Systems and methods for generating true return on advertising spend using short-term and long-term factor analysis of advertising effectiveness

Abstract

A method includes receiving a first product value; determining an attrition value based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the attrition value; determining a return value based on return data; generating a third product value by adjusting the second product value using the return value; determining a lifetime customer value based on at least the attrition value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining a natural ranking value based on natural location data and a value of the natural location data; generating a fifth product value by adjusting the fourth product value using the natural ranking value; and generating an actionable report describing an effectiveness of a target product in a computer networking market based on at least the fifth product value.

Description

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to U.S. non-provisional application No. 18 / 585,688, filed February 23, 2024, the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to business systems and methods, and more specifically, to using short-term and long-term factors to analyze advertising effectiveness in order to generate a true return on advertising sales value. Background Technology

[0004] E-commerce systems are well-known in the art and are an effective means of facilitating the exchange of products, goods, and services from one party to another. Typically, an e-commerce system is represented by a marketplace where numerous products are offered for sale, and people as customers can browse or select products and choose what they want to buy. Such marketplaces can be managed by companies including eBay®, Amazon®, Wayfair®, Costco®, Walmart®, and Target®. With the advent of digital marketplaces, sellers can list products for anyone with an internet connection to purchase. Often, many sellers offer the same or similar products. Shoppers (e.g., users accessing a digital marketplace via the internet) can filter and browse all these products to find what they are looking for.

[0005] One of the common problems associated with typical commerce systems and digital marketplaces is the density of potential products for sale. For example, when a shopper wants to buy a product, they might start with a search engine that provides hundreds or thousands of results. Unlike “physical” marketplaces (e.g., physical markets), shoppers search at least one specific digital marketplace, or even multiple potential ones, which can yield thousands of results. Any particular product can easily be overwhelmed by the sheer volume of search results. This can make it difficult for sellers to get their products noticed and sold.

[0006] Furthermore, the seller may have recently created a product or recently placed it on a digital marketplace, but may not know the extent to which they should focus on promoting that product. In this example, the seller may not know what target Ad CoS (Amount of Advertising Sales) is appropriate to see long-term benefits rather than short-term profits. When product density is high in the marketplace, spending more money to achieve or exceed the minimum return enables greater recognition in those digital marketplaces, thereby unlocking more potential sales.

[0007] Additionally, sellers can use various metrics to determine the return on investment (ROI) regarding, for example, advertising expenditure. For instance, Return on Advertising Expenditures (ROAS) is one of the most widely used metrics in advertising. ROAS is calculated by dividing advertising sales by advertising expenditure and measures how many dollars of revenue are generated for every dollar spent on advertising. However, such ROAS calculations may ignore variables that could limit the effectiveness and / or accuracy of ROAS calculations. Summary of the Invention

[0008] The various systems and methods disclosed herein are developed in response to the state of the art and, in particular, to problems and needs in the field that have not yet been fully addressed by the currently available digital markets.

[0009] One aspect of the disclosed implementation includes a system comprising a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive a first product value for a target product; determine a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generate a second product value by adjusting the first product value using the cannibalization value; determine a return value for the target product based on return data; generate a third product value by adjusting the second product value using the return value; determine a lifetime customer value for the target product based at least on the cannibalization value; generate a fourth product value by adjusting the third product value using the lifetime customer value; determine a natural ranking value for the target product based on natural location data and the value of the natural location data; generate a fifth product value by adjusting the fourth product value using the natural ranking value; and generate an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0010] One aspect of the disclosed implementation includes a method comprising: receiving a first product value for a target product; determining a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the cannibalization value; determining a return value for the target product based on return data; generating a third product value by adjusting the second product value using the return value; determining a lifetime customer value for the target product based at least on the cannibalization value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining a natural ranking value for the target product based on natural location data and the value of the natural location data; generating a fifth product value by adjusting the fourth product value using the natural ranking value; and generating an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0011] One aspect of the disclosed implementation is a computing device including a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive a first product value from a computer networking market for a target product; determine a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generate a second product value by subtracting the cannibalization value from the first product value; determine a return value for the target product based on return data; generate a third product value by subtracting the return value from the second product value; determine a lifetime customer value for the target product based at least on the cannibalization value; generate a fourth product value by adding the lifetime customer value to the third product value; determine a natural ranking value for the target product based on natural location data and the value of the natural location data; generate a fifth product value by adding the natural ranking value to the fourth product value; and generate an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0012] These and other aspects of this disclosure are disclosed in the following detailed description of the embodiments, the appended claims, and the drawings. Attached Figure Description

[0013] The present disclosure can be best understood by reading in conjunction with the accompanying drawings and the following detailed description. It should be emphasized that, by convention, the various features in the drawings are not drawn to scale. Instead, for clarity, the dimensions of the various features have been arbitrarily enlarged or reduced.

[0014] Figure 1 A schematic block diagram illustrating the principles of the system according to this disclosure is shown in general.

[0015] Figure 2A This illustrates the principles based on this disclosure. Figure 1 A schematic block diagram of a computing device in the form of a smartphone.

[0016] Figure 2B This illustrates the principles based on this disclosure. Figure 1 Desktop computer form computing devices and Figure 1 A schematic block diagram of a server in the first server form.

[0017] Figure 3 This is a schematic block diagram illustrating a computing device and server operating a digital market according to the principles of this disclosure.

[0018] Figure 4 This is a schematic block diagram illustrating computing devices and servers that host digital markets including the attributes of target and competing products, based on the principles of this disclosure.

[0019] Figure 5This is a schematic block diagram illustrating a computing device including a graphical user interface according to the principles of this disclosure.

[0020] Figure 6 This is a flowchart illustrating a method for evaluating a product based on the principles of this disclosure.

[0021] Figure 7 This is a flowchart illustrating a method for providing a competitive evaluation of a target product in the market based on the principles of this disclosure.

[0022] Figure 8 This is a schematic block diagram illustrating a computing device and server operating a digital market according to the principles of this disclosure.

[0023] Figure 9 It is a graph of multiple search terms plotted at points representing the frequency and similarity of search terms associated with the target product relative to competing products, based on the principles of this disclosure.

[0024] Figure 10 It is a graphical representation of multiple search terms drawn at points representing the relevance and search volume of search terms associated with the target product relative to competing products, based on the principles of this disclosure.

[0025] Figure 11 This is a flowchart of a method for determining the true return on advertising sales based on the principles of this disclosure. Detailed Implementation

[0026] The following discussion relates to various embodiments of this disclosure. While one or more of these embodiments may be preferred, the disclosed embodiments should not be construed as or otherwise used to limit the scope of this disclosure (including the scope of the claims). Furthermore, those skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is intended only as an illustrative example of that embodiment and is not intended to imply that the scope of this disclosure (including the claims) is limited to that embodiment.

[0027] As described, sellers in digital marketplaces can use various metrics to determine return on investment (ROI) regarding, for example, advertising spending. ROAS, for instance, is one of the most widely used metrics in advertising. ROAS is calculated by dividing advertising sales by advertising expenditure and measures how many dollars of revenue are generated for every dollar spent on advertising.

[0028] However, such ROAS calculations may overlook variables that could limit the effectiveness and / or accuracy of ROAS calculations. For example, typical ROAS values ​​(e.g., those generated by associated ROAS calculations) may fail to account for important factors that savvy advertisers consider when evaluating the effectiveness of their ads. These factors include: the likelihood that shoppers would purchase the advertised product even without the ad; products purchased after engaging with the ad but later returned; the lifetime value of customers acquired through the ad; the impact of the ad on organic rankings; and how digital marketplaces handle the product's search engine ranking position on associated platforms.

[0029] Therefore, systems and methods that can be configured to improve the return on sales value of advertising, such as those described herein, may be desirable. In some implementations, the systems and methods described herein can be configured to provide improved ROAS calculations by analyzing the advertising effectiveness of a target product (e.g., in a digital marketplace) using short-term and long-term factors.

[0030] In some implementations, the systems and methods described herein can be configured to calculate an improved ROAS value for a target product based on: the cannibalization value of the target product (e.g., removing a sponsored position from a mirrored organic search engine ranking position, since these positions are likely to generate sales regardless of the sponsored position), the return value of the target product (e.g., sales returns, since returns do not constitute realized revenue), the customer lifetime value of the target product (e.g., the value of incremental sales, which extends beyond the first attribution purchase, such that repeat purchases by loyal customers increase the long-term value of the original sponsored position), the market search engine organic ranking value of the target product (e.g., this may be referred to herein as organic ranking value) (e.g., advertising data that can inform search engines that the target product is relevant to new keywords and improve the organic ranking of the target product), and the review value of the target product (e.g., this may include or be associated with data related to customer reviews and / or user-generated content (UGC) obtained from advertising, which may indicate an increase in the value of the target product and may improve its market presence to contribute to the growth of the target product).

[0031] The systems and methods described herein can be configured to determine the ROAS value of a target product. The systems and methods described herein can be configured to adjust the ROAS value of a target product based on cannibalization value. For example, the systems and methods described herein can be configured to determine the ROAS value by multiplying it by 1 minus the keyword click share value of the target product. The systems and methods described herein can be configured to subtract return value from the product of the ROAS value and 1 minus the keyword click share value. The systems and methods described herein can be configured to add the customer lifetime value of the target product. The systems and methods described herein can be configured to calculate customer lifetime value by multiplying the brand new customer percentage (NTB%) by the product lifetime value and then multiplying it by the value representing an advertising order minus cannibalization value.

[0032] The systems and methods described in this paper can then be configured to add organic ranking value. Specifically, the systems and methods described in this paper can be configured to generate or determine organic ranking value by multiplying the incremental click value by the conversion rate value and then by the product price value.

[0033] The system and method described in this paper can then be configured to add comment value. Specifically, the system and method can be configured to calculate comment value by multiplying the incremental conversion value by the comment probability value and then by the economic value of the incremental comment.

[0034] In some embodiments, the systems and methods described herein can be configured to receive a first product value for a target product. In some embodiments, the first product value can be determined based on a comparison between the advertised sales value and the advertised expenditure value of the target product. The first product value can be received from a computer networking market. The systems and methods described herein can then be configured to determine cannibalization value for the target product based on at least one of experimental data and non-experimental data. Experimental data can be associated with: at least one intentional experiment of advertising data configured to identify the incremental effect of advertising on a product associated with the computer networking market; at least one natural experiment of advertising data configured to discover naturally occurring advertising scenarios for a product associated with the computer networking market; other suitable experiments; or combinations thereof. Non-experimental data can be associated at least with the product position and market share of the product associated with the computer networking market.

[0035] The systems and methods described herein can then be configured to generate a second product value by adjusting the value of a first product using a cannibalization value. For example, the systems and methods described herein can be configured to generate a second product value by subtracting a cannibalization value from the value of a first product. The systems and methods described herein can then be configured to determine a return value for a target product based on return data. Return data may include return data provided by a computer network marketplace and / or return data not provided by a computer network marketplace.

[0036] The systems and methods described herein can then be configured to generate a third product value by adjusting the value of a second product using return value. For example, the systems and methods described herein can be configured to generate a third product value by subtracting the return value from the value of a second product. The systems and methods described herein can then be configured to determine lifetime customer value for a target product based at least on cannibalization value, new customer data provided by computer-networked markets, future lifetime value, and / or any other suitable value.

