Systems and methods for real-time bidding

Abstract

A real-time bidding method includes receiving user input data, generating a first machine learning model that generates a predicted expected performance based on the user input data, and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

Description

SYSTEMS AND METHODS FOR REAL-TIME BIDDINGCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This PCT patent application claims the benefit of and priority to U.S. provisional patent application serial no. 63 / 563,090. filed March 8, 2024, the entire disclosure of which is hereby incorporated by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to commerce systems and methods, and more specifically, to systems and methods for real-time bidding.BACKGROUND

[0003] Commerce systems are well known in the art and are effective means to allow for the transaction of products, commodities, services and the like from one party to another. Commonly, commerce systems are embodied by a market, where many products are offered for sale and people that are customers are able to shop or brow se the products and select items for purchase. Such markets may be managed by companies that include Ebay®, Amazon®, Wayfair®, Costco®, Walmart®, and Target®, among others. With the advent of digital marketplaces, sellers are allow ed to list products for purchase to anyone with an internet connection. Commonly, many sellers will offer the same or similar products. Shoppers (e g., users accessing digital marketplaces via the internet) are able to sort through and brow se all of these products to find what they are looking for.SUMMARY OF THE DISCLOSURE

[0004] The various systems and methods of the present disclosure have been developed in response to the present state of the art, and in particular, in response to the problems and needs in the art that have not yet been fully solved by currently available digital marketplaces.

[0005] An aspect of the disclosed embodiments includes a real-time bidding system that includes a computing device, and a memory. The memory includes instructionsthat, when executed by the computing device, cause the real-time bidding system to: receive user input data; generate at least one model for a target spend value based on the user input data; and adjust at least one bid on at least one of at least one keyword and at least one product, based on output from the at least one model.

[0006] Another aspect of the disclosed embodiments includes a real-time bidding system. The system includes a processor, and a memory. The memory includes instruct ons that, when executed by the processor, cause the processor to: receive user input data; generate a first machine learning model that generates a predicted expected performance based on the user input data; and adjust at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0007] Another aspect of the disclosed embodiments includes a real-time bidding method. The method includes receiving user input data, generating a first machine learning model that generates a predicted expected performance based on the user input data, and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0008] These and other aspects of the present disclosure are disclosed in the following detailed description of the embodiments, the appended claims, and the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary', the dimensions of the various features are arbitrarily expanded or reduced for clarity.

[0010] FIG. 1 generally illustrates a schematic block diagram illustrating a system according to the principles of the present disclosure.

[0011] FIG. 2 A is a schematic block diagram illustrating a computing device in the form of the smartphone of FIG. 1, according to the principles of the present disclosure.

[0012] FIG. 2B is a schematic block diagram illustrating a computing device in the form of the desktop computer of FIG. 1, and a server in the form of the first server of FIG. 1. according to the principles of the present disclosure.

[0013] FIG. 3 is a schematic block diagram illustrating a computing device and a server in operating a digital marketplace, according to the principles of the present disclosure.

[0014] FIG. 4 is a schematic block diagram illustrating a computing device and a server in hosting a digital marketplace that includes attributes of a target product and a competing product, according to the principles of the present disclosure.

[0015] FIG. 5 is a schematic block diagram illustrating a computing device that includes a graphic user interface according to the principles of the present disclosure.

[0016] FIG. 6 is a flow diagram illustrating a method of evaluating a product, according to the principles of the present disclosure.

[0017] FIG. 7 is a flow diagram illustrating a method of providing a competitive assessment of a target product on a marketplace, according to the principles of the present disclosure.

[0018] FIG. 8 is a schematic block diagram illustrating a computing device and a server in operating a digital marketplace, according to the principles of the present disclosure.

[0019] FIG. 9 is a graphic representation of a plurality of search terms plotted at points that represent a frequency and similarities in search terms associated with a target product relative to competing products, according to the principles of the present disclosure.

[0020] FIG. 10 is a graphic representation of a pl urality of search terms plotted at points that represent relevance and volume of search terms associated with a target product relative to competing products, according to the principles of the present disclosure.

[0021] FIG. 11 is a graphical representation of a model output according to the principles of the present disclosure.

[0022] FIG. 12 is a flow diagram generally illustrating a real -rime bidding method according to the principles of the present disclosure.DETAILED DESCRIPTION

[0023] The following discussion is directed to various embodiments of the disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to imply that the scope of the disclosure, including the claims, is limited to that embodiment.

[0024] In commerce systems, pay per click advertising operates on a cost-per-click auction, where bids are set at the target (e.g., keyword or product) level. Using the auction, bids are set to be competitive, and advertisements (ads) serve until the campaign budget cap is reached. Once the campaign budget cap is reached, advertising turns off and is no longer able to serve.

[0025] Typically, the industry standard protocol is to increase bids to spend more and decrease bids to spend less, often using a bidding algorithm that adjusts based on efficiency targets. Such bid adjustments happen without regard to the campaign budget and require manual inputs to spend a desired amount.

[0026] Accordingly, systems and methods, such as those described herein, configured improve real-time bidding, may be desirable. In some embodiments, the systems andmethods described herein may be configured to provide optimal cost per click bidding which simultaneously or substantially simultaneously takes into account desired spend and target efficiency. The systems and methods described herein may be configured to use machine learning, data extraction best practices, and data monitoring to create the optimal cost per click bidding system.

[0027] In some embodiments, the systems and methods described herein may be configured to maximize a user performance or success criteria, above a minimum threshold, while spending a user defined budget.

[0028] The systems and methods described herein may be configured to receive user input from a user computing device. For example, a user may interact with the user computing device to provide, at a user interface, input that includes the desired spend and the period for the desired spend amount. For example, the user can specify $100 for a single day, week, month, etc. The user input may further include products and / or product information associated with the advertising budget.

