Method for operating electronic device for generating report on basis of advertising data provided from online advertising medium and calculating prediction data for advertising effect and computer-readable medium

Abstract

A method for operating an electronic device is disclosed. The method for operating an electronic device according to the present disclosure comprises the steps of: obtaining result data based on an advertising history of an online advertising medium from at least one external server corresponding to the online advertising medium; and applying the result data to templates set in advance for each item, and thereby automatically generating a report for describing the advertising effect of the online advertising medium.

Description

An electronic device operating method and a computer-readable medium for generating reports based on advertising data provided from online advertising media and producing prediction data on advertising effectiveness

[0001] The present disclosure relates to an operating method of an electronic device, and more particularly, to an operating method of an electronic device for automatically generating a report for explaining the advertising effect of an advertisement performed by receiving result data according to an advertising history from an external server corresponding to an online advertising medium.

[0002] With the recent advancement of digital technology, online advertising media provided by search service providers, social media companies, etc. are growing rapidly.

[0003] Unlike the online advertisements of the past that were simply displayed on search engines, the emergence of social media has led to a wide variety of advertisement forms. Platform companies that rely on a large number of users, such as those on social media, also target and provide effective advertisements to each user.

[0004] Meanwhile, online advertising media are officially disclosing data collected as advertising progresses through APIs and other means, and the need for data collection and utilization in a different way from traditional offline advertising media is increasing, as data can be received immediately.

[0005] Additionally, unlike traditional offline advertising, online advertising allows for immediate user feedback, so data provided by online advertising media may include advertising effectiveness such as clicks per impression, clicks per impression, and conversion rates, and may also include advertising effectiveness categorized by the target audience to whom the advertisement was displayed (e.g., categorized by gender, age, interests, category, etc.).

[0006] Data acquired through the above-mentioned online advertising media is provided in the form of a large amount of data on advertising effectiveness, and each online advertising media provides data in different formats. Therefore, those who wish to utilize the data must manually compile the data one by one and create reports. This poses a risk of human error, and there is a disadvantage in that a lot of resources are consumed in creating reports.

[0007] Furthermore, in order to create documents related to budget allocation and predicted advertising performance for proposing advertising products to be published in online advertising media, the collected data had to be processed according to the perspective of the person in charge. This has led to the disadvantages of difficulty in accurately reflecting the market situation depending on the perspective of the person in charge, in addition to the aforementioned human error and massive resource consumption.

[0008] The purpose of the present disclosure is to provide an operating method of an electronic device that automatically generates a report for explaining the advertising effectiveness 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.

[0009] In addition, the present disclosure aims to provide an operating method of an electronic device that obtains prediction data for an advertisement by inputting advertisement period, advertisement cost, online advertisement medium, etc. into an artificial intelligence model trained based on result data and advertisement history.

[0010] The purposes of the present disclosure are not limited to those mentioned above, and other purposes and advantages of the present disclosure not mentioned above can be understood through the following description and will be more clearly understood through the embodiments of the present disclosure. Furthermore, it will be readily apparent that the purposes and advantages of the present disclosure can be realized by the means and combinations thereof set forth in the claims.

[0011] A method of operating an electronic device according to one embodiment of the present disclosure includes a step of obtaining result data according to an advertising history of an online advertising medium from at least one external server corresponding to the online advertising medium, and a step of automatically generating a report for explaining an advertising effect of the online advertising medium by applying the result data to a template set for each item.

[0012] At this time, the operating method of the electronic device may include a step of training an artificial intelligence model to output prediction data on the advertising effect based on the advertising history and the result data, and the prediction data may include at least one of the expected number of exposures, the expected number of clicks, the expected number of clicks compared to the expected exposures, the expected number of purchases, the expected number of purchases compared to the expected clicks, and the purchase amount for at least one advertisement from the online advertising medium.

[0013] In this case, the operating method of the electronic device may include a step of obtaining the prediction data by inputting input data including at least one of an advertising period, an advertising cost, and an online advertising medium into the artificial intelligence model.

