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

Abstract

To provide an information processing device, an information processing method, and an information processing program that can support efficient implementation of marketing initiatives.SOLUTION: An information processing device comprises: an implementation condition information acquisition unit for acquiring implementation condition information indicating implementation conditions of marketing initiatives implemented in the past; an achievement information acquisition unit for integrating, via an API, with an external tool that measures results achieved by implementing the marketing initiatives and acquiring the results measured by the external tool as achievement information; an evaluation index reception unit for receiving an evaluation index in a numerical value representing the achievement information; and a storage control unit for associating the achievement information with the evaluation index and also storing the achievement information as actual result information of the marketing initiatives in association with the implementation condition information.SELECTED DRAWING: Figure 5

Description

[Technical Field]

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

[0002] As the internet becomes more widespread, marketing strategies using the internet are on the rise. One example of internet advertising is listing ads. Listing ads are ads that are displayed in conjunction with keywords that customers search for on search engines, and are considered an effective advertising method that can appeal to customers with a high purchasing intent. For keywords that have many users wishing to display ads, the ads of advertisers who have submitted high bids are given priority in displaying.

[0003] When conducting online marketing activities, it is important to measure and analyze data related to advertising effectiveness, such as the number of times an advertisement is displayed, the number of times it is clicked, and the amount of money spent, and systems for measuring such advertising effectiveness have emerged (see, for example, Patent Document 1).

[0004] Data on advertising effectiveness is scattered across advertising media and systems, and the granularity of the data can vary. Data granularity refers to the item names and indicators used in the data. This makes the task of integrating data related to advertising effectiveness complicated, which can be a significant burden when analyzing the effectiveness of marketing initiatives. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-159264 Summary of the Invention [Problem to be solved by the invention]

[0006] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide an information processing device, an information processing method, and an information processing program that can reduce the workload when analyzing the effectiveness of marketing measures carried out using the Internet. [Means for solving the problem]

[0007] In other words, the information processing device of the first aspect is characterized by comprising an implementation condition information acquisition unit that acquires implementation condition information indicating the implementation conditions of marketing measures implemented in the past, a performance information acquisition unit that connects via an API with an external tool that measures the results obtained by implementing the marketing measures and acquires the results measured by the external tool as performance information, an evaluation index receiving unit that receives a numerical evaluation index representing the performance information, and a memory control unit that associates the performance information with the evaluation index and also associates it with the implementation condition information and stores it as performance information of the marketing measures.

[0008] In a second aspect, in an information processing device according to the first aspect, when there are multiple pieces of result information obtained as results obtained by implementing a marketing campaign, the evaluation index receiving unit may receive multiple evaluation indexes to be assigned to each of the multiple pieces of result information indicating the results, and the memory control unit may associate the multiple pieces of result information with the respective evaluation indexes and store them as performance information of the marketing campaign in association with implementation condition information.

[0009] A third aspect may be characterized in that, in the information processing device of the first aspect, when the results obtained by implementing a marketing campaign are measured using multiple external tools, the result information acquisition unit cooperates with each of the multiple external tools via an API and acquires result information from each of the multiple external tools, and the memory control unit integrates the multiple result information acquired from each of the multiple external tools, associates it with an evaluation index, and stores it as performance information of the marketing campaign in association with implementation condition information.

[0010] In a fourth aspect, in the information processing device according to the first aspect, the evaluation index may include an item name and an index of the outcome information.

[0011] A fifth aspect may be an information processing device according to the first aspect, which includes a graph generation unit that generates a graph showing changes in results over time based on the results information, a note receiving unit that receives notes about events that occurred during a period corresponding to the changes over time, a note display unit that displays the notes at a position on the graph corresponding to the time when the events occurred, and a measure name display unit that displays the name of the marketing measure at a position on the graph corresponding to the implementation period of the marketing measure included in the implementation condition information associated with the results information.

[0012] In a sixth aspect, in the information processing device of the first aspect, the memory control unit may be configured to store implementation condition information indicating the implementation conditions for each of multiple marketing measures implemented in the past and outcome information indicating the results obtained by implementing each of the multiple marketing measures in association with each other as multiple pieces of performance information, and to acquire a prediction model derived based on the multiple pieces of performance information; an unimplemented information receiving unit that receives unimplemented condition information indicating the implementation conditions for marketing measures that have not been implemented; and a prediction unit that inputs the unimplemented condition information into the prediction model to predict prediction information indicating the predicted results when the unimplemented marketing measures are implemented.

[0013] In a seventh aspect, in the information processing device according to the first aspect, the evaluation index may be the number of times an advertisement related to the implementation condition information is displayed, a click rate indicating the rate at which the advertisement is clicked, or the achievement rate of the results targeted by the advertiser of the implementation condition information.

[0014] As for an eighth aspect, in the information processing device according to the sixth aspect, the prediction model may be a learning model that has previously been machine-learned to determine the correspondence between the implementation condition information and the result information.

[0015] In a ninth aspect, in the information processing device according to the sixth aspect, the prediction model may be an equation model derived in advance by performing multiple regression analysis of the correspondence between the implementation condition information and the result information.

[0016] In a tenth aspect, in the information processing device according to the sixth aspect, the memory control unit associates improvement condition information indicating the improvement conditions for each of a plurality of improvement measures implemented in the past for marketing campaigns with improvement result information indicating the improvement results obtained by implementing each of the plurality of improvement measures, and stores the associated information as a plurality of pieces of improvement performance information; the prediction model acquisition unit acquires an improvement prediction model derived based on the plurality of pieces of improvement performance information; the unimplemented information receiving unit receives the improvement conditions for the unimplemented improvement measures as unimplemented improvement information; and the prediction unit inputs the unimplemented improvement information into the improvement prediction model, thereby predicting predicted improvement information indicating a prediction of the improvement results when the unimplemented improvement measures are implemented.

[0017] The information processing method according to the eleventh aspect is characterized in that the computer performs the following steps: an implementation condition information acquisition step of acquiring implementation condition information indicating the implementation conditions of marketing measures implemented in the past; a performance information acquisition step of linking with an external tool that measures the results obtained by implementing the marketing measures via an API and acquiring the results measured by the external tool as performance information; an evaluation index reception step of receiving a numerical evaluation index representing the performance information; and a storage control step of associating the performance information with the evaluation index and with the implementation condition information and storing it as performance information of the marketing measures.

[0018] The information processing program according to the twelfth aspect is characterized in that it causes a computer to realize the following: an implementation condition information acquisition function that acquires implementation condition information indicating the implementation conditions of marketing measures implemented in the past; a performance information acquisition function that connects via an API with an external tool that measures the results achieved by implementing the marketing measures and acquires the results measured by the external tool as performance information; an evaluation index reception function that accepts a numerical evaluation index representing the performance information; and a storage control function that associates the performance information with the evaluation index and with the implementation condition information and stores it as performance information of the marketing measures. [Effects of the Invention]

[0019] The information processing device etc. of the present invention is characterized by comprising an implementation condition information acquisition unit that acquires implementation condition information indicating the implementation conditions of marketing measures implemented in the past, a performance information acquisition unit that connects via an API with an external tool that measures the results obtained by implementing the marketing measures and acquires the results measured by the external tool as performance information, an evaluation index receiving unit that accepts a numerical evaluation index representing the performance information, and a storage control unit that associates the performance information with the evaluation index and with the implementation condition information and stores it as performance information of the marketing measures, thereby enabling support for the efficient implementation of marketing measures using the Internet. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram for explaining an overview of an information processing device according to this embodiment. [Figure 2] FIG. 2 is a diagram for explaining an overview of the information processing device according to this embodiment. [Figure 3] FIG. 3 is a diagram for explaining an outline of the information processing device according to this embodiment from a functional aspect. [Figure 4] FIG. 4 is a diagram illustrating an example of the hardware configuration of the information processing device according to this embodiment. [Figure 5] FIG. 5 is a block diagram illustrating an example of the functional configuration of the information processing device according to this embodiment. [Figure 6] FIG. 6 is a diagram for explaining an example of a policy registration screen displayed by the information processing device according to this embodiment. [Figure 7] FIG. 7 is a diagram for explaining settings for automatic data import from an external tool of the information processing apparatus according to this embodiment. [Figure 8] FIG. 8 is a diagram for explaining a case where data is collected from an external tool having a data configuration granularity different from that of the information processing device according to this embodiment. [Figure 9] FIG. 9 is a diagram for explaining the allocation of data items of the information processing device according to this embodiment to data items of external tools (tool A, tool B, and tool C). [Figure 10] Figure 10 is a diagram for explaining how data collected from multiple external tools (tool A, tool B, and tool C) is collected as the same item for marketing measures registered in the information processing device of this embodiment. [Figure 11] FIG. 11 is a diagram for explaining an example of a list of measures displayed by the information processing device according to this embodiment. [Figure 12] FIG. 12 is a diagram for explaining the setting of specifications for a report to be output by the information processing device according to this embodiment. [Figure 13] FIG. 13 is a diagram for explaining a detailed display of a marketing measure, which is an example of a report output by the information processing device according to this embodiment. [Figure 14] FIG. 14 is a diagram for explaining a report including a time series graph, which is an example of a report of the information processing device according to this embodiment. [Figure 15] FIG. 15 is a diagram for explaining a report including a time-series line graph, which is an example of a report of the information processing device according to this embodiment. [Figure 16] FIG. 16 is a diagram for explaining the report input screen shown in FIG. 15 of the information processing device according to this embodiment. [Figure 17]Figure 17 is a diagram illustrating a report that displays a table showing breakdown data on the number of sessions of a web page, which is an example of a report of an information processing device according to this embodiment, alongside a table showing the implementation period of registered events. [Figure 18] FIG. 18 is a diagram illustrating a report including a table that displays how the advertising effectiveness changes by changing the creative used in the advertisement, which is an example of a report of the information processing device according to this embodiment. [Figure 19] FIG. 19 is a diagram for explaining an improvement pattern of an algorithm used by an improvement prediction model of the information processing device according to this embodiment. [Figure 20] FIG. 20 is a flowchart of the information processing program according to this embodiment. [Figure 21] FIG. 21 is a flowchart of an information processing program according to another first embodiment. [Figure 22] FIG. 22 is a flowchart of an information processing program according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0021] (Overview of the information processing device 10 according to this embodiment) An information processing device 10 according to an embodiment of the present disclosure will be described with reference to Fig. 1 to Fig. 19. First, an overview of the information processing device 10 according to this embodiment will be described with reference to Fig. 1, Fig. 2, and Fig. 3. The information processing device 10 is a so-called computer, and includes, for example, a workstation, a server, a personal computer (hereinafter referred to as a PC), a notebook PC, a tablet PC, and a smartphone.

[0022] The information processing device 10 automatically acquires and stores measurement data from external tools, and predicts the results of marketing measures that have not yet been implemented, thereby supporting the effective implementation of marketing measures that are implemented using the Internet. The user of the information processing device 10 can adjust the content of the marketing measures that have not yet been implemented based on the predicted results.

[0023] The storage unit 10d, which will be described later, provided in the information processing device 10 stores a plurality of pieces of performance information. Multiple performance information refers to information obtained by associating implementation condition information, which indicates the implementation conditions for each of multiple marketing measures implemented in the past, with performance information, which indicates the results obtained by implementing each of the multiple marketing measures. As shown in Figure 1, as past performance 1 of marketing measures, implementation condition information indicating the implementation conditions for each of multiple marketing measures implemented in the past and result information indicating the results are associated with each other and stored in memory unit 10d as multiple performance information. The information processing device 10 predicts 3 the results of marketing measures that have not yet been implemented using a prediction model 2 derived by referring to a plurality of pieces of performance information stored in a storage unit 10d (see FIG. 1).