[0029] The systems and methods described herein may be configured to receive, via a user selection made using the user interface, input indicating the marketplace(s) the budget, period, and products apply to. The systems and methods described herein may be configured to receive user selection indicating a minimum performance framework to fulfill per currency unit spent. For example, minimum $5 in revenue per $1 spent, minimum 2 new customers per $1 spent, 300 user impressions per $1 spent, etc. It should be understood that the user input may include any suitable input to fine tune or further direct the systems and methods described herein, such as specifying which groupings of target should receive different minimum performance requirements or budget expectations, or other suitable information.

[0030] The systems and methods described herein may be configured to, based on the user input for the specified time period, products, and marketplaces, estimate a distribution of relative expected advertising spend in order to determine whether spend is above, below, or on target. For example, if the time period (e.g., the user’s specified time period) is a single day, 3 PM will generally have a higher anticipatedspend than 3 AM, and the systems and methods described herein may be configured to recede from a machine learning model the relative spend throughout a day. The output of the model is a predicted distribution of user demand for each sub-period (e.g., hour) over the specified duration and marketplace.

[0031] In order to predict relative spend of each sub-period, the machine learning model uses a tree-based, neural network, or Bayesian architecture to ingest data, such as historical advertising spend by subdivided period, date and hour related elements, product and brand categories, demand behavior of similar products, advertising behavior of similar products, and / or the like. For example, if a user desires to spend $100 for a single day on a marketplace for a group of products, given the product's history, category, brand, and other relevant information, the model anticipates that 5.3% of advertising spend would take place for the one-hour sub-period beginning at 9:00 AM. As live data arrives, the model continues to update and adjust to adapting conditions.

[0032] The systems and methods described herein may be configured to generate a population of potential targets to consider for each product. The systems and methods described herein may be configured to generate a significant quantity of potential targets, with their associated data and relevance, that meet a minimum filtering relevance for consideration in the process.

[0033] The systems and methods described herein may be configured to generate two models. The systems and methods described herein may be configured to use a first model of the two generated models, to estimate the performance metric given different potential bid levels. The systems and methods described herein may be configured to use a second model of the two generated models to estimate the relevant cost for each target given different potential bid levels. Additionally, or alternatively, the two models can be combined into a single model to generate output indicating performance per currency unit.

[0034] Each performance metric may include a ratio of performance per currency unit spent. The systems and methods described herein may be configured to maximize theperformance ratio while meeting the desired spend, responsive to the actual spend being above the minimum threshold ratio. For example, the systems and methods described herein may be configured to maximize revenue per dollar spent, while spending $100 in a week.

[0035] The first model (e.g., the performance model) may output the expected performance, (e.g.. $50 of revenue), for any given bid and target. The first model may use a tree-based, neural network, Bayesian, or other architecture. The inputs to the first model may include: output metrics, the historical performance and bids of the target, historical performance of similar targets, product information, competitor information, category information, other target attributes, and / or the like.

[0036] The systems and methods described herein may be configured to fine tune the first model using the training inputs to meet the custom loss function according to user preferences captured through the interface and historical behavior. Specifically, advertising system risk preferences towards performance outcomes can be both asymmetric and change with magnitude. For example, a user may not be indifferent between an ROI of 0. 1 over versus under a target, and a result that is 0.5 off a target may not simply be 5 times worse than a result that is 0. 1. Accordingly, the first model is trained and fine tuned based on user preferences on performance. User preferences may be captured explicitly per user in direct questions or can be inferred from the preferences of the general population of users, such as average behavior.

[0037] The second model (e.g., the cost model) may output the expected cost in the specified currency unit, (e.g., $10 of cost), for any given bid and target. Unlike the performance model, this is always cost rather than the user input specified. The second model may use a tree-based, neural network, Bayesian, or other architecture. The inputs to the second model may include: output metrics, the historical cost and bids for the target, historical performance of similar targets, product information, competitor information, category information, other target attributes, and / or the like.

[0038] The systems and methods described herein may be configured to fine tune the second model using training inputs to meet the custom loss function according to userpreferences captured through the interface and historical behavior. Specifically, advertising system risk preferences towards spending outcomes can be both asymmetric and change with magnitude. For example, a user may not be indifferent between spending $ 100 over target versus $ 100 under a target, and a result that is $500 off a target may not simply be 5 times worse than a result that is off by $100. Accordingly, the second model may be trained and fine tuned based on user preferences on spending in both magnitude and direction.

[0039] The systems and methods described herein may be configured to combine the two models to generate both ratios and anticipated spend. For example, a $0.50 bid for product ABC on target keyword "example" is estimated to produce $3.00 in revenue and $1.00 in cost, producing a revenue to cost ratio of 3. If the user requires a performance ratio of at least 4, then only lower bids, with associated lower revenue, lower cost, but higher ratios would be considered. As described, a single model to directly estimate the ratio may also be used depending on the conditions and accuracy. The final output of the model is a downward sloping curve (e.g., as is generally illustrated in FIG. 11) where, in aggregate across all targets, the total spend is the input and the ratio of performance per currency unit acts as the output, as depicted below. The systems and methods described herein may be configured to generate the output for any combination of period or sub-period, marketplaces, products, groups of products, etc. desired. Performance outcomes below the required performance threshold may be discarded.

[0040] In some embodiments, the systems and methods described herein may be configured to, knowing the desired amount of spend per sub-period, and an ability to select the collection of bids and targets that will achieve that desired spend at the highest performance, select all of the targets and bids associated with the desired spend and performance where the estimated spend that will sum to the desired spend. This collection of targets and bids is then sent into the marketplace for each subperiod.

[0041] The systems and methods described herein may be configured to provide realtime spend and performance monitoring throughout the period to achieve the desireduser spend both above the minimum performance and maximizing performance. The systems and methods described herein may be configured to incorporate the latest performance and spend into the models to generate updated estimates of performance and cost. As real-time performance of both spend and performance are generated, the latest data can be fed back into the models to learn through reinforcement and adjust to the latest market conditions and improve future predictions. The updated models can then make predictions for the remaining sub-periods of the day.

