KR102674678B1 - Method of operating electric apparatus and computer readable medium for generating report based on advertising data provided from online advertising media and calculating predictive data on advertising effectiveness - Google Patents

Abstract

Disclosed is an operating method of an electronic device. according to the present disclosure, the operating method of the electronic device comprises the steps of: obtaining result data according to an advertisement history of an online advertisement medium from at least one external server corresponding to the online advertisement medium; and automatically generating a report for explaining an advertisement effect of the online advertisement medium by applying the result data to a template preset for each item.

Description

A method of operating an electronic device and a computer-readable medium that generates a report based on advertising data provided from online advertising media and calculates predictive data on advertising effectiveness { METHOD OF OPERATING ELECTRIC APPARATUS AND COMPUTER READABLE MEDIUM FOR GENERATING REPORT BASED ON ADVERTISING DATA PROVIDED FROM ONLINE ADVERTISING MEDIA AND CALCULATING PREDICTIVE DATA ON ADVERTISING EFFECTIVENESS }

The present disclosure relates to a method of operating an electronic device, and more specifically, to receive result data according to advertising history from an external server corresponding to an online advertising medium and automatically generate a report to explain the advertising effect of the performed advertising. It relates to a method of operating an electronic device for.

As digital technology has recently developed, online advertising media provided by search service providers and SNS companies has grown significantly.

Unlike online advertisements that were simply posted on search engines in the past, the forms of advertisements have recently become very diverse with the advent of SNS, and platform companies based on large numbers of users, such as SNS, target and provide effective advertisements for each user.

Meanwhile, online advertising media officially disclose data collected according to advertising progress based on APIs, etc., and the need for data collection and data utilization in a different way from existing offline advertising media in that data can be received immediately. This is increasing.

In addition, unlike traditional offline advertising, online advertising provides immediate user feedback, so data provided by online advertising media includes advertising effects such as clicks per exposure, number of clicks per exposure, and purchase conversion ratio. Advertising effects classified by target (e.g., gender, age, interest, category, etc.) to which the advertisement was exposed may be included.

The data obtained through the above-mentioned online advertising media provides advertising effectiveness in the form of a large amount of data, and each online advertising media provides data in different formats, so the person in charge who wants to utilize the data must manually collect the data one by one. Because reports must be compiled in aggregate, there is a risk of human error, and there is a disadvantage in that a lot of resources are consumed in report writing.

Furthermore, in order to create documents related to budget allocation and predicted advertising performance for proposing advertising products posted on online advertising media, the collected data has no choice but to be processed according to the perspective of the person in charge. In addition to errors and the consumption of large amounts of resources, there have been disadvantages in that it is difficult to accurately reflect market conditions depending on the perspective of the person in charge.

Registration Patent Notice No. 10-0819703

The purpose of the present disclosure is to provide a method of operating an electronic device that automatically generates a report to explain the advertising effect of an advertising medium according to a preset template using result data based on advertising history from an external server corresponding to various online advertising media. There is.

In addition, the present disclosure provides a method of operating an electronic device that obtains predictive data for advertisements by inputting advertisement period, advertisement cost, online advertisement medium, etc. into an artificial intelligence model trained based on result data and advertisement history. There is a purpose.

The objects of the present disclosure are not limited to the purposes mentioned above, and other objects and advantages of the present disclosure that are not mentioned can be understood by the following description and will be more clearly understood by the examples of the present disclosure. Additionally, it will be readily apparent that the objects and advantages of the present disclosure can be realized by the means and combinations thereof indicated in the patent claims.

A method of operating an electronic device according to an embodiment of the present disclosure includes obtaining result data according to the advertising history of the online advertising medium from at least one external server corresponding to the online advertising medium, and storing the result data for each item. It includes automatically generating a report to explain the advertising effect of the online advertising medium by applying it to the set template.

At this time, the method of operating the electronic device may include training an artificial intelligence model to output prediction data about the advertisement effect based on the advertisement history and the result data, and the prediction data may include the online advertisement. It may include at least one of the expected number of exposures, expected number of clicks, expected number of clicks compared to expected exposure, expected number of purchases, expected number of purchases compared to expected clicks, and purchase amount for at least one advertisement carried out by the media.

In this case, the method of operating the electronic device may include obtaining the prediction data by inputting input data including at least one of an advertisement period, an advertisement cost, and an online advertisement medium into the artificial intelligence model.