[0037] The systems and methods described herein can then be configured to generate a fourth product value by adjusting the third product value using lifetime customer value. For example, the systems and methods described herein can be configured to generate a fourth product value by adding the lifetime customer value to the third product value. The systems and methods described herein can then be configured to determine an organic ranking value for a target product based on organic location data and the value of that organic location data.

[0038] The systems and methods described herein can then be configured to generate a fifth product value by adjusting the fourth product value using the organic ranking value. For example, the systems and methods described herein can be configured to generate a fifth product value by adding the organic ranking value to the fourth product value. The systems and methods described herein can then be configured to generate an actionable report describing the effectiveness of a target product in the computer networking market, based at least on the fifth product value.

[0039] Reference Figure 1 The schematic block diagram illustrates a system 100 based on the principles of this disclosure. System 100 can be used to provide benefits to one or more users 110, which may include a first user 112, a second user 114, a third user 116, and a fourth user 118, as shown below. Figure 1As shown. Each of the users 110 may use one of a variety of computing devices 120, which may include any of a variety of devices that perform computing steps, including but not limited to a desktop computer 122 used by the first user 112, a laptop computer 124 used by the second user 114, a smartphone 126 used by the third user 116, a camera device 128 used by the fourth user 118, etc. The systems and methods presented herein can be performed on any type of computing device.

[0040] Computing devices 120 may optionally connect to each other and / or other resources. Such connections may be wired or wireless and may be achieved using any known wired or wireless communication standards, including but not limited to Ethernet, 802.11a, 802.11b, 802.11g, and 802.11n, Universal Serial Bus (USB), Bluetooth, cellular, Near Field Communication (NFC), Bluetooth Smart, ZigBee, etc. Figure 1 In the illustration, wired communication is shown with solid lines and wireless communication is shown with dashed lines, as an example.

[0041] Routing can be achieved by using router 130 and / or by other means. Figure 1 Communication between various components. Router 130 can be of any type known in the art and can be designed for wired and / or wireless communication via any known communication standard, including but not limited to those listed herein. Router 130 may include, for example: a first router 132 that facilitates communication to and / or from desktop computer 122; a second router 134 that facilitates communication to and / or from laptop computer 124; a third router 136 that facilitates communication to and / or from smartphone 126; and a fourth router 138 that facilitates communication to and / or from camera device 128.

[0042] Router 130 can facilitate communication between computing device 120 and one or more networks 140, which can include any type of network, including but not limited to local area networks (LANs) (e.g., LAN 142) and wide area networks (e.g., WAN 144). In some embodiments, LAN 142 may be a network serving entities such as businesses, non-profit entities, government organizations, etc. WAN 144 can provide communication for multiple entities and / or individuals, and in some embodiments may be the Internet. LAN 142 can communicate with WAN 144. If desired, one or more routers or other devices may be used to facilitate such communication.

[0043] Network 140 may store information on server 150 or other information storage devices. As shown, first server 152 may be connected to local area network 142 and thus may communicate with devices connected to local area network 142, such as desktop computer 122 and laptop computer 124. Second server 154 may be connected to wide area network 144 and thus may communicate with devices connected to wide area network 144, such as smartphone 126 and camera device 128. If desired, second server 154 may be a web server providing web pages, network connectivity services, executable code designed to operate on the Internet, and / or other functions facilitating the provision of information and / or services on wide area network 144.

[0044] Reference Figure 2A The schematic block diagram illustrates an exemplary computing device 120, which enables the implementation of the present disclosure in a standalone computing environment. The computing device may be, for example... Figure 1 The smartphone 126. However, this specification envisions that the computing device 120 may include... Figure 1 Any computing device or any other type of computing device among those computing devices 120 described herein.

[0045] As shown, smartphone 126 may include processor 210 designed to execute instructions on data. Processor 210 may be of various types, including microprocessors with x86-based architectures or other architectures known in the art, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), etc. Processor 210 may optionally include multiple processing elements or "cores". Processor 210 may include a cache providing temporary storage for data generated incidentally as a result of the operation of processor 210.

[0046] The smartphone 126 may also include memory 220, which may be volatile memory, such as random access memory (RAM). Memory 220 may include one or more memory modules. Memory 220 may include executable instructions, data referenced by such executable instructions, and / or any other data that may advantageously make the processor 210 readily accessible.

[0047] The smartphone 126 may also include a data storage device 230, which may be a non-volatile memory such as a hard disk drive or flash memory. The data storage device 230 may include one or more data storage elements. The data storage device 230 may store executable code, such as an operating system and / or various programs to run on the smartphone 126. The data storage device 230 may also store data to be used by such programs. For the systems and methods of this disclosure, the data storage device 230 may store computer-executable code associated with the evaluation module 232, text analysis module 238, screening module 235, comparison module 234, recommendation module 236, and competitiveness score generation module 233. The data storage device 230 may also include data associated with: descriptive terms 241 related to the target product and / or competing products, relevant descriptive terms 242 associated with either the target product or competing products, competitiveness scores 239, and actionable reports 237. The data stored in the data storage 230 can be retained on the data storage 230 for any length of time, and some data can be created or overwritten at any time to facilitate the methods described herein.

[0048] Smartphone 126 may also include one or more wired transmitters / receivers 240, which can facilitate communication between smartphone 126 and any other device (e.g., Figure 1 Wired communication between other computing devices 120, servers 150, and / or routers 130. The wired transmitter / receiver 240 can communicate via any known wired protocol (including but not limited to...). Figure 1 The wired transmitter / receiver 240 may communicate with any of the wired protocols described herein. In some implementations, the wired transmitter / receiver 240 may include an Ethernet adapter, a Universal Serial Bus (USB) adapter, etc.

[0049] Smartphone 126 may also include one or more wireless transmitters / receivers 250, which can facilitate communication between Smartphone 126 and any other device (e.g., Figure 1 Wireless communication between other computing devices 120, servers 150, and / or routers 130. The wireless transmitter / receiver 250 can communicate via any known wireless protocol (including but not limited to...). Figure 1The wireless transmitter / receiver 250 may communicate with any of the wireless protocols described herein. In some implementations, the wireless transmitter / receiver 250 may include a Wi-Fi adapter, a Bluetooth adapter, a cellular adapter, etc. Either the wired transmitter / receiver 240 or the wireless transmitter / receiver 250 may be associated with the network interface device (NID) 280. The network interface device 280 may provide connectivity to any network via the Internet, such as a wide area network (WAN), a local area network (LAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), a wireless wide area network (WWAN), or other networks.

[0050] Smartphone 126 may also include one or more user inputs 260, said one or more user inputs 260 from a user (e.g. Figure 1 Input is received by any user in user 110. User 110 described herein may be referred to as the seller of the target product. User input 260 may be integrated into smartphone 126, or may be separate from smartphone 126 and connected to smartphone 126 via a wired or wireless connection that may operate via wired transmitter / receiver 240 and / or wireless transmitter / receiver 250. User input 260 may include elements such as touch screen, buttons, keyboard, mouse, trackball, trackpad, stylus, digitizer, digital camera, microphone, and / or other user input devices known in the art.

[0051] Smartphone 126 may also include one or more user outputs 270, which output to a user (e.g. Figure 1 The user output 270 provides output to any user in user 110. The user output 270 may be integrated into the smartphone 126, or it may be separate from the smartphone 126 and connected to it via a wired or wireless connection that may operate via a wired transmitter / receiver 240 and / or a wireless transmitter / receiver 250. The user output 270 may include elements such as a display screen, speaker, vibration device, LED or other light, and / or other output devices known in the art. In some embodiments, one or more of the user inputs 260 may be combined with one or more of the user outputs 270, as is the case with a touchscreen. In some embodiments, the user output 270 may present a graphical user interface to a user through which the user interacts with the smartphone 126 to influence the methods and processes described herein.

[0052] The smartphone 126 may include various other components not shown or described herein. Those skilled in the art will recognize, with the aid of this disclosure, that in addition to combining… Figure 2AAny such component, other than those shown and described or as an alternative to these components, may be used to perform the contents of this disclosure.

[0053] The smartphone 126 can execute the contents of this disclosure in an independent computing environment, i.e., without relying on communication with other devices such as other computing devices 120 or server 150. This specification also envisions that any of the following modules—evaluation module 232, competitiveness score generation module 233, comparison module 234, screening module 235, recommendation module 236, and text analysis module 238—can be distributed across multiple computing devices (e.g., ...). Figure 1 The computing devices 120) are located between and / or distributed across any server (e.g., Figure 1 Between 150 and 150). In other embodiments, the present disclosure can be utilized in different computing environments. (The following is a continuation of the previous sentence, but the context is unclear: "will be combined with...") Figure 2B An example of a client / server environment is shown and described.

[0054] Reference Figure 2B The schematic block diagram shows Figure 1 Desktop computer 122 form computing device and Figure 1 The first server 152 is a server in the form of a computing device that can cooperate to enable the implementation of the present disclosure in a client / server architecture. As shown, the desktop computer 122 can be a "dumb terminal" configured to operate in conjunction with the first server 152.

[0055] Therefore, the desktop computer 122 may only have the ability to communicate with the user (e.g., Figure 1 The desktop computer 122 may include the hardware required for interaction with the first user 112 and communication with the first server 152. Therefore, the desktop computer 122 may include one or more user inputs 260, one or more user outputs 270, one or more wired transmitters / receivers 240, and / or one or more wireless transmitters / receivers 250. Similarly, any of the wired transmitters / receivers 240 or the wireless transmitters / receivers 250 may be associated with the NID 280a. The NID 280a may provide connectivity to any network via the Internet, such as a wide area network (WAN), local area network (LAN), wireless local area network (WLAN), wireless personal area network (WPAN), wireless wide area network (WWAN), or other networks in which the first server 152 is a part. These components may be combined as follows. Figure 2A As described.

[0056] Computational functions (excluding those incidentally arising from receiving input from and delivering output to the user) can be executed, in whole or in part, at the first server 152. Therefore, the processor 210, memory 220, data memory 230, wired transmitter / receiver 240, and wireless transmitter / receiver 250 can be housed within the first server 152. These components can also be combined as follows. Figure 1 As described by A.

[0057] In operation, the desktop computer 122 can receive input from the user via user input 260. The user input can be delivered to the first server 152 via wired transmitter / receiver 240 and / or wireless transmitter / receiver 250. The user input can be further transmitted by any intermediate device, such as the first router 132 and any other device in the local area network 142 that needs to transmit the user input from the first router 132 to the first server 152.

[0058] The first server 152 can perform any necessary processing steps in response to receiving user input. The first server 152 can then send user output to the user via wired transmitter / receiver 240 and / or wireless transmitter / receiver 250. This user output can be further transmitted by any intermediate device, such as the first router 132 and any other device in the local area network 142 (or alternatively, the wide area network 144) that needs to transmit the user output from the first server 152 to the first router 132. The user output can then be provided to the user via user output 270. In some embodiments, user output 270 can present a graphical user interface to the user, displaying a list of relevant descriptive terms 242 for the target product and competing products, and displaying an actionable report describing the expected performance of the target product in the computer networking market relative to at least one natural competitor also present in the computer networking market, according to the methods described herein.