[0042] The systems and methods described herein may be configured to, based on the estimated model pacing on targeted spend, shift up or down the collecting of bids and targets throughout the period to achieve the closest possible spend to match the desired spend. For example, with a user target spend of $100 for a 24 hour period, minimum performance metric is 4, 12 hours of the period has passed by, $55 has been spent, the performance metric is 6, and the expected spend from the step two model is 44% (or $44). In this scenario the systems and methods described herein may be spending over the expected target by $11. First, the latest performance and spend may be incorporated into the models. Second, because spend is running over estimated target pacing, a selection of lower bids that corresponds with an estimated $45 spend may be selected to complete the day in order to achieve the desired spend of $55 (thus far) + $45 (new estimate given updated targets, bids, and estimates) = $100. This may result in higher estimated performance, as bids have become more conservative and performance per currency unit improves.

[0043] Referring to FIG. 1, a schematic block diagram illustrates a system 100 according to the principles of the disclosure. The system 100 may be used for the benefit of 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 show n in FIG. 1. Each of the users 110 may use one of a variety of computing devices 120, which may include any of a wide variety of devices that carry out computational 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 128 used by the fourth user 118, and the like. The system and method presented herein may be carried out on any type of computing device.

[0044] The computing devices 120 may optionally be connected to each other and / or other resources. Such connections may be wired or wireless, and may be implemented through the use of any known wired or wireless communication standard, including but not limited to Ethernet, 802. I la, 802. 1 lb, 802. 11g, and 802. 1 In, universal serial bus (USB), Bluetooth, cellular, near-field communications (NFC), Bluetooth Smart, ZigBee, and the like. In FIG. 1, by way of example, wired communications are shown with solid lines and wireless communications are shown with dashed lines.

[0045] Communications between the various elements of FIG. 1 may be routed and / or otherwise facilitated through the use of routers 130. The routers 130 may be of any type known in the art, and may be designed for wired and / or wireless communications through any known communications standard including but not limited to those listed herein. The routers 130 may include, for example, a first router 132 that facilitates communications to and / or from the desktop computer 122, a second router 134 that facilitates communications to and / or from the laptop computer 124. a third router 136 that facilitates communications to and / or from the smartphone 126, and a fourth router 138 that facilitates communications to and / or from the camera 128.

[0046] The routers 130 may facilitate communications between the computing devices 120 and one or more networks 140, which may include any type of networks including but not limited to local area networks such as a local area network 142, and wide area networks such as a wide area network 144. In some embodiments, the local area network 142 may be a network that services an entity such as a business, nonprofit entity, government organization, or the like. The wide area network 144 may provide communications for multiple entities and / or individuals, and in some embodiments, may be the Internet. The local area network 142 may communicate with the wide area network 144. If desired, one or more routers or other devices maybe used to facilitate such communication.

[0047] The networks 140 may store information on serv ers 150 or other information storage devices. As shown, a first server 152 may be connected to the local area network 142, and may thus communicate with devices connected to the local area network 142 such as the desktop computer 122 and the laptop computer 124. Asecond server 154 may be connected to the wide area network 144, and may thus communicate with devices connected to the wide area network 144. such as the smartphone 126 and the camera 128. If desired, the second server 154 may be a web server that provides web pages, web-connected services, executable code designed to operate over the Internet, and / or other functionality that facilitates the provision of information and / or services over the wide area network 144.

[0048] Referring to FIG. 2A, a schematic block diagram illustrates an exemplary computing device of the computing devices 120 that may enable implementation of the disclosure in a standalone computing environment. The computing device may be, for example, the smartphone 126 of FIG. 1. The present specification, however, contemplates that the computing device 120 may include any of those computing devices 120 described in FIG. 1 or any other type of computing device.

[0049] As shown, the smartphone 126 may include a processor 210 that is designed to execute instructions on data. The processor 210 may be of any of a wide variety of ty pes, including microprocessors with x86-based architecture or other architecture known in the art, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGA's), and the like. The processor 210 may optionally include multiple processing elements, or "cores." The processor 210 may include a cache that provides temporary storage of data incident to the operation of the processor 210.

[0050] The smartphone 126 may further include memory 220, which may be volatile memory such as random-access memory (RAM). The memory 220 may include one or more memory modules. The memory 220 may include executable instructions, data referenced by such executable instructions, and / or any other data that may beneficially be made readily accessible to the processor 210.

[0051] The smartphone 126 may further include a data store 230, which may be nonvolatile memory such as a hard drive, flash memon . and / or the like. The data store 230 may include one or more data storage elements. The data store 230 may store executable code such as an operating system and / or various programs to be run on the smartphone 126. The data store 230 may further store data to be used by suchprograms. For the system and method of the present disclosure, the data store 230 may store computer executable code associated with an assessment module 232, a text analytics module 238, a filtering module 235, a comparison module 234, a recommendation module 236, and a competitivity score generating module 233. The data store 230 may further include data associated with descriptive terms 241 related to a target product and / or a competing product, relevant descriptive terms 242 associated with either of the target product or a competing product, a competitivity score 239, and an actionable report 237. This data stored by the data store 230 may be maintained on the data store 230 for any length of time and some data may be created or overwritten at any time to facilitate the methods described herein.

[0052] The smartphone 126 may further include one or more wired transmitter / receivers 240, which may facilitate wired communications between the smartphone 126 and any other device, such as the other computing devices 120, the servers 150, and / or the routers 130 of FIG. 1. The wired transmitter / receivers 240 may communicate via any known wired protocol, including but not limited to any of the wired protocols described in FIG. 1. In some embodiments, the wired transmitter / receivers 240 may include Ethernet adapters, universal serial bus (USB) adapters, and / or the like.