[0024] As shown in FIG. 2, planning information 4, results 5, analysis 6, and improvement proposals 7 are recorded and centrally managed in the memory unit 10d. The planning information 4 is information on the conditions for implementing marketing measures and is planned by the marketing department of a company or the like. The results 5 are data on the results obtained by implementing marketing measures, obtained by an external tool described below. The results 5 may include both quantitative data and qualitative data. The external tool refers to an advertising effectiveness measurement tool described below, which is connected to the information processing device 10 via an information and communication network 20 (see FIG. 4). Quantitative data refers to the number of impressions, the number of clicks, the click-through rate, the cost per click, the number of conversions, the conversion rate, the cost per conversion, and the like, measured by an external tool. Qualitative data refers to the quality and change of things expressed in words rather than numbers, and specifically refers to data on the results of advertising obtained through interview surveys, feedback surveys, questionnaire surveys, etc., such as the thoughts, impressions, and images evoked by people who saw the advertisement. Qualitative data may also include the market situation and environment at the time the qualitative data was acquired, the trends of competitors, the company's own trends, and the occurrence of weather, disasters, and social events. Qualitative data is often closely related to changes in quantitative data, and is therefore stored along with the date and time it was acquired.

[0025] Analysis 6 is the result of analyzing Actual Results 5 and the result of analyzing Planning Information 4 based on Actual Results 5. Improvement Proposal 7 is the condition for improving the marketing measures obtained based on Analysis 6. The planning information 4, results 5, analysis 6, and improvement proposal 7 are associated with each marketing measure and stored in the memory unit 10d, and the recording of the marketing measure information and data collection 8 are performed. For example, the planning information 4A, results 5A, analysis 6A, and improvement proposal 7A of a marketing measure 8A are associated with each other, bundled together, and stored in the memory unit 10d. The planning information 4B, results 5B, analysis 6B, and improvement proposal 7B of a marketing measure 8B are associated with each other, bundled together, and stored in the memory unit 10d. The plurality of pieces of performance information stored in the storage unit 10d are used for policy budget formulation and budget / actual management 11. The plurality of pieces of performance information stored in the storage unit 10d are also used for information reference and data analysis 12.

[0026] (Overview of functions of information processing device 10) Next, the main functions provided to the user of the information processing device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram for explaining an overview of the information processing device 10 from a functional aspect. (Login 13) When the user starts using the information processing device 10, the information processing device 10 requests the user to perform a login operation 13 in order to verify the validity of the user's identity.

[0027] (Common Menu 13a) The information processing device 10 provides a common menu 13a to a user who has logged in 13. The common menu 13a provides options such as budgeting, budget / actual management 14, information reference / data analysis 16, and settings 17. Budget formulation and budget / actual management 14 is one of the options displayed on the common menu 13a that the user can select when they want to receive support from the information processing device 10 in formulating a budget for marketing measures and managing the budget / actual of marketing measures. Information reference / data analysis 16 is one of the options displayed on the common menu 13a that can be selected when one wishes to refer to information about past marketing measures registered in the information processing device 10 or to analyze data related to past marketing measures. The setting 17 is one of the options displayed on the common menu 13a that can be selected by the user when making various settings of the information processing device 10.

[0028] (Budget formulation, budget and actual management 14) The information processing device 10 displays to the user a campaign list 14a, which is a list of registered past marketing campaigns. The campaign list 14a provides the user with the data necessary for budgeting marketing campaigns and managing budgets and actuals, allowing the user to understand the progress of actual results against the budget and confirm whether the target is likely to be achieved smoothly or whether course corrections are necessary. The campaign list 14a also includes hyperlinks for moving to each screen, allowing the user to move to each screen from the campaign list 14a. These screens include a campaign information detail display 112 that displays detailed information on registered past marketing campaigns, and a CVR (conversion rate) time series graph report 114.

[0029] (Information Recording / Data Collection 15) The measure registration / editing 15a is one of the functions provided to the user of the information processing device 10, and allows the user to register and edit information related to past marketing measures. By aggregating and managing the contents of plans and projects, performance data, outcome data, reviews, inferences about problems, and improvement proposals for each marketing measure, the scattering of information is prevented and the information is turned into an asset. The data link setting 15b is one of the functions provided to the user of the information processing device 10, and performs settings for linking the data identifier of data to be aggregated as a marketing measure with the marketing measure to be registered in the information processing device 10. Specifically, the data link setting 15b performs various settings for linking various data automatically acquired from external tools (described later) with data in the information processing device 10.

[0030] (Information Reference / Data Analysis 16) The information processing device 10 provides the user with the functions of measure details 16a and report 16b in the information reference / data analysis 16, enabling a simple search for information on past marketing measures that have been collected and registered, realizing data management that makes it easy to follow past learnings, and allowing the user to refer to measures that will be the next step and review their strategy. The policy details 16a is one of the functions provided to the user of the information processing device 10, and refers to a function of displaying to the user a policy information details display 112 associated with a hyperlink embedded in the policy list 14a. Report 16b is one of the functions provided to the user of the information processing device 10, and refers to a function that creates a report for the user based on information about registered past marketing measures. Based on the information about the collected and registered marketing measures, it is possible to create a report that allows for highly flexible aggregation and analysis from various perspectives, thereby realizing the analysis desired by the user. The report 16b has functions of report creation 16c, report display 16d, and report download 16e. Report Creation 16c refers to the function of extracting data from the aggregated and registered information on marketing measures using specified search criteria, editing the extracted data, and saving it as a report together with the search criteria used. The report display 16d embeds a hyperlink associated with the created report in the policy information details display 112, allowing the user to easily reach the report. Report Download 16e is a function for downloading saved reports, and specifically, the user can obtain the reports as CSV (Comma Separated Value) files.

[0031] (Setting 17) The information processing device 10 has a setting 17, which is a function that allows the user to change and set the operation of the information processing device 10 and the display format on a monitor or the like from the existing ones to suit the user's preferences and needs. The setting 17 includes a media linkage 17a, an event management 17e, a goal management 17g, a measure item management 17i, a measure group management 17k, and a user management 17l. The medium linking 17a includes a medium linking setting 17b, an authentication 17c, and a CSV upload 17d. The media linkage settings 17b perform settings for API linkage when the information processing device 10 imports measurement data from an external tool, and settings for linking data item names and indicators that differ for each external tool to corresponding items and indicators in the information processing device 10. API integration refers to expanding functionality by linking data using APIs with external software and programs. The authentication 17c performs settings for receiving authentication that enables access to an external tool that cooperates with the information processing device 10. The CSV upload 17d has a function of uploading a CSV file containing advertisement information to an external tool that cooperates with the information processing device 10. Event management 17e includes event registration / editing 17f, which allows users to register and edit events as needed. Registered event information may be acquired by a memo receiving unit 34 (described later) and displayed in a report by a memo display unit 35. An event is an event hosted by a company to introduce its products or services, such as an exhibition, seminar, conference, or promotional event. Events are not limited to events hosted by the company itself, but may also be events hosted by other companies that have an impact on the company's products or services, or national events. The goal management 17g includes a goal registration / editing 17h, which allows users to register and edit goals for their marketing initiatives. The policy item management 17i includes a policy item registration / editing 17j, which can register and edit various items of marketing policies, such as the aggregation date (year / month / day), customer name, campaign, number of downloads, number of web applications, implementation period of the marketing policy (start date, end date), KPI target, KPI performance, budget amount, cost, etc. The measure group management 17k has a function of managing groups for grouping and classifying registered marketing measures. The user management 17l has a function of registering and editing the account and password assigned to the user used for user authentication performed in the login 13, and the address and telephone number of the user as contact information.

[0032] (Hardware configuration of the information processing device 10 according to this embodiment) Next, an example of the hardware configuration of the information processing device 10 will be described with reference to Fig. 4. Fig. 4 is a diagram for explaining an example of the hardware configuration of the information processing device 10. The information processing device 10 includes a communication interface 10a, a read only memory (ROM) 10b, a random access memory (RAM) 10c, a storage unit 10d, a calculation unit 10e, and an input / output interface 10f.

[0033] The communication interface 10a has a function of transmitting and receiving data handled by the information processing device 10 to and from other devices via an information communication network 20 including the Internet and an intranet.

[0034] The storage unit 10d can be used as a storage device for the information processing device 10, and is configured, for example, by a hard disk drive, a solid state drive, or a flash memory, and 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, when there is a huge amount of data related to past performance 1, the data can be efficiently managed, and the searchability and accessibility are excellent, allowing required information to be quickly referenced.

[0035] In addition, the memory unit 10d of the information processing device 10 stores the information processing program described below, an OS (Operating System) required for the operation of the information processing device 10, various other applications, and various data used by the applications. The OS is a type of application installed in the information processing device 10 and has a function of managing basic control of the information processing device 10 .

[0036] The information processing device 10 stores an information processing program in the ROM 10b or the storage unit 10d, and loads the information processing program into a main memory configured by the RAM 10c, etc. The calculation unit 10e accesses the main memory into which the information processing program has been loaded, and executes the information processing program.

[0037] The calculation unit 10e includes a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), etc., and is realized by a logic circuit (hardware) or a dedicated circuit formed on an integrated circuit (IC) chip, an LSI (Large Scale Integration), etc.

[0038] The input / output interface 10f transmits and receives data to and from devices external to the information processing device 10. The external devices are an input device 10g and an output device 10h that input and output data to and from the information processing device 10. The input device 10g is, for example, a keyboard and a mouse, and the output device 10h is, for example, a monitor, a printer, and a speaker.

[0039] (Functional configuration of information processing device 10) An example of the functional configuration of the information processing device 10 will be described with reference to Fig. 5. Fig. 5 is a block diagram for describing an example of the functional configuration of the information processing device 10. By executing the information processing program described below, the information processing device 10 has functional units such as an implementation condition information acquisition unit 29, a performance information acquisition unit 30, an evaluation index reception unit 31, a memory control unit 32, a graph generation unit 33, a memo reception unit 34, a memo display unit 35, a measure name display unit 36, a prediction model acquisition unit 37, an unimplemented information reception unit 38, and a prediction unit 39 in the calculation unit 10e.

[0040] The implementation condition information acquisition unit 29 acquires implementation condition information indicating implementation conditions of marketing measures implemented in the past. The implementation condition information acquisition unit 29 acquires implementation condition information input on the policy registration screen 40 (see FIG. 6).

[0041] The information processing device 10 displays a measure registration screen 40 (see FIG. 6) on the monitor of the output device 10h. The campaign registration screen 40 is used to acquire implementation condition information indicating the implementation conditions for each of multiple marketing campaigns that have been implemented in the past. The campaign registration screen 40 may also be used to acquire unimplemented condition information indicating the implementation conditions for marketing campaigns that have not yet been implemented, as described below. The user inputs implementation condition information into blank spaces on the policy registration screen 40 using the keyboard, mouse, etc. of the input device 10g. Furthermore, acquisition of the implementation condition information, outcome information, and unimplemented condition information may be performed using generative artificial intelligence (AI). For example, a document file containing plan information 4 indicating implementation conditions of a marketing measure, implementation condition information, and unimplemented condition information may be input to the generation AI, and the implementation conditions may be automatically acquired and formatted using natural language processing and stored as data in the storage unit 10d. The implementation condition information acquisition unit 29 acquires implementation condition information that indicates the implementation conditions of marketing measures that have been implemented in the past and that have been input from the measure registration screen 40.

[0042] When acquiring data with different item names between advertising effectiveness measurement tools, the information processing device 10 can aggregate the data as the same item. For example, the number of "applications completed on a web page" is displayed as "number of conversions" in tool A, as "number of conversions" in tool B, and as "number of conversions" in tool C. When acquiring the number of "applications completed on a web page" from each of tool A, tool B, and tool C, the information processing device 10 can aggregate the data from tool A, tool B, and tool C by unifying the item name to "number of conversions," for example. As a result, even if results are obtained for one marketing campaign using three different advertising effectiveness measurement tools, namely, tool A, tool B, and tool C, the information processing device 10 can tally the results as a single unified item, "number of conversions."

[0043] The implementation condition information may include at least one of the implementation period of the marketing measure, the name of the person in charge, and the budget amount. The implementation condition information may also include all items entered on the measure registration screen 40, or may be selected from all items by the user or an information processing program (described later).

[0044] The policy registration screen 40 will be described with reference to Fig. 6. Fig. 6 is a diagram for explaining an example of the policy registration screen 40 displayed by the information processing device 10. The campaign registration screen 40 includes a campaign name input field 41, a campaign classification input field 42, an appeal input field 43, an implementation period input field 44, a person in charge input field 45, a KPI indicator / target value input field 46, a budget amount input field 47, a memo input field 48, an attachment field 49, an upload button 49a, a save button 50, and a results information linking button 51.