[0014] At this time, the step of obtaining the prediction data may include a step of setting a threshold range for the prediction data according to a user input and a step of excluding prediction data exceeding an upper limit value of the threshold range and prediction data below a lower limit value of the threshold range.

[0015] Meanwhile, the operating method of the electronic device may include a step of setting a target range for an advertising effect according to a user input, a step of inputting a plurality of different input data into the artificial intelligence model to select at least one input data such that predicted data included in the target range is output, and a step of recommending the selected input data.

[0016] Alternatively, the method of operating the electronic device may include a step of generating a plurality of advertising schedules for one or more online advertising media for a specific period based on a user input, and a step of obtaining prediction data for the specific period based on prediction data for each advertisement included in the plurality of advertising schedules.

[0017] In addition, the method of operating the electronic device may include a step of setting a target range for the advertising effect of the specific period according to a user input, a step of randomly generating a plurality of different advertising schedules for the specific period, a step of inputting a plurality of advertising schedules constituting each of the plurality of advertising schedules into the artificial intelligence model to select at least one advertising schedule that outputs prediction data included in the target range, and a step of recommending the selected advertising schedule.

[0018] Meanwhile, the present disclosure includes a non-transitory computer-readable medium having stored thereon at least one instruction that is executed by a processor of an electronic device to cause the electronic device to perform a control method for automatically generating a report for explaining the advertising effectiveness of an online advertising medium.

[0019] The method of operating the electronic device of the present disclosure automatically generates and provides a report of a preset template, thereby reducing the resources consumed for report creation by manually compiling result data and creating a report, and preventing human errors due to manual work.

[0020] Additionally, based on artificial intelligence models, it is possible to effectively deliver media mix proposals that reflect market conditions to advertisers, including predictive data on advertising effectiveness.

[0021] FIG. 1 is a diagram illustrating communication between an electronic device and multiple external servers according to one embodiment of the present disclosure;

[0022] FIG. 2 is a flowchart for explaining the operation of an electronic device according to an embodiment of the present disclosure;

[0023] FIG. 3 is a flowchart illustrating an operation of an electronic device according to an embodiment of the present disclosure to obtain prediction data based on an artificial intelligence model.

[0024] FIG. 4 is a diagram for explaining prediction data output based on an artificial intelligence model according to an embodiment of the present disclosure;

[0025] FIG. 5 is a flowchart illustrating an operation of an electronic device according to an embodiment of the present disclosure to select and recommend input data to achieve a target range;

[0026] FIG. 6 is a drawing for explaining the configuration of an electronic device according to one embodiment of the present disclosure.

[0027] Before describing the present disclosure in detail, the description method of the specification and drawings will be described.

[0028] First, the terms used in this specification and claims are general terms selected based on their functions in the various embodiments of the present disclosure. However, these terms may vary depending on the intentions of those skilled in the art, legal or technical interpretations, and the emergence of new technologies. Furthermore, some terms may have been arbitrarily selected by the applicant. These terms may be interpreted according to the meanings defined in this specification. In the absence of a specific definition, they may be interpreted based on the overall content of this specification and common technical knowledge in the relevant field.

[0029] Additionally, the same reference numbers or symbols in each drawing attached to this specification represent parts or components that perform substantially the same functions. For convenience of explanation and understanding, the same reference numbers or symbols are used in different embodiments. In other words, even if components with the same reference numbers are all depicted in multiple drawings, the multiple drawings do not necessarily represent a single embodiment.

[0030] Additionally, terms including ordinal numbers, such as "first," "second," etc., may be used in this specification and claims to distinguish between components. These ordinal numbers are used to distinguish identical or similar components from each other, and the use of these ordinal numbers should not be interpreted in a limited manner. For example, components associated with these ordinals should not be restricted in their order of use or arrangement by their numbers. If necessary, each ordinal number may be used interchangeably.