[0053] The smartphone 126 may further include one or more wireless transmitter / receivers 250, which may facilitate wireless communications between the smartphone 126 and any other device, such as the other computing devices 120, the servers 150, and / or the routers 130 of FIG. 1. The wireless transmitter / receivers 250 may communicate via any known wireless protocol, including but not limited to any of the wireless protocols described in FIG. 1. In some embodiments, the wireless transmitter / receivers 250 may include Wi-Fi adapters, Bluetooth adapters, cellular adapters, and / or the like. Either of the wired transmitter / receiver(s) 240 or wireless transmitter / receiver(s) 250 may be associated with a network interface device (NID) 280. The network interface device 280 may provide connectivity to, via the Internet, any network, e.g., a wide area network (WAN), a local area network (LAN), wireless local area network (WLAN), a wireless personal area network (WPAN), a wireless wide area network (WWAN), or other networks.

[0054] The smartphone 126 may further include one or more user inputs 260 that receive input from a user such as any of the users 110 of FIG. 1. The users 110 described herein, may be referred to as a seller of a target product. The user inputs 260 may be integrated into the smartphone 126, or may be separate from the smartphone 126 and connected to it by a wired or wireless connection, which may operate via the wired transmitter / receivers 240 and / or the wireless transmitter / receivers 250. The user inputs 260 may include elements such as a touch screen, buttons, keyboard, mouse, trackball, track pad, stylus, digitizer, digital camera, microphone, and / or other user input devices known in the art.

[0055] The smartphone 126 may further include one or more user outputs 270 that provide output to a user such as any of the users 1 10 of FIG. 1. The user outputs 270 may be integrated into the smartphone 126, or may be separate from the smartphone 126 and connected to it by a wired or wireless connection, which may operate via the wired transmitter / receivers 240 and / or the wireless transmitter / receivers 250. The user outputs 270 may include elements such as a display screen, speaker, vibration device, LED or other lights, and / or other output devices known in the art. In some embodiments, one or more of the user inputs 260 may be combined w ith one or more of the user outputs 270, as may be the case with a touch screen. In some embodiments, the user outputs 270 may present to a user a graphical user interface by w hich the user may interact with the smartphone 126 in order to affect the methods and processes described herein.

[0056] The smartphone 126 may include various other components not shown or described herein. Those of skill in the art will recognize, with the aid of the present disclosure, that any such components may be used to carry out the present disclosure, in addition to or in the alternative to the components shown and described in connection with FIG. 2A.

[0057] The smartphone 126 may be capable of carrying out the present disclosure in a standalone computing environment, i.e., without relying on communication with other devices such as the other computing devices 120 or the servers 150. The present specification further contemplates that any of the assessment module 232,competitivity score generating module 233, comparison module 234, filtering module 235, recommendation module 236, and text analytics module 238 may be distributed amongst a number of computing devices (e.g.. computing devices 120 of FIG. 1) and / or amongst any server (e.g., 150 of FIG. 1). In other embodiments, the present disclosure may be utilized in different computing environments. One example of a client / server environment will be shown and described in connection with FIG. 2B.

[0058] Referring to FIG. 2B, a schematic block diagram illustrates a computing device in the form of the desktop computer 122 of FIG. 1, and a server in the form of the first server 152 of FIG. 1, which may cooperate to enable practice of the disclosure with client / server architecture. As shown, the desktop computer 122 may be a "dumb terminal," made to function in conjunction with the first server 152.

[0059] Thus, the desktop computer 122 may have only the hardware needed to interface with a user (such as the first user 112 of FIG. 1) and communicate with the first server 152. Thus, the desktop computer 122 may include one or more user inputs 260, one or more user outputs 270, one or more wired transmitter / receivers 240, and / or one or more wireless transmitter / receivers 250. Again, either of the wired transmitter / receiver(s) 240 or wireless transmitter / receiver(s) 250 may be associated with a N1D 280a. The N1D 280a may provide connectivity to. via the Internet, any network, e.g., a wide area netw ork (WAN), a local area network (LAN), wireless local area network (WLAN), a wireless personal area netw ork (WPAN), a wireless wide area network (WWAN), or other networks in which the first server 152 forms a part of. These components may be as described in connection with FIG. 2A.

[0060] Computing functions (apart from those incidents to receiving input from the user and delivering output to the user) may be carried out wholly or partially at the first server 152. Thus, the processor 210, memory 220, data store 230, wired transmitter / receivers 240, and wireless transmitter / receivers 250 may be housed in the first server 152. These components may also be as described in connection with FIG. 1A.

[0061] In operation, the desktop computer 122 may receive input from the user via the user inputs 260. The user input may be delivered to the first sen' er 152 via the wired transmitter / receivers 240 and / or wireless transrmtter / receivers 250. This user input may be further conveyed by any intervening devices, such as the first router 132 and any other devices in the local area network 142 that are needed to convey the user input from the first router 132 to the first server 152.

[0062] The first server 152 may conduct any processing steps needed in response to receipt of the user input. Then, the first server 152 may transmit user output to the user via the wired transmitter / receivers 240, and / or wireless transmitter / receivers 250. This user output may be further conveyed by any intervening devices, such as the first router 132 and any other devices in the local area network 142 (or, alternatively, a wide area netw ork 144) that are needed to convey the user output from the first server 152 to the first router 132. The user output may then be provided to the user via the user outputs 270. In some embodiments, the user outputs 270 may present to a user a graphical user interface that, according to the methods described herein, display a listing of relevant descriptive terms 242 of the target product and competitive product as well as display an actionable report that describes a projected performance of the target product in a computer-networked marketplace relative to the at least one organic competing product also presented on the computer-networked marketplace.