[0059] Reference Figure 3 The schematic block diagram illustrates a computing device 322 (similar to) operating a digital market according to the principles of this disclosure. Figure 1 Any of the computing devices shown) and server 350 (similar to Figure 1The computing device and the server (any of the servers shown) can collaborate to enable the implementation of this disclosure in a client / server architecture. As illustrated, computing device 322 can be operatively coupled to server 350 via NID 380 as described herein. This operative coupling enables computing device 322 to access digital marketplace 382 on which target and competing products are sold when appropriate. Digital marketplace 382 can be any web-accessible website listing multiple products, allowing users to view, rate, purchase, and perform other tasks related to digital commerce when they visit such websites. Digital marketplace 382 can be managed by companies including eBay®, Amazon®, Wayfair®, Costco®, Walmart®, and Target®. After purchasing a product, the consumer can have it shipped to their home or workplace for consumption. In some implementations, digital marketplace 382 can be any of multiple websites on which server 350 provides storage and processing resources.

[0060] As described herein, computing device 322 may include processor 310, memory 320, user input 360, user output 370, and data memory 330, the operation of which is similar to that of a combination of Figure 2A and 2B The data storage 330 may include the modules described herein, including evaluation module 332, competitiveness score generation module 333, comparison module 334, screening module 335, recommendation module 336, and text analysis module 338.

[0061] During operation, evaluation module 332 can evaluate certain attributes of the target product. As described herein, the target product is a specific product that a user (e.g., a seller) of computing device 322 seeks to discover in a particular market its competitiveness. For example, the target product could be a product that the user is selling or wants to sell on a digital marketplace 382 hosted by server 350. To determine the competitiveness of the target product, evaluation module 332 can access certain data about the target product present on server 350. Evaluation module 332 can access this data by sending a data request via NID 380 through a wired (e.g., via wired transmitter / receiver 340) or wireless (e.g., via wireless transmitter / receiver 350) connection.

[0062] Data requests can be requests for attributes of the target product. While any number of attributes about the target product can be requested, evaluation module 332 can request specific attributes that will be used to generate an actionable report 337 about the target's competitiveness in digital marketplace 382. A first attribute can describe a rating provided by at least one buyer of the target product on digital marketplace 382. Typically, digital marketplace 382 provides consumers with a graphical user interface (GUI) that allows them to rate products they purchase on digital marketplace 382. In a particular implementation, consumers / buyers of the target product can use a 5-star rating system to rate the target product. A one-star rating would indicate that the consumer / buyer rated the target product poorly, while a five-star rating would indicate that the consumer / buyer rated the target product very well. Therefore, evaluation module 332 can use each star rating or the average of those star ratings as input to create actionable report 337.

[0063] The second attribute may include reviews and / or user-generated content associated with the target product. Similarly, the digital marketplace 382 typically provides a GUI that allows consumers of the target product to input text describing their experience with the product. This text may include specific positive or negative keywords describing the consumer's experience with the target product. In some implementations, using this data, the evaluation module 332 may enable the text analysis module 338 to parse each review against these keywords describing the target product. Furthermore, the text analysis module 338 may also extract keywords describing certain features of the target product. As an example, the text analysis module 338 may extract the phrase "ergonomic handle," which not only describes that the target product includes a handle but also that the handle is an "ergonomic" handle, thus giving the consumer who provided the review a perception of how well the target product fits or feels.

[0064] The third attribute can be similar to the second attribute; evaluation module 332 determines the number of reviews associated with the target product presented on the digital marketplace 382. The number of reviews can indicate the level of engagement with the target product, whether it is derogatory or positive. Together with the text content of these reviews, the number of reviews associated with the target product can be used to help create actionable reports based on the target product's engagement within the digital marketplace 382.

[0065] The fourth attribute can include the listed price of the target product. Although the amount charged for purchasing the product may not indicate the value of the target product, the amount charged relative to other similar competing products can indicate its value or current price (whether accurate or not).

[0066] The fifth attribute may also include the target product's ranking relative to at least one natural competitor. This ranking can be the result of the target product's average or cumulative rating relative to natural competitors. Typically, the digital marketplace 382 allows buyers to list natural competitors and the target product using average ratings. By doing so, the evaluation module 332 can understand the target product's ranking relative to at least one natural competitor and use this information to generate an actionable report 337.

[0067] Evaluation module 332 can also determine at least one similar attribute of a natural competitor product, which is similar to those attributes that evaluation module 332 has found for the target product. In the context of this specification, the term "natural competitor product" is intended to be understood as any product ranked on the digital marketplace 382 based on consumer reviews. Therefore, a "natural" competitor product is one that has naturally ranked based on past consumer reviews, rather than one that may have gained this position by being given "top shelf" privileges after payment. This natural ranking nature of products on the digital marketplace 382 is generally intended to provide potential consumers with evidence that others appreciate the product. A "competitor" product is any product similar to the target product but sold by a seller other than the seller of the target product. The "similarity" of the target product relative to at least one natural competitor product depends on the data obtained by text analysis module 338, and particularly on the analysis of descriptive terms 341 associated with each of these types of products. In a particular embodiment, text analysis module 338 can also obtain descriptive data associated with each of the target product and natural competitor products based on a list of them. Similarly, the digital marketplace 382 allows for the posting of product descriptions alongside each product, outlining its features, physical characteristics, and perceived advantages as a premium product. All of this is presented as textual information on the GUI to potential consumers to entice them to purchase the product. The text analysis module 338 can analyze this text and use parsing processing to extract keywords for comparing text associated with the target product with text associated with naturally competing products.

[0068] When the computing device 322 obtains attributes associated with the target product and at least one natural competitor product via the evaluation module 332, descriptive terms 341 describing these attributes can be listed for use by the filtering module 335 in some embodiments. The filtering module 335 can be used to filter the descriptive terms 341 to only those relevant descriptive terms 342 that lead to the purchase of the target product in the digital marketplace 382. For example, some descriptive terms 341 may correctly or incorrectly include the color or color scheme of the target product or natural competitor product. While some consumers may appreciate a particular color of a product, these may not be the deciding factors for attracting consumers to purchase the target product or natural competitor product. This may be especially true when the purchase history associated with the target product or natural competitor product indicates that no product of a particular color has significantly outsold another color. In this specific example, although the color of the product is a descriptive term 341 that the text analysis module 338 has parsed from the product, it may not necessarily be a relevant descriptive term 341, and such information can be filtered out by the filtering module 335 to obtain only those relevant descriptive terms 342 that are associated with any target product or naturally competing products.

[0069] In a more general example, the filtering module 335 can narrow down the range of descriptive terms 341 of interest by analyzing metrics collected about sufficiently "mature" keywords (e.g., sales > 2), as emerging keywords may lack sufficient data to influence predictions of purchases of the target product or naturally competing products. Based on the keywords used to search for the product, click-through rates and conversion rates (clicks leading to purchases) associated with any given product can be considered. In these examples, the lack of data for a particular descriptive term 341 can also filter out that particular descriptive term 341 to obtain relevant descriptive terms 342 as described herein. It should also be understood that descriptive terms 341 can be filtered by the filtering module 335 for any other reason to obtain relevant descriptive terms 342, and these other reasons are considered in this specification.

[0070] After determining the relevant descriptive terms 342, these relevant descriptive terms 342 can be sent to the comparison module 334 to compare those relevant descriptive terms 342 of the target product with those relevant descriptive terms 342 associated with at least one natural competitor product. Although this specification describes the comparison process as being performed between a single natural competitor product (e.g., "at least one") and the target product, any number of natural competitors products can be compared with the target product. In a particular example, the comparison module 334 can compare the top 10 natural competitors products with the target product.

[0071] During the execution of comparison module 334 by processor 310, descriptive terms 341 may be compared to generate a competitiveness score 339 via competitiveness score generation module 333 executed by processor 310. In some embodiments, the competitiveness score may use any processing or algorithm for defining the extent to which a target product can or cannot compete with any discovered naturally competing products.

[0072] During operation, the recommendation module 336 may receive the competitiveness score 339 and other data from the digital marketplace 382 hosted by the server 350. This other data may include revenue data (if any) associated with organic competitors and the target product. For example, if a click-through rate for any given product (e.g., the target product or an organic competitor) leads to a purchase, this conversion rate data, along with the product's pricing data, may be passed to the recommendation module 336. The recommendation module 336 can then provide recommendations describing whether the target product can (or cannot) compete with at least one organic competitor. In some implementations, a threshold competitiveness score may be set such that the report provided by the recommendation module 336 instructs the seller of the target product whether to continue selling the product on the digital marketplace 382. Alternatively, if the competitiveness score does not meet the threshold, the competitiveness score generation module 333 may not forward the competitiveness score to the recommendation module 336 to generate an actionable report 337. Alternatively or additionally, if the competitiveness score does not meet the threshold, the competitiveness score generation module 333 may pass a threshold failure signal to the recommendation module 336, indicating that the target product is not competitive. When the threshold competitiveness score is not reached, the recommendation module 336 can provide the seller with an instruction not to recommend that the seller initiate or continue to sell the target product on the digital marketplace 382.

[0073] Upon reaching the threshold competitiveness score, the recommendation module 336 can provide additional economic data describing the price point and ACoS statistics used to increase revenue. Similarly, the seller of the target product may not know what target Ad Cost of Sales (ACoS) is appropriate to achieve in order to see long-term revenue rather than short-term profit, or at what price point to sell the target product. The recommendation module 336 provides this information based on the competitiveness score 339 generated by the competitiveness score generation module 333 and the revenue data received from the digital marketplace 382. In a specific example, the revenue potential of the target product can be determined by the recommendation module 336 through calculating ad spend profit, ad spend potential, and revenue potential. Ad spend profit can be calculated by multiplying the target ACoS by the price of the target product. The target ACoS can be determined and set by the seller based on available capital, or it can be set by the seller based on the percentage of revenue gained so far from selling the target product on the digital marketplace 382 and manufacturing costs. The ad spend potential can then be calculated by multiplying the monthly opportunity unit (OU) by the spend profit margin. The monthly OU can be calculated based on the target product's click-through conversion rate, which depends on the sales volume of the target product after a buyer views the product. The revenue potential can then be calculated by multiplying OU by the price of the target product. The revenue potential of each of the target product and naturally competing products can be ranked to determine the target product's position within the digital market 382.

[0074] In some implementations, the recommendations presented by the recommendation module 336 can be refined (e.g., actionable report 337) by inputting an estimated bid amount from the digital marketplace 382 to “win” ad placement for the target product. In addition to selling products, the digital marketplace 382 can also present ads to potential buyers of one or more products. These ads can appear as banners or other sub-sections of the GUI presented to buyers, or as pop-up ads. These forms of advertising present alternative products that potential buyers are seeking to purchase in real time. These ads can present the target product and persuade buyers to buy the target product instead of competitors' products. Therefore, investment may be required to increase the number of purchases of the target product. The system and method can also present the seller of the target product in the actionable report 337: how much additional investment might be needed to win ad placement based on keywords associated with the target product and entered into searches by potential users. For example, the required investment can be calculated by multiplying the estimated bid amount by the product of the target product's click-through rate and the number of impressions (e.g., usages) of specific keywords associated with the target product and organic competing products used to search for those products. The return on investment (ROI) can then be calculated by subtracting the required investment from the return on investment and multiplying by the advertising expenditure potential. Products with no (or low) potential receive suggested outputs via the recommendation module 336 and its actionable report 337 regarding why they are not competitive or have poor conversion rates, allowing these attributes of the target product to be improved for future potential, or the money spent on selling the target product to be reallocated for other purposes.