At this time, the step of obtaining the prediction data includes setting a threshold range for the prediction data according to a user input, and excluding prediction data exceeding the upper limit of the threshold range and prediction data below the lower limit of the threshold range. It may include steps to:

Meanwhile, the method of operating the electronic device includes setting a target range for advertising effect according to user input, inputting a plurality of different input data into the artificial intelligence model, and outputting predicted data included in the target range. It may include the step of selecting at least one input data that is suitable for use and the step of recommending the selected input data.

Alternatively, the method of operating the electronic device may include generating a plurality of advertisement schedules for one or more online advertising media for a specific period according to a user input, and based on prediction data for each advertisement included in the plurality of advertisement schedules. It may include obtaining prediction data for the specific period.

In addition, the method of operating the electronic device includes setting a target range for advertising effects in the specific period according to a user input, randomly generating a plurality of different advertising schedules for the specific period, and the plurality of different advertising schedules. inputting a plurality of advertisement schedules constituting each of the advertisement schedules into the artificial intelligence model, selecting at least one advertisement schedule to output predicted data included in the target range, and recommending the selected advertisement schedule. may include.

Meanwhile, the present disclosure is a non-transitory device storing at least one instruction, which is executed by a processor of an electronic device and causes the electronic device to perform a control method for automatically generating a report for explaining the advertising effect of an online advertising medium. Includes computer-readable media.

The operating method of the electronic device of the present disclosure automatically generates and provides a report of a preset template, thereby reducing the resources consumed in report creation by manually collecting result data and creating a report, and preventing human error due to manual work. can do.

In addition, based on an artificial intelligence model, it is possible to effectively deliver a media mix proposal that reflects market conditions to advertisers by including predicted data on advertising effectiveness.

1 is a diagram illustrating communication between an electronic device and a plurality of external servers according to an embodiment of the present disclosure;
2 is a flowchart for explaining the operation of an electronic device according to an embodiment of the present disclosure;
3 is a flowchart illustrating an operation in which an electronic device acquires prediction data based on an artificial intelligence model according to an embodiment of the present disclosure;
4 is a diagram illustrating prediction data output based on an artificial intelligence model according to an embodiment of the present disclosure;
5 is a flowchart illustrating an operation in which an electronic device selects and recommends input data to achieve a target range according to an embodiment of the present disclosure;
FIG. 6 is a diagram for explaining the configuration of an electronic device according to an embodiment of the present disclosure.

Before explaining the present disclosure in detail, the description method of the present specification and drawings will be explained.

First, the terms used in the specification and claims are general terms selected in consideration of their functions in various embodiments of the present disclosure. However, these terms may vary depending on the intention of technicians working in the relevant technical field, legal or technical interpretation, and the emergence of new technologies. Additionally, some terms are arbitrarily selected by the applicant. These terms may be interpreted as defined in this specification, and if there is no specific term definition, they may be interpreted based on the overall content of this specification and common technical knowledge in the relevant technical field.

In addition, the same reference numbers or symbols in each drawing attached to this specification indicate parts or components that perform substantially the same function. For convenience of explanation and understanding, the same reference numerals or symbols are used in different embodiments. That is, even if all components having the same reference number are shown in multiple drawings, the multiple drawings do not represent one embodiment.

Additionally, in this specification and claims, terms including ordinal numbers such as “first”, “second”, etc. may be used to distinguish between components. These ordinal numbers are used to distinguish identical or similar components from each other, and the meaning of the term should not be interpreted limitedly due to the use of these ordinal numbers. For example, the order of use or arrangement of components combined with these ordinal numbers should not be limited by the number. If necessary, each ordinal number may be used interchangeably.

In this specification, singular expressions include plural expressions, unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “consist of” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are intended to indicate the presence of one or more other It should be understood that this does not exclude in advance the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof.

In embodiments of the present disclosure, terms such as “module”, “unit”, “part”, etc. are terms to refer to components that perform at least one function or operation, and these components are hardware or software. It may be implemented or may be implemented through a combination of hardware and software. In addition, a plurality of "modules", "units", "parts", etc. are integrated into at least one module or chip, except in cases where each needs to be implemented with individual specific hardware, and is integrated into at least one processor. It can be implemented as:

Additionally, in an embodiment of the present disclosure, when a part is connected to another part, this includes not only direct connection but also indirect connection through other media. In addition, the meaning that a part includes a certain component does not mean that other components are excluded, but that other components can be further included, unless specifically stated to the contrary.

