in the web information 19, or a model in which an algorithm or mathematical formula has been constructed.

[0060] (Web text information extraction unit 30) The web text information extraction unit 30 extracts the text contained in the website 15 from the web information 19 as web text information. The web text information extraction unit 30 extracts the characters contained in the web information 19 (i.e., the content of the website 15) from the web information 19 acquired by the web information acquisition unit 28 as web text information. The web character information extraction unit 30 may input web information 19 into a learning model capable of recognizing characters and extract the characters contained in the web information 19 as web character information, or it may use a rule-based system to extract the characters contained in the web information 19 as web character information. The learning model used by the web character information extraction unit 30 is a model that has been trained to recognize and extract characters contained in the web information 19, or a model in which an algorithm or mathematical formula has been constructed. The web text information extraction unit 30 extracts, for example, the text "3LDK, close to the station!" contained within an image on the website 15.

[0061] (Web image feature information extraction unit 31) The web image feature information extraction unit 31 analyzes the web image information, converts the features of the images contained in the website 15 into text, and extracts them as web image feature information. The web image feature information extraction unit 31 analyzes the web image information extracted by the web image information extraction unit 29, converts the features of the images contained in the website 15 into text, and extracts them as web image feature information. The web image feature information extraction unit 31 may input the web image information extracted by the web image information extraction unit 29 into a learning model capable of recognizing images, and extract web image feature information by converting the features of images contained in the website 15 into text. The learning model used by the web image feature information extraction unit 31 is a model that has been trained to recognize images contained in the website 15 and convert the features of those images into text, or a model in which an algorithm or mathematical formula has been constructed. Alternatively, the web image feature information extraction unit 31 may input web image information into LLAVA, convert the features of the images contained in the website 15 into text, and extract web image feature information. LLAVA (Large Language and Vision Assistant) is an LLM that can generate text from images, and it refers to a model that combines an image encoder with an LLM. The web image feature information extraction unit 31 extracts, for example, the features of an image contained in the website 15 as "visual representation of a bright sky, surrounding environment, and transportation infrastructure."

[0062] (Advertising image information extraction unit 32) The advertising image information extraction unit 32 extracts images of advertisements used in the first marketing initiative from the results information 18 as advertising image information. The advertising image information extraction unit 32 extracts images of advertisements used in the first marketing initiative as advertising image information from the results information 18, which shows the results of the implementation of the first marketing initiative measured by the external tool 12. The advertising image information extraction unit 32 may also input the results information 18 into a learning model capable of recognizing images and extract advertising image information. The learning model used by the advertising image information extraction unit 32 is a model that has been trained to recognize images included in the result information 18 and extract advertising images, or a model in which an algorithm or mathematical formula has been constructed.

[0063] (Advertisement text information extraction unit 33) The advertising text information extraction unit 33 extracts the text of advertisements used in the first marketing initiative from the results information 18 as advertising text information. The advertising text information extraction unit 33 extracts the text of advertisements used in the first marketing initiative as advertising text information from the results information 18, which shows the results of the implementation of the first marketing initiative measured by the external tool 12. The advertising text information extraction unit 33 may also input the results information 18 into the learning model and extract the advertising text information. The learning model used by the advertising text information extraction unit 33 is a model that has been trained to recognize characters contained in the outcome information 18 and extract text from advertisements, or a model in which an algorithm or mathematical formula has been constructed.

[0064] (Advertising image feature information extraction unit 34) The advertising image feature information extraction unit 34 analyzes the advertising image information, converts the features of the advertising image into text, and extracts it as advertising image feature information. The advertising image feature information extraction unit 34 analyzes the advertising image information extracted from the results information 18, which shows the results of the implementation of the first marketing measure measured by the external tool 12, and extracts the features of the images of the advertisements used in the first marketing measure as text, thereby extracting them as advertising image feature information. The advertising image feature information extraction unit 34 may also input advertising image information into a learning model or LLAVA capable of recognizing images, convert the features of the advertising image into text, and extract advertising image feature information. The learning model used by the advertising image feature information extraction unit 34 is a model that has been trained to analyze advertising image information and convert the features of the advertising image into text, or a model in which an algorithm or mathematical formula has been constructed.

[0065] (Reference result information acquisition unit 35) The reference results information acquisition unit 35 acquires information related to the second marketing measures, which are different from the first marketing measures and are currently being implemented or have been implemented in the past, as reference results. The second marketing initiative refers to a marketing initiative other than the first marketing initiative, which is the subject of estimation in Planning Information 20. The reference results information acquisition unit 35 may, similar to the results information acquisition unit 27, acquire the results of the implementation of the second marketing measures, both currently implemented and in the past, as measured by the external tool 12, as reference results information via the means of linking with the external tool 12 provided by the external tool 12.