[0031] 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 indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but should be understood not to preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0032] In the embodiments of the present disclosure, terms such as "module," "unit," "part," etc. are terms used to refer to components that perform at least one function or operation, and such components may be implemented as hardware or software, or a combination of hardware and software. In addition, a plurality of "modules," "units," "parts," etc. may be integrated into at least one module or chip and implemented as at least one processor, except in cases where each needs to be implemented as a separate, specific hardware.

[0033] Additionally, in the embodiments of the present disclosure, when a part is said to be connected to another part, this includes not only a direct connection but also an indirect connection through another medium. Furthermore, unless specifically stated otherwise, the statement that a part includes a certain component does not exclude other components, but rather implies that other components may be included.

[0034] FIG. 1 is a diagram illustrating communication between an electronic device and multiple external servers according to one embodiment of the present disclosure.

[0035] According to FIG. 1, an electronic device (100) can communicate with a plurality of external servers (200).

[0036] The electronic device (100) is configured to receive data from an external server (200) and apply it to a preset template to generate a report.

[0037] 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.

[0038] An external server (200) is configured to communicate with an electronic device (100) and provide data to the electronic device (100).

[0039] 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 the server of an online advertising medium.

[0040] At this time, the result data according to the advertising history may correspond to various data aggregated as a result of the actual advertising execution, such as the number of exposures, number of clicks, number of clicks compared to exposures, number of purchases, and number of purchases compared to clicks for the executed advertisement.

[0041] FIG. 2 is a flowchart illustrating the operation of an electronic device according to an embodiment of the present disclosure.

[0042] According to FIG. 2, the electronic device (100) can obtain result data according to advertising history from an external server (200) corresponding to an online advertising medium (S210).

[0043] The electronic device (100) can automatically generate a report to explain advertising effectiveness based on the result data (S220). Specifically, the electronic device (100) can generate a report in a predetermined format by applying the acquired result data to a preset template for each item.

[0044] Additionally, the electronic device (100) can obtain data on future advertising effectiveness as well as reports on advertising history. To this end, the electronic device (100) may include at least one artificial intelligence model.

[0045] In relation to this, FIG. 3 is a flowchart for explaining an operation of an electronic device according to an embodiment of the present disclosure to obtain prediction data based on an artificial intelligence model.

[0046] According to FIG. 3, the electronic device (100) can train an artificial intelligence model to output prediction data on advertising effectiveness based on advertising history and result data (S310).

[0047] Specifically, the resulting data can be categorized into weekly, monthly, quarterly, semi-annual, and annual data based on advertising history and used as training data for artificial intelligence models.

[0048] The predicted data is a predicted value of an item of the result data, and may be configured to include at least one of the expected number of exposures, the expected number of clicks, the expected number of clicks compared to the expected exposures, the expected number of purchases, the expected number of purchases compared to the expected clicks, and the purchase amount for at least one advertisement run from an online advertising medium in response to the result data.

[0049] In this case, the electronic device (100) can obtain prediction data through an ARIMA (AutoRegressive Integrated Moving Average) model to analyze the pattern of time series data and predict the change of items over time, but is not limited thereto, and can obtain prediction data by training various artificial intelligence models such as a regression analysis model, LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit).

[0050] And, the electronic device (100) can obtain prediction data by inputting input data into an artificial intelligence model (S320).

[0051] At this time, the input data input into the artificial intelligence model may include at least one of advertising period, advertising cost, and online advertising medium.

[0052] FIG. 4 is a diagram for explaining prediction data output based on an artificial intelligence model according to an embodiment of the present disclosure.

[0053] Referring to Figure 4, the advertising period is set to start on October 19, 2020, and end on January 31, 2021, based on user input. Industry categories can be set based on user input based on predefined categories, such as major, minor, and minor. Accordingly, the AI ​​model can only output prediction data based on advertising data matching the corresponding category.

[0054] The predicted data output as a result of the input value may include expected impressions, expected clicks, expected purchases, CPM (cost per impression), CPC (cost per click), expected CTR (clicks per impression), expected CVR (clicks per purchase), and advertising purchase costs based on the advertising products provided by the advertising medium.