[0045] Detailed information about the marketing measures is acquired by the implementation condition information acquisition unit 29 from the measure registration screen 40 and registered in the information processing device 10. The measure classification and appeal type of the marketing measure, the person in charge, and the type of KPI indicators and target indicators can be selected from multiple candidates displayed in the measure classification input field 42, appeal input field 43, person in charge input field 45, and KPI indicator and target value input field 46, respectively, so that each can be set according to the user's needs. The registered marketing measures are displayed in the measure list 14a.

[0046] The campaign name input field 41 is an input field for the name of the marketing campaign to be registered. The name can be determined by the user. The name of the marketing campaign entered in the campaign name input field 41 may be displayed in a report by the campaign name display unit 36, which will be described later. The measure classification input field 42 is an input field for the classification of the marketing measure to be registered. Examples of classifications include CVR improvement measures, PV (Page View) acquisition measures, CPA (Cost Per Acquisition) improvement measures, awareness improvement measures, KGI (Key Goal Indicator) measures, KPI (Key Performance Indicator) measures, white papers, in-house webinars, co-hosted webinars, and questionnaire surveys. The appeal input field 43 is used to input the type of appeal for the marketing measure to be registered. Appeal means appealing to consumers about the appeal of a product or service and increasing their desire to purchase. Examples of appeals for marketing measures include advertising the features of your company's products or services, or points of differentiation from other companies, or advertising unique strengths that only your company has, or products with technology or added value that other companies cannot imitate.

[0047] The implementation period input field 44 is an input field for inputting the implementation period of the marketing measure to be registered, and is used to input the start and end dates. The implementation period entered in the implementation period input field 44 is referenced when the memo display unit 35 displays a memo at a position on the graph corresponding to the time when the event occurred, and is also referenced when the measure name display unit 36 ​​displays the measure name of the marketing measure at a position on the graph corresponding to the implementation period of the marketing measure. The person in charge input field 45 is a field for inputting the name of the person in charge of the marketing measure to be registered. The person in charge name entered in the person in charge input field 45 may be displayed by the measure name display unit 36 ​​in a graph in the report together with the measure name of the marketing measure. The KPI indicator / target value input field 46 is used to input the KPI indicators and target values ​​for the marketing initiative being registered. KPI (key performance indicator) indicators are used to evaluate the results of marketing initiatives and understand their progress. They are intermediate indicators for achieving KGI. Specifically, KPI indicators include the number of times the advertisement related to the implementation condition information is displayed (also known as the number of impressions), the click-through rate (also known as the CTR, or click-through rate) indicating the percentage of clicks on the displayed advertisement, the achievement rate of the advertiser's target results (also known as the CVR, or conversion rate), the number of MALs (marketing accepted leads), or the number of MQLs (marketing qualified leads). MALs refer to leads that will be the target of lead nurturing in the future. Leads are potential customers, and lead nurturing refers to marketing activities aimed at moving leads to the purchasing phase. MQLs refer to leads selected from MALs as having a high probability of success. The KPI indicator / target value input field 46 may have, for example, 300 MALs and 10 MQLs.

[0048] To effectively implement the PDCA cycle, it is necessary to determine target values ​​and the time period for achieving those targets. The PDCA cycle is a concept that aims to improve the quality of management by circulating a hypothesis-verification process of Plan, Do, Check (measurement and evaluation), and Action (measures and improvement). Unless target values ​​and target achievement periods are determined, it is impossible to evaluate implemented marketing measures, and appropriate measures cannot be taken to lead to the next improvement measures, which may result in a one-off effort. Therefore, before implementing a marketing measure, users set KPI indicators, target values, and implementation period. Marketing measure results are recorded based on the set implementation period. Even if results improve outside the implementation period, they are not tallied as marketing measure results. Users evaluate marketing measures by comparing the results of marketing measures during the implementation period with KPI indicators and target values. In this way, the target value and the period for achieving the target are determined, and the results of the marketing measures during the implementation period are tallied, allowing the marketing measures to be evaluated. A common method for narrowing the period for tallying the results of a marketing measure is to set a period for collecting the results, but a unique function is to set the implementation period before the marketing measure is implemented and tallie the results of the marketing measure based on the implementation period.

[0049] The budget input field 47 is a field for inputting the budget amount of the marketing measure to be registered. The memo input field 48 is a field for inputting notes regarding the marketing measure to be registered. The attachment field 49 is an input field for registering a file related to the marketing initiative to be registered, such as an image file or a video file, in association with the marketing initiative to be registered. The attachment file is, for example, a creative image used in the marketing initiative to be registered, and by clicking the upload button 49a, the attachment file is stored in the storage unit 10d. The save button 50 is clicked or touched when the content input on the policy registration screen 40 is to be saved in the storage unit 10d. The result information linking button 51 is a button that moves to a setting screen for configuring data acquired from external tools such as an advertising effectiveness measurement tool, a customer management tool, and an order management tool, and result information of marketing measures.

[0050] The result information acquisition unit 30 cooperates with an external tool that measures the results obtained by implementing a marketing measure via an API, and acquires the results measured by the external tool as result information.

[0051] An API (Application Programming Interface) refers to an interface that connects an external tool and the information processing device 10, and links the external tool connected via the information communication network 20 with the information processing device 10, enabling the information processing device 10 to automatically acquire measurement data acquired by the external tool as result information in accordance with a predetermined program. By linking data using the API function of the external tool, the information processing device 10 can automatically import the measurement data of the external tool as result information.

[0052] Performance information showing the results obtained by implementing marketing measures is obtained from external tools such as advertising effectiveness measurement tools, customer relationship management (CRM) tools, marketing automation (MA) tools, and order management tools. The outcome information may include not only quantitative data obtained by an external tool, but also qualitative data obtained by an interview survey or the like. Since the performance information includes information that changes over time, the performance information that changes over time is stored in the chronological order in which it was acquired. The graph generation unit 33, which will be described later, generates a graph showing the change over time in the results obtained by implementing the marketing measures, based on the performance information that changes over time. The result information acquisition unit 30 acquires measurement data measured by an external tool as result information indicating the results obtained by implementing a marketing measure. An advertising effectiveness measurement tool is a tool that measures the effectiveness of a placed advertisement, i.e., how effective the advertisement was. Indicators for measuring the effectiveness of an advertisement include awareness, number of visitors to the site, click rate, and number of inquiries. An example of an advertising effectiveness measurement tool is AdEbis (registered trademark). Methods for measuring advertising effectiveness include, for example, measuring the number of times an advertisement is displayed, click rate, and achievement rate of results as evaluation indicators. A customer relationship management (CRM) tool is a tool for aggregating and managing customer information, specifically recording and managing information such as the customer's company information, department name, job title, contact information, purchase and behavior history, etc. Zoho CRM (registered trademark) is an example of a CRM tool. Marketing automation (MA) tools are tools for automating marketing activities, specifically, they can automate and streamline the management of customer information and the nurturing of potential customers (leads). An example of an MA tool is Sales Force (registered trademark). An order management tool is a system that efficiently manages the series of transactions and tasks that occur in order placement and receipt operations, and specifically, is a tool that can centrally manage order placement and receipt operations on the cloud. An example of an order management tool is the cart system of an EC (Electric Commerce) site. The evaluation index may be the number of times the advertisement relating to the implementation condition information is displayed, the click rate indicating the rate at which the advertisement is clicked, or the rate at which the advertiser of the implementation condition information achieves the results they are aiming for.

[0053] The number of views refers to the number of times an ad is displayed, also known as the number of impressions. Click-through rate is the percentage of people who click on an ad among those who see it, and is also called CTR (Click Through Rate). The success rate is the percentage of people who clicked on an ad and then purchased a product or signed up for a service. It is also called the CVR (Conversion Rate).

[0054] The advertising effectiveness measurement tool, CRM tool, MA tool, order management tool, and the like are not limited to those provided outside the information processing device 10 as external tools, but may be provided inside the information processing device 10. Furthermore, the memory control unit 32 is not limited to acquiring performance information indicating the results obtained by implementing marketing measures from an advertising effectiveness measurement tool or the like, but may also acquire the information through user input or through processing of the information processing program described below.

[0055] The evaluation index receiving unit 31 receives a numerical evaluation index representing the outcome information. The storage control unit 32 associates the outcome information with the evaluation index and with the implementation condition information, and stores the information as performance information of the marketing measure.

[0056] When there are multiple pieces of outcome information acquired as the outcome obtained by implementing a marketing measure, the evaluation index receiving unit 31 receives multiple evaluation indexes to be assigned to each of the multiple pieces of outcome information indicating the outcome. The storage control unit 32 associates the multiple pieces of performance information with the respective evaluation indexes and with implementation condition information, and stores the information as performance information of the marketing measures.

[0057] The evaluation indexes used are KPIs (Key Performance Indicators), which are parameters that companies implementing marketing measures should set as targets. Specifically, as mentioned above, KPIs include the number of times an advertisement related to the implementation condition information is displayed, the click rate indicating the percentage of times the displayed advertisement is clicked, the achievement rate of the results targeted by the advertiser of the implementation condition information, the number of MALs (Marketing Accepted Leads), or the number of MQLs (Marketing Qualified Leads).

[0058] When the results obtained by implementing a marketing measure are measured using multiple external tools, the result information acquisition unit 30 cooperates with each of the multiple external tools via an API and acquires result information from each of the multiple external tools. The storage control unit 32 integrates the multiple pieces of performance information acquired from the multiple external tools, associates them with the evaluation index, and stores them as performance information of the marketing measures in association with the implementation condition information. The evaluation index may include the item name and index of the outcome information.

[0059] Next, settings for automatically importing data from an external tool into the information processing device 10 will be described with reference to Fig. 7. Fig. 7 is a diagram for explaining settings for automatically importing data from an external tool into the information processing device 10. Here, settings for automatically importing data from AdEbis, an advertising effectiveness measurement tool, into the information processing device 10 will be described as an example of an external tool. Automatic import means that by selecting a data identifier for an external tool that corresponds to automatic import, the external tool will link with the registered marketing measures and acquire performance information measured by the external tool at a predetermined granularity at predetermined time intervals, for example, every day, every 12 hours, or every week, based on the API (Application Programming Interface) provided by the external tool. Granularity refers to the item names and indicators assigned to the performance information. The example AdEbis data identifiers are account ID, media type, ad group 1, ad group 2, and ad ID, and the user can identify the performance information measured by AdEbis by determining the account ID, media type, ad group 1, ad group 2, and ad ID. Specifically, automatic acquisition means that the information processing device 10 sends a request via HTTP communication to the endpoint of the API provided by the external tool to send the result information acquired by the external tool to the information processing device 10, and the external tool sends the result information to the information processing device 10 in response to the request.

[0060] In the example of AdEbis, ads are configured by combining four elements: media type, ad group 1, ad group 2, and ad ID. The media type indicates the type of media in which the advertisement is displayed, and includes, for example, listing advertisements, display advertisements, SNS advertisements, video advertisements, and native advertisements. Ad Group 1 and Ad Group 2 are groups for further classifying advertisements and are set according to the purpose and target of the advertisement. Ad Group 1 manages broad categories of advertisement campaigns, and can be set, for example, by product type or advertisement purpose (brand awareness, product purchase, website visits, etc.). Ad Group 2 further classifies advertisements under Ad Group 1, and can manage, for example, advertisements for specific product lines, campaigns by region, or advertisements for specific user segments (age range, gender, interests, etc.). An ad ID is a unique identifier assigned to each ad, allowing us to track the performance of a particular ad and adjust the ad as needed.

[0061] FIG. 7(a) shows the data link setting screen 21, and FIG. 7(b) shows the data link addition screen. 7(a) displays information about marketing measures that have already been registered in the information processing device 10 and are to be linked with external tools. The marketing measure information 21a includes a measure name 21b, a measure classification 21c, an implementation period 21d, and a KPI index / target value 21e. The campaign name 21b is the name of a marketing campaign, and is exemplified as “GA4 Report Guide: Introductory Edition.” Note that GA4 refers to Google (registered trademark) Analytics 4. The measure classification 21c is a classification type of marketing measures, and is exemplified by white paper. The implementation period 21d is the implementation period of the marketing measure, and is exemplified as 2023 / 08 / 01 to 2023 / 09 / 30. KPI indicators and target values ​​21e are KPI indicators and target values ​​for marketing measures, and examples include a target value of 300 MALs and a target value of 10 MQLs.