[0066] (Reference: Planning Information Acquisition Section 36) The reference planning information acquisition unit 36 ​​acquires information used in the planning of the second marketing measure as reference planning information. The reference planning information acquisition unit 36 ​​acquires as reference planning information information information that was used in the planning of a second marketing measure, which is different from the first marketing measure that is the subject of estimation of planning information 20. The reference plan information acquisition unit 36 ​​may acquire, as reference plan information, plan information for ongoing and past second marketing measures registered in the external tool 12, via the means of linking with the external tool 12 provided by the external tool 12.

[0067] (See Web Information Acquisition Section 37) The reference web information acquisition unit 37 designates the website used in the implementation of the second marketing measure as a reference website and acquires the content of the said reference website as reference web information. The reference web information acquisition unit 37 may acquire the content of the website of the URL registered in the external tool 12 as reference web information, similar to the web information acquisition unit 28. The reference web information acquisition unit 37 may also acquire website content as reference web information using a learning model. The learning model used by the reference web information acquisition unit 37 is a model that has been trained to acquire website content, or has an algorithm or mathematical formula built into it.

[0068] (Reference: Advertisement Information Acquisition Unit 38) The reference advertising information acquisition unit 38 recognizes advertisements used in the second marketing initiative as reference advertisements and acquires information related to those reference advertisements as reference advertising information. The reference advertising information acquisition unit 38 may acquire reference advertising information for the second marketing initiative from the reference performance information for the second marketing initiative acquired by the reference performance information acquisition unit 35 from the external tool 12. The reference advertisement information acquisition unit 38 may acquire reference advertisement information from reference result information using a learning model. The learning model used by the reference advertisement information acquisition unit 38 is a model that has been trained to acquire reference advertisement information from reference result information, or has an algorithm or mathematical formula constructed for it.

[0069] (Reference Information Storage Control Unit 39) The reference information storage control unit 39 stores reference result information, reference project information, reference advertisement information, and reference web information in the storage unit 10d as reference information by associating them with each other. The reference information storage control unit 39 associates the reference result information acquired by the reference result information acquisition unit 35, the reference plan information acquired by the reference plan information acquisition unit 36, the reference advertisement information acquired by the reference advertisement information acquisition unit 38, and the reference web information acquired by the reference web information acquisition unit 37 with each other and stores them as reference information in the storage unit 10d. The reference information consists of the results information, planning information, advertising information, and web information of the second marketing initiative, which the estimation unit 45 refers to when estimating the planning information 20 of the first marketing initiative.

[0070] (Similar Ad Search Section 40) The similar ad search unit 40 searches for reference ad information related to reference ads similar to the ads used in the first marketing initiative, based on the ad image information and ad text information. The similar ad search unit 40 may search the reference information for reference ads that are similar to the ads used in the first marketing initiative, based on a comparison of the ad image information and ad text information of the first marketing initiative with the reference ads. The similar ad search unit 40 searches for reference information related to reference ads that are similar to the ads used in the first marketing initiative from the reference information stored in the memory unit 10d. Specifically, the similar ad search unit 40 searches for, for example, referral ads that were run in the same region and at the same time as the ads used in the first marketing initiative, referral ads for the same products as those used in the first marketing initiative, and referral ads for the same LP (Landing Page) as those used in the first marketing initiative, considering them similar to the ads used in the first marketing initiative. Furthermore, the similar ad search unit 40 may determine that there are no reference ads similar to the ad used in the first marketing initiative.

[0071] (Related information extraction unit 41) The related information extraction unit 41 extracts reference performance information, reference project information, and reference web information associated with the reference advertisement information searched by the similar advertisement search unit 40 from the reference information. The related information extraction unit 41 extracts reference performance information, reference planning information, and reference web information associated with the reference advertising information searched by the similar ad search unit 40 from the reference information related to the second marketing initiative stored in the memory unit 10d.

[0072] (IR information acquisition unit 42) The IR information acquisition unit 42 acquires IR information from businesses implementing the first marketing initiative. The IR information acquisition unit 42 may use a crawler to acquire IR information of businesses implementing the first marketing initiative, or it may accept IR information provided by such businesses. A crawler is a program that periodically retrieves documents, images, and other data from websites on the internet and automatically creates a database of them. It is also known as a bot, spider, or robot.

[0073] (Competitor Information Acquisition Department 43) The Competitor Information Acquisition Unit 43 acquires information about other businesses that compete with the business implementing the first marketing initiative, as competitor information. The competitor information acquisition unit 43 may use a crawler to acquire competitor information about other businesses that compete with the business implementing the first marketing measure, or it may accept competitor information provided by such business.

[0074] (Industry analysis information acquisition department 44) The Industry Analysis Information Acquisition Unit 44 acquires information regarding the analysis of the industry in which the first marketing initiative will be implemented, as industry analysis information. The Industry Analysis Information Acquisition Unit 44 may acquire articles, reports, papers, industry journals, etc., that analyze the industry in which the first marketing initiative is implemented, as industry analysis information. The industry analysis information acquisition unit 44 may acquire industry analysis information using a crawler, or it may acquire industry analysis information provided by the business operator implementing the first marketing measure.