[0075] Figure 4 This is a schematic block diagram illustrating a computing device 420 and a server 452 hosting a digital marketplace 482 containing attributes of a target product and competing products. The computing device and server can collaborate to enable the implementation of this disclosure in a client / server architecture. As described herein, an evaluation module 432 can evaluate certain attributes of the target product. The target product, as described herein, is a specific objective sought by a user of the computing device 420 (e.g., a seller) to discover the competitiveness of that product within a particular market. For example, the target product could be a product that a user is selling or wants to sell on the digital marketplace 482 hosted by the server 452. To determine the competitiveness of the target product, the evaluation module 432 can access certain data about the target product present on the server 452. The evaluation module 432 can access the data by sending a data request via a wired (e.g., via a wired transmitter / receiver 440) or wireless (e.g., via a wireless transmitter / receiver 450) connection through NID 480.

[0076] Data requests can be requests for attributes of the target product. While any number of attributes about the target product can be requested, the evaluation module 432 can request specific attributes that will be used to generate an actionable report on the target's competitiveness in the digital marketplace 482. A first attribute can describe a rating provided by at least one buyer of the target product on the digital marketplace 482. Typically, the digital marketplace 482 provides consumers with a graphical user interface (GUI) that allows them to rate products they purchase on the digital marketplace 482. In a particular implementation, consumers / buyers of the target product can use a 5-star rating system to rate the target product. A one-star rating would indicate that the consumer / buyer rated the target product poorly, while a five-star rating would indicate that the consumer / buyer rated the target product very well. Therefore, the evaluation module 432 can use each star rating, or the average of those star ratings, as input to create an actionable report.

[0077] The second attribute may include content 486 of reviews and descriptions associated with the target product. Similarly, the digital marketplace 482 typically provides a GUI that allows consumers of the target product to input text describing their experience with the product. This text may include specific positive or negative keywords describing the consumer's experience with the target product. In some implementations, using this data, the evaluation module 432 may enable the text analysis module 438 to parse each review against these keywords describing the target product. Furthermore, the text analysis module 438 may also extract keywords describing certain features of the target product. As an example, the text analysis module 438 may extract the phrase "ergonomic handle," which not only describes that the target product includes a handle but also that the handle is an "ergonomic" handle, giving the consumer who provided the review a perception that they like the fit of the target product.

[0078] The third attribute could be the number of reviews (484) associated with the target product presented on the digital marketplace (482). The number of reviews (482) can indicate the level of engagement with the target product, whether it is negative or positive. Together with the text content of these reviews, the number of reviews associated with the target product can be used to help create actionable reports based on the target product's engagement within the digital marketplace (482).

[0079] The fourth attribute could be the target product's list price of 485. While the amount charged for purchasing a product may not indicate its value, the amount charged relative to other similar competing products can indicate its value or current price (whether accurate or not).

[0080] The fifth attribute may also include the target product's ranking relative to at least one natural competitor product 487. This ranking may be the result of the target product's average or cumulative rating relative to natural competitors. Typically, the digital marketplace 382 allows buyers to list natural competitors and the target product using average ratings. By doing so, the evaluation module 432 can understand the target product's ranking relative to at least one natural competitor product and use this information to generate an actionable report.

[0081] Each of these target product attributes can be requested by computing device 420 and its evaluation module 432, and delivered by server 452 upon request. Furthermore, similar attributes associated with at least one natural competing product can also be requested by computing device 420 and sent to computing device 420. These natural product attributes may include competitor product rating 488, competitor product review count 489, competitor product price 490, competitor product content 491, and competitor product ranking 492. Each of these competing product attributes may be similar to those associated with the target product and described herein in conjunction with the target product.

[0082] Figure 5 This is a schematic block diagram illustrating a computing device 520, which includes a graphical user interface 522 for enabling the implementation of the present disclosure within a client / server architecture. The graphical user interface 522 can be used by a seller of a target product to evaluate the competitiveness of the target product as described herein. As described herein, the computing device 520 includes a filtering module 535. The filtering module 535 can be used to filter descriptive terms 541 to only those relevant descriptive terms 542 that would lead to the purchase of the target product in a digital marketplace.

[0083] The filtering module 535 may include various types of filters to filter descriptive terms 541 as relevant descriptive terms 542. These filters may include an impression filter 524, a click-through rate filter 526, and a conversion rate filter 528, each of which may cause the removal of descriptive terms 541 that do not lead to the purchase of the target product or any naturally competing product. As described herein, the impression filter 524 may be provided with the number of times an advertisement (whether a banner, button, or text link) associated with the target product or competing product has been (or will be) shown to a potential buyer and has resulted in the purchase of that product. Therefore, the impression filter 524 may filter out instances where a potential buyer did not see the advertisement or was not shown the advertisement but did make a purchase. The click-through rate filter 526 may filter out descriptive terms that, despite the wording of the advertisement, did not lead to the selection of the advertisement or the purchase of the product. The conversion rate filter 528 may filter out descriptive terms that, despite the wording of the advertisement, were selected by a potential buyer but did not lead to the purchase of the product.

[0084] By filtering descriptive terms via the filtering module 535 and its associated filters 524, 526, and 528, the GUI 522 can display to the seller of the target product the relevant descriptive terms 542 used in analyzing the competitiveness of the target product. Although Figure 5 The use of specific filters 524, 526, and 528 to filter descriptive terms 541 is shown, but this specification envisions that any criterion can be used to filter descriptive terms 541.

[0085] In some implementations, system 100 and / or any computing device or other device or system described herein may perform the methods described herein. However, the methods described herein as performed are not intended to be limiting, and any type of software executing on a controller or processor may perform the methods described herein without departing from the scope of this disclosure.

[0086] Figure 6 This is a flowchart illustrating a method 600 for evaluating a product according to the principles of this disclosure. Method 600 may begin at block 605 with the use of an evaluation module executed by a processor to evaluate attributes of a target product. As described herein, the evaluation of the target product (or any other competing product) may indicate certain attributes of the target product. While any number of attributes regarding the target product may be requested, the evaluation module may request specific attributes that will be used to generate an actionable report on the target's competitiveness in the digital market.

[0087] At box 610, method 600 may further include listing relevant descriptive terms for the target product that describe the attributes of the target product. This list of relevant descriptive terms may also be obtained by the processor of the computing device executing an evaluation module. In some embodiments, this list of relevant descriptive terms may have been generated based on a selection of all descriptive terms generated for the target product, as described herein. Some irrelevant information may exist and may be filtered out from the descriptive terms generated based on the attributes of the target product, and therefore need not appear in the operational report.

[0088] Method 600 may continue at block 615, wherein a computer networking marketplace is accessed via NID, and at least one natural competitor product matching at least one descriptive term is identified. This identification may implement an evaluation module to compare the descriptive term associated with the target product with any generated descriptive terms associated with any natural competitor product. In some embodiments, the matching process of descriptive terms associated with the target product and descriptive terms associated with natural competitors may be performed before or after the screening of descriptive terms by a screening module as described herein. When performed before screening, more natural competitors may be matched; when performed after screening, relatively fewer natural competitors may be matched due to a smaller list of relevant descriptive terms.

[0089] Method 600 may further include, at block 620, comparing descriptive terms of the target product with descriptive terms associated with at least one natural competing product to generate a competitiveness score. This may be done via executing a comparison module 620 executed by a processor. During the execution of the comparison module by the processor, descriptive terms may be compared to generate a competitiveness score using a competitiveness score generation module executed by the processor. In some embodiments, the competitiveness score may use any processing or algorithm for defining the degree to which the target product can or cannot compete with any discovered natural competing products.

[0090] At box 625, method 600 may further include generating an actionable report describing the expected performance of the target product relative to at least one natural competitor in the computer networking marketplace. The actionable report may be generated via a recommendation module executed by a processor. During operation, the recommendation module may receive the competitiveness score and other data from the server-hosted digital marketplace. This other data may include revenue data (if any) associated with the natural competitors and the target product. For example, conversion rate data and product pricing data may be passed to the recommendation module if a click-through rate for any given product (e.g., the target product or a natural competitor) leads to a purchase. The recommendation module can then provide recommendations describing whether the target product can (or cannot) compete with at least one natural competitor. In some implementations, a threshold competitiveness score may be set such that the report provided by recommendation module 336 instructs the seller of the target product whether to continue selling the product in the digital marketplace. Alternatively, if the competitiveness score does not meet the threshold, the competitiveness score generation module may not forward the competitiveness score to the recommendation module to generate the actionable report. When the threshold competitiveness score is not met, the recommendation module only provides the seller with an instruction not to recommend that the seller initiate or continue selling the target product in the digital marketplace.

[0091] Upon reaching a threshold competitiveness score, the recommendation module can provide additional economic data describing the price point and ACoS statistics used to increase revenue. Similarly, the seller of the target product may not know what target ACoS is appropriate to achieve in order to see long-term revenue rather than short-term profits, or at what price to sell the target product. The recommendation module provides this information based on the competitiveness score generated by the competitiveness score generation module and revenue data received from the digital marketplace. In a specific example, the revenue potential of the target product can be determined by the recommendation module through calculations of ad spend profit, ad spend potential, and revenue potential. Ad spend profit can be calculated by multiplying the target ACoS by the price of the target product. The target ACoS can be determined and set by the seller based on available capital, or it can be set by the seller based on the percentage of revenue gained so far from selling the target product on the digital marketplace and manufacturing costs. The ad spend potential can then be calculated by multiplying the monthly opportunity unit (OU) by the spend profit margin. The monthly OU can be calculated based on the target product's click-through conversion rate, which is the result of sales of the target product after a buyer views the product. The revenue potential can then be calculated by multiplying the OU by the price of the target product. It is possible to rank the revenue potential of each of the target product and naturally competing products to determine the target product's position within the digital market.

[0092] At this point, method 600 can end.

[0093] Figure 7 This is a flowchart illustrating a method 700 for providing a competitive evaluation of a target product in the market according to the principles of this disclosure. Here, method 700 may begin at block 705 by evaluating the target product to determine its attributes. In some embodiments, the evaluation may be performed via the execution of an evaluation module. In some embodiments, the evaluation may be performed by requesting descriptive terms about the target product at a GUI. Additionally or alternatively, the evaluation may be performed by the evaluation module accessing a digital marketplace to retrieve descriptive terms via a text analysis module, as described herein. Additionally or alternatively, certain input devices, such as digital camera devices, may be used to image the target product and infer certain characteristics of the product, such as size, color, texture, etc.

[0094] Method 700 may continue at block 710, wherein, during processor execution, a digital marketplace is accessed to identify at least one natural competitor product of the target product. In this embodiment, the evaluation module may access certain data about the target product, such as descriptive terms, and cross-reference those descriptive terms to determine whether at least one descriptive term matches any competing product listed in the digital marketplace.

[0095] At box 715, method 700 may include calculating a competitiveness score relating to the ability of a target product to compete with at least one natural competitor. This processing may be performed when a competitiveness score generator is executed by a processor of a computing device accessing a digital marketplace. In some embodiments, the competitiveness score may use any processing or algorithm for defining the degree to which the target product can or cannot compete with any discovered natural competitors.