[0062] When the user clicks or touches the details display button 21f shown in FIG. 7(a), a campaign information details display 112, which will be described later and includes detailed information about the marketing campaign, is displayed. Touch operation refers to an operation performed by directly touching icons or buttons displayed on a tablet PC or smartphone display that has a built-in touch sensor with a finger or pen. A click operation refers to pressing a button on a mouse, which is an input device for a computer, to perform an input operation on an icon or button displayed on a display. The policy linkage setting list 22 shown in FIG. 7(a) displays the data linkage settings that have already been registered, and includes a KPI column 22a, an external tool data identifier column 22b, and an action column 22c. The KPI column 22a displays the KPI indicators of the marketing measures that are the targets of collaboration with external tools. The external tool data identifier column 22b displays measurement data of the external tool linked to the KPI indicator displayed in the KPI column 22a, specifically the contents of the data linkage settings. The external tool data identifier column 22b specifically displays KPI, linked service, medium type, ad group 1, ad group 2, ad ID, and indicators to be automatically linked. The action column 22c has an embedded hyperlink that takes you to the data link setting addition screen 24 when a link setting is added, and is used when changing five already registered link settings (22d, 22e, 22f, 22g, and 22h). The policy linkage setting list 22 lists five linkage settings (22d, 22e, 22f, 22g, and 22h) as an example. The save button 22i has a function of saving the changed linkage settings displayed on the data linkage setting addition screen 24. The back button 22j has a function of returning from the data link setting addition screen 24 to the data link setting screen 21.

[0063] (First Linkage Setting 22d) The contents of the data linkage settings in the first linkage setting 22d are as follows. KPI:MAL Collaborative service: Ad Ebisu All traffic Media type: Display_Facebook (KWM) Ad Group 1: GA4 Appeal Ad Group 2: All Advertising ID: All Automatically linked indicator: CV (M_White Paper)

[0064] (Second Link Setting 22e) The contents of the data linkage settings in the second linkage settings 22e are as follows: KPI:MAL Collaborative service: Ad Ebisu All traffic Media type: Email_House email newsletter, white paper Ad Group 1: GA4 Measures Ad Group 2: All Advertising ID: All Automatically linked indicator: CV (M_White Paper)

[0065] (Third Link Setting 22f) The contents of the data linkage settings in the third linkage setting 22f are as follows: KPI:MAL Collaborative service: Ad Ebisu All traffic Media type: External media_Advertisement Ad Group 1: GA4 Measures Ad Group 2: All Advertising ID: All Automatically linked indicator: CV (M_White Paper)

[0066] (4th Link Setting 22g) The contents of the data linkage settings of the fourth linkage setting 22g are as follows: KPI:MAL Collaborative service: Ad Ebisu All traffic Media type:MarkeZine (registered trademark) Ad Group 1: GA4 Measures Ad Group 2: All Advertising ID: All Automatically linked indicator: CV (M_White Paper)

[0067] (5th link setting 22h) The contents of the data linkage settings for the 5th linkage setting 22h are as follows: KPI:MQL Collaborative service: Salesforce · All traffic Media type: MQL report Ad Group 1: Phone Inquiries Ad Group 2: All Advertising ID: All Automatically linked indicator: Number of appointments obtained

[0068] If the user wants to add a new data linkage setting, by clicking or touching the Add Linkage Setting button 23 displayed on the data linkage setting screen 21, the Add Data Linkage Setting screen 24 will be displayed as a pop-up screen at the front of the operation screen. 7(b) includes a KPI input field 24a, a linked service input field 24b, a medium type input field 24c, an ad group 1 input field 24d, an ad group 2 input field 24e, an ad ID input field 24f, an automatically linked indicator input field 24g, and a performance information selection button 24h. These input fields 24a to 24g may be filled in by selecting from options in a pre-registered pull-down menu, or by directly entering characters.

[0069] The KPI input field 24a is a field for inputting a KPI index to be linked with the performance information measured by an external tool, and MAL is exemplified. The linked service input field 24b is a field for inputting external tools to be linked with the KPI entered above, and shows the total traffic measured by AD EBISU as an example. The total traffic tab means that the measurement results of inflow traffic, including traffic inflow from sources other than advertising, inflow from organic search, direct inflow, and inflow from external links, are displayed. The medium type input field 24c is an input field for the type of advertising medium, and is exemplified by a display advertisement on Facebook (registered trademark). The ad group 1 input field 24d is an input field for the type of ad group 1, and GA4 (Google Analytics 4) appeal is shown as an example. The ad group 2 input field 24e is an input field for the type of ad group 2, and shows that all of the types of ad group 2 are selected. The advertisement ID input field 24f is an input field for inputting the type of advertisement ID, and in this example, all types of advertisement ID are selected. The automatically linked indicator input field 24g is an input field for an indicator to be automatically linked, and shows, for example, that the number of downloads of M_White Paper is used as an indicator of CV (conversion). When the result information selection button 24h is clicked or touched, the contents input in these input fields 24a to 24g are selected and stored in the information processing device 10.

[0070] Next, referring to Figures 8 to 10, we will explain the case where data is aggregated from an external tool with a data configuration granularity different from that of the information processing device 10 in this embodiment, and specifically, we will explain how to deal with the case where the data configuration of the marketing measures registered in the information processing device 10 differs from the data configuration of the external tool. The data configuration granularity refers to the item names and indices assigned to the data, and in some cases refers to the level of detail. FIG. 8 is a diagram for explaining a case where data is collected from an external tool having a data configuration granularity different from that of the information processing device 10. In FIG.

[0071] 8 shows the relationship between a data configuration example 26 of an external tool and a data configuration example 28 of the information processing device 10. It is assumed that marketing measures X and Y are registered in the information processing device 10. The data configuration example 26 of the external tool includes measurement data of an advertising effectiveness measurement tool 26a, measurement data of an MA tool 26b, and measurement data of an order management system 26c. The measurement data of the advertising effectiveness measurement tool 26a is the number of requests for information and the number of completed purchases when advertising is placed on Google Ads and external media. The measurement data of the MA tool 26b is the number of appointments obtained and the number of contracts concluded when campaign A and campaign B are implemented. The measurement data of the order management system 26c is the confirmed sales amounts of Offer A and Offer B.

[0072] Marketing strategy X tally up the number of requests for information when an advertisement is placed on Google Ads and the number of requests for information when an advertisement is placed on external media, as measured by the advertising effectiveness measurement tool 26a, as the number of KPI "leads." In addition, the marketing measure X tally up the number of appointments acquired when the campaign A is implemented, measured by the MA tool 26b, as the number of KPI "leads." Marketing strategy Y compiles the number of completed purchases when advertising on Google Ads, measured by advertising effectiveness measurement tool 26a, and the number of completed purchases when advertising on external media, as the number of KPI "completed purchases." In addition, marketing strategy Y aggregates the confirmed sales amount from offer A measured by the order management tool as the amount of the KPI "sales."

[0073] As described above, the number of brochure requests measured by the advertising effectiveness measurement tool 26a can be linked to the number of "leads," the KPI of marketing measure X. This is because, even if the data configuration granularity of the external tool (i.e., the data index) and the data configuration granularity of the marketing measure X (i.e., the data index) are different, the data index of the external tool is redefined to match the data index of marketing measure X. Furthermore, as mentioned above, marketing strategy X has two KPIs, "leads" and "appointments," and measurement data from different external tools can be linked to match the indicators of marketing strategy X. Therefore, by linking the identifiers and indices of the external tool to be imported with the KPIs of the marketing measures registered in the information processing device 10, data of different granularities (indices) of the external tool can be aggregated and grouped.

[0074] 9 and 10, a method of aggregating and grouping data in the information processing device 10 when the data configuration granularity of measurement data from external tools (tool A, tool B, and tool C) differs from the data configuration granularity of the information processing device 10 will be described. Fig. 9 is a diagram for explaining assignment of data item names of the information processing device 10 to data item names of the external tools (tool A, tool B, and tool C), and Fig. 10 is a diagram for explaining aggregation of data aggregated from multiple external tools (tool A, tool B, and tool C) as the same item for marketing measures registered in the information processing device 10.

[0075] 9 is a screen for setting item names to be assigned to the measurement data of external tools (tool A, tool B, and tool C) acquired by the information processing device 10 when the data is acquired by the information processing device 10. The corresponding item setting screen 52 includes a tool A item name column 52a, a tool B item name column 52b, a tool C item name column 52c, and an information processing device 10 item name column 52d. The first items 52e of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are the negotiation date, the contract date, and the purchase date, respectively. The second items 52f of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are the supplier name, the customer name, and the client name, respectively. The third items 52g of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are the person in charge name, the staff name, and the clerk name, respectively. The fourth items 52h of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are the campaign name, the promotion name, and the measure name, respectively. The fifth items 52i of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are the number of conversions, the number of conversions, and the number of conversions, respectively. The sixth items 52j of the item name column 52a of tool A, the item name column 52b of tool B, and the item name column 52c of tool C are area, region, and district, respectively.

[0076] As described above, the item names of the first to sixth items of tool A, the item names of the first to sixth items of tool B, and the item names of the first to sixth items of tool C are all different. When acquiring the measurement data of tool A, the measurement data of tool B, and the measurement data of tool C, the information processing device 10 assigns the item names of the first to sixth items of the information processing device 10 as follows. When the first item 52e of the measurement data of tool A, tool B, and tool C is acquired and compiled by the information processing device 10, the compilation date (year, month, and day) is assigned as the item name and compiled. When the second item 52f of the measurement data of tool A, tool B, and tool C is acquired and compiled by the information processing device 10, the supplier name is assigned as the item name and the data is compiled. The third item 52g of the measurement data of tool A, tool B, and tool C is not captured by the information processing device 10. When the fourth item 52h of the measurement data of tool A, tool B, and tool C is acquired and tallied by the information processing device 10, campaign is assigned as the item name and tallied. When the fifth item 52i of the measurement data of tool A, tool B, and tool C is acquired and tallied by the information processing device 10, the number of WEB applications is assigned as the item name and tallied. When the sixth item 52j of the measurement data of tool A, tool B, and tool C is acquired and compiled by the information processing device 10, the area is assigned as the item name and compiled.

[0077] FIG. 10 shows a summary result 58 in which the measurement data of tools A, B, and C are summarized, grouped, and displayed by the information processing device 10. The counting result 58 includes a marketing measure name column 58a, an external tool name column 58b, and a number of web applications column 58c. The XX measure displayed in the marketing measure name column 58a is displayed as a group by aggregating measurement data from tools A, B, and C as external tools. The sum of the measurement value 58e of tool A, the measurement value 58f of tool B, and the measurement value 58g of tool C is displayed as a total value 58d. By doing this, even if the item names and indices, which are the data configuration granularity of the measurement data of tool A, tool B, and tool C, are different from the item names and indices of the information processing device 10, the data can be aggregated in the information processing device 10.

[0078] The memory control unit 32 stores, as multiple pieces of performance information, implementation condition information indicating the implementation conditions for each of multiple marketing measures implemented in the past and result information indicating the results obtained by implementing each of the multiple marketing measures. The prediction model acquisition unit 37 acquires a prediction model derived based on a plurality of pieces of performance information. The prediction model is a learning model that has previously been machine-learned to determine the correspondence between implementation condition information and result information. The learning model is machine-learned by providing a plurality of pieces of performance information stored in the storage unit 10d as learning data. The machine learning is performed using supervised learning. The training data used in the machine learning is teacher data having a structure in which an example problem and a correct answer are paired, and pairs of the performance information stored in the memory unit 10d, in which the implementation condition information is an example problem and the outcome information is a correct answer, are used as the teacher data. Supervised learning consists of two processes: learning and recognition / prediction. First, rules and patterns are learned using example data and correct answers. Then, the learned rules and patterns are used to recognize and predict newly input data for which the correct answer is not yet known. The learning model according to this embodiment learns rules and patterns by studying information on conditions for implementing past marketing initiatives and information on results indicating their outcomes. The learning model then uses the learned rules and patterns to recognize information on unimplemented conditions that indicate the conditions for implementing marketing initiatives that have not yet been implemented, and predicts information on results indicating the results of those initiatives.