[0075] (Estimation part 45) Based on the outcome information 18, the estimation unit 45 uses a learning model to estimate the information used in planning the first marketing measure as planning information 20. The learning model used in the estimation unit 45 is a model that has been trained to estimate the planning information 20 of the first marketing measure based on the outcome information 18, or a model in which an algorithm or mathematical formula has been constructed. The learning model may be an algorithm, mathematical model, statistical model, machine learning model, generative AI, or large-scale language model. The estimation unit 45 may, for example, use artificial intelligence provided by OpenAI (registered trademark) as a learning model, or it may use a combination of multiple types of artificial intelligence with different properties as a learning model. The estimation unit 45 may estimate, as planning information 20, basic information of the first marketing initiative (overview (title) / target period (implementation period) / status / initiative group / person in charge / budget amount, etc.), or information related to the plan of the first marketing initiative. Furthermore, the estimation unit 45 may estimate the hypotheses or background used in planning the first marketing strategy, or the product, target audience (age group, region, interests, etc.), or appeal axis (low price appeal, function appeal, brand appeal, etc.).

[0076] The estimation unit 45 may further consider web text information and web image feature information to estimate the information used in planning the first marketing strategy as planning information 20. The estimation unit 45 may further estimate the planning information 20 by taking into account web text information extracted from web information 19 and web image feature information, which is textualized representation of the features of images contained in the website 15.

[0077] The estimation unit 45 may further consider the advertising text information and advertising image feature information to estimate the information used in planning the first marketing strategy as planning information 20. The estimation unit 45 may further estimate the planning information 20 of the first marketing initiative by taking into account the advertising text information of the advertisement used in the first marketing initiative, and the advertising image feature information, which is a text representation of the image features of the advertisement used in the first marketing initiative.

[0078] The estimation unit 45 may further estimate the project information 20 by taking into account any or a combination of the reference results information, reference project information, and reference web information extracted by the related information extraction unit 41. Therefore, the estimation unit 45 may further estimate the planning information 20 by taking into account any of the reference results information, reference planning information, and reference web information of the second marketing measure. Furthermore, the estimation unit 45 may further estimate the planning information 20 by taking into account any of the following: a combination of the reference results information and reference planning information of the second marketing measure, a combination of the reference planning information and reference web information, and a combination of the reference web information and reference results information. Furthermore, the estimation unit 45 may further estimate the planning information 20 by taking into account a combination of the reference results information, reference planning information, and reference web information of the second marketing measure. As a result, the estimation unit 45 can estimate the planning information 20 with high accuracy by referring to reference information regarding a second marketing initiative that uses advertisements similar to those used in the first marketing initiative.

[0079] The estimation unit 45 may also estimate the planning information 20 by further taking into account the comparison results obtained by comparing the outcome information 18 with the pre-implementation outcome information. The estimation unit 45 may further estimate the planning information 20 by taking into account the comparison results obtained by comparing the results information before and after the implementation of the first marketing measure.

[0080] The estimation unit 45 may also estimate the planning information 20 by further taking into account the IR information. The estimation unit 45 estimates the planning information 20 by taking into account the IR information of the business operator implementing the first marketing measure, which is the target of the estimation of the planning information 20, thereby enabling more accurate estimation.

[0081] The estimation unit 45 may also estimate the planning information 20 by taking into account competitor information. The estimation unit 45 estimates the planning information 20 by taking into account competitor information regarding competitors of the business operator implementing the first marketing measure that is the target of the planning information 20 estimation, thereby enabling more accurate estimation.

[0082] The estimation unit 45 may also estimate the planning information 20 by further incorporating industry analysis information. The estimation unit 45 further incorporates industry analysis information regarding the industry in which the first marketing measure, which is the target of estimation of the planning information 20, is implemented, thereby estimating the planning information 20 with higher accuracy.

[0083] The planning information 20 estimated by the estimation unit 45 is used to reduce the burden of acquiring the planning information 20, which is the first step in the MCM 16 described above. Therefore, if a user of the information processing device 10 obtains permission to access existing performance information 17, they can use MCM 16 to review the first marketing initiative and implement the PDCA cycle for that first marketing initiative, even if they do not possess the planning information 20 for the first marketing initiative.

[0084] When measuring the results of the implementation of the first marketing initiative using multiple external tools 12, the estimation unit 45 may use a learning model based on the integrated results information 18 to estimate the information used in planning the first marketing initiative as planning information 20. The learning model used by the estimation unit 45 is a model in which learning has been performed to estimate the planning information 20 of the first marketing measure based on a plurality of integrated outcome information 18, or an algorithm or mathematical formula has been constructed. The estimation unit 45 may use a learning model to estimate the information used in planning the first marketing measure as planning information 20, based on the multiple results information 18 that were integrated when stored in the memory unit 10d.