FIG. 1 is a diagram illustrating communication between an electronic device and a plurality of external servers according to an embodiment of the present disclosure.

According to FIG. 1, the electronic device 100 can communicate with a plurality of external servers 200.

The electronic device 100 is configured to receive data from the external server 200 and apply it to a preset template to generate a report.

The electronic device 100 may correspond to a device or system including at least one computer. For example, the electronic device 100 may correspond to a server device, or may correspond to a terminal device such as a desktop PC, laptop PC, tablet PC, or smartphone.

The external server 200 is configured to communicate with the electronic device 100 and provide data to the electronic device 100.

At this time, the data provided by the external server 200 may correspond to result data based on advertising history. Accordingly, the external server 200 may correspond to a server of an online advertising medium.

At this time, the result data according to the advertising history may correspond to various data aggregated as a result of the actual advertisement execution, such as the number of exposures to the performed advertisement, the number of clicks, the number of clicks compared to exposure, the number of purchases, and the number of purchases compared to clicks.

Figure 2 is a flowchart for explaining the operation of an electronic device according to an embodiment of the present disclosure.

According to FIG. 2, the electronic device 100 may obtain result data according to advertising history from an external server 200 corresponding to an online advertising medium (S210).

The electronic device 100 may automatically generate a report to explain the advertising effect based on the resulting data (S220). Specifically, the electronic device 100 can generate a report in a predetermined format by applying the obtained result data to a preset template for each item.

Additionally, the electronic device 100 may obtain data on future advertising effects as well as reports on advertising history. To this end, the electronic device 100 may include at least one artificial intelligence model.

In relation to this, FIG. 3 is a flowchart illustrating an operation in which an electronic device acquires prediction data based on an artificial intelligence model according to an embodiment of the present disclosure.

According to FIG. 3, the electronic device 100 can train an artificial intelligence model to output prediction data about advertising effectiveness based on advertising history and result data (S310).

Specifically, the resulting data can be classified into weekly, monthly, quarterly, semi-annual, and yearly data according to advertising history and used as training data for an artificial intelligence model.

Predicted data is a value that predicts items of result data, including the expected number of exposures, expected number of clicks, expected number of clicks compared to expected exposure, expected number of purchases, and expected clicks for at least one advertisement performed from an online advertising medium in response to the result data. It may be configured to include at least one of the expected number of purchases and the purchase amount.

In this case, the electronic device 100 may acquire predicted data through an ARIMA (AutoRegressive Integrated Moving Average) model for predicting changes in items over time by analyzing patterns of time series data, but is not limited to this. Predictive data can be obtained by training various artificial intelligence models such as analysis models, LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit).

Then, the electronic device 100 may obtain prediction data by inputting input data to the artificial intelligence model (S320).

At this time, input data input to the artificial intelligence model may include at least one of advertising period, advertising cost, and online advertising media.

Figure 4 is a diagram for explaining prediction data output based on an artificial intelligence model according to an embodiment of the present disclosure.

Referring to Figure 4, according to user input, the advertising period start date is set to October 19, 2020, and the end date is set to January 31, 2021. Industry categories can be set according to user input based on items preset as major, medium, and small categories. Accordingly, the artificial intelligence model can output predicted data only based on advertising data matching the corresponding category.

The predicted data output as a result of the corresponding input values are expected exposure, expected clicks, expected number of purchases, CPM (cost set according to the number of impressions), and CPC (cost set according to the number of clicks) calculated according to the advertising product provided by the advertising medium. , it can include expected CTR (number of clicks to impressions), expected CVR (number of purchases to clicks), and corresponding advertising purchase costs.

At this time, in order to output effective advertising media and advertising products, predicted data may be calculated by ranking based on the purpose set according to user input (ex. maximizing purchase, maximizing exposure, etc.). Alternatively, predicted data may be output based on user input where the user directly selects a specific advertising medium and advertising product.

At this time, prediction data may be calculated based on a threshold range set according to user input. This reflects the fact that predicted data calculated out of the distribution of other predicted data may have relatively low reliability.