[0096] Method 700 may further include generating an actionable report at box 720 based on the target product's ability to compete with at least one natural competitor. During operation, a recommendation module executed by the processor may receive a competitiveness score and other data from a digital marketplace hosted on a server. This other data may include revenue data (if any) associated with the natural competitors and the target product. For example, if a click-through rate for any given product (e.g., the target product or a natural competitor) leads to a purchase, this conversion rate data, along with the product's pricing data, may be passed to the recommendation module. The recommendation module can then provide recommendations describing whether the target product can (or cannot) compete with at least one natural competitor. In some implementations, a threshold competitiveness score may be set such that the report provided by the recommendation module instructs the seller of the target product whether to continue selling the product on the digital marketplace. Alternatively, if the competitiveness score does not meet the threshold, the competitiveness score generation module may not forward the competitiveness score to the recommendation module to generate an actionable report. When the threshold competitiveness score is not met, the recommendation module only provides the seller with an instruction not to recommend that the seller initiate or continue selling the target product on the digital marketplace. At this point, method 700 may terminate.

[0097] Figure 8 This is a schematic block diagram illustrating a computing device 822 and a server 852 operating a digital marketplace 882, which can cooperate to enable the implementation of this disclosure in a client / server architecture. (Except as in the following...) Figures 1 to 7 The described report provides an actionable overview of the target's competitiveness in the digital market 882. Figure 3 In addition to (337), the computing device 822 may also describe an actionable report 837 describing the sustainable and feasible growth of products over time on an e-commerce platform (e.g., digital marketplace 882), and provide a winning report 804 describing the probability of winning each search term (e.g., associating the target product with the search term) at any given point in time and the estimated cost of winning those search terms. In some implementations, the actionable report 837 and the winning report 804 may provide users with instructions on how to optimize advertising and search engine implementation to increase revenue.

[0098] As described herein, computing device 822 may include processor 810, memory 820, user input 860, user output 870, and data memory 830, operating similarly to, for example, combined with Figure 2A and Figure 2B The data storage 830 may include those modules described herein, including comparison module 834 and receiving module 899.

[0099] The described computing device 822 may include, as in conjunction with herein, Figure 3 This refers to any module described, data storage 830, or data stored on a computer. In the embodiments described herein, similar combinations can be used. Figure 3 The comparison module 334 described is used to provide an operational report 837. Although these modules (e.g., comparison module 834) can be similar to... Figure 3 The modules described in [the document], but Figure 8 The modules within can perform additional and different processing as described herein in order to provide actionable reports indicating the status of optimized advertising and search engine implementation 837.

[0100] In some implementations, computing device 822 can initially identify any competing products that compete with the target product at any point in time. Computing device 822 can do this by accessing a search engine 894 associated with digital marketplace 882 via processor 810 and NID 880 of computing device 822. When accessing search engine 894, processor 810 can retrieve data describing the frequency of occurrence of one or more search terms associated with the target product. Additionally, processor 810 can obtain data related to the ranking of those search terms. This data can describe situations where the target product and any competing products are associated with the same search terms. Furthermore, this data can describe the degree of similarity in the ranking of search terms associated with the target product and each competing product. For example, in the case of the target product being athletic shoes, some relevant search terms may include running, hiking, basketball, tennis, sole, shoelaces, and marathon, as well as other potential terms associated with the target product, athletic shoes. The data may also include which competing products also have similar rankings on these search terms. For example, a product that matches 9 out of 10 search terms "ranks higher" than a product that matches 4 out of 10 search terms for the target product.

[0101] In a particular embodiment, processor 810 may use search query websites such as Google®, Trends®, etc., to access the data. Processor 810 may use these types of websites to access multiple search queries targeting specific terms associated with any target product and any number of competing products. Processor 810 may access search query websites to automatically access search query queries in order to obtain the data used herein by computing device 822. While specific search query websites are contemplated herein, this specification also contemplates other search query databases that processor 810 may access, whether or not those databases are accessible to a user via a website.

[0102] The computing device 822 also includes a machine learning module 896. The machine learning module 896 can construct multiple mathematical models that provide a competition set report 898 describing the set of competing products that compete with the target product. Like each machine learning module 896, the machine learning module 896 can be "trained" using multiple sets of search terms and rankings for the target product and multiple sets of search terms and rankings for competing products as input. Similarly, the multiple sets of search terms and rankings for the target product and multiple sets of search terms and rankings for competing products can be accessed by the processor 810 via a specific search query website or database.

[0103] In some embodiments, the machine learning module 896 may determine such relevance when executed by the processor 810. In other embodiments, the machine learning module 896 may determine such relevance based on any machine learning or neural network methods known in the art or developed in the future. In particular embodiments, the machine learning module 896 may implement unsupervised learning clustering techniques. For example, in some embodiments, the machine learning module may use a hierarchical neural network topology to model the relationship between multiple sets of target product search terms and rankings and multiple sets of competing product search terms and rankings. In some embodiments, such a neural network may include an input layer (e.g., multiple sets of target product search terms and rankings and multiple sets of competing product search terms and rankings) and an output layer, the input layer including a known, recorded set of values ​​for each of these parameters, settings, indicators, and data metrics, and the output layer including a predicted optimal competing set report 898 based on the known, recorded set of values ​​in the input layer. In some embodiments, the machine learning module 896 may propagate the input through the various layers of the neural network to predict or forecast the optimal competing set report 898 based on known, recorded search term metrics and compare these predicted values ​​with the optimal search terms to be presented in the competing set report 898. In some implementations, using a backpropagation method, the machine learning module 896 can then use the difference between the predicted value and the known optimal value to adjust the weight matrix of the neural network, which describes how changes in each of the search term data metrics might affect the optimal search term to be presented in the competitive set report 898.

[0104] Using the output layer, computing device 822 can provide learned competing search terms, which are determined as optimal search terms (if any search terms have been specified) and are based on similar and frequent search terms detected at search engine 894 of digital marketplace 882 during use of computing device 822. These learned optimal search terms can be suggested to the user or executed automatically. In some implementations, the suggestions may include indicators and may be graphically displayed at a user interface (e.g., a graphical user interface) for user approval before further processing by processor 810 of computing device 822.

[0105] Figure 9 The image shows an example representation of a graph. This example graph can indicate the position of each search term for competing products relative to the target product based on frequency. Figure 9 Each point (e.g., a circle) on the graph represents a search term. Each representative search term is... Figure 9 The graph is arranged at points defining the frequency with which a term appears alongside search terms related to the target product, and at positions indicating similarity or dissimilarity of the search term relative to search terms associated with the target product. In this example graph, the further right any given search term is, the more similar the competitor's search term is to the target product's. Additionally, the further left any given search term is, the less similar the competitor's search term is to the target product's. Furthermore, the closer any given search term is to the top of the graph, the more generic the search term is relative to the target product, while the closer any given search term is to the bottom of the graph, the more niche the search term is relative to the target product. In some implementations, it may be most desirable to have a target product whose associated search terms are more generic and similar to those of competing products. This indicates that the target product is competing with relatively well-known competitors. The processing described herein can help provide users with reports indicating how to adjust ad revenue to focus on search terms that are more generic and similar to those of competing products.

[0106] In some implementations, the machine learning module 896 can repeatedly perform forward and backward propagation using different input node values ​​to fine-tune any weighted or unweighted matrix. In this way, in some implementations, the machine learning module 896 can adaptively learn how changes in multiple sets of target product search terms and rankings, as well as multiple sets of competitor product search terms and rankings, can affect the data reflected in the competition set report 898. In such implementations, the weight matrices associated with the layers of the neural network model can mathematically describe these relevances for a single target product. In some implementations, the neural network model (including the specification of node values ​​in the input layers and the number of layers) along with the weight matrices associated with each layer can form a trained machine learning classifier, algorithm, or mathematical model for generating any competition set report 898 as described herein.

[0107] As described herein, the output from the currently trained machine learning module 896 is a competition set report 898. Using the competition set report 898, computing device 822 can determine, via processor 810 and NID 880, the current performance of search terms most relevant to the target product and to the competition set defined in the competition set report 898. In this process, two variables are identified: the frequency of a word's general occurrence in searches (e.g., total search volume, i.e., the number of times people search for the word per day) and the frequency of the word's occurrence in searches associated with the competition set report 898. More specifically, in some embodiments, the most general and similar search terms found among the target product and each competing product are provided to comparison module 834, which searches for those search terms defined in the competition set report 898 via processor 810 executed at search engine 894. In this process, processor 810 can access search engine 894 or any other search engine at digital marketplace 882 and obtain search term metadata describing the current performance of each search term among the most relevant search terms in the competition set defined in competition set report 898, which is related to the target product. Comparison module 834 can compare these most relevant search terms from competition set report 898 and provide this data to the user in the form of actionable report 837. In some embodiments, the data describing the most relevant search terms in the competition set in actionable report 837, related to the target product, can be provided to the user via a graphical representation.

[0108] Figure 10 The image shows an example graphical representation of the current performance of search terms related to the target product. For example... Figure 10As shown, the closer a search term (e.g., indicated by a circle) is to the right of the graph, the higher its search volume or the more frequently it appears compared to other search terms, indicating a relatively higher relevance to competing products. Additionally, the closer a search term is to the left of the graph, the lower its search volume or the less frequently it appears compared to other search terms, indicating a relatively lower relevance to competing products. Furthermore, the closer a search term is to the top of the graph, the higher its relevance compared to other search terms, indicating a relatively higher relevance to competing products. Moreover, the lower a search term is on the graph, the lower its relevance compared to other search terms, indicating a relatively lower relevance to competing products. The most frequently searched and most relevant terms can also be provided to the comparison module 834 and used to further define the sustainable and feasible growth of the target product over time in, for example, the digital market 882.

[0109] With such Figure 10 The most relevant and frequent search terms are identified and presented in the actionable report 837. The computing device 822 can also quantify the opportunity that these search terms, when associated with the target product, will increase revenue and profit margin on the target product. In some implementations, the processor 810 can execute the revenue module 899 to receive the relevant and most frequent search terms from the actionable report 837 and provide output to the user in the form of an increased revenue metric. The revenue module can calculate the increased revenue using the following formula when executed by the processor 810:

[0110] Increased revenue = Number of impressions Click-through rate Conversion rate Number of orders Price Equation 1

[0111] In some implementations, within the context of Equation 1, the number of impressions can be defined as the search volume for each of the most relevant and frequent search terms. In some implementations, the number of impressions can be measured by the number of times an advertisement associated with the target product is presented to any given user during or after the most relevant and frequent search terms are entered into search engine 894. Processor 810 can retrieve this data by accessing a specific database or by accessing a search query website as described herein.

[0112] In some implementations, the click-through rate (CTR) in Equation 1 can be defined as: an estimate of the probability of receiving a click associated with the ranking of each of the most relevant and frequent search terms provided in the actionable report 837 along a curve. For example, the ranking can be set to include the first CTR (e.g., 20% of clicks), the second CTR (14% of clicks), up to the tenth CTR (6% of clicks), and extending to any number of the most relevant and frequent search terms. The processor 810 can retrieve this data by accessing a specific database or by accessing a search query website as described herein.

[0113] In some implementations, the conversion rate in Equation 1 can be defined as the percentage of most relevant and frequent search terms that are clicked, associated with the target product, and converted into sales (e.g., resulting in sales of the target product). Processor 810 can retrieve this data by accessing a specific database or by accessing a search query website as described herein.