[0055] At this time, predictive data can be generated in rank order based on user input, with the goal of generating effective advertising media and products (e.g., maximizing purchases, maximizing exposure, etc.). Alternatively, predictive data can be generated based on user input, where the user directly selects specific advertising media and products.

[0056] At this time, predicted data may be generated based on a threshold range set by user input. This reflects the fact that predicted data generated outside the distribution of other predicted data may have relatively low reliability.

[0057] Specifically, the electronic device (100) can set a threshold range for prediction data according to user input, and can exclude prediction data exceeding the upper limit of the threshold range and prediction data below the lower limit of the threshold range.

[0058] In relation to this, referring to FIG. 4, when predictive data is acquired for each advertising medium and advertising product, a parameter range (=critical range) can be determined based on user input. It can be confirmed that the predictive data for Media 1 to Media 5 presented in FIG. 4 reflects the exclusion of predictive data for advertising media and advertising products that were calculated to fall below the bottom 20% or exceed 80%.

[0059] Meanwhile, the electronic device (100) may also output an advertising period, advertising cost, and online advertising medium to achieve the target input data.

[0060] In this regard, FIG. 5 is a flowchart illustrating an operation of an electronic device according to an embodiment of the present disclosure to select and recommend input data to achieve a target range.

[0061] According to FIG. 5, the electronic device (100) can set a target range for advertising effect according to user input (S510).

[0062] A target range may correspond to an item of predicted data set to a certain value. For example, the electronic device (100) may set a target range by setting the expected number of purchases to a certain value or range.

[0063] The electronic device (100) can input multiple input data into an artificial intelligence model and select input data to output predicted data included in the target range (S520).

[0064] That is, the electronic device (100) can obtain various prediction data matching different input data as they are input. In an embodiment, when the electronic device (100) obtains prediction data for one input data, it is possible to obtain multiple prediction data different from the prediction data matching the input data before the change by changing at least one of the items (e.g., advertising period, advertising cost, online advertising medium, etc.) included in the input data.

[0065] At this time, the fluctuation range of the input data can be determined based on the difference between the generated predicted data and the target range. For example, the electronic device (100) can calculate the ratio of the difference between the generated predicted data and the target range based on the input data. Based on the calculated ratio, the electronic device (100) can set the fluctuation range for changing the input data.

[0066] Alternatively, if the ratio is greater than a certain value, the electronic device (100) may determine that many attempts will be required to achieve the target range with the fluctuation of the input data, and may stop the fluctuation of the input data and set new random input data to perform the process of generating predicted data included in the target range again.

[0067] And, the electronic device (100) can identify and select prediction data included in the target range among the plurality of generated prediction data.

[0068] Accordingly, the electronic device (100) can recommend the selected input data (S530).

[0069] Meanwhile, the electronic device (100) may also generate an advertising schedule including multiple advertising schedules.

[0070] Specifically, the electronic device (100) can generate an advertising schedule including multiple advertising schedules for one or more online advertising media for a specific period based on user input, and can obtain prediction data for a specific period based on prediction data of an artificial intelligence model for input data matching each of the multiple advertising schedules.

[0071] That is, the electronic device (100) can produce prediction data on the advertising effectiveness of the entire advertising schedule for a specific period based on an artificial intelligence model.

[0072] Accordingly, the user can perform a feedback process to obtain an optimized advertising schedule by modifying at least one advertising schedule that constitutes an advertising schedule based on prediction data for the advertising schedule and re-inputting the modified advertising schedule into the electronic device (100).

[0073] Alternatively, in the same manner as the embodiment of FIG. 5, the electronic device (100) may set a target range for an advertising effect for a specific period and obtain an advertising schedule matching the range.

[0074] Specifically, the electronic device (100) can set a target range for an advertising effect for a specific period of time according to user input, and randomly generate multiple different advertising schedules for the specific period of time.