Specifically, the electronic device 100 may set a threshold range for prediction data according to user input, and may exclude prediction data exceeding the upper limit of the threshold range and prediction data below the lower limit of the threshold range.

Relatedly, referring to FIG. 4, when prediction data for each advertising medium and advertising product is obtained, a parameter range (=critical range) may be determined according to user input. It can be seen that the predicted data for media 1 to 5 presented in Figure 4 reflects the exclusion of the predicted data for advertising media and advertising products calculated as less than the bottom 20% or calculated as more than 80%.

Meanwhile, the electronic device 100 may output the advertising period, advertising cost, and online advertising medium to achieve the target input data.

In this regard, FIG. 5 is a flowchart illustrating an operation in which an electronic device selects and recommends input data to achieve a target range according to an embodiment of the present disclosure.

According to FIG. 5, the electronic device 100 can set a target range for advertising effect according to user input (S510).

The target range may correspond to an item of prediction data being set to a certain value. For example, the electronic device 100 may set the target range by setting the expected number of purchases to a certain value or range.

The electronic device 100 may input a plurality of input data into an artificial intelligence model and select input data that outputs predicted data included in the target range (S520).

That is, the electronic device 100 can obtain various prediction data matching a plurality of different input data. In an embodiment, when the electronic device 100 obtains prediction data for input data, the electronic device 100 changes at least one of the items included in the input data (e.g., advertisement period, advertisement cost, online advertisement medium, etc.) to change the prediction data. It is possible to obtain a plurality of prediction data that are different from prediction data matching all input data.

At this time, the variation of the input data may be determined according to the difference between the calculated predicted data and the target range. For example, the electronic device 100 may calculate a ratio of the difference between prediction data calculated according to input data and the target range. According to the calculated ratio, the electronic device 100 can set a change width for changing the input data.

Alternatively, if the ratio is above a certain value, the electronic device 100 determines that many attempts will be required to achieve the target range due to the variation of the input data, so stops the variation of the input data and sets random new input data. By doing so, the process of calculating predicted data within the target range can be performed again.

Additionally, the electronic device 100 may identify and select prediction data included in the target range among the plurality of calculated prediction data.

Accordingly, the electronic device 100 may recommend the selected input data (S530).

Meanwhile, the electronic device 100 may generate an advertisement schedule that includes a plurality of advertisement schedules.

Specifically, the electronic device 100 may generate an advertising schedule including a plurality of advertising schedules for one or more online advertising media for a specific period according to a user input, and input data matching each of the plurality of advertising schedules. Based on the predicted data of the artificial intelligence model, predicted data for a specific period can be obtained.

That is, the electronic device 100 is capable of calculating predicted data on the advertising effect of the entire advertising schedule for a specific period based on the artificial intelligence model.

Accordingly, the user can perform a feedback process to obtain an optimized advertising schedule by modifying at least one advertising schedule constituting the advertising schedule based on prediction data about the advertising schedule and re-entering it into the electronic device 100. there is.

Alternatively, in the same manner as the embodiment of FIG. 5, the electronic device 100 may set a target range for the advertising effect of a specific period and obtain an advertising schedule matching that range.

Specifically, the electronic device 100 may set a target range for advertising effects in a specific period according to user input and randomly generate a plurality of different advertising schedules for a specific period.

By inputting input data matching the plurality of advertising schedules constituting each of the randomly generated plurality of advertising schedules into the artificial intelligence model, the electronic device 100 can obtain a plurality of prediction data for the advertising schedule and make predictions. You can select and recommend an advertising schedule that outputs predicted data corresponding to the target range among the data.

As described above, before inputting each of the plurality of randomly generated advertising schedules into the artificial intelligence model, the electronic device 100 may perform preprocessing on each randomly generated advertising schedule.

Specifically, in a plurality of advertising schedules that constitute a randomly generated advertising schedule, the electronic device 100 may change the order or period difference of each advertising schedule based on synergy between different online advertising media.

For example, the randomly generated advertising schedule is the first advertising schedule (ex. January to April advertising for medium A), the second advertising schedule (ex. March to June advertising for medium B), and the third advertising schedule. Assume that the schedule (ex. July-October advertisement for C media) is included. Here, when the synergy between media A and media C is relatively large, the electronic device 100 changes the second advertising schedule and the third advertising schedule, respectively, and generates an advertising schedule for medium C for 3 to 6 months to media A There is an overlapping period with the first advertising schedule for medium B, and an advertising schedule can be created from July to October for medium B.