[0085] The following describes an example of how the information processing device 10 can be used. Assuming there are two advertisements, A and B, selling the same product, traditionally, advertisements A and B were compared as follows. Advertisement A, using ad copy XX, has a low CPA (cost per acquisition) and a high CVR (conversion rate). Ad B, which uses ad copy △△, has a high CPA (cost per acquisition) and a low CVR (conversion rate). In comparisons between the conventional advertisement A and advertisement B, it was sometimes difficult to see the improvements in advertisement B. On the other hand, if the planning information 20 estimated by the estimation unit 45 is, for example, the target and appeal axis, then advertisement A and advertisement B can be compared as follows. Advertisement A, using ad copy XX, targets families with children in their 30s, emphasizes safety and security, and has a low CPA (cost per acquisition) and a high CVR (conversion rate). Advertisement B, using ad copy △△, targets couples in their 20s, emphasizes convenience as its selling point, resulting in a high CPA (cost per acquisition) and a low CVR (conversion rate). In this case, by comparing ad B with ad A, it becomes easier to hypothesize that ad B has a good target audience but a poor appeal, resulting in a high CPA (cost per acquisition) and a low CVR (conversion rate). This makes it easier for even less experienced marketing personnel to formulate hypotheses and conduct analysis.

[0086] (Memory control unit 46) The memory control unit 46 associates the results information 18 and the planning information 20 and stores them in the memory unit 10d. When measuring the results of the first marketing initiative using multiple external tools 12, the memory control unit 46 integrates the multiple results information 18 obtained from each of the multiple external tools 12 into items that are categorized according to the content of the results information 18 and stores them in the memory unit 10d.

[0087] (List of marketing strategies) Referring to Figure 8, we will now describe the policy list 70, which displays a list of first marketing measures registered based on the planning information 20 estimated by the information processing device 10. Figure 8 is a diagram illustrating an example of the policy list 70 of first marketing measures for which planning information 20 has been estimated by the information processing device 10 according to this embodiment. The policy list 70 includes a performance aggregation period input field 71, a new registration button 72, a new registration selection button 72a, a policy classification column 73, a policy ID column 74, a policy name column 75, a person in charge column 76, an aggregation axis column 77, a start date column 78, an end date column 79, a KPI target column 80, a KPI performance column 81, a KPI performance column (8 / 1~8 / 31) 82, a budget amount column 83, and a cost column 84. All of these may be estimated by the estimation unit 45, or any missing parts of these may be estimated by the estimation unit 45.

[0088] The performance aggregation period input field 71 is for entering the period during which the performance information 17 was aggregated. The New Registration button 72 is a button for clicking or touching when registering a new policy category and the first marketing policy. The New Registration Selection button 72a is a button for selecting either the policy category or the first marketing policy. Column 63, "Policy Classification," displays the policy classification of the registered first marketing policy, used to group and classify the first marketing policy. Column 74, Policy ID, displays the Policy ID assigned to the first registered marketing policy.

[0089] The policy name column 75 displays the policy name assigned to the first registered marketing policy. Column 76 displays the name of the person who was assigned to the first marketing initiative that was registered. The aggregation axis column 77 displays the names of the tools used to measure the effectiveness of the advertisements. While advertising effectiveness is measured using advertising effectiveness measurement tools, it is not limited to these, and website access analysis tools may also be used. In this embodiment, the effectiveness measurement of the first marketing measure displayed in the list of measures 70 uses Google Analytics 4, which is a website access analysis tool for website 15. The Start Date column 78 displays the start date of the implementation period for the first registered marketing initiative. The End Date column 79 displays the end date of the implementation period for the registered first marketing initiative. Column 80 of the KPI target column displays the target value for the total KPI of the registered first marketing initiative.

[0090] The KPI performance column 81 displays the total value of performance information 18, which indicates the results obtained from the implementation of the registered first marketing initiative. Specifically, it displays the total value of performance information 18 obtained by the advertising effectiveness measurement tool. The KPI performance column (8 / 1~8 / 31)82 displays the total value of performance information showing the results obtained from the implementation of the registered first marketing initiative during the period from 8 / 1 to 8 / 31. Specifically, it displays the total value of performance information 18 obtained by the advertising effectiveness measurement tool for the period from 8 / 1 to 8 / 31. Column 83 displays the total budget amount for the registered first marketing initiative. Cost column 84 displays the total cost incurred for implementing the registered first marketing initiative, specifically the total cost incurred for implementing the first marketing initiative.

[0091] Additionally, the policy list 70 displays the screen switching button 85. The screen switching button 85 includes the policy details display button 85a and the KPI performance trend display button 85b. The Policy Details Display button 85a is clicked or touched when the user wants to display Policy Details Display 92 (see Figure 9), which shows detailed information about the first marketing policy selected by the user from the list of first marketing policies displayed in Policy List 70. The KPI performance trend display button 85b is clicked or touched when the user wants to display the KPI performance trend display report 94 (see Figure 10), which shows the performance trend of the KPI of the first marketing measure selected by the user from the list of first marketing measures displayed in the measure list 70.