[0114] In some implementations, the basket size can be defined as the number of items purchased in each conversion. In some implementations, this number can be the average of multiple purchases. For example, if multiple conversions have been detected, processor 810 can calculate how many target products were purchased in any given time (e.g., the number placed in the "shopping cart" to be purchased at digital marketplace 882). This value can be at least equal to 1 or greater. Similarly, processor 810 can retrieve this data by accessing a specific database or by accessing a search query website as described herein.

[0115] In some implementations, the price of the target product may be the manufacturer's suggested retail price. In some implementations, the quantitative value of the price in Equation 1 is the average price of the target product or other branded products resulting from the purchase across any number of digital marketplaces 882, minus any discounts or promotions associated with those sales. The processor 810 may retrieve this data by accessing a specific database, such as an access search query website as described herein, or by accessing sales data from a database maintained by the manufacturer of the target product.

[0116] In some implementations, any one of the impression count, click-through rate, conversion rate, order volume, and price values ​​in Equation 1 can be enhanced by a weight value. In this implementation, the weight value can either strengthen or weaken the effect of any of these values ​​in Equation 1 to better determine the revenue increase or opportunity for the seller of the target product, thereby increasing that revenue. Because actual real-time data is used in Equation 1, the seller of the target product or the user of computing device 822 can know in real time whether to leverage the increased product views or sales to increase the target product's visibility relative to any competitor's products.

[0117] In some implementations, the value associated with the click-through rate (CTR) in Equation 1 can significantly alter the decision of the user of computing device 822 to take action (e.g., to provide more ads supporting the target product). In some implementations, the CTR (which is associated with improving the search ranking of the target product from its current position on a search term to its potential ranking position on a search phrase) can be weighted to accommodate situations where the importance of this value increases. For example, a given search term that could improve the organic search ranking of any search term from 20th to 5th would increase the CTR by an estimated 3x. The ranking improvement could also be partly due to an increase in impressions, particularly if the target product, which previously had no ranking on a search term, achieves a search ranking of 10th. In this example, this would increase the number of clicks from zero (due to zero impressions) to the estimated number of clicks associated with a 10th ranking on that search term. As output, processor 810 can provide an increased revenue report 802 via revenue module 899, which describes how, if any, revenue related to the sale of the target product increased.

[0118] In some cases, certain search terms may not be applicable to the target product, but could potentially increase revenue if applicable. In the context of advertising, these currently unapplicable search terms can be referred to as "unavailable." These unavailable search terms may be those that, for some reason, were initially irrelevant to the target product or have not yet been associated with it, because the platform data associated with the digital marketplace 882 lacks data related to the target product. In some implementations, the machine learning module 896 may also be trained and used to receive data related to the characteristics of the target product, the target product's current competitors, and the current state of the e-commerce search term algorithm to determine the "winning probability" of search terms. The winning probability of a search term can be defined as: the probability of winning each search term (e.g., associating the target product with the search term) at any given point in time, and the estimated cost of winning those search terms.

[0119] Machine learning module 896 can be trained with winning inputs as described herein to provide winning report 804. Some of the inputs to this model include any number of inputs, and the description of certain types of inputs does not imply a limitation on the breadth of inputs for obtaining winning reports in machine learning module 896, and this specification considers these additional and different inputs. By way of example, inputs may include the current and historical prices of both the target product and competing products. This historical pricing can be retrieved from one or more digital marketplaces 882 via the execution of processor 810 and NID 880 as described herein. In this particular example, processor 810 may enable NID 880 to access one or more digital marketplaces 882 via a wired (wired transmitter / receiver 840) or wireless (wireless transmitter / receiver 850) connection, locate instances of the target product and competing products being sold, and retrieve their historical pricing values.

[0120] Another input to the machine learning module 896 may include current review ratings and review counts, as well as historical review ratings and review counts, associated with the target product and competing products. This review rating and review count data can be retrieved from one or more digital marketplaces 882 via the execution of processor 810 and NID 880 as described herein. Digital marketplaces 882 typically provide a GUI that allows consumers of the target and competing products to input text describing their experience with the target and competing products, as well as ranking assessments of those products in the form of a digital rating system or a star rating system. In this particular example, processor 810 may enable NID 880 to access one or more digital marketplaces 882 via a wired or wireless connection and find review ratings and review counts associated with the target and competing products being sold, and provide this review rating and review count data to the machine learning module 896.

[0121] Another input to the machine learning module 896 can include content similarity scores for any search terms related to the target product and competing products. In some implementations, these scores can be generated based on the provided data. Figure 9 In, for example, any given search term in Figure 9 The further to the right on the graph, the more similar the search terms for competing products are to those for the target product. In a specific example, Figure 9 The x-axis (bottom) of the graph or its associated data can be used to determine the similarity score. For example... Figure 9 As shown, the similarity score can be based on Figure 9The similarity score is a positive or negative score for the numerical ranking on the x-axis. In this example, the similarity score can be a positive or negative weight reflected in the winning report 804 provided by the processor 810 when executing the machine learning module 896. In this implementation, the processor 810 can again enable the NID 880 to access one or more digital marketplaces 882 via a wired or wireless connection and retrieve multiple sets of target product search terms and rankings associated with the target product and competing products being sold, as well as multiple sets of competing product search terms and rankings. This data is then provided to the machine learning module 896.

[0122] Furthermore, other inputs to the machine learning module 896 may include platform-specific information, such as the average best-selling rank (BSR) of any given digital marketplace 882 associated with the target product and any number of competing products. The BSR can vary across any given digital marketplace 882, but these rankings can be averaged across multiple digital marketplaces 882 to obtain the value. In this implementation, the processor 810 may again enable the NID 880 to access one or more digital marketplaces 882 via a wired or wireless connection and retrieve the BSR data. This data is then provided to the machine learning module 896.

[0123] Other inputs to the machine learning module 896 can include projected search volume for search terms and click distribution. Combining this type of data provided to the machine learning module 896, it is possible to create... Figure 10 The expected search volume for the search terms is retrieved from the data in the graph. This data, describing the frequency of any given search term appearing in searches associated with the target product and competing products, can be accessed by processor 810 and provided as input to machine learning module 896. Additionally, any click distribution describing the number of clicks received for any given search term can be accessed by processor 810 and NID 880 at search engine 894 of digital marketplace 882.

[0124] Further inputs to the machine learning module 896 may include historical changes in the ranking of search terms related to the target product and the search phrase product. At any given time, the search engine 894 may have fluctuations in the content being searched on the internet. These search terms may be ranked, and their historical rankings may change over time based on numerous social, political, environmental, and economic factors. This historical data can be retrieved from the search engine 894 by the processor 810 and NID 880 and provided to the machine learning module 896.

[0125] Another example input to the machine learning module 896 may include targeted advertising spending associated with search terms related to the target product. This data can be maintained on any database accessible to the processor 810 of the computing device 822. In a particular embodiment, this data describing the targeted advertising spending associated with search terms related to the target product may be maintained by the seller of the target product on a private database, and the user of the computing device 822 may have secure access to that database. This type of data may also be provided to the machine learning module 896.

[0126] Utilizing all the data from these different types of data obtained by processor 810 via NID 880, machine learning module 896 can construct multiple mathematical models that provide a winning report 804 describing the probability of winning each search term (e.g., associating a target product with a search term) and the estimated cost of winning those search terms at any given time. As with each machine learning module 896, machine learning module 896 can be “trained” using the winning factors described herein. In some embodiments, machine learning module 896 can implement one or more nonparametric and parametric learning techniques. For example, in some embodiments, the machine learning module can use a hierarchical neural network topology to model the relationships between each of the multiple sets of winning ability factors. In some embodiments, such a neural network can include an input layer (e.g., winning factors) and an output layer, the input layer including a known, recorded set of values ​​for each of these parameters, settings, indicators, and data metrics, and the output layer including a predicted winning report 804 based on the known, recorded values ​​in the input layer. In some implementations, the machine learning module 896 can propagate the input through the layers of a neural network to predict or forecast the optimal winning probability of a search term based on known, recorded search term metrics, and compare these predictions with the optimal search term to be presented in the winning report 804. In some implementations, using backpropagation, the machine learning module 896 can then use the difference between the predictions and the known optimal values ​​to adjust the weight matrix of the neural network, which describes how changes in each of the search term data metrics might affect the optimal search term to be presented in the winning report 804.

[0127] Using the output layer, computing device 822 can provide learned competing search terms, which are determined as optimal search terms (if any search terms have been specified) and are based on winning search terms detected at search engine 894 of digital marketplace 882 or other databases during use of computing device 822. These learned optimal search terms can be suggested to the user or implemented automatically. In some implementations, the suggestions may include indicators and may be graphically displayed at the user interface for user approval before implementing further processing by processor 810 of computing device 822.

[0128] In some implementations, the machine learning module 896 can repeatedly perform forward and backward propagation using different input node values ​​to fine-tune any weighted or unweighted matrix. In some implementations, the machine learning module 896 can adaptively learn how changes in winning factors can affect the data reflected in the winning report 804 in such a way. In such implementations, the weight matrices associated with the layers of the neural network model can mathematically describe these correlations for a single target product. In some implementations, the neural network model (including the specification of node values ​​in the input layers and the number of layers) along with the weight matrices associated with each layer can form a trained machine learning classifier, algorithm, or mathematical model for generating any winning report 804 as described herein.

[0129] As described herein, the output from the currently trained machine learning module 896 is a winning report 804. Using the winning report 804, computing device 822 can determine the probability of achieving the desired change in revenue based on the required investment, via processor 810 and NID 880. In some implementations, the required investment can be calculated using the following equation:

[0130] Required investment = Expected bid (Number of times displayed) Click pass rate) Equation 2

[0131] The return on investment (ROI) can then be calculated using the following equation:

[0132] ROI = Increased Revenue (Based on the estimated duration of the required investment) Equation 3

[0133] Using Equations 2 and 3, users can then prioritize advertising and search engine optimization (SEO) actions for target products with search terms that have a high return on investment (ROI). In this way, computing device 822 can execute machine learning module 896 to achieve a second objective: determining the "winning potential" of search terms while applying additional funds to advertising and SEO.

[0134] In some implementations, the processor 810 may also perform advertising spend profit calculations, advertising spend potential calculations, and revenue potential calculations to specifically determine how much additional advertising funding needs to be applied to the target product. Similarly, advertising spend profit can be calculated by multiplying the target ACoS by the price of the target product. The target ACoS can be determined and set by the seller based on available capital, or it can be set by the seller based on the percentage of revenue gained to date from sales of the target product on digital marketplace 382 and manufacturing costs. Advertising spend potential can then be calculated by multiplying the monthly opportunity unit (OU) by the spend profit margin. The monthly OU can be calculated based on the target product's click-through rate, which depends on sales of the target product after a buyer views the product. Revenue potential can then be calculated by multiplying the OU by the price of the target product. The revenue potential of each of the target products can be ranked to determine the target product's position within digital marketplace 882. The search terms presented in the winning report 804 can be sorted by revenue potential to identify the best opportunities for revenue growth for the target products. To refine the recommendations, the process can continue to input estimated bid amounts from digital marketplace 882 required to win ad placements for these keywords. In this way, the execution of processor 810 can initiate these calculations to predict the number of clicks needed and the necessary costs to achieve potential growth. The equation for performing this calculation can be found in Equation 2 of this paper.