[0079] The prediction model may also be an equation model derived in advance by performing multiple regression analysis on the correspondence between the implementation condition information and the outcome information. The equation model is expressed by the following formula (1). Multiple regression analysis uses multiple explanatory variables X i (i=1, 2, 3, 4, ...) to calculate the regression equation (formula (1)) that represents the response variable Y. Coefficient β of each explanatory variable i (i=0, 1, 2, 3, 4, ...) is a partial regression coefficient, which is calculated by the least squares method based on a scatter diagram showing the correspondence between the implementation condition information of multiple marketing measures taken in the past recorded in memory unit 10d and the result information showing the results.

[0080]

number

[0081] The unimplemented information receiving unit 38 receives implementation conditions for unimplemented marketing measures as unimplemented condition information. The unimplemented condition information refers to implementation condition information that indicates the implementation conditions of marketing measures that have not yet been implemented. The unimplemented condition information is input by the user to the measure registration screen 40, and the input content related to the input is stored in the memory unit 10d by the memory control unit 32.

[0082] The prediction unit 39 inputs the unimplemented condition information into the prediction model, thereby predicting prediction information indicating the predicted results when the unimplemented marketing measure is implemented.

[0083] The memory control unit 32 may store improvement condition information indicating the improvement conditions for each of multiple improvement measures implemented in the past in marketing strategies, and improvement result information indicating the improvement results obtained by implementing each of the multiple improvement measures, in association with each other, as multiple improvement performance information. The prediction model acquisition unit 37 acquires an improved prediction model derived based on a plurality of pieces of improvement performance information. The unimplemented information receiving unit 38 receives the improvement conditions of the unimplemented improvement measures as unimplemented improvement information. The unimplemented information receiving unit 38 acquires unimplemented condition information input on the measure registration screen 40 (see FIG. 6). The prediction unit 39 inputs the unimplemented improvement information into the improvement prediction model, thereby predicting predicted improvement information that indicates a prediction of the improvement results when the unimplemented improvement measures are implemented. By inputting unimplemented improvement information into the improvement prediction model, predicted improvement information is predicted, which indicates the improvement results when unimplemented improvement measures are implemented.

[0084] The improvement prediction model is a learning model that has previously learned the correspondence between improvement condition information and improvement result information by machine learning. The improvement learning model is learned by machine learning by receiving a plurality of pieces of improvement result information stored in the storage unit 10d as learning data. The machine learning is performed using supervised learning. The training data used in the machine learning is teacher data having a structure in which an example problem and a correct answer are paired, and pairs of improvement condition information as an example problem and improvement result information as a correct answer from the improvement performance information stored in the memory unit 10d are used as the teacher data. The improvement-learning model according to this embodiment learns rules and patterns by studying improvement condition information indicating improvement measures to improve past marketing measures and improvement result information indicating the results thereof. The improvement-learning model then uses the learned rules and patterns to recognize unimplemented improvement information indicating unimplemented improvement measures and predicts improvement result information indicating the results of those improvements.

[0085] The improvement prediction model may be an equation model derived in advance by performing multiple regression analysis on the correspondence between the improvement condition information and the improvement result information. The equation model is expressed by the above formula (1). Multiple regression analysis uses multiple explanatory variables X i (i=1, 2, 3, 4, ...) to calculate the regression equation (formula (1)) that represents the response variable Y. Coefficient β of each explanatory variable i (i=0, 1, 2, 3, 4, ...) is a partial regression coefficient, which is calculated by the least squares method based on a scatter diagram showing the correspondence between improvement condition information of improvement measures for multiple marketing measures implemented in the past, which are recorded in memory unit 10d, and improvement result information showing the results thereof. In this embodiment, multiple explanatory variables X i are the items of unimplemented improvement information indicating the improvement conditions for improvement measures of unimplemented marketing measures, and the objective variable Y is improvement result information indicating the improvement results when improvement measures of unimplemented marketing measures are implemented.

[0086] Next, an example of a report from the information processing device 10 will be described with reference to Fig. 11 to Fig. 18. Fig. 11 to Fig. 18 are diagrams for explaining an example of a report from the information processing device 10. The information processing device 10 generates a report to visualize the accumulated data. The report is displayed on a monitor or printed on a paper medium. The format of the report generated by the information processing device 10 can be changed by the user to suit the user's purpose.

[0087] The measure list 14a that displays a list of registered marketing measures will be described with reference to Fig. 11. Fig. 11 is a diagram for explaining an example of the measure list 14a that the information processing device 10 displays. The measure list 14a is a list of a plurality of pieces of performance information stored in the storage unit 10d by the storage control unit 32. The policy list 14a includes an actual results aggregation period input field 61, a new registration button 62, a new registration selection button 62a, a policy classification column 63, a policy ID column 64, a policy name column 65, a person in charge column 66, an aggregation axis column 67, a start date column 68, an end date column 69, a KPI target column 70, a KPI actual results column 71, a KPI actual results column (8 / 1~8 / 31) 72, a budget amount column 73, and a cost column 74.

[0088] The result aggregation period input field 61 is a field for inputting the period for which the result information was aggregated. The new registration button 62 is a button for clicking or touching when executing new registration of a measure classification and a marketing measure. The new registration selection button 62a is a button for selecting one of the measure classification and the marketing measure. The measure classification column 63 displays the measure classification of the marketing measure for grouping and classifying the registered marketing measures. The implementation condition information of the registered marketing measures is sorted and grouped according to the measure classification entered in the measure classification input field 42 on the measure registration screen 40. The campaign ID column 64 displays the campaign ID assigned to the registered marketing campaign.

[0089] The campaign name column 65 displays the campaign name given to the registered marketing campaign, specifically the campaign name entered in the campaign name input field 41 on the campaign registration screen 40 . The person in charge column 66 displays the name of the person in charge of the registered marketing measure, specifically the name entered in the person in charge input field 45 on the measure registration screen 40. The aggregation axis column 67 displays the name of the tool used to measure the effectiveness of the advertisement. The effectiveness of the advertisement is measured using an advertising effectiveness measurement tool, but is not limited to this and a website access analysis tool may also be used. In this embodiment, the effectiveness of the marketing measures displayed in the measure list 14a is measured using Google (registered trademark) Analytics 4, a website access analysis tool.

[0090] The start date column 68 displays the start date of the implementation period of the registered marketing measure, specifically the start date entered in the implementation period input field 44 on the measure registration screen 40 . The end date column 69 displays the end date of the implementation period of the registered marketing measure, specifically the end date entered in the implementation period input field 44 on the measure registration screen 40 . The KPI target column 70 displays the target value of the total KPI value of the registered marketing measure, specifically the numerical value entered in the KPI indicator / target value input field 46 on the measure registration screen 40.

[0091] The KPI performance column 71 displays the total value of performance information indicating the results obtained by implementing the registered marketing measures, specifically, the total value of performance information obtained by the advertising effectiveness measurement tool. The KPI performance column (8 / 1-8 / 31) 72 displays the total value of performance information indicating the results obtained by implementing the registered marketing measures from 8 / 1 to 8 / 31, specifically the total value of performance information acquired by the advertising effectiveness measurement tool from 8 / 1 to 8 / 31. Note that this is not limited to 8 / 1 to 8 / 31, and can be set to any period desired by the user. The budget column 73 displays the total budget amount of the registered marketing measures, specifically the budget amount entered in the budget amount input field 47 on the measure registration screen 40. The cost column 74 displays the total cost incurred in implementing the registered marketing measures, specifically, the total cost incurred in implementing the registered marketing measures obtained by the advertising effectiveness measurement tool. In the measure list 14a, the KPI performance column 71, the KPI performance column (8 / 1 to 8 / 31) 72, and the cost column 74 of the marketing measures that have not been implemented or the marketing measures for which no result information is registered are left blank.

[0092] In addition, the policy list 14a displays a screen switching button 75. The screen switching buttons 75 include a measure details display button 75a and a KPI performance transition display button 75b. The campaign details display button 75a is clicked or touched when the user wishes to display campaign information details display 112 (see FIG. 13) showing detailed information about a marketing campaign selected by the user from the marketing campaigns listed in the campaign list 14a. The KPI performance trend display button 75b is clicked or touched when the user wishes to display a KPI performance trend display report 114 (see Figure 15), 140 (see Figure 14), which displays the trend in KPI performance of a marketing measure selected by the user from the marketing measures listed in the measure list 14a.

[0093] Next, the setting of specifications for a report generated by the information processing device 10 will be described with reference to Fig. 12. Fig. 12 is a diagram for explaining the setting of specifications for a report output by the information processing device 10. The specifications of the table 79 included in the report display screen 77 that is generated by the information processing device 10 and displayed on a monitor or the like can be changed to specifications desired by the user. The report display screen 77 displays a report setting area 78 and a table 79 . The report setting area 78 includes a period input field 78 a , a search condition input field 78 b , a calculation axis input field 78 c , and a report column setting area 93 . The period input field 78a is a field for inputting the acquisition period of the performance data used to generate the table 79, and is exemplified as August 1, 2023 to August 31, 2023. The search condition input field 78b is a field for inputting search conditions used to extract performance data. The aggregation axis input field 78c is a field for inputting the aggregation axis of Table 79, and examples of appeals are shown. The input to the aggregation axis input field 78c is reflected as an appeal theme in the appeal theme display column 94a of Table 79, and an example of this is ITP (Intelligent Tracking Prevention). ITP is a tracking prevention function built into Safari (registered trademark), a web browser from Apple (registered trademark). ITP can affect web advertising, conversion measurement, and the like.

[0094] The report column setting area 93 is an area for setting the item names of the columns of table 79, and includes a second column setting area 93a, a third column setting area 93b, a fourth column setting area 93c, a fifth column setting area 93d, a sixth column setting area 93e, a seventh column setting area 93f, and an eighth column setting area 93g. The second column setting area 93a is an area for setting the item name of the second column 94b of the table 79, and examples of the policy types are shown. The third column setting area 93b is an area for setting the item name of the third column 94c of the table 79, and an example of the person in charge is shown. The fourth column setting area 93c is an area for setting the item name of the fourth column 94d of the table 79, and examples of the names of measures are shown. The fifth column setting area 93d is an area for setting the item name of the fifth column 94e of the table 79, and examples of targets are shown. The target is the target of the marketing measure. The sixth column setting area 93e is an area for setting the item name of the sixth column 94f of the table 79, and CV is shown as an example. The seventh column setting area 93f is an area for setting the item name of the seventh column 94g of the table 79, and CPA is shown as an example. The eighth column setting area 93g is an area for setting the item name of the eighth column 94h of the table 79, and an example of cost is shown. The table 79 is generated based on the item names of each column set in the report column setting area 93 and is displayed on the report display screen 77.

[0095] The detailed campaign information display 112 of the marketing campaign will be described with reference to Fig. 13. Fig. 13 is a diagram for explaining the detailed campaign information display, which is an example of a report output by the information processing device 10. The campaign information detail display 112 displays detailed information about the marketing campaign registered in the information processing device 10, and reflects the implementation condition information indicating the implementation conditions of the marketing campaign input from the campaign registration screen 40 described above. The policy information details display 112 includes a policy name display section 112a, a policy classification display section 112b, an appeal (aggregation axis) display section 112c, an implementation period display section 112d, a person in charge display section 112e, a KPI indicator / target value display section 112f, a budget amount display section 112g, a memo display section 112h, an attachment link display section 112i, an initial registration date display section 112j, a last update date display section 112k, and a results data link setting button 112l.