[0063] Referring to FIG. 3, a schematic block diagram illustrating a computing device 322 (similar to any one of the computing devices shown in FIG. 1) and a server 350 (similar to any of the servers shown in FIG. 1) operating a digital marketplace, which may cooperate to enable practice of the disclosure with client / server architecture, according to the principles of the disclosure. As shown, the computing device 322 may be operatively coupled to the server 350 via the NID 380 as described herein. This operative coupling allows the computing device 322 to access, when appropriate, a digital marketplace 382 on which a target product and competitive product are sold. The digital marketplace 382 may be any network accessible website that lists a number of products that, when accessed by a user, allow s a user to review' products, rate products, purchase products among other tasks associated with digital commerce. The digital marketplace 382 may be managed by companies that include Ebay®.Amazon®, Wayfair®, Costco®, Walmart®, and Target®, among others. Upon purchase of a product, a consumer may have the purchased product sent to the consumer's home or business for consumption. In some embodiments, the digital marketplace 382 may be any of a plurality of websites that the server 350 provides storage and processing resources for.

[0064] As described herein, the computing device 322 may include a processor 310, a memory 320, user inputs 360, user outputs 370 and a data store 330 that operate similar to those similar elements described in connection with FIGS. 2A and 2B. The data store 330 may include those modules described herein including an assessment module 332, a competitivity score generating module 333, a comparison module 334, a filtering module 335, a recommendation module 336, and a text analytics module 338.

[0065] During operation, the assessment module 332 may assess certain attributes of a target product. The target product as described herein is a specific target product a user (e.g., seller) of the computing device 322 is seeking to discover the competitivity of the product within a certain market. For example, the target product may be a product the user is selling or would like to sell on the digital marketplace 382 hosted by the server 350. In order to know the competitiveness of the target product, the assessment module 332 may access certain data about the target product present on the server 350. The data may be accessed by the assessment module 332 by sending data requests via the NID 380 either via a wired (e.g., via the wired transmitter / receiver(s) 340)) or a wireless (e.g., via the wireless transmitter / receiver(s) 350) connection.

[0066] The data request may be a request for attributes regarding the target product. Although any number of attributes about the target product may be requested, the assessment module 332 may request specific attributes that will be used to develop an actionable report 337 regarding the competitivity of the target in the digital marketplace 382. A first attribute may be descriptive of the ratings provided by at least one purchaser of the target product on the digital marketplace 382. Often, digital marketplaces 382 provide graphical user interfaces (GUIs) to consumers that allowsthose consumers to rate the products they purchase on the digital marketplace 382. In a specific embodiment, a 5-star rating system may be used by a consumer / purchaser of the target product to rate the target product. A one-star rating would indicate a poor assessment by the consumer / purchaser of the target product while a 5-star rating would indicate a very good assessment of the target product by the consumer / purchaser. The assessment module 332 may, accordingly, take each starrating or an average of those star-ratings as input for use in creating the actionable report 337.

[0067] A second attribute may include the reviews and / or user generated content associated with the target product. Again, digital marketplaces 382 often provide a GUI that allow the consumer of the target product to enter text descriptive of the consumers' experiences with the target product. This text may include specific positive keywords or negative keywords that describe the consumers' experience with the target product. With this data, the assessment module 332 may cause a text analytics module 338 to, in some embodiments, parse each review for these keywords that describe the target product. Still further, the text analytics module 338 may also extract key ords descriptive of certain features of the target product. As an example, the wording "ergonomic handle" may be extracted by the text analytics module 338 describing not only that the target product includes a handle, but that that handle is an "ergonomic" handle giving a perception that the consumer giving that review likes the fit or feel of the target product.

[0068] A third attribute may be similar to the second attribute in that the assessment module 332 determines the number of the reviews associated with the target product presented on the digital marketplace 382. The number of reviews may indicate a level of involvement with the target product either for the disparaging of the target product or the approval of the target product. Along with the textual substance of these reviews, the number of reviews associated with the target product may be used to help create the actionable report based on the involvement within the digital marketplace 382 with the target product.

[0069] A fourth attribute may include the listed price of the target product. Although the amount charged to purchase a product may not be indicative of the value of the target product, the charged amount relative to other similar competing products may be indicative of its worth or current price point (whether incorrect or correct).

[0070] A fifth attribute may also include a ranking of the target product relative to at least one organic competing product. This ranking may be a result of an average or accumulative rating of the target product relative to the organic competing product. Often, the digital marketplaces 382 allow purchasers to list organic competing products and the target product by an average rating. By doing so the assessment module 332 may understand the ranking of the target product relative to the at least one organic competing product and use this information to develop the actionable report 337.

[0071] The assessment module 332 may also determine similar attributes of an at least one organic competing product similar to those attributes discovered by the assessment module 332 for the target product. In the context of the present specification the term "organic competing product" is meant to be understood as any product that, based on consumer reviews, is ranked on the digital marketplace 382. An "organic" competing product is therefore a naturally ranked product based on those reviews provided by past consumers as opposed to those products that may be given "top shelf' preference after payment to achieve such status. This organic ranking nature of products on the digital marketplace 382 is often done to provide potential consumers with evidence that others appreciate that product. A "competing" product is any product that is similar to the target product but sold by another seller apart from the seller of the target product. The "similarity" of the target product relative to the at least one organic competing product is dependent on the data obtained by the text analytics module 338 and specifically the analysis of descriptive terms 341 associated with each of these types of products. In a specific embodiment, the text analytics module 338 may also obtain descriptive data associated with each target product and organic competing product per their listing. Again, digital marketplaces 382 allow descriptions of products to be posted alongside each product that describes its functionalities, its physical characteristics, and its alleged advantages as superiorproducts. All of this is presented to a potential consumer on a GUI as textual information used to entice the consumer to purchase the products. The text analytics module 338 may analyze this text and. using a parsing process, extract keywords used to compare the text associated with the target product to the text associated with the organic competing product.