[0135] The ROI can be further calculated using the following equation:

[0136] ROI = Advertising Spending Potential (Return on Investment - Required Investment) Equation 4

[0137] Because this process utilizes both advertising and search engine optimization techniques to target high-win-rate terms, increasing associated impressions, clicks, and conversion rates, the processing applied to the target product can continuously adapt and adjust. When the target product succeeds on new search terms, the set of competing products defined in Competitive Set Report 898 shifts to comparisons with larger and more mainstream competing products. As the set of competing products defined in Competitive Set Report 898 shifts, the set of competing search terms also shifts. As reviews, search terms, seller rankings, and other attributes change, so does the win rate of each search term and the associated required investment. With the shift in win rates, new search terms are prioritized, and the process iterates continuously to ensure that revenue associated with the target product increases proportionally.

[0138] In some implementations, computing device 322 (e.g., and / or system 100, computing device 420, computing device 520, and / or computing device 822) can be configured to generate an improved ROAS value based on short-term and long-term factors to analyze advertising effectiveness. For example, computing device 322 can receive and / or calculate a first ROAS value for the target product (e.g., by dividing the advertised sales value of the target product by the advertised expenditure value of the target product). Computing device 322 can generate a second ROAS value for the target product by removing sales that the target product might have earned in the absence of advertising from the first ROAS value. For example, computing device 322 uses the click share (e.g., organic click share) of each keyword associated with the target product to determine which advertised sales to remove (e.g., by modeling the probability that sales would have been completed anyway using experimental and non-experimental methods). For example, if computing device 322 determines that the click share is 50%, then computing device 322 reduces the sales by 50%. In another example, if computing device 322 determines that the click share is 1%, then computing device 322 reduces the sales by 1%.

[0139] The computing device 322 can generate a third ROAS value for the target product by removing the advertised sales revenue of the returned products. For example, the computing device 322 can determine the return rate value of a first product (e.g., indicating the product value of the target product returned after the initial sale) and can generate a third ROAS value by subtracting the return rate value from a second ROAS value. In some embodiments, the return rate value may correspond to or be associated with the value of returned products of the target product, including lost revenue, product loss, and / or any other suitable costs associated with the returned products.

[0140] The computing device 322 can generate a fourth ROAS value for the target product by adding the customer lifetime revenue acquired through advertising to the number of advertising sales. For example, the computing device 322 can determine the lifetime revenue value by multiplying the percentage of advertising customers who are first-time buyers of the advertised brand by the lifetime value of shoppers who purchase the advertised product. The computing device 322 can generate a fourth ROAS value for the target product by adding the lifetime revenue value to the third ROAS value of the target product.

[0141] The computing device 322 can generate a fifth ROAS value for the target product by adding any attributable movement in the organic ranking to the fourth ROAS value of the target product. For example, the computing device 322 can identify the share of clicks for each keyword targeting the target product before and after ad serving associated with the target product. If the computing device 322 determines that the target product's ranking has improved over time (e.g., and possibly directly due to ad traffic and purchase results), the computing device 322 can determine that the improvement is attributable to advertising, and the computing device 322 generates the fifth ROAS value by adding the organic ranking value (e.g., the additional clicks and sales corresponding to the improved ranking) to the fourth ROAS value of the target product.

[0142] The computing device 322 can generate the sixth ROAS value of the target product by dividing the fifth ROAS value of the target product by the advertising expenditure value of the target product.

[0143] In some implementations, computing device 322 may generate an improved ROAS value for the target product based on the following:

[0144] ((Platform-reported revenue - (Platform-reported return value + Estimated unreported return value)) Encroachment rate + (new brand customers) Encroachment rate Future lifetime customer value) + Impact of improved organic ranking + (Review probability) Incremental number of comments + probability of UGC (Incremental UGC count) Exponential decay value / Advertising expenditure

[0145] The platform-reported revenue and / or platform-reported ad spend are received from the marketplace (e.g., or the platform) and indicate the ad spend revenue for the target product. Cannibalization rate is a deduction in ROAS and indicates the percentage of sales that would have been generated without advertising (e.g., because other ads for other products associated with the target product might appear alongside it in the marketplace). Cannibalization can be detected through experimental and non-experimental sources. Experimental sources can include: (i) A / B testing (e.g., intentional tests to evaluate the incremental effect of advertising, where inputs include the product and ad timing, and outputs include incremental sales), where the A / B test output is generalized to a larger context value; and (ii) natural experiments (e.g., discovering naturally occurring scenarios similar to A / B tests that were not intentionally set up for testing purposes, and / or scenarios where the advertising budget changes independently of performance, such as when a brand determines to abruptly stop or start advertising over a period of time), where the results of natural experiment outputs are utilized in a manner similar to A / B testing. Non-experimental sources (e.g., indicators of whether changes to relevant inputs such as advertising produce outputs that follow the expected modeling path, such as total sales) may include observed effects based on relevant cannibalizing factors (e.g., product and brand placements in search, market share, etc.) and may include: (i) organic ranking by brand, by product, and by search term (e.g., the position of a product in the organic rankings on a marketplace page); (ii) traffic and click estimation models by search term, used to model the frequency with which search terms are searched daily and the total number of times users click on products across the entire page, using platform data; (iii) data from the platform, where available, that informs the number of clicks received by products and / or brands on search terms; and (iv) clicks per location on the page, using ranking positions from click estimations and, where available, creating models that estimate the number of clicks received per location on the page.

[0146] In some implementations, cannibalization can also be based on the brand's position on the page and experimental and non-experimental results for each brand and / or keyword pair. The computing device 322 can estimate the total number of clicks a brand might receive without advertising and compare it to the total number of clicks with advertising, according to the following:

[0147]

[0148] Return value is a short-term deduction from ROAS. Return value is deducted from advertised sales. The computing device 322 may determine the return value based on: (i) the return value and associated costs reported by the platform (e.g., indicating that previous advertised sales have been returned, which can be deducted directly from the advertised sales); and (ii) the return value not reported by the platform (e.g., the platform may not report whether advertised sales have been returned, or the platform only reports advertised sales returned within a limited time period (e.g., three days). If the platform does not report the return value, the computing device 322 may use other data sources (e.g., the general return rate on the platform and extrapolate it to the advertised data) to estimate the return value. If the platform only reports within a limited period, the computing device 322 may utilize the platform's advertised return rate within the period of data provided and extrapolate to the period when the platform does not report (e.g., if the platform only reports returns for the first seven days, the computing device 322 may combine the reported return value with the total return rate to estimate the overall return rate for several weeks following the purchase). The computing device 322 may combine the total return value with the period of advertised sales returns to estimate subsequent return values.

[0149] Lifetime Customer Value (LTV) is a long-term increase in ROA and indicates the long-term value of newly acquired customers. Computing device 322 can calculate LTV as follows:

[0150] New brand customers Encroachment rate Future Lifetime Value

[0151] New brand customers include those making their first purchase from the brand. Computing device 322 can receive data from the platform and / or use external data sources and general traffic data to infer and generalize potential new brand customers. Future lifetime value is a value indicating repeat purchase rate and expected revenue over an estimated period, which can be modeled as a decay function created using a nonparametric distribution via a bootstrapping approach. Computing device 322 can combine repeat purchase rate with repeat purchase revenue and can accordingly discount future cash flows to generate total future lifetime value. Computing device 322 can receive repeat purchase rate, or it can use a combination of new brand customers and sales history to infer a customer's repeat purchase rate. Computing device 322 can receive repeat purchase revenue from the platform, which can indicate the "number of orders" from repeat purchases or the total revenue per purchase. Discounting cash flows indicates the value of calculated future cash flows. Computing device 322 can discount the calculated future cash flows to generate the net present value of future new customer revenue.

[0152] Organic ranking is a long-term driver of ROAS (Return on Ads). The search engine algorithm of an ad exposure platform determines whether a target product is relevant (e.g., if the search engine finds the product relevant, it can improve the keyword's organic ranking position). The value of organic ranking can be defined as follows:

[0153]

[0154] Where: i = advertising day, n = complete attribution period (e.g., the next 365 days). =Keyword conversion rates estimated using historical averages and / or machine learning models, along with clicks with and without ads, are variables that compare a page's historical position to a hypothetical position on a page without ads, using historical platform data. Organic position data can correspond to the platform's organic ranking for a search term before, during, and after advertising. The value of organic position can be based on a cannibalized value that provides incremental value to the position on the page.

[0155] Reviews and / or other user-generated content are long-term adders to ROAS. Review data and / or other user-generated content data can create incremental value for the product. Advertising accelerates the process of acquiring new reviews to reach a threshold where the original number of reviews no longer serves as a warning sign for consumers. Computing device 322 can determine the value of a review based on: (i) an exponential decay function of the review value (e.g., which may include a model for determining the impact of an increasing number of reviews (e.g., 1→2, 13→14, 230415→230416) on clicks and conversion rates), and (ii) the probability of generating a review (e.g., where computing device 332 determines the probability of a sale generating a review when requested from historical platform data).

[0156] In some implementations, computing device 322 may receive a first product value for a target product. Computing device 322 may determine cannibalization value for the target product based on at least one of experimental data and non-experimental data. Experimental data may be associated with: at least one intentional experiment of advertising data configured to identify the incremental effect of advertising on a product associated with a computer network market; at least one natural experiment of advertising data configured to discover naturally occurring advertising scenarios for a product associated with a computer network market; other suitable experiments; or combinations thereof. Non-experimental data may be associated at least with the product position and market share of the product associated with the computer network market.

[0157] The computing device 322 can generate the value of a second product by adjusting the value of a first product using the erosion value. For example, the computing device 322 can generate the value of a second product by subtracting the erosion value from the value of the first product. The computing device 322 can determine the return value for a target product based on return data.

[0158] The computing device 322 can generate a third product value by adjusting the value of the second product using the return value. For example, the computing device 322 can generate a third product value by subtracting the return value from the value of the second product. The computing device 322 can determine the lifetime customer value for the target product based at least on erosion value, new customer data provided by the computer networking market, future lifetime value, and / or any other suitable value.

[0159] Computing device 322 can generate a fourth product value by adjusting the value of a third product using lifetime customer value. For example, computing device 322 can generate a fourth product value by adding the lifetime customer value to the value of the third product. Computing device 322 can determine the natural ranking value for a target product based on natural location data and the value of natural location data.

[0160] Computing device 322 can generate a fifth product value by adjusting the value of a fourth product using the natural ranking value. For example, computing device 322 can generate a fifth product value by adding the natural ranking value to the value of the fourth product. Computing device 322 can generate an actionable report describing the effectiveness of the target product in the computer networking market, based at least on the value of the fifth product.

[0161] Figure 11 This is a flowchart illustrating, in general, a method 1100 for calculating an improved ROAS value for a target product according to the principles of this disclosure. At 1102, method 1100 receives a first product value for the target product.

[0162] At 1104, method 1100 determines the cannibalization value for the target product based on at least one of experimental data and non-experimental data.

[0163] At 1106, method 1100 generates the value of the second product by adjusting the value of the first product through nibbling value.

[0164] At 1108, Method 1100 uses return data to determine the return value for the target product.

[0165] At 1110, method 1100 generates the value of the third product by adjusting the value of the second product using the value of returned goods.

[0166] At 1112, Method 1100 determines lifetime customer value for a target product based at least on erosion value.

[0167] At 1114, Method 1100 uses lifetime customer value to adjust the third product value to generate the fourth product value.