[0096] The campaign name display section 112a displays the name of the registered marketing campaign, and reflects the information entered in the campaign name input field 41 on the campaign registration screen 40. The policy classification display section 112b displays the classification of the registered marketing policy, and reflects the information input in the policy classification input field 42 on the policy registration screen 40. The appeal (aggregation axis) display section 112c displays the type of appeal of the registered marketing campaign, and reflects the information input in the appeal input field 43 on the campaign registration screen 40. The implementation period display section 112d displays the implementation period of the registered marketing campaign, and reflects the information entered in the implementation period input field 44 of the campaign registration screen 40. The person in charge display section 112e displays the name of the person in charge of the registered marketing campaign, and reflects the information entered in the person in charge input field 45 on the campaign registration screen 40. The KPI index / target value display section 112f displays the KPI index and the target value for the KPI index of the registered marketing measure, and reflects the information entered in the KPI index / target value input field 46 on the measure registration screen 40. The budget display section 112g displays the budget amount of the registered marketing measure, and reflects the information entered in the budget input field 47 on the measure registration screen 40. The memo display section 112h displays memos related to the registered marketing measures, and reflects the information entered in the memo entry field 48 on the measure registration screen 40. The attachment link display section 112i displays the hyperlink address of the file related to the registered marketing measure, and reflects the hyperlink address of the attachment file uploaded from the attachment field 49 on the measure registration screen 40. The first registration date display section 112j displays the date on which detailed information about the registered marketing measure was first registered. The last update date display section 112k displays the date on which detailed information about the registered marketing measures was last registered. The result data link setting button 112l is a button for displaying the data link setting addition screen 24 shown in FIG. 7(b), and sets up a link with an external tool for measuring KPI result data.

[0097] The KPI performance transition display report 140 shown in FIG. 14 shows the number of sessions of a web page in a time series graph 141, and also includes a table 150 in which the registered events are arranged in chronological order. The graph generating unit 33 generates a graph showing the change in the results over time based on the results information. The graph generating unit 33 generates a time series graph 141 that indicates the time series change of the results based on the result information acquired by the result information acquiring unit 30. The table 148 includes an event occurrence date column 146 and an event summary column 147 . The event occurrence date column 146 displays the date on which each event (event 142, event 143, event 144, and event 145) occurred. In the event summary column 147, a summary of each event (event 142, event 143, event 144, and event 145) is displayed. The report 140 shown in FIG. 14 can be used to analyze the correlation between the changes in the time series graph 141 and the events displayed in the table 148.

[0098] A report 114 relating to KPI performance trends shown in Fig. 15 displays a time-series line graph 114a showing changes over time in CVR used as an index of KPI. Fig. 15 is a diagram for explaining a report including the time-series line graph 114a, which is an example of a report of the information processing device 10. In the report 114 on the KPI performance trend, the measurement data of the CVR of the implemented marketing measures is represented as a line graph 114a. In the line graph 114a, the vertical axis represents the CVR and the horizontal axis represents the implementation period of the marketing measures. The graph generating unit 33 generates a line graph 114a that indicates the time series change of the results based on the result information acquired by the result information acquiring unit 30. In the report 114 on the KPI performance trends, the names of the implemented marketing measures and the persons in charge (114b, 114c, 114d) as well as memos (114e, 114f, 114g) are displayed together with the line graph 114a. Notes are a user's memorandum in which the user writes down their observations, analyses, learnings, events, and points of attention, and are acquired by the memo receiving unit 34 of the information processing device 10 and displayed by the memo display unit 35 along with graphs in various reports.

[0099] The memo display section 35 displays the memo at a position on the graph corresponding to the time when the event occurred. The notes may be descriptions of events that occurred during the implementation of the marketing campaign, and the events may be, for example, each event (event 142, event 143, event 144, and event 145) displayed in table 148, in which case the time when the event occurred may be the date of the event. The memo display unit 35 displays memos accepted by the memo accepting unit 34, which will be described later. The measure name display unit 36 ​​displays the name of the marketing measure at a position on the graph that corresponds to the implementation period of the marketing measure included in the implementation condition information associated with the result information. The measure name display unit 36 ​​displays the name of the marketing measure by drawing a lead line from the position on the graph corresponding to the implementation period of the marketing measure, as shown in Fig. 15. If multiple marketing measures are implemented, the names of the marketing measures are displayed by drawing a lead line from the position on the graph corresponding to the implementation period of each marketing measure, as shown in Fig. 15.

[0100] The entries of the policy names and their responsible persons (114b, 114c, 114d) and the notes (114e, 114f, 114g) shown in FIG. 15 each have three entries, but this is not limited to the example of three entries, and the entries may be two or less, or four or more. If implementation condition information indicating the implementation conditions of a marketing measure has already been registered via the measure registration screen 40, the contents of the registered implementation condition information will be reflected in the measure name and the person in charge (114b, 114c, 114d) and notes (114e, 114f, 114g) and will be plotted together with the time series line graph 114a in the report 114 on KPI performance trends.

[0101] When the user clicks or touches the campaign name and the person in charge (114b, 114c, 114d), the campaign information details display 112 (see FIG. 13) for the marketing campaign is displayed. When the user clicks or touches the memo (114e, 114f, 114g), the entire text of the memo is displayed. Therefore, the report 114 on KPI performance trends allows the cause of changes in the line graph 114a to be inferred based on the information on the implementation conditions of the marketing measures. Furthermore, by entering external factors that influenced the changes in the line graph 114a and other special notes in the notes (114e, 114f, 114g), the implemented marketing measures can be reviewed.

[0102] With reference to Fig. 16, the input of the policy name and the person in charge (114b, 114c, 114d) and memos (114e, 114f, 114g) displayed in Fig. 15 will be described. Fig. 16 is a diagram for explaining the input screen 115 of the report 114 shown in Fig. 15 of the information processing device 10. An input screen 115 for a report 114 relating to KPI performance trends is displayed on the monitor of the information processing device 10 by a predetermined operation. The input screen 115 includes a policy information input area 115 a and a memo input field 116 . The campaign information input area 115a is an area for inputting campaign information for marketing campaigns, and based on the input information, the campaign name and the person in charge (114b, 114c, 114d) are displayed in a report 114 on KPI performance trends along with a line graph 114a.

[0103] The memo receiving unit 34 receives memos relating to events that occurred during a period corresponding to a change over time. The memo receiving unit 34 receives a memo input into the memo input field 116 . The memo receiving unit 34 also receives the policy information of the marketing policy input in the policy information input area 115a. The policy information input area 115a includes a policy name input field 115b, a person in charge (enterer) input field 115c, a purpose input field 115d, an implementation period input field 115e, a budget (distribution costs / production costs / labor costs) input field 115f, a save button 115g, a cancel button 115h, an input item expansion button 115i, and an input template switch button 115j. The campaign name input field 115b is a field for inputting the name of the marketing campaign. The person in charge (enterer) input field 115c is an input field for the name of the person in charge (enterer) of the marketing measure. The purpose input field 115d is a field for inputting the purpose of the marketing measure. The implementation period input field 115e is a field for inputting the implementation period of the marketing measure. The budget (distribution costs / production costs / labor costs) input field 115f is a field for inputting the budget (distribution costs / production costs / labor costs) of the marketing measure. The save button 115g is a button for saving the above-mentioned input information and storing it in the information processing device 10. The cancel button 115h is a button for canceling the saving of the input information and deleting the input information. The input item expansion button 115i is a button for increasing the number of input items for the policy information of the marketing policy, and when clicked or touched, an input field is added. The input template switching button 115j is a button for switching the template of the policy information input area 115a to another format, and when clicked or touched, the template is switched to another format. The campaign information input area 115a can register detailed information about a marketing campaign, and can collect information about the campaign, such as planning information at the planning stage of the marketing campaign, deliverables after implementation, and reviews. The memo input field 116 is a field where external factors of changes in the time-series line graph 114a and other special notes can be entered.

[0104] The report 149 shown in FIG. 17 includes a table 150 showing the breakdown data of the number of sessions of the web page and a table 160 showing the implementation period of the registered events. The table 150 includes a measure classification column 151 , a medium type column 152 , a total number of sessions column 153 , and a breakdown column 154 of the number of sessions. The campaign classification column 151 indicates the classification of the goals of the marketing campaign, and in the table 150, an increase in MAL and an increase in MQL are displayed. The medium type column 152 displays the type of advertising medium used in the marketing measure, and the table 152 displays display advertising, e-mail magazine advertising, external media A, external media B, and telephone inquiries.

[0105] Display advertising refers to advertisements that appear on websites or apps in the form of images, videos, or text. An e-mail newsletter is an email that is periodically sent to all users by a company or a web or e-commerce site operator who wishes to subscribe. The total number of sessions column 153 displays the total number of sessions during the implementation period of the marketing measure. The session count breakdown column 154 displays the breakdown of the number of sessions during the implementation period of the marketing measure.

[0106] Table 160 displays a list of registered events. The table 160 includes an event name column 161, an implementation period column 162, and an implementation period breakdown column 163. Table 160 shows that among the registered events (164, 165, 166, 167, 168), the event implemented during the period 169 from October 1st to October 10th was creative change 168. According to the report 149, the breakdown data of the session count breakdown column 154 can be viewed in chronological order, and even if the impact of the event is partial, the relationship between the breakdown data and the implementation of the event can be easily confirmed based on the change in the breakdown data and the date of the event.

[0107] FIG. 18 is a report 119 that displays how the effectiveness of an advertisement changes when the creative used in the advertisement is changed. The report 119 includes an implementation period 120, a creative column 121, an impression count column 122, a CTR column 123, a click count column 124, a CVR column 125, a conversion count column 126, a cost column 127, a CPA column 128, a landing page column 129, and campaign details 130.

[0108] The implementation period 120 indicates the implementation period of the marketing measure. The creative column 121 displays the creative of the advertisement. The number of impressions column 122 indicates the number of impressions of the creative. The CTR column 123 shows the click-through rate of the creative. The click number column 124 indicates the number of clicks on the creative. The CVR column 125 shows the conversion rate of the creative. The CV column 126 indicates the number of conversions for the creative.

[0109] The cost column 127 indicates the cost of the creative. The CPA column 128 indicates the cost per acquisition of the creative. The LP column 129 indicates the URL of the landing page where the creative will appear. In the campaign details 130, a link to detailed information on the marketing campaign using the creative is embedded. According to Report 119, it is possible to analyze which creative will produce the best advertising results.

[0110] An improvement pattern of the algorithm used by the improvement prediction model of the information processing device 10 will be described with reference to Fig. 19. Fig. 18 is a diagram for explaining an improvement pattern of the algorithm used by the improvement prediction model of the information processing device 10. The following describes a case where the improvement prediction model recommends improvement conditions for a marketing measure. The data required for learning the improvement prediction model are the performance target values ​​for each objective of the marketing measure, the period for achieving the target, and performance data for past marketing measures. The goals of the marketing measure are, for example, an increase in CV (conversion), an improvement in CPA (cost per acquisition), an improvement in CVR (conversion rate), an improvement in CPC (cost per click), etc.

[0111] CV (Conversion) is an indicator that represents the number of goals achieved by companies and other organizations in marketing strategies. The method for determining the improvement prediction model involves using multiple regression analysis, machine learning models, or simple daily calculations to perform budget-to-actual management and determine whether the actual performance value is likely to reach the target performance value, and what the actual performance value will be if the actual performance value does not reach the target value. Referring to FIG. 18, a pattern will be described in which the improvement prediction model presents improvement measures that can eliminate the causes of problems in marketing measures.

[0112] The improvement prediction model selects an improvement pattern three times (improvement pattern 1, improvement pattern 2, and improvement pattern 3) to recommend improvement conditions for a marketing measure. Improvement Pattern 1 In selecting improvement pattern 1, the improvement prediction model selects whether the improvement target of the marketing measure to be improved is an improvement in CPA 80 or an increase in CV 81. Improving CPA by 80 means improving the cost of marketing efforts required to acquire one conversion (CV). Increasing conversions81 refers to increasing the number of conversions (CVs) that can be obtained through marketing measures.

[0113] Improvement Pattern 2 In selecting improvement pattern 2, the improvement prediction model selects an intermediate target for achieving the improvement target selected in improvement pattern 1. When an improvement in CPA 80 is selected as the improvement target, the improvement prediction model selects an improvement in CVR 82 or an improvement in CPC 83 as the intermediate target. When an increase in CV 81 is selected as the improvement target, the improvement prediction model selects an improvement in CPC 83 or an increase in inflow 84 as the intermediate target. Improving CVR 82 means improving the rate at which an advertiser achieves the results they are aiming for (CVR: Conversion Rate). CPC (Cost Per Click) refers to the price that an advertiser is charged when a customer clicks on an ad they have placed. Specifically, it refers to the amount that the advertiser pays each time the ad is clicked. CPC improvement 83 means keeping the CPC low, and by keeping the CPC low, advertisers can get more clicks. Increasing traffic84 refers to increasing the number of visitors to the website of a company implementing a marketing strategy.