[0072] When the computing device 322, via the assessment module 332, has obtained the attributes associated with the target product and the at least one organic competing product, the descriptive terms 341 describing these attributes may be listed for consumption by, in some embodiments, a filtering module 335. The filtering module 335 may be used to filter the descriptive terms 341 to only those relevant descriptive terms 342 that have resulted in the purchase of the target product in the digital marketplace 382. For example, some descriptive terms 341 may, rightly or wrongly, include a color or color scheme of the target product or organic competing product. Although some consumers may appreciate a specific color of a product, these may not be deciding factors used to entice a consumer to purchase the target product or organic competing product. This may be especially true where, as indicated by purchase histories associated with the target product or organic competing product indicate that any particular color of product was not overwhelmingly purchased over another color. In this specific example, although the color of the product is a descriptive term 341 the text analytics module 338 had parsed out from the products, it may not necessarily be a relevant descriptive term 341 and such information may be filtered out by the filtering module 335 to obtain only those relevant descriptive terms 342 associated with any of the target product or organic competing product.

[0073] In a more general example, the filtering module 335 may narrow down the descriptive terms 341 of interest by analyzing metrics collected on sufficiently "mature" keywords (e.g.. sales > 2) as budding keywords that may lack sufficient data to influence predictions in purchasing the target product or organic competing product. The click-rate and conversion rate (clicks that result in a purchase) associated with any given product may be taken into consideration based on the keywords used to search for the products. In these examples, a lack of data regarding a specific descriptive term 341 may also filter out that specific descriptive term 341 in order toobtain the relevant descriptive terms 342 as described herein. It is also appreciated that the descriptive terms 341 may be filtered by the filtering module 335 based on any other reason to obtain relevant descriptive terms 342 and the present specification contemplates these other reasons.

[0074] With the relevant descriptive terms 342 being determined, these relevant descriptive terms 342 may be sent to a comparison module 334 to compare those relevant descriptive terms 342 of the target product to those relevant descriptive terms 342 associated with the at least one organic competing product. Although the present specification describes this comparison process as being conducted between a single organic competing product (e.g.. "at least one") to the target product, any number of organic competing products may be compared to the target product. In a specific example, the top 10 ranked organic competing products may be compared to the target product by the comparison module 334.

[0075] During execution of the comparison module 334 by the processor 310, the descriptive terms 341 may be compared to generate, with a competitivity score generating module 333 executed by the processor 310, a competitivity' score 339. In some embodiments, the competitivity score may use any process or algorithm used to define how the target product can or cannot compete with any of the discovered organic competing products.

[0076] During operation, a recommendation module 336 may receive this competitivity score 339 along with other data from the digital marketplace 382 hosted by the server 350. Among this other data may include revenue data associated with the organic competing products and the target product (if available). For example, where a click-rate of any given product (e.g., target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module 336. The recommendation module 336 may then provide a recommendation descriptive of the ability (or inability ) of the target product to compete with the at least one organic competing product. In some embodiments, a threshold competitivity score may be set such that the report provided by the recommendation module 336 indicates to the seller of thetarget product whether to proceed to sell that product on the digital marketplace 382. Alternatively, where the competitivity score has not met the threshold the competitivity score generating module 333 may not forward the competitivity score onto a recommendation module 336 to generate the actionable report 337. Alternatively, or additionally, where the competitivity score has not met the threshold the competitivity score generating module 333 may pass a threshold failure signal onto to the recommendation module 336 indicative of a non-competitive status of the target product. When the threshold competitivity score is not reached, the recommendation module 336 may provide an indication to the seller that it is not recommended that the seller initiate or continue to sell the target product on the digital marketplace 382.

[0077] Where the threshold competitivity score is reached, the recommendation module 336 may provide additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue. Again, a seller of the target product may not know what appropriate target advertising cost of sale (ACoS) to meet and what price point to sell the target product at in order to see long term gains in lieu of short-term profits. The recommendation module 336 provides this information based on the competitivity score 339 generated by the competitivity score generating module 333 and revenue data received from the digital marketplace 382. In a specific example, the revenue potential of the target product may be determined by the recommendation module 336 calculating an ad spend margin, an ad spend potential, and a revenue potential. The ad spend margin may be calculated by multiplying a target ACoS by the price of the target product. A target ACoS may be determined and set by the seller based on available capital or may be set by the seller based on the fraction of the revenue received thus far from the sale of the target product on the digital marketplace 382 and costs of manufacturing. Ad spend potential may then be calculated by multiplying monthly opportunity’ units (OU) by the spend margin. The monthly OUs may be calculated as a result of the conversion rate of clicks to the target product that is the result of sales of the target product after a purchaser has viewed the product. The revenue potential may then be calculated by multiplying the OU with the price of the target product. This revenue potential of each of the targetproducts and organic competing products may be ranked to determine the placement of the target product within the digital marketplace 382.

[0078] In some embodiments, the recommendation (e.g., the actionable report 337) presented by the recommendation module 336 may be refined by inputting an estimated bid amount from the digital marketplace 382 required to "win" advertising slots for the target product. The digital marketplace 382, along with selling products, may also engage in presenting advertisements to a potential purchaser of one or more products. These advertisements may be presented in a banner or other sub-section of the GUI presented to the purchaser or as a pop-up window advertisement. These forms of advertisements present, in real-time, alternative products for which the potential purchaser is seeking to purchase. These advertisements may present the target product and persuade the purchaser to purchase the target product rather than a competitors' products. Thus, investments may be required to increase the purchasing instances of the target product. The present systems and methods may also present to the seller of the target product, on the actionable report 337, how much additional investment may be needed to win advertising slots based on the keywords associated with the target product and entered into a search by a potential user. For example, the investment needed may be calculated by multiplying the projected bid amount by the product of the click rate of the target product and the impressions (e.g., uses) for specific keywords associated with the target product and the organic competing product used to search for those products. A return on investment (ROI) may then be calculated by subtracting the investment needed from an investment payoff term and multiplying that by the ad spend potential. Products with no (or low) potential receive suggestion outputs as to why they are not competitive or have bad conversion rates by the recommendation module 336 and its actionable report 337, so that these attributes of the target product can be improved for future potential or the money spent to sell the target product can be reallocated for other uses.