[0092] The KPI performance trend display report 94 shown in Figure 10 displays a time-series line graph 94a showing the change in CVR, which is used as a KPI indicator, over time. Figure 10 is a diagram illustrating an example of a report from the information processing device 10, which includes a time-series line graph 94a. In the KPI Performance Trend Display Report 94, the measured CVR data for the first marketing initiative implemented is represented as a line graph 94a. In line graph 94a, the vertical axis represents CVR, and the horizontal axis represents the implementation period of the first marketing initiative. In the KPI performance trend display report 94, the name of the first marketing initiative implemented and the person in charge of it (94b, 94c, 94d), as well as notes (94e, 94f, 94g), are displayed together with the line graph 94a. Notes are a user's personal record of observations, analyses, lessons learned, events, and points to note. The entries for policy names and their responsible persons (94b, 94c, 94d) and memos (94e, 94f, 94g) shown in Figure 10 are three each, but this is not limited to the three examples; there may be two or fewer, or four or more.

[0093] (Regarding information processing methods and information processing programs) Next, with reference to Figure 11, an example of an information processing program according to this embodiment of the present invention will be described along with the information processing method according to this embodiment. Figure 11 is an example of a flowchart of an information processing program according to this embodiment of the present invention. The information processing method is executed by the arithmetic unit 10e of the information processing device 10 based on the information processing program.

[0094] The information processing program includes an outcome information acquisition step S27, an estimation step S45, and a memory control step S46, among others. The information processing program enables the arithmetic unit 10e of the information processing device 10 to implement functions such as result information acquisition, estimation, and memory control. These functions are executed in the order shown in the flowchart of Figure 11, but the order can be changed as needed. Note that detailed explanations of each function are omitted as they overlap with the descriptions of the various functional units of the information processing device 10 mentioned above.

[0095] The results information acquisition function acquires information regarding the results of the implementation of marketing measures as results information 18 (S27: Results Information Acquisition Step).

[0096] The estimation function uses a learning model to estimate the information used in planning marketing measures as planning information 20, based on the outcome information 18 (S45: estimation step).

[0097] The memory control function associates the result information 18 and the planning information 20 and stores them in the memory unit 10d (S46: memory control step).

[0098] (Information processing programs and information processing methods according to other embodiments) Referring to Figure 12, an information processing program according to another embodiment will be described along with other information processing methods. Figure 12 is an example of a flowchart of an information processing program according to another embodiment. The information processing program according to another embodiment shown in Figure 12 is another embodiment of the information processing program according to the present embodiment shown in Figure 11, and differs from the information processing program according to the present embodiment shown in Figure 11 in that it adds the following steps: web information acquisition step S28, web image information extraction step S29, web text information extraction step S30, web image feature information extraction step S31, advertisement image information extraction step S32, advertisement text information extraction step S33, advertisement image feature information extraction step S34, reference result information acquisition step S35, reference project information acquisition step S36, reference web information acquisition step S37, reference advertisement information acquisition step S38, reference information storage control step S39, similar advertisement search step S40, related information extraction step S41, IR information acquisition step S42, competitor information acquisition step S43, and industry analysis information acquisition step S44.

[0099] The following describes an information processing program according to another embodiment shown in Figure 12, along with an information processing method according to the other embodiment. The information processing program according to another embodiment shown in Figure 12 includes a result information acquisition step S27, a web information acquisition step S28, a web image information extraction step S29, a web text information extraction step S30, a web image feature information extraction step S31, an advertisement image information extraction step S32, an advertisement text information extraction step S33, an advertisement image feature information extraction step S34, a reference result information acquisition step S35, a reference project information acquisition step S36, a reference web information acquisition step S37, a reference advertisement information acquisition step S38, a reference information storage control step S39, a similar advertisement search step S40, a related information extraction step S41, an IR information acquisition step S42, a competitor information acquisition step S43, an industry analysis information acquisition step S44, an estimation step S45, and a storage control step S46, among others.

[0100] The information processing program according to another embodiment shown in Figure 12 implements the following functions for the arithmetic unit 10e: result information acquisition function, web information acquisition function, web image information extraction function, web text information extraction function, web image feature information extraction function, advertising image information extraction function, advertising text information extraction function, advertising image feature information extraction function, reference result information acquisition function, reference project information acquisition function, reference web information acquisition function, reference advertising information acquisition function, reference information storage control function, similar advertising search function, related information extraction function, IR information acquisition function, competitor information acquisition function, industry analysis information acquisition function, estimation function, and storage control function. These functions are executed in the order shown in the flowchart of Figure 12, but the order can be changed as appropriate. Since each function overlaps with the description of the information processing program shown in Figure 11 above, the overlapping explanations will be omitted. Furthermore, since each function overlaps with the description of the various functional units of the information processing device 10 above, detailed explanations will be omitted.

[0101] The web information acquisition function acquires the content of the website 15 used in the implementation of the first marketing measure as web information 19 (S28: Web Information Acquisition Step).

[0102] The web image information extraction function extracts images contained in the website 15 from the web information 19 as web image information (S29: Web image information extraction step).