[0075] By inputting input data matching multiple advertising schedules that constitute each of multiple randomly generated advertising schedules into an artificial intelligence model, the electronic device (100) can obtain multiple prediction data for the advertising schedules, and select and recommend an advertising schedule that outputs prediction data corresponding to a target range among the prediction data.

[0076] Before inputting each of the plurality of randomly generated advertising schedules as described above into the artificial intelligence model, the electronic device (100) can perform preprocessing on each of the randomly generated advertising schedules.

[0077] Specifically, in a plurality of advertising schedules that constitute a randomly generated advertising schedule, the electronic device (100) can change the order or period difference of each advertising schedule based on the synergy between different online advertising media.

[0078] For example, assume that the randomly generated advertising schedule includes a first advertising schedule (e.g., advertising for medium A from January to April), a second advertising schedule (e.g., advertising for medium B from March to June), and a third advertising schedule (e.g., advertising for medium C from July to October). Here, if the synergy between medium A and medium C is relatively large, the electronic device (100) can change the second advertising schedule and the third advertising schedule, respectively, to generate an advertising schedule for medium C from March to June so that there is an overlapping period with the first advertising schedule for medium A, and generate an advertising schedule for medium B from July to October.

[0079] To this end, the electronic device (100) can calculate synergy between different online advertising media. Specifically, the electronic device (100) can define a unit synergy score for different online advertising media and calculate a synergy score by applying a time difference to the unit synergy score. For example, if the unit synergy score between media A and media C is 0 or greater, the smaller the time difference between the advertising schedules for media A and media C or the longer the overlapping period, the higher the synergy score can be calculated.

[0080] 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.

[0081] At this time, the artificial intelligence model can be produced using as input variables the advertising schedules of media A and media C, advertising products provided by each media, and advertising formats.

[0082] Accordingly, the artificial intelligence model of the present disclosure may include the artificial intelligence model described above, and may define a unit synergy score between different media based on a GNN (Graph Neural Network) model that identifies correlations between nodes, a reinforcement learning model that optimizes a combination of online advertising media, etc.

[0083] FIG. 6 is a drawing for explaining the configuration of an electronic device according to one embodiment of the present disclosure.

[0084] According to FIG. 6, the electronic device (100) may include a memory (110), a communication unit (120), and a processor (130).

[0085] The memory (110) is a configuration for storing an operating system (OS) for controlling the overall operation of components of the electronic device (100) and at least one instruction or data related to the components of the electronic device (100).

[0086] The memory (110) may include non-volatile memory such as ROM, flash memory, etc., and may include volatile memory composed of DRAM, etc. In addition, the memory (110) may include a hard disk, SSD (Solid state drive), etc.

[0087] Additionally, the memory (110) may include at least one artificial intelligence model (10) for producing prediction data on the advertising effectiveness of an online advertising medium.

[0088] The communication unit (120) is a component for the electronic device (100) to communicate with external devices such as an external server (200). The communication unit (120) may include circuits, modules, chips, etc. for communicating using various wired and wireless communication methods. The communication unit (120) may also be connected to external devices through various networks.

[0089] Depending on the area or scale, a network may be a personal area network (PAN), a local area network (LAN), or a wide area network (WAN), and depending on the openness of the network, it may be an intranet, an extranet, or the Internet.

[0090] The communication unit (120) can be connected to external devices through various wireless communication methods such as LTE (long-term evolution), LTE-A (LTE Advance), 5G (5th Generation) mobile communication, CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), 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), Zigbee, and LoRa.

[0091] Additionally, the communication unit (120) may be connected to external devices through a wired communication method such as Ethernet, an optical network, USB (Universal Serial Bus), or ThunderBolt.

[0092] In addition, the communication unit (120) may be configured to utilize various communication methods / technologies that will be newly designed in the future.

[0093] The processor (130) is a component for controlling the overall operation of the electronic device (100). Specifically, the processor (130) is connected to the memory (110) and executes at least one instruction stored in the memory (110) to perform operations according to various embodiments of the present disclosure.