To this end, the electronic device 100 can calculate synergy between different online advertising media. Specifically, the electronic device 100 may define a unit synergy score for different online advertising media and calculate the synergy score by applying a period difference to the unit synergy score. For example, if the unit synergy score between media A and media C is 0 or more, the synergy score can be calculated higher as the difference in period between the advertising schedule for media A and the advertising schedule for media C is small or the overlapping period is long. .

At this time, the electronic device 100 may include at least one artificial intelligence model for defining a unit synergy score based on the correlation between medium A and medium C.

At this time, the corresponding artificial intelligence model can be calculated using the advertising schedules of media A and media C, advertising products and advertising formats provided by each media, etc. as input variables.

Accordingly, the artificial intelligence model of the present disclosure may include the above-described artificial intelligence model, and may be based on a GNN (Graph Neural Network) model that identifies correlations between nodes, a reinforcement learning model that optimizes the combination of online advertising media, etc. Unit synergy scores between different media can be defined.

FIG. 6 is a diagram for explaining the configuration of an electronic device according to an embodiment of the present disclosure.

According to FIG. 6, the electronic device 100 may include a memory 110, a communication unit 120, and a processor 130.

The memory 110 is configured to store an operating system (OS) for controlling the overall operation of the components of the electronic device 100 and at least one instruction or data related to the components of the electronic device 100. .

The memory 110 may include non-volatile memory, such as ROM or flash memory, and may include volatile memory, such as DRAM. Additionally, the memory 110 may include a hard disk, solid state drive (SSD), etc.

Additionally, the memory 110 may include at least one artificial intelligence model 10 for calculating prediction data about the advertising effect of an online advertising medium.

The communication unit 120 is a component that allows the electronic device 100 to communicate with an external device such as the external server 200. The communication unit 120 may include circuits, modules, chips, etc. for communicating through various wired and wireless communication methods. The communication unit 120 may be connected to external devices through various networks.

Depending on the area or size, the network may be a personal area network (PAN), a local area network (LAN), or a wide area network (WAN). Depending on the openness of the network, it may be an intranet, It may be an extranet, or the Internet.

The communication unit 120 supports long-term evolution (LTE), LTE Advance (LTE-A), 5th Generation (5G) mobile communication, code division multiple access (CDMA), wideband CDMA (WCDMA), and universal mobile telecommunications system (UMTS). , WiBro (Wireless Broadband), GSM (Global System for Mobile Communications), DMA (Time Division Multiple Access), WiFi (Wi-Fi), WiFi Direct, Bluetooth, BLE (Bluetooth Low Energy), NFC (near field communication), It can be connected to external devices through various wireless communication methods such as Zigbee and LoRa.

Additionally, the communication unit 120 may be connected to external devices through a wired communication method such as Ethernet, optical network, USB (Universal Serial Bus), or Thunderbolt.

In addition, the communication unit 120 may be configured to utilize various communication methods/technologies that will be newly designed in the future.

The processor 130 is configured to overall control the electronic device 100. Specifically, the processor 130 may perform operations according to various embodiments of the present disclosure by being connected to the memory 110 and executing at least one instruction stored in the memory 110.

The processor 130 may include a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU. An artificial intelligence-specific processor may be designed with a hardware structure specialized for training or use of a specific artificial intelligence model.

The processor 130 according to an embodiment of the present disclosure may obtain prediction data by inputting the above-described input data into the artificial intelligence model 10 of the memory 110.

Meanwhile, the various embodiments described above may be implemented by combining two or more embodiments as long as they do not conflict or contradict each other.

Meanwhile, the various embodiments described above may be implemented in a recording medium that can be read by a computer or similar device using software, hardware, or a combination thereof.

According to hardware implementation, embodiments described in the present disclosure include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), and field programmable gate arrays (FPGAs). ), processors, controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

In some cases, embodiments described herein may be implemented in the processor itself. According to software implementation, embodiments such as procedures and functions described in this specification may be implemented as separate software modules. Each of the software modules described above may perform one or more functions and operations described herein.

Meanwhile, computer instructions or computer programs for performing processing operations in an electronic device according to various embodiments of the present disclosure described above may be stored in a non-transitory computer-readable medium. You can. Computer instructions or computer programs stored in such non-transitory computer-readable media, when executed by a processor of a specific device, cause the specific device to perform processing operations of the electronic device according to the various embodiments described above.