[0114] Improvement Pattern 3 In selecting improvement pattern 3, the improvement prediction model selects specific measures for achieving the intermediate goal selected in improvement pattern 2. When improvement of CVR 82 is selected as the intermediate goal, the improvement prediction model selects specific measures such as improvement of the site / LP 86, EFO 87, advertising operation 88, or adaptation of the creative to the LP 89. When CPC improvement 83 is selected as the intermediate goal, the improvement prediction model selects advertising operation 90 as a specific measure. When an increase in inflow 84 is selected as the intermediate goal, the improvement prediction model selects SEO measures 91 or press / SNS transmission 92 as specific measures.

[0115] Site / LP improvements 86 are improvements made using loading speed 100 and form completion rate 101 as evaluation indicators. A loading speed of 100 refers to how quickly the site appears when you view it. Form delivery rate 101 refers to the rate at which a set goal is achieved, for example, the rate at which a user goes from "viewing an inquiry form" to "completed inquiry." Form delivery rate can be measured using web analytics tools such as Google Analytics.

[0116] EFO (Entry Form Optimization)87 refers to a measure to optimize forms so that customers can apply or make inquiries smoothly, and specifically refers to improving the form abandonment rate102, which is an evaluation index. The form abandonment rate102 refers to the percentage of customers who reach an input form on the web but abandon it without pressing the submit button.

[0117] Ad management 88 refers to excluding keywords and adjusting segment placements to improve the CVR 103, which is an evaluation indicator. Keyword exclusion means excluding certain search terms from the ads that will be displayed, so that the ads will only be displayed to customers who search for important keywords related to the client's products or services. Segment placement adjustment refers to adjusting the segment into which the market is subdivided and the placement where the advertisement is displayed.

[0118] Adapting creatives to the landing page (LP) 89 refers to adjusting and adapting the advertising production to the LP in order to improve the quality score 104, which is an evaluation index, and to improve the usability of the LP. The Quality Score 104 is a numerical index of the quality of an ad, and is evaluated for each keyword registered when placing an ad, and is assigned a score from 1 to 10. The Quality Score 104 is determined by the following three factors: estimated click-through rate, ad relevance, and landing page usability.

[0119] The estimated click-through rate (CTR) is the estimated percentage of clicks on an ad. The estimated clicks are determined based on the ad's past CTR and impression count. The relevance of an ad refers to the degree of relevance of the ad to the keywords searched by the customer. Landing page usability is an indicator that shows how useful the page (landing page) that leads to after clicking on an ad is to the customer who clicked on the ad, and how useful it is for the purpose of their search.

[0120] Ad management 90 involves improving ad ranking, improving ad placement, and adjusting bids. The evaluation indicators are CPC, CTR 105, and ad rank 106. Ad rank 106 is a reference value used to determine the ranking of Google (registered trademark) ads. Ad rank 106 is calculated for each auction, and top ad slots are allocated to ads with higher ranks. Ad rank 106 is determined by the following four factors: bid price, ad quality, the context of the customer's search, and the content of ad display options.

[0121] A bid is the maximum amount you are willing to pay for a click on your ad. The quality of an advertisement is a concept for evaluating how useful or convenient an advertisement is for a customer. The context of a customer's search takes into account a variety of factors, including the search keywords entered by the customer, their location at the time of the search, the device they are using, the time of the search, and other ads and search results displayed on the same page. The content of the ad display options refers to whether or not ad display options such as callout display options and site link display options are set and their contents.

[0122] SEO (Search Engine Optimization) measures 91 are measures taken to make a company's website appear at the top of search engines, and are intended to increase the inflow of customers from search results, or sales and lead acquisition, by improving the evaluation indexes of organic search inflow 107 and listing order 108. Organic search inflow 107 refers to inflow of customers from search results that appear naturally on the search engine results page, i.e., not from advertisements.

[0123] Press / SNS communication92 is a marketing measure for companies and organizations to widely disseminate information about themselves, such as by creating a press release and disseminating information, or by using SNS (Social Networking Service). Press / SNS communication92 leads to improvements in the evaluation indexes CTR (click-through rate)109 and ad impressions110.

[0124] Next, we will explain the automatic analysis of results obtained using an advertising effectiveness measurement tool. For example, when automatically detecting outliers, the data required as training data is time-series data on traffic and results. It automatically detects outliers that deviate from the standard range (generated using past data as training data) during the implementation period of a marketing campaign, and automatically detects outliers (sudden increases / decreases) in the numerical values ​​that indicate results. For example, when automatically performing cause inference analysis, the data required as training data is the history of marketing measures and changes, as well as time-series data on trends in the world. Using the time-series data of anomalies detected by automatic anomaly detection analysis and the data from cause inference analysis, it is possible to statistically determine which factors have a high influence, and extract the factors that influenced the analysis results of automatic anomaly detection analysis and their degree of influence.

[0125] Examples of banner (LP) elements that affect performance include (1) who / (2) what / (3) how it is communicated, (4) what kind of attitude change you want to induce, and (5) design components. (1) Who refers to the persona in marketing. (2) What refers to the unique strengths of a product or service. (3) How refers to how the message is conveyed to the user. For example, whether it is conveyed through the impact of an image or through the message itself. (4) What kind of attitude change do you want to bring about? This refers to how you want to change the user's thoughts and emotions during the purchasing process. (5) Design components refer to the elements to be included in the banner or LP, such as price, product name, person, image color, etc.

[0126] The report of the information processing device 10 will be described below. A format and interface that is easy to take over and that makes it easy to express qualitative analysis is one that allows reports to be saved (so you can see who created them and when), so you can access past reports and check what kind of reporting was done. The data relating to (1) to (3) below shows what should be communicated to users so that they can understand the state of past marketing and what actions can be encouraged. Here, instead of providing a report, details of marketing initiatives can be displayed on the marketing initiative management screen, making it easier to take over. (1) What to convey History of past improvement actions (when, who, why, and what was done). (2) Data The results of each action (yes / no against the goal, changes over time). (3) Prompting action Based on the above (1) and (2), the status of past measures (marketing) can be understood, and based on the improvement history, a decision can be made as to how to proceed, i.e., "Is there room for improvement (it is necessary to separately explore what other methods can be taken) or should it be stopped?" When it comes to how to proceed specifically, if it is an initiative that has been implemented in the past (or a similar initiative), have them consider goals, budgets, plans, etc., based on how much money was spent and what kind of performance was likely to be achieved.

[0127] (Information processing method and information processing program) Next, an information processing program according to this embodiment of the present invention will be described together with an information processing method with reference to Fig. 20. Fig. 20 is a flowchart of the information processing program according to this embodiment of the present invention. The information processing method is executed by the calculation unit 10e of the information processing device 10 based on the information processing program.

[0128] The information processing program includes an implementation condition information acquisition step S29, a result information acquisition step S30, an evaluation index reception step S31, and a storage control step S32. The information processing program causes the calculation unit 10e of the information processing device 10 to realize an implementation condition information acquisition function, a result information acquisition function, an evaluation index reception function, and a storage control function. These functions are executed in the order shown in the flowchart of Fig. 20, but the order can be changed as appropriate. Note that each function overlaps with the description of the various functional units of the information processing device 10 described above, and therefore detailed description thereof will be omitted.

[0129] The implementation condition information acquisition function acquires implementation condition information indicating the implementation conditions of marketing measures that have been implemented in the past (S29: implementation condition information acquisition step).

[0130] The result information acquisition function connects via an API with an external tool that measures the results obtained by implementing a marketing measure, and acquires the results measured by the external tool as result information (S30: result information acquisition step).

[0131] The evaluation index receiving function receives a numerical evaluation index that represents the outcome information (S31: evaluation index receiving step).

[0132] The storage control function associates the result information with the evaluation index and also with the implementation condition information, and stores the information as the result information of the marketing measure (S32: storage control step).

[0133] (Information processing program and information processing method according to another first embodiment) An information processing program according to another first embodiment will be described together with an information processing method with reference to Fig. 21. Fig. 21 is a flowchart of the information processing program according to another first embodiment. The information processing program of Figure 21 is another embodiment of the information processing program of Figure 20, and differs from the information processing program of Figure 20 in that a graph generation step S33, a memo acceptance step S34, a memo display step S35, and a measure name display step S36 have been added.

[0134] The information processing program according to FIG. 21 will be described below together with the information processing method. The information processing program of FIG. 21 includes an implementation condition information acquisition step S29, a result information acquisition step S30, an evaluation index reception step S31, a memory control step S32, a graph generation step S33, a memo reception step S34, a memo display step S35, and a measure name display step S36. The information processing program according to Fig. 21 causes the calculation unit 10e to realize an implementation condition information acquisition function, a result information acquisition function, an evaluation index reception function, a memory control function, a graph generation function, a memo reception function, a memo display function, and a measure name display function. These functions are executed in the order shown in the flowchart of Fig. 21, but the order can be changed as appropriate. The various functions overlap with the description of the information processing program according to Fig. 20, and therefore the overlapping description will be omitted.

[0135] The graph generation function generates a graph showing the change in the outcome over time based on the outcome information (S33: graph generation step).

[0136] The memo receiving function receives memos about events that occurred during a period corresponding to a change over time (S34: memo receiving step).

[0137] The memo display function displays a memo at a position on the graph corresponding to the time when the event occurred (S35: memo display step).

[0138] The measure name display function displays the measure name of the marketing measure at a position on the graph corresponding to the implementation period of the marketing measure included in the implementation condition information associated with the result information (S36: measure name display step).

[0139] (Information processing program and information processing method according to another second embodiment) An information processing program according to another second embodiment will be described together with an information processing method with reference to Fig. 22. Fig. 22 is a flowchart of the information processing program according to another second embodiment. The information processing program of Figure 22 is another embodiment of the information processing program of Figures 20 and 21, and differs from the information processing program of Figure 21 in that a prediction model acquisition step S37, an unimplemented information reception step S38, and a prediction step S39 have been added.

[0140] The information processing program according to FIG. 22 will be described below together with the information processing method. The information processing program of Figure 22 includes an implementation condition information acquisition step S29, a result information acquisition step S30, an evaluation index reception step S31, a memory control step S32, a graph generation step S33, a memo reception step S34, a memo display step S35, a measure name display step S36, a prediction model acquisition step S37, an unimplemented information reception step S38, and a prediction step S39. The information processing program of Fig. 22 causes the calculation unit 10e to realize an implementation condition information acquisition function, a result information acquisition function, an evaluation index reception function, a memory control function, a graph generation function, a memo reception function, a memo display function, a measure name display function, a prediction model acquisition function, an unimplemented information reception function, and a prediction function. Note that these functions are executed in the order shown in the flowchart of Fig. 22, but the order can also be changed as appropriate. The various functions overlap with the description of the information processing program of Fig. 21 above, and therefore the overlapping description will be omitted.

[0141] The prediction model acquisition function acquires a prediction model derived based on a plurality of pieces of performance information (S37: prediction model acquisition step).

[0142] The unimplemented information receiving function receives unimplemented condition information indicating the implementation conditions of the marketing measures that have not been implemented (S38: unimplemented information receiving step).

[0143] The prediction function inputs unimplemented condition information into a prediction model, thereby predicting prediction information indicating the predicted results when unimplemented marketing measures are implemented (S39: prediction step).

[0144] According to the information processing device 10 of the embodiment described above, even if the item names and indices of the performance information measured by the external tool are different from those of the information processing device 10, the performance information measured by the external tool can be associated with the evaluation indices received by the evaluation index receiving unit 31 when the performance information measured by the external tool is imported into the information processing device 10. Furthermore, according to the information processing device 10 according to the embodiment described above, it is possible to link the measurement data measured by each of the plurality of external tools to each of the plurality of types of outcome information indicating the outcome of the implementation of the marketing measures. Furthermore, according to the information processing device 10 of the embodiment described above, when single performance information indicating the results of a marketing initiative is obtained from multiple external tools, even if the item names and indices of the measurement data of the external tools are different, the measurement data can be unified by associating the evaluation indices received by the evaluation index receiving unit 31 when the measurement data is imported into the information processing device 10.