[0079] FIG. 4 is a schematic block diagram illustrating a computing device 420 and a server 452 in hosting a digital marketplace 482 that includes attributes of a target product and a competing product, which may cooperate to enable practice of the disclosure with client / server architecture. As described herein, the assessment module432 may assess certain atributes of a target product. The target product as described herein is a specific target a user (e.g., seller) of the computing device 420 is seeking to discover the competitivity of the product within a certain market. For example, the target product may be a product the user is selling or would like to sell on the digital marketplace 482 hosted by the server 452. In order to know the target product’s competitiveness, the assessment module 432 may access certain data about the target product present on the server 452. The data may be accessed by the assessment module 432 by sending data requests via the NID 480 either via a wired (e.g., via the wired transmiter / receiver(s) 440)) or a wireless (e.g., via the wireless transmitter / receiver(s) 450) connection.

[0080] The data request may be a request for atributes regarding the target product. Although any number of atributes about the target product may be requested, the assessment module 432 may request specific atributes that will be used to develop an actionable report regarding the competitivity of the target in the digital marketplace 482. A first attribute may be descriptive of the ratings 483 provided by at least one purchaser of the target product on the digital marketplace 482. Often, digital marketplaces 482 provide graphical user interfaces (GUIs) to consumers that allows those consumers to rate the products they purchase on the digital marketplace 482. In a specific embodiment, a 5-star rating system may be used by a consumer / purchaser of the target product to rate the target product. A one-star rating would indicate a poor assessment by the consumer / purchaser of the target product while a 5-star rating would indicate a very good assessment of the target product by the consumer / purchaser. The assessment module 432 may, therefor, take each star-rating or an average of those star-ratings as input for use in creating the actionable report.

[0081] A second atribute may include the content 486 of the reviews and description associated with the target product. Again, digital marketplaces 482 often provide a GUI that allow the consumer of the target product to enter text descriptive of the consumers' experiences with the target product. This text may include specific positive keywords or negative keywords that describe the consumers' experience with the target product. With this data, the assessment module 432 may cause a text analytics module 438 to. in some embodiments, parse each review that describes thetarget product. Still further, the text analytics module 438 may also extract keywords descriptive of certain features of the target product. As an example, the wording "ergonomic handle" may be extracted by the text analytics module 438 describing not only that the target product includes a handle, but that that handle is an "ergonomic" handle giving a perception that the consumer giving that review likes the fit of the target product.

[0082] A third attribute may be the number of the reviews 484 associated with the target product presented on the digital marketplace 482. The number of reviews 482 may indicate a level of involvement with the target product either for the disparaging of the target product or the approval of the target product. Along with the textual substance of these reviews, the number of reviews associated with the target product may be used to help create the actionable report based on the involvement within the digital marketplace 482 with the target product.

[0083] A fourth attribute may include the listed price 485 of the target product. Although the amount charged to purchase a product may not be indicative of the value of the target product, the changed amount relative to other similar competing products may be indicative of its worth or current price point (whether incorrect or correct).

[0084] A fifth attribute may also include a ranking 487 of the target product relative to at least one organic competing product. This ranking may be a result of an average or accumulative rating of the target product relative to the organic competing product. Often, the digital marketplaces 382 allow purchasers to list organic competing products and the target product by an average rating. By doing so the assessment module 432 may understand the ranking of the target product relative to the at least one organic competing product and use this information to develop the actionable report.

[0085] Each of these target product attributes may be requested by the computing device 420 and its assessment module 432 and delivered by the server 452 upon request. Even further, similar attributes related to at least one organic competing product may also be requested by and sent to the computing device 420. Theseorganic product attributes may include competing product ratings 488, competing product review numbers 489, competing product prices 490, competing product content 491, and competing product rank 492. Each of these competing product attributes may be similar to those attributes associated and described herein in connection with the target product.

[0086] FIG. 5 is a schematic block diagram illustrating a computing device 520 that includes a graphic user interface 522 used to enable practice of the disclosure within a client / server architecture. The graphic user interface 522 may be used by a seller of a target product to evaluate the competiti vity of the target product as described herein. As described herein, the computing device 520 includes a filtering module 535. The filtering module 535 may be used to filter the descriptive terms 541 to only those relevant descriptive terms 542 that have resulted in the purchase of the target product in the digital marketplace.

[0087] The filtering module 535 may include a number of types of filters to filter the descriptive terms 541 into the relevant descriptive terms 542. These filters may include an impression filter 524, a click-rate filter 526, and a conversion-rate filter 528 each of which may result in the removal of descriptive terms 541 that do not result in purchases of the target product or any organic comparison product. As described herein, the impression filter 524 may be provided with a number of times an ad associated with the target product or competing product (whether it's a banner, button, or text link) has been (or will be) exposed to a potential purchaser and has resulted in a purchase of that product. The impression filter 524 may therefore, filter out those instances where a potential purchaser did not see or was not shown an ad but did result in a purchase. Click-rate filter 526 may filter out those descriptive terms that, despite the wording of the ad, did not result in a selection of the ad or a purchase of the product. The conversion-rate filter 528 may filter out those descriptive terms that, despite the wording of the ad and a selection by the potential purchaser of the ad, did not result in a purchase of the product.

[0088] By filtering the descriptive terms via the filtering module 535 and its associated filters 524, 526. 528, the GUI 522 may be able to display to a seller of thetarget product those relevant descriptive terms 542 that apply in the analysis of how competitive the target product is. Although FIG. 5 shows the use of specific filters 524, 526. 528 to filter the descriptive terms 541. the present specification contemplates that the descriptive terms 541 may be filtered using any criteria.

[0089] In some embodiments, the system 100 and / or any computing device or other devices or systems described herein, may perform the methods described herein. However, the methods described herein as performed herein are not meant to be limiting, and any type of software executed on a controller or processor can perform the methods described herein without departing from the scope of this disclosure.