[0168] At 1116, Method 1100 determines the natural ranking value for a target product based on natural location data and the value of natural location data.

[0169] At 1118, method 1100 generates the fifth product value by adjusting the fourth product value using the natural ranking value.

[0170] At 1120, Method 1100 generates an actionable report describing the effectiveness of the target product in the computer networking market, based at least on the fifth product value.

[0171] In some implementations, the system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive a first product value for a target product; determine a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generate a second product value by adjusting the first product value using the cannibalization value; determine a return value for the target product based on return data; generate a third product value by adjusting the second product value using the return value; determine a lifetime customer value for the target product based at least on the cannibalization value; generate a fourth product value by adjusting the third product value using the lifetime customer value; determine a natural ranking value for the target product based on natural location data and the value of the natural location data; generate a fifth product value by adjusting the fourth product value using the natural ranking value; and generate an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0172] In some implementations, the first product value is determined based on a comparison between the advertised sales value and the advertised expenditure value of the target product. In some implementations, the first product value is received from a computer network marketplace. In some implementations, experimental data is associated with at least one intentional experiment of advertising data configured to identify incremental effects of advertising on products associated with the computer network marketplace. In some implementations, experimental data is associated with at least one natural experiment of advertising data configured to discover naturally occurring advertising scenarios for products associated with the computer network marketplace. In some implementations, non-experimental data is associated at least with the product position and market share of the product associated with the computer network marketplace. In some implementations, adjusting the first product value using cannibalization value includes subtracting cannibalization value from the first product value. In some implementations, return data includes return data provided by the computer network marketplace. In some implementations, adjusting the second product value using return value includes subtracting return value from the second product value. In some implementations, lifetime customer value is further determined based on new customer data and future lifetime value provided by the computer network marketplace. In some implementations, adjusting the third product value using lifetime customer value includes adding the lifetime customer value to the third product value. In some implementations, using the natural ranking value to adjust the value of the fourth product includes adding the natural ranking value to the value of the fourth product.

[0173] In some implementations, a method includes: receiving a first product value for a target product; determining a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the cannibalization value; determining a return value for the target product based on return data; generating a third product value by adjusting the second product value using the return value; determining a lifetime customer value for the target product based at least on the cannibalization value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining an organic ranking value for the target product based on organic location data and the value of the organic location data; generating a fifth product value by adjusting the fourth product value using the organic ranking value; and generating an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0174] In some implementations, experimental data is associated with at least one of the following: (i) at least one intentional experiment of advertising data configured to identify the incremental effect of advertising on a product associated with the computer networking market; and (ii) at least one natural experiment of advertising data configured to identify naturally occurring advertising scenarios on a product associated with the computer networking market. In some implementations, non-experimental data is associated at least with the product position and market share of the product associated with the computer networking market. In some implementations, adjusting the first product value using cannibalization value includes subtracting cannibalization value from the first product value. In some implementations, adjusting the second product value using return value includes subtracting return value from the second product value. In some implementations, adjusting the third product value using lifetime customer value includes adding lifetime customer value to the third product value. In some implementations, adjusting the fourth product value using organic ranking value includes adding organic ranking value to the fourth product value.

[0175] In some implementations, the computing device includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive a first product value for a target product from a computer networking market; determine a cannibalization value for the target product based on at least one of experimental data and non-experimental data; generate a second product value by subtracting the cannibalization value from the first product value; determine a return value for the target product based on return data; generate a third product value by subtracting the return value from the second product value; determine a lifetime customer value for the target product based at least on the cannibalization value; generate a fourth product value by adding the lifetime customer value to the third product value; determine an organic ranking value for the target product based on organic location data and the value of organic location data; generate a fifth product value by adding the organic ranking value to the fourth product value; and generate an actionable report describing the effectiveness of the target product in the computer networking market based at least on the fifth product value.

[0176] In some implementations, the computing device includes a processor and a memory, and the memory includes instructions that, when executed by the processor, cause the processor to: receive a first product value from a computer networking market for a target product; determine a cannibalization value for the target product based on at least one of experimental data and non-experimental data; and generate a second product value for the target product based at least on the cannibalization value.

[0177] Any method disclosed herein includes one or more steps or actions for performing the described method. Method steps and / or actions may be interchanged with each other. In other words, the order and / or use of specific steps and / or actions may be modified unless a specific order of steps or actions is required for the correct operation of the implementation.

[0178] The term "first" used in the claims to describe a feature or element does not necessarily imply the presence of a second or additional such feature or element. Elements described in the means-plus-function format are intended to be interpreted in accordance with 35 USC §112 Para. 6. It will be apparent to those skilled in the art that changes may be made to the details of the above embodiments without departing from the basic principles of this disclosure.

[0179] The foregoing discussion is intended to illustrate the principles and various implementations of this disclosure. Once the foregoing disclosure is fully understood, many variations and modifications will become apparent to those skilled in the art. The appended claims are intended to be construed as encompassing all such variations and modifications.

[0180] The word “example” as used herein means intended as an example, instance, or illustration. Any aspect or design described herein as an “example” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, the use of the word “example” is intended to present the concept in a concrete manner. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise stated or becomes clear from the context, “X comprises A or B” is intended to mean any natural inclusion arrangement. That is, if X comprises A; X comprises B; or X comprises both A and B, then “X comprises A or B” is satisfied in any of the foregoing cases. Additionally, unless otherwise specified or clearly indicated by the context to be a singular form, the articles “a” and “an” as used in this application and the appended claims should generally be construed as meaning “one or more.” Furthermore, unless so described, the use of the terms “implementation” or “an implementation” throughout the document is not intended to mean the same implementation or manner of implementation.

[0181] The systems, algorithms, methods, instructions, etc., described herein can be implemented in hardware, software, or any combination thereof. Hardware may include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, or any other suitable circuitry. In the claims, the term "processor" should be understood to include any of the foregoing hardware, individually or in combination. The terms "signal" and "data" are used interchangeably.

[0182] As used herein, the term "module" can include packaged functional hardware units designed for use with other components, instruction sets executable by a controller (e.g., a processor executing software or firmware), processing circuitry systems configured to perform specific functions, and self-contained hardware or software components that interface with a larger system. For example, a module can include application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), circuits, digital logic circuits, analog circuits, combinations of discrete circuits, logic gates, and other types of hardware or combinations thereof. In other embodiments, a module can include memory storing instructions executable by a controller to implement the features of the module.

[0183] Furthermore, in one aspect, for example, the system described herein may be implemented using a general-purpose computer or general-purpose processor having a computer program that, when executed, implements any of the corresponding methods, algorithms, and / or instructions described herein. Additionally or alternatively, for example, a special-purpose computer / processor may be utilized, which may include other hardware for performing any of the methods, algorithms, or instructions described herein.

[0184] Furthermore, all or part of the embodiments of this disclosure may take the form of a computer program product accessible from, for example, a computer-usable or computer-readable medium. A computer-usable or computer-readable medium can be any device that can, for example, tangibly contain, store, transmit, or transport a program for use by or in conjunction with any processor. The medium can be, for example, an electronic, magnetic, optical, electromagnetic, or semiconductor device. Other suitable media are also available.

[0185] The embodiments, implementations, and aspects described above have been provided to facilitate a clear understanding of this disclosure, but are not intended to limit it. Rather, this disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, and its scope should be interpreted in the broadest possible sense to cover all such modifications and equivalent structures permitted by law.

Claims

1. A system comprising: processor; as well as The memory includes instructions that, when executed by the processor, cause the processor to: Receive the first product value for the target product; The cannibalization value is determined for the target product based on at least one of experimental data and non-experimental data. The value of the second product is generated by adjusting the value of the first product using the erosion value; The return value is determined for the target product based on return data. The third product value is generated by adjusting the value of the second product using the return value; The lifetime customer value for the target product is determined at least based on the erosion value. The fourth product value is generated by adjusting the third product value using the lifetime customer value. The natural ranking value is determined for the target product based on natural location data and the value of natural location data. The fifth product value is generated by adjusting the fourth product value using the natural ranking value. At least based on the value of the fifth product, generate an actionable report describing the effectiveness of the target product in the computer networking market.

2. The system according to claim 1, wherein, The first product value is determined based on a comparison between the advertising sales value and advertising expenditure value of the target product.

3. The system according to claim 1, wherein, The value of the first product is received from the computer networking market.

4. The system according to claim 1, wherein, The experimental data is associated with at least one intentional experiment of advertising data configured to identify the incremental effect of advertising on products associated with the computer network market.

5. The system according to claim 1, wherein, The experimental data is associated with at least one natural experiment of advertising data configured to discover naturally occurring advertising scenarios that indicate products associated with the computer network market.

6. The system according to claim 1, wherein, The non-experimental data is at least associated with the product position and market share of products related to the computer networking market.

7. The system according to claim 1, wherein, Adjusting the value of the first product using the erosion value includes subtracting the erosion value from the value of the first product.

8. The system according to claim 1, wherein, The return data includes return data provided by the computer network marketplace.

9. The system according to claim 1, wherein, Adjusting the value of the second product using the return value includes subtracting the return value from the value of the second product.

10. The system according to claim 1, wherein, The lifetime customer value is further determined based on new customer data provided by the computer networking market and future lifetime value.

11. The system according to claim 1, wherein, Adjusting the value of the third product using the lifetime customer value includes adding the lifetime customer value to the value of the third product.

12. The system according to claim 1, wherein, Adjusting the value of the fourth product using the natural ranking value includes adding the natural ranking value to the value of the fourth product.

13. A method comprising: Receive the first product value for the target product; The cannibalization value is determined for the target product based on at least one of experimental data and non-experimental data. The value of the second product is generated by adjusting the value of the first product using the erosion value; The return value is determined for the target product based on return data. The third product value is generated by adjusting the value of the second product using the return value; The lifetime customer value for the target product is determined at least based on the erosion value. The fourth product value is generated by adjusting the third product value using the lifetime customer value. The natural ranking value is determined for the target product based on natural location data and the value of natural location data. The fifth product value is generated by adjusting the fourth product value using the natural ranking value. At least based on the value of the fifth product, generate an actionable report describing the effectiveness of the target product in the computer networking market.

14. The method according to claim 13, wherein, The experimental data is associated with at least one of the following: (i) at least one intentional experiment of advertising data configured to identify the incremental effect of advertising for products associated with the computer network market; and (ii) at least one natural experiment of advertising data configured to discover naturally occurring advertising scenarios for products associated with the computer network market.

15. The method according to claim 13, wherein, The non-experimental data is at least associated with the product position and market share of products related to the computer networking market.

16. The method according to claim 13, wherein, Adjusting the value of the first product using the erosion value includes subtracting the erosion value from the value of the first product.

17. The method according to claim 13, wherein, Adjusting the value of the second product using the return value includes subtracting the return value from the value of the second product.

18. The method according to claim 13, wherein, Adjusting the value of the third product using the lifetime customer value includes adding the lifetime customer value to the value of the third product.

19. The method according to claim 13, wherein, Adjusting the value of the fourth product using the natural ranking value includes adding the natural ranking value to the value of the fourth product.

20. A computing device, comprising: processor; as well as The memory includes instructions that, when executed by the processor, cause the processor to: To capture the primary product value from the computer networking market for the target product; The cannibalization value is determined for the target product based on at least one of experimental data and non-experimental data. A second product value is generated for the target product based at least on the erosion value.

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