[0145] Furthermore, the information processing device 10 according to the present embodiment described above can predict the results of marketing measures that have not yet been implemented, thereby enabling support for the effective implementation of marketing measures. Furthermore, according to the information processing device 10 according to the present embodiment described above, it is possible to predict the improvement results of improvement measures for marketing measures that have not yet been implemented, thereby enabling support for the effective implementation of marketing measures. Furthermore, according to the information processing device 10 according to the present embodiment, the performance information stored in the storage unit 10d is displayed on the monitor as a measure list 14a, which allows for unified management of past marketing measures.

[0146] The present invention is not limited to the information processing device 10, the information processing method, and the information processing program according to the above-described embodiments, and can be embodied in various other modified examples or application examples without departing from the spirit of the present invention as set forth in the claims. Also, although the term "information" is used in the above-described embodiments, the term "information" can be replaced with "data," and the term "data" can be replaced with "information." [Explanation of symbols]

[0147] 1. Past achievements 2. Prediction Model 3 Results of unimplemented marketing measures 4. Project Information 4A Project Information 4B Planning Information 5. Achievements 5A Achievements 5B Achievements 6 Analysis 6A Analysis 6B Analysis 7 Improvement proposal 7A Improvement plan 7B Improvement plan 8. Recording policy information and collecting data 8A Measures 8B Measures 10. Information processing equipment 10a Communication Interface 10b ROM (Read Only Memory) 10c RAM (Random Access Memory) 10d storage section 10e Calculation unit 10f Input / Output Interface 10g input device 10h output device 11 Policy budget formulation and budget management 12 Information Reference / Data Analysis 13. Login 13a Shared Menu 14 Budget formulation and budget management 14a List of measures 15 Information Recording / Data Collection 15a Policy Registration / Editing 15b Data link settings 16 Information Reference / Data Analysis 16a Measure details 16b Custom Reports 16c Reporting 16d Report Display 16e Report Download 17 Settings 17a Media cooperation 17b Media linkage settings 17c Certification 17d CSV upload 17e Event Management 17f Events (Register / Edit) 17g Goal management 17h Goal (Register / Edit) 17i Measure item management 17j Policy Items (Registration / Editing) 17k Policy Group Management 17l User Management 20 Information and Communications Networks 21 Data link setting screen 21a Marketing strategy information 21b Measure name 21c Measure classification 21d Implementation period 21e KPI indicators / goals 21f Details button 22 Policy Collaboration Settings List 22a KPI column 22b External tool data identifier column 22c Action Column 22d First link setting 22e Second Link Setting 22f Third link setting 22g 4th Link Setting 22h 5th link setting 22i Save button 22j Back button 23 Add link setting button 24 Data link setting addition screen 24a KPI input field 24b Linked service input field 24c Media type input field 24d Ad Group 1 input field 24e Ad Group 2 input field 24f Advertising ID input field 24g Automatically linked indicator input field 24h Results Data Selection Button 26 Example of data structure for external tools 26a First data configuration example 26b Second data configuration example 26c Third data configuration example 28 Example of data structure of information processing device 28a Example of data configuration for marketing strategy X 28b Example of data structure for marketing strategy Y 29 Implementation condition information acquisition unit 30 Results information acquisition department 31 Evaluation Index Reception Department 32 Memory control unit 33 Graph Generation Unit 34 Memo Reception 35 Memo display section 36 Measure name display area 37 Prediction model acquisition unit 38 Unimplemented Information Reception Department 39 Prediction Department 40 Policy registration screen 41 Measure name input field 42 Policy classification input field 43 Appeal input field 44 Policy agency input field 45 Person in charge input field 46 KPI indicators and target value input fields 47 Budget input field 48 Notes entry field 49 Attachment field 49a Upload button 50 Save button 51 Button to proceed to linking results data 52 Supported item setting screen 52a Tool A item name column 52b Tool B item name column 52c Tool C Item Name Column 52d Item name string of information processing device 52e 1st item 52f 2nd item 52g 3rd item 52h 4th item 52i Item 5 53j Item 6 55 Data import condition setting screen 55a Marketing strategy information 55b Add link setting button 56 Result data condition input area 56a KPI input field 56b Linked service input field 56c Media Type 56d Ad Group 1 input field 56e Ad Group 2 input field 56f Advertising ID input field 56g Automatically linked indicator input field 56h Results data selection button 58 Counting Results 58a Marketing strategy name column 58b External tool name string 58c Web application sequence 58d Total 58e (Tool A) Measurement 58f (Tool B) measurement 58g (Tool C) measurement 61. Entry field for the period of calculation of results 61a Apply button 62 New registration button 62a New registration selection button 63 Measure classification column 64 Measure ID column 65 Measure name column 66 Staff Column 67 Aggregation Axis Column 68 Start date column 69 End Date Column 70 KPI target column 71 KPI performance column 72 KPI Results Column (8 / 1~8 / 31) 73 Budget column 74 Cost Column 75 Screen switching button 75a Measure details display button 75b KPI performance display button 77 Report display screen 78 Report Settings Area 78a Period input field 78b Search criteria input field 78c Aggregation axis input field 79 table 80 CPA improvement 81 CV increase 82 CVR improvement 83 CPC improvement 84 Increased inflow 86 Site / Lap Improvement 87 EFO 88 Advertising Management 89 Creative x LP Adaptation 90 Advertising Management 91 SEO measures 92 Press / SNS 93 Report Columns Settings Area 93a 2nd row setting area 93b 3rd column setting area 93c 4th column setting area 93d 5th column setting area 93e 6th column setting area 93f 7th column setting area 93g 8th column setting area 94 Report name display area 94a Appeal Theme Display Column 94b 2nd column display column 94c 3rd column display column 94d 4th column display column 94e 5th column display column 94f 6th column display column 94g 7th column display column 94h 8th column display column 96 CSV download button 97 Report save button 100 Loading Speed 101 Form delivery rate 102 form abandonment rate 103 CVR 104 Quality Score 105 CPC·CTR 106 Ad Rank 107 organic search traffic 108 rankings 109 CTR 110 impressions 112 Detailed policy information display 112a Measure name display section 112b Measure classification display section 112c Appeal (aggregation axis) display section 112d Implementation period display section 112e Person in charge display section 112f KPI indicators and target value display section 112g Budget display section 112h Memo display 112i Attachment link display area 112j Initial registration date display section 112k Last updated date display section 112l Results data link setting button 114 Report on KPI performance trends 114a Time series line graph 114b 1st measure 114c Second measure 114d Third measure 114e First Memo 114f Second Memo 114g Third Memo 115 Input screen 115a Policy information input area 115b Entry field for the name of this measure 115c Person in charge (enterer) input field 115d Purpose input field 115e Implementation period input field 115f Budget (distribution costs / production costs / labor costs) input field 115g Save button 115h Cancel button 115i Input item expansion button 115j Input template switching button 116 Memo entry field 119 Reports (Time Series Graph Report 114) 120 Implementation period 121 Creative Row 122 Impression Column 123 CTR column 124 Click column 125 CVR row 126 CV row 127 cost column 128 CPA column 129 LP row 130 Measure details column 140 KPI performance trend display report 141 Time Series Graph 142~145 Events 146 Event occurrence date column 147 Event Summary Column 148 table 149 Report 150 tables 151 Measure classification column 152 Media type column 153 Session Total Sequence 154 Session Breakdown Column 160 tables 161 Event Name Column 162 Implementation period column 162 163 Implementation period breakdown 164 ●●GA4 featured in the media 165 Email Newsletter Distribution "How to Visualize GA4 Data" 166 Introduction Support Campaign 167 Newsletter "What is the threshold in GA4?" 168 Creative Changes 169 period

Claims

1. an implementation condition information acquisition unit that acquires implementation condition information indicating implementation conditions of marketing measures implemented in the past; an outcome information acquisition unit that cooperates with an external tool that measures outcomes achieved by implementing the marketing measures via an API and acquires the outcomes measured by the external tool as outcome information; an evaluation index receiving unit that receives a numerical evaluation index representing the outcome information; a storage control unit that associates the outcome information with the evaluation index and with the implementation condition information and stores the information as performance information of the marketing measure; An information processing device comprising:

2. In the case where there are multiple pieces of result information obtained as the results obtained by implementing the marketing measures, the evaluation index receiving unit receives a plurality of evaluation indexes to be assigned to each of a plurality of pieces of outcome information indicating the outcome; 2. The information processing apparatus according to claim 1, wherein the storage control unit associates the plurality of pieces of performance information with the respective evaluation indexes and with the implementation condition information, and stores the pieces of performance information as performance information of the marketing measures.

3. When measuring the results obtained by implementing the marketing measures using multiple external tools, the result information acquisition unit cooperates with each of the plurality of external tools via an API and acquires the result information from each of the plurality of external tools; The information processing device described in claim 1, characterized in that the memory control unit integrates the multiple pieces of performance information obtained from each of the multiple external tools, associates them with the evaluation index, and stores them as performance information of the marketing campaign in association with the implementation condition information.

4. 2. The information processing apparatus according to claim 1, wherein the evaluation index includes an item name and an index of the outcome information.

5. a graph generating unit that generates a graph showing a change in the outcome over time based on the outcome information; a memo receiving unit that receives memos about events that occurred during a period corresponding to the change over time; a memo display unit that displays the memo at a position on the graph that corresponds to the time when the event occurred; a measure name display unit that displays the name of the marketing measure at a position on the graph that corresponds to the implementation period of the marketing measure included in the implementation condition information associated with the result information; 2. The information processing apparatus according to claim 1, further comprising:

6. the storage control unit associates implementation condition information indicating implementation conditions for each of a plurality of marketing measures implemented in the past with outcome information indicating outcomes obtained by implementing each of the plurality of marketing measures, and stores the information as a plurality of pieces of performance information; a prediction model acquisition unit that acquires a prediction model derived based on the plurality of pieces of performance information; an unimplemented information receiving unit that receives unimplemented condition information indicating implementation conditions of unimplemented marketing measures; a prediction unit that predicts prediction information indicating a predicted outcome when the unimplemented marketing measure is implemented by inputting the unimplemented condition information into the prediction model; 2. The information processing apparatus according to claim 1, further comprising:

7. 2. The information processing device according to claim 1, wherein the evaluation index is the number of times the advertisement relating to the implementation condition information is displayed, a click rate indicating the rate at which the advertisement is clicked, or the achievement rate of the results targeted by the advertiser of the implementation condition information.

8. The information processing apparatus according to claim 6 , wherein the prediction model is a learning model that has previously been machine-learned to learn the correspondence between the implementation condition information and the result information.

9. 7. The information processing apparatus according to claim 6, wherein the prediction model is an equation model derived in advance by performing multiple regression analysis on the correspondence between the implementation condition information and the result information.

10. the storage control unit associates improvement condition information indicating improvement conditions for each of a plurality of improvement measures implemented in the past in the marketing measures with improvement result information indicating improvement results obtained by implementing each of the plurality of improvement measures, and stores the information as a plurality of pieces of improvement performance information; the prediction model acquisition unit acquires an improved prediction model derived based on the plurality of pieces of improvement performance information; the unimplemented information receiving unit receives improvement conditions for unimplemented improvement measures as unimplemented improvement information; The information processing device according to claim 6, characterized in that the prediction unit predicts predicted improvement information indicating a prediction of improvement results when the unimplemented improvement measures are implemented by inputting the unimplemented improvement information into the improvement prediction model.

11. The computer an implementation condition information acquisition step of acquiring implementation condition information indicating implementation conditions of marketing measures implemented in the past; an outcome information acquisition step of linking with an external tool that measures outcomes achieved by implementing the marketing measures via an API and acquiring the outcomes measured by the external tool as outcome information; an evaluation index receiving step of receiving a numerical evaluation index representing the outcome information; a storage control step of associating the outcome information with the evaluation index and with the implementation condition information and storing the information as performance information of the marketing measure; An information processing method characterized by carrying out the above.

12. On the computer, An implementation condition information acquisition function that acquires implementation condition information indicating the implementation conditions of marketing measures implemented in the past; a result information acquisition function that links with an external tool that measures results obtained by implementing the marketing measures via an API and acquires the results measured by the external tool as result information; an evaluation index receiving function that receives a numerical evaluation index representing the outcome information; a storage control function that associates the outcome information with the evaluation index and with the implementation condition information and stores the information as performance information of the marketing measure; An information processing program characterized by realizing the above.

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