[0103] The web text information extraction function extracts the text contained in the website 15 from the web information 19 as web text information (S30: Web Text Information Extraction Step).

[0104] The web image feature information extraction function analyzes web image information, converts the features of the images contained in the website 15 into text, and extracts them as web image feature information (S31: Web Image Feature Information Extraction Step).

[0105] The advertising image information extraction function extracts images of advertisements used in the first marketing initiative from the results information 18 as advertising image information (S32: advertising image information extraction step).

[0106] The ad text information extraction function extracts the text of the advertisements used in the first marketing initiative from the performance information 18 as ad text information (S33: Ad Text Information Extraction Step).

[0107] The ad image feature information extraction function analyzes the ad image information, converts the features of the ad image into text, and extracts it as ad image feature information (S34: Ad Image Feature Information Extraction Step).

[0108] The reference outcome information acquisition function acquires information related to the results of the second marketing initiative, which is different from the first marketing initiative and is currently being implemented or has been implemented in the past, as reference outcomes (S35: Reference outcome information acquisition step).

[0109] The reference plan information acquisition function acquires information used in the planning of the second marketing initiative as reference plan information (S36: Reference Plan Information Acquisition Step).

[0110] The reference web information acquisition function uses the website used in the implementation of the second marketing measure as a reference website and acquires the content of that reference website as reference web information (S37: Reference Web Information Acquisition Step).

[0111] The referral advertising information acquisition function identifies advertisements used in the second marketing initiative as referral advertisements and acquires information related to those referral advertisements as referral advertising information (S38: Referral advertising information acquisition step).

[0112] The reference information storage control function stores reference result information, reference project information, reference advertisement information, and reference web information as reference information in the storage unit 10d (S39: Reference Information Storage Control Step).

[0113] The similar ad search function searches for reference ad information related to reference ads similar to the ads used in the first marketing initiative, based on ad image information and ad text information (S40: Similar Ad Search Step).

[0114] The related information extraction function extracts reference performance information, reference project information, and reference web information associated with the referenced ad information searched using the similar ad search function from the reference information (S41: related information extraction step).

[0115] The IR information acquisition function acquires IR information from businesses implementing the first marketing initiative (S42: IR information acquisition step).

[0116] The competitor information acquisition function acquires information about other businesses that compete with the business implementing the first marketing initiative as competitor information (S43: Competitor Information Acquisition Step).

[0117] The industry analysis information acquisition function acquires information related to the analysis of the industry in which the first marketing initiative will be implemented as industry analysis information (S44: Industry Analysis Information Acquisition Step).

[0118] (Effects of the information processing device 10) According to the information processing device 10 of the above embodiment, the planning information 20 for the first marketing measure is estimated based on performance information 17 obtained from an external tool 12 and a website 15, thereby reducing the burden of obtaining the planning information 20 for the first marketing measure.

[0119] Furthermore, according to the information processing device 10 of the embodiment described above, a user of the information processing device 10 can obtain planning information 20 for the first marketing initiative simply by obtaining access rights to the data held by the external tool 12. Therefore, a user of the information processing device 10 can promote the PDCA cycle of the first marketing initiative and continuously improve the first marketing initiative simply by obtaining access rights to the data held by the external tool 12.

[0120] Furthermore, according to the information processing device 10 of the embodiment described above, when measuring the results of the implementation of the first marketing measure using multiple external tools 12, even if the data structure granularity of the measurement data from the multiple external tools 12 differs from the data structure granularity of the information processing device 10, the data can be integrated and grouped in the information processing device 10 by assigning the data item name of the information processing device 10 to each item of the acquired measurement data, as described above with reference to Figure 6.

[0121] Furthermore, the present invention is not limited to the information processing apparatus 10, information processing method, and information processing program according to the above-described embodiment, and can be implemented by various other modifications or applications without departing from the gist of the present invention as described in the claims. Also, although the word "information" is used in the above-described embodiment, the word "information" can be replaced with "data," and the word "data" can be replaced with "information." [Explanation of symbols]