[0094] The processor (130) may include a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-only processor such as a GPU or VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. The artificial intelligence-only processor may be designed with a hardware structure specialized for training or utilizing a specific artificial intelligence model.

[0095] A processor (130) according to one embodiment of the present disclosure can obtain prediction data by inputting the above-described input data into an artificial intelligence model (10) of a memory (110).

[0096] 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.

[0097] Meanwhile, the various embodiments described above may be implemented in a recording medium readable by a computer or similar device using software, hardware, or a combination thereof.

[0098] In terms of hardware implementation, the embodiments described in the present disclosure may be implemented using at least one of Application Specific Integrated Circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

[0099] In some cases, the embodiments described herein may be implemented within the processor itself. In a software implementation, the embodiments, such as the procedures and functions described herein, may be implemented as separate software modules. Each of the software modules described above may perform one or more of the functions and operations described herein.

[0100] Meanwhile, computer instructions or computer programs for performing processing operations in electronic devices according to the various embodiments of the present disclosure described above may be stored in a non-transitory computer-readable medium. When executed by a processor of a specific device, the computer instructions or computer programs stored in the non-transitory computer-readable medium cause the specific device to perform the processing operations of the electronic device according to the various embodiments described above.

[0101] A non-transitory computer-readable medium refers to a medium that permanently stores data and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specific examples of non-transitory computer-readable media include CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, and ROMs.

[0102] Although the preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary skill in the art to which the present disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present disclosure.

Claims

1. In the method of operating an electronic device, A step of 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 An operating method of an electronic device, comprising: a step of automatically generating a report for explaining the advertising effect of the online advertising medium by applying the result data to a preset template for each item; 2. In paragraph 1, The method of operation of the above electronic device is: A step of training an artificial intelligence model to output prediction data on advertising effectiveness based on the above advertising history and the above result data; The above prediction data is An operating method of an electronic device, comprising at least one of an expected number of exposures, an expected number of clicks, an expected number of clicks relative to expected exposures, an expected number of purchases, an expected number of purchases relative to expected clicks, and a purchase amount for at least one advertisement from the online advertising medium.

3. In paragraph 2, The method of operation of the above electronic device is: A method of operating an electronic device, comprising: a step of obtaining the prediction data by inputting input data including at least one of an advertising period, an advertising cost, and an online advertising medium into the artificial intelligence model.

4. In paragraph 3, The step of obtaining the above prediction data is: A step of setting a threshold range for the above prediction data according to user input; and A method of operating an electronic device, comprising: a step of excluding prediction data exceeding an upper limit value of the threshold range and prediction data below a lower limit value of the threshold range.

5. In paragraph 3, The method of operation of the above electronic device is: A step for setting a target range for advertising effectiveness based on user input; A step of inputting a plurality of different input data into the artificial intelligence model and selecting at least one input data that outputs predicted data included in the target range; and A method of operating an electronic device, comprising: a step of recommending the selected input data; 6. In paragraph 3, The method of operation of the above electronic device is: A step of generating an advertising schedule comprising a plurality of advertising schedules for one or more online advertising media for a specific period of time based on user input; and An operating method of an electronic device, comprising: a step of obtaining prediction data for the specific period based on prediction data of the artificial intelligence model for input data matching each of the plurality of advertising schedules; 7. In paragraph 6, The method of operation of the above electronic device is: A step of setting a target range for advertising effectiveness for a specific period based on user input; A step of randomly generating multiple different advertising schedules for the above specific period; A step of inputting a plurality of advertising schedules constituting each of the plurality of advertising schedules into the artificial intelligence model to select at least one advertising schedule that causes prediction data included in the target range to be output; and A method of operating an electronic device, comprising: a step of recommending the selected advertising schedule; 8. A non-transitory computer-readable medium having stored thereon at least one instruction that is executed by a processor of an electronic device to cause the electronic device to perform the control method of claim 1.

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