A non-transitory computer-readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as registers, caches, and memories. Specific examples of non-transitory computer-readable media may include CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, etc.

In the above, preferred embodiments of the present disclosure have been shown and described, but the present disclosure is not limited to the specific embodiments described above, and may be used in the technical field pertaining to the disclosure without departing from the gist of the disclosure as claimed in the claims. Of course, various modifications can be made by those skilled in the art, and these modifications should not be understood individually from the technical ideas or perspectives of the present disclosure.

100: electronic device
200: external server
110: memory
10: Artificial intelligence model
120: Department of Communications
130: processor

Claims (8)

In a method of operating an electronic device,
Obtaining result data according to the advertising history of the online advertising medium from at least one external server corresponding to the online advertising medium; and
Applying the result data to a preset template for each item to automatically generate a report to explain the advertising effect of the online advertising medium,
The method of operating the electronic device is,
Training a first artificial intelligence model to output prediction data about advertising effectiveness based on the advertising history and the result data; and
Comprising: obtaining the prediction data by inputting input data including at least one of an advertisement period, an advertisement cost, and an online advertisement medium into the first artificial intelligence model,
The predicted data is
Characterized by including at least one of the expected number of exposures, expected number of clicks, expected number of clicks compared to expected exposure, expected number of purchases, expected number of purchases compared to expected clicks, and purchase amount for at least one advertisement carried out from the online advertising medium, ,
The method of operating the electronic device is,
Setting a target range for advertising effectiveness according to user input;
Inputting a plurality of different input data into the first artificial intelligence model and selecting at least one input data that outputs predicted data included in the target range; and
Comprising: recommending the selected input data,
The plurality of different input data are,
Characterized by outputting predicted data included in the target range through input data in which at least one of the advertising period, advertising cost, and online advertising medium of the input data input to the first artificial intelligence model is set to change,
The step of selecting at least one input data that causes predicted data included in the target range to be output,
calculating a ratio of the difference between the predicted data calculated according to the variable input data and the target range;
setting a variation range for changing the input data based on the calculated ratio; and
If the calculated ratio is greater than a certain value, stopping the change of the input data and generating random new input data,
The method of operating the electronic device is,
generating an advertising schedule including a plurality of advertising schedules for one or more online advertising media for the specific period according to user input; and
Comprising: obtaining predicted data for the specific period based on predicted data of the first artificial intelligence model for input data matching each of the plurality of advertising schedules,
The method of operating the electronic device is,
Setting a target range for advertising effectiveness in the specific period according to user input;
Randomly generating a plurality of different advertising schedules for the specific period;
Inputting a plurality of advertising schedules constituting each of the plurality of advertising schedules into the first artificial intelligence model, and selecting at least one advertising schedule to output prediction data included in the target range; and
Comprising: recommending the selected advertising schedule,
The step of selecting at least one advertisement schedule includes:
Calculating the unit synergy score by inputting at least one of the advertising schedule, advertising product, and advertising format of each medium into a second artificial intelligence model trained to calculate the unit synergy score by identifying the correlation between the different online advertising media. step;
calculating a synergy score between the different online advertising media by applying a period difference between the different online advertising media to the calculated unit synergy score; and
A step of changing at least one of the order and period differences of a plurality of advertisement schedules included in the advertisement schedule according to the calculated synergy score,
The step of calculating the synergy score is,
If the unit synergy score is a positive number, the smaller the difference between the advertising schedules of each medium and the longer the overlapping period, the higher the synergy score is calculated,
The step of changing at least one of the order and period difference in the advertising schedule is,
A method of operating an electronic device that changes the advertising schedule so that the higher the synergy score between the different advertising media, the difference in the period of advertising schedules for advertising products of the different advertising media is minimized or the overlapping period is maximized. delete delete According to paragraph 1,
The step of obtaining the prediction data is,
setting a threshold range for the prediction data according to user input; and
Excluding predicted data exceeding the upper limit of the threshold range and excluding prediction data less than the lower limit of the threshold range. delete delete delete A non-transitory computer-readable medium storing at least one instruction, which is executed by a processor of an electronic device and causes the electronic device to perform the control method of claim 1.