[0122] 10 Information Processing Devices 10a communication interface 10b ROM (Read Only Memory) 10c RAM (Random Access Memory) 10d storage section 10e Arithmetic Unit 10f Input / Output Interface 10g input device 10h output device 11. Information and Communication Networks 12 External Tools 12a External Tool A 12b External Tool B 13 User terminals 15 Websites 16. MCM (Marketing Campaign Management) 17. Performance Information 18. Results Information 19. Web Information 20. Project Information 27 Results information acquisition department 28 Web Information Acquisition Department 29 Web Image Information Extraction Unit 30 Web Text Information Extraction Unit 31 Web Image Feature Information Extraction Unit 32. Advertising Image Information Extraction Unit 33. Advertising text information extraction unit 34. Advertising Image Feature Information Extraction Unit 35 Reference result information acquisition department 36 Reference Planning Information Acquisition Department 37 Reference Web Information Acquisition Section 38 Reference Advertisement Information Acquisition Unit 39 Reference Information Storage Control Unit 40 Similar Ad Search Department 41 Related Information Extraction Unit 42 IR Information Acquisition Department 43 Competitor Information Acquisition Department 44 Industry analysis information acquisition department 45 Estimation part 46 Memory Control Unit 62. Supported Item Settings Screen 62a Item name column of Tool A 62b Item name column for Tool B 62c Tool C item name column 62d Item name list of information processing device 62e 1st item 62f 2nd item 62g 3rd item 62h 4th item 62i Item 5 62j Item 6 68 Summary Results 68a List of Marketing Initiatives 68b External tool name list 68c WEB application sequence 68d Total Value 68e (Measurement value of tool A) 68f (Measurement value of Tool B) 68g (measured value of tool C) List of 70 policies 71 Input field for performance aggregation period 71a Apply button 72 New Registration Button 72a New Registration Selection Button 73 Measure classification column 74 Measure ID column 75 Measure name column 76 Column of Person in Charge 77 Aggregation axis columns 78 Start date column 79 End date column 80 KPI target column 81 KPI Performance Column 82 KPI Performance Column (8 / 1~8 / 31) 83 Budget Amount Column 84 Cost column 92 Detailed policy information display 92a Measure name display section 92b Measure classification display section 92c Appeal (aggregation axis) display section 92d Implementation Period Display Section 92e Person in charge display section 92f KPI Indicator / Target Value Display Section 92g Budget amount display section 92h Memo display section 92i Attachment link display section 92j Initial registration date display section 92k Last updated date display section 92l Results Data Linking Settings Button 94 KPI Performance Trend Display Report 94a Time-series line graph 94b 1st measure 94c Second measure 94d Third Measure 94e First Memo 94f Second memo 94g Third memo 99 Report (Time Series Graph Report 114) 100 Implementation period 101 Creative Columns 102 Display Count Column 103 CTR column 104 Click column 105 CVR column 106 CV row 107 cost column 108 CPA column 109 LP row 110 Measure details column

Claims

1. A results information acquisition unit acquires results information that includes the URL of the website used to implement the first marketing measure, and is used as results information. A web information acquisition unit that acquires the content of the aforementioned website as web information, A web image information extraction unit extracts images contained in the website from the aforementioned web information as web image information, A web text information extraction unit extracts text contained in the website from the aforementioned web information as web text information, A web image feature information extraction unit analyzes the aforementioned web image information, converts the features of the images contained in the website into text, and extracts them as web image feature information. Based on the aforementioned results information, the aforementioned web text information, and the aforementioned web image feature information, an estimation unit uses a learning model to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information. A storage control unit that stores the aforementioned results information and the aforementioned planning information in a storage unit in association with each other, An information processing device equipped with the following features.

2. A performance information acquisition unit acquires performance information that includes information relating to the results of the implementation of the first marketing measure, which includes at least images and text of advertisements used in the first marketing measure. An advertising image information extraction unit extracts the image of the advertisement as advertising image information from the aforementioned results information, An advertising text information extraction unit extracts the text of the advertisement as advertising text information from the aforementioned performance information, An advertising image feature information extraction unit that performs image analysis on the aforementioned advertising image information, converts the features of the advertising image into text, and extracts them as advertising image feature information. Based on the aforementioned results information, the aforementioned advertising text information, and the aforementioned advertising image feature information, an estimation unit uses a learning model to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information. A storage control unit that stores the aforementioned results information and the aforementioned planning information in a storage unit in association with each other, An information processing device equipped with the following features.

3. The aforementioned results information acquisition unit, The results of the implementation of the first marketing measure, as measured by the external tool, are acquired as result information through the means of linking with the external tool provided by the external tool. The aforementioned web information acquisition unit, The information processing device according to claim 1, characterized in that it accesses the website by referring to the URL of the website included in the aforementioned results information and obtains the aforementioned web information.

4. A reference result information acquisition unit acquires information relating to a second marketing measure, which is different from the first marketing measure, and which uses the results of the second marketing measure that is currently being implemented or has been implemented in the past as reference results, and acquires information relating to said reference results as reference result information. A reference planning information acquisition unit acquires information used in the planning of the second marketing measure as reference planning information, A reference website information acquisition unit that uses the website used in the implementation of the second marketing measure described above as a reference website and acquires the content of said reference website as reference website information, A reference advertising information acquisition unit that uses the advertisements used in the second marketing measure described above as reference advertisements and acquires information related to said reference advertisements as reference advertising information, A reference information storage and control unit that stores the aforementioned reference results information, the aforementioned reference project information, the aforementioned reference advertisement information, and the aforementioned reference web information in a storage unit as reference information, A similar ad search unit searches for reference ad information related to the reference ad that is similar to the ad used in the first marketing measure, based on the ad image information and the ad text information, A related information extraction unit extracts the reference performance information, the reference project information, and the reference web information associated with the reference advertisement information found in the similar advertisement search unit from the reference information. Furthermore, The information processing apparatus according to claim 2, characterized in that the estimation unit further takes into account any or any combination of the reference result information, the reference plan information, and the reference web information extracted by the related information extraction unit to estimate the plan information.

5. The aforementioned results information acquisition unit acquires information regarding the results obtained from the implementation of the first marketing measure, specifically the results prior to the implementation of the first marketing measure, as pre-implementation results information. The information processing device according to claim 1 or 2, characterized in that the estimation unit estimates the planning information by further taking into account the comparison results obtained by comparing the outcome information with the pre-implementation outcome information.

6. The system further includes an IR information acquisition unit that acquires IR information of businesses implementing the aforementioned first marketing measures, The information processing apparatus according to claim 1 or 2, characterized in that the estimation unit estimates the planning information by further taking into account the IR information.

7. The system further includes a competitor information acquisition unit that acquires information about other businesses that compete with the business implementing the first marketing measure described above, as competitor information. The information processing apparatus according to claim 1 or 2, characterized in that the estimation unit estimates the planning information by further taking into account the competitor information.

8. The system further includes an industry analysis information acquisition unit that acquires information regarding the analysis of the industry in which the first marketing measure is implemented, as industry analysis information. The information processing apparatus according to claim 1 or 2, characterized in that the estimation unit estimates the planning information by further taking into account the industry analysis information.

9. When measuring the results of the first marketing measure using multiple external tools, The aforementioned results information acquisition unit cooperates with each of the multiple external tools via the means for coordinating with the external tools provided by each of the multiple external tools, and acquires the results information from each of the multiple external tools. The memory control unit integrates the multiple results information obtained from each of the multiple external tools into items that are categorized according to the content of the results information and stores them in the memory unit. The information processing device according to claim 3, characterized in that the estimation unit estimates the planning information using a learning model based on the integrated plurality of outcome information.

10. On the computer, A step to acquire results information, which includes information regarding the results of the implementation of the first marketing measure, including the URL of the website used to implement the first marketing measure, A web information acquisition step for obtaining the content of the aforementioned website as web information, A web image information extraction step of extracting images contained in the website from the aforementioned web information as web image information, A web text information extraction step, which extracts the text contained in the website from the aforementioned web information as web text information, A web image feature information extraction step involves performing image analysis on the aforementioned web image information, converting the features of the images contained in the website into text, and extracting them as web image feature information. An estimation step in which, based on the aforementioned results information, the aforementioned web text information, and the aforementioned web image feature information, a learning model is used to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information; A memory control step of associating the aforementioned results information and the aforementioned planning information and storing them in the memory unit, An information processing method that enables execution of [the specified action].

11. On the computer, A step to acquire results information, which involves acquiring results information that includes at least images and text of advertisements used in the first marketing initiative, and which is information that is obtained as results information. From the aforementioned results information, an advertising image information extraction step is performed to extract the image of the advertisement as advertising image information, From the aforementioned performance information, an advertising text information extraction step is performed to extract the text of the advertisement as advertising text information, The advertising image information is analyzed to extract the features of the advertising image into text, which is then extracted as advertising image feature information. An estimation step in which, based on the aforementioned results information, the aforementioned advertising text information, and the aforementioned advertising image feature information, a learning model is used to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information; A memory control step of associating the aforementioned results information and the aforementioned planning information and storing them in the memory unit, An information processing method that enables execution of [the specified action].

12. On the computer, A function for acquiring results information that obtains information as results information, including the URL of the website used to implement the first marketing measure, and A web information acquisition function that acquires the content of the aforementioned website as web information, A web image information extraction function that extracts images contained in the website from the aforementioned web information as web image information, A web text information extraction function that extracts text contained in the aforementioned website from the aforementioned web information as web text information, A web image feature information extraction function that analyzes the aforementioned web image information, converts the features of the images contained in the website into text, and extracts them as web image feature information. Based on the aforementioned results information, the aforementioned web text information, and the aforementioned web image feature information, an estimation function is provided that uses a learning model to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information. A memory control function that stores the aforementioned results information and the aforementioned planning information in a memory unit in association with each other, An information processing program that makes this possible.

13. On the computer, A performance information acquisition function that acquires performance information that includes information relating to the results of the implementation of the first marketing measure, which includes at least the images and text of the advertisements used in the first marketing measure. Based on the aforementioned performance information, an advertising image information extraction function is provided to extract the image of the advertisement as advertising image information, An advertising text information extraction function extracts the text of the advertisement as advertising text information from the aforementioned performance information, An advertising image feature information extraction function that analyzes the aforementioned advertising image information, converts the features of the advertising image into text, and extracts them as advertising image feature information. Based on the aforementioned results information, the aforementioned advertising text information, and the aforementioned advertising image feature information, an estimation function is provided that uses a learning model to estimate information used in the planning of the first marketing measure, including the products and target audience of the first marketing measure, as planning information. A memory control function that stores the aforementioned results information and the aforementioned planning information in a memory unit in association with each other, An information processing program that makes this possible.

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