[0090] FIG. 6 is a flow diagram illustrating a method 600 of evaluating a product, according to the principles of the disclosure. The method 600 may begin at block 605 with assessing attributes of a target product using an assessment module executed by a processor. As described herein, the assessment of the target product (or any other competing product) may indicate certain attributes of the target product. Although any number of attributes about the target product may be requested, the assessment module may request specific attributes that will be used to develop an actionable report regarding the competitivity of the target in the digital marketplace.

[0091] At block 610, the method 600 may further include listing relevant descriptive terms of the target product descriptive of the attributes of the target product. This listing of the relevant descriptive terms may also be conducted by the assessment module being executed by the processor of the computing device. This list of relevant descriptive terms, in some embodiments, may have been generated based on the filtering of all descriptive terms generated for the target product as described herein. There may be some irrelevant information that may be filtered out of the descriptive terms generated from the attributes of the target product that would not need to show up in the actionable report.

[0092] The method 600 may continue at block 615 with accessing a computer- networked marketplace, via a NID, and identifying at least one organic competing product matching at least one descriptive term. This identification may implement theassessment module to compare the descriptive terms associated with the target product to any generated descriptive terms associated with any organic competing product. In some embodiments, this matching process of descnptive terms related to the target product to descriptive terms related to the organic competing product may be conducted before or after the filtering of descriptive terms by a filtering module as described herein. When conducted before, more organic competing products may be matched where, when conducted after the filtering, relatively less organic competing products may be matched due to the smaller list of relevant descriptive terms.

[0093] The method 600 may also include comparing the descriptive terms of the target product to descriptive terms associated with the at least one organic competing product to generate a competitivity score at block 620. This may be done via execution of a comparison module 620 executed by the processor. During execution of the comparison module by the processor, the descriptive terms may be compared to generate, with a competitivity score generating module executed by the processor, a competitivity score. In some embodiments, the competitivity score may use any process or algorithm used to define how the target product can or cannot compete with any of the discovered organic competing products.

[0094] At block 625. the method 600 may further include generating an actionable report descriptive of a projected performance of the target product in the computer- networked marketplace relative to the at least one organic competing product. The actionable report may be generated via the execution of a recommendation module by the processor. During operation, a recommendation module may receive this competitivity score along with other data from the digital marketplace hosted by the server. Among this other data may include revenue data associated with the organic competing products and the target product (if available). For example, where a clickrate of any given product (e.g.. target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module. The recommendation module may then provide a recommendation descriptive of the ability7(or inability ) of the target product to compete with the at least one organic competing product. In some embodiments, a threshold competitivity score may be set such that the report provided by therecommendation module 336 indicates to the seller of the target product whether to proceed to sell that product on the digital marketplace. Alternatively, where the competitivity score has not met the threshold the competitivity score generating module may not forward the competitivity score onto a recommendation module to generate the actionable report. When the threshold competitivity score is not reached, the recommendation module simply provides an indication to the seller that it is not recommended that the seller initiate or continue to sell the target product on the digital marketplace.

[0095] Where the threshold competitivity score is reached, the recommendation module may provide additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue. Again, a seller of the target product may not know what appropriate target ACoS to meet and what price point to sell the target product at in order to see long term gains in lieu of short-term profits. The recommendation module provides this information based on the competitivity score generated by the competitivity score generating module and revenue data received from the digital marketplace. In a specific example, the revenue potential of the target product may be determined by the recommendation module calculating an ad spend margin, an ad spend potential, and a revenue potential. The ad spend margin may be calculated by multiplying a target ACoS by the price of the target product. A target ACoS may be determined and set by the seller based on available capital or may be set by the seller based on the fraction of the revenue received thus far from the sale of the target product on the digital marketplace and costs of manufacturing. Ad spend potential may then be calculated by multiplying monthly opportunity units (OU) by the spend margin. The monthly OUs may be calculated as a result of the conversion rate of clicks to the target product that is the result of sales of the target product after a purchaser has viewed the product. The revenue potential may then be calculated by multiplying the OU with the price of the target product. This revenue potential of each of the target products and organic competing products may be ranked to determine the placement of the target product within the digital marketplace.

[0096] At this point, the method 600 may end.

[0097] FIG. 7 is a flow diagram illustrating a method 700 of providing a competitive assessment of a target product on a marketplace, according to the principles of the disclosure. Here, the method 700 may begin with evaluating a target product to determine attributes of the target product at block 705. In some embodiments, the evaluation may be conducted via the execution of an assessment module. In some embodiments, the assessment may be conducted by requesting, at a GUI, descriptive terms regarding the target product. Additionally, or alternatively, the evaluation may be made by an assessment module accessing a digital marketplace to retrieve descriptive terms via a text analytics module as described herein. Additionally, or alternatively, certain input devices such as a digital camera may be used to image the target product and extrapolate certain features of the product such as size, color, texture, among others.

[0098] The method 700 may continue at block 710 with accessing the digital marketplace to determine at least one organic competing product to the target product upon execution of the processor. In this embodiment, the assessment module may access certain data about the target product such as the descriptive terms and crossreference those descriptive terms to determine if at least one descriptive term matches any competing product listed on the digital marketplace.

[0099] At block 715, the method 700 may include calculating a competitivity score related to the ability of the target product to compete with the at least one organic competing product. This process may be conducted upon execution of a competitivity' score generator by the processor of the computing device accessing the digital marketplace. In some embodiments, the competitivity score may use any process or algorithm used to define how the target product can or cannot compete with any of the discovered organic competing products.

[0100] The method 700 may further include generating an actionable report based on the ability of the target product to compete with the at least one organic competing product at block 720. During operation, a recommendation module, executed by the processor, may receive the competitivity score along with other data from the digital marketplace hosted by the server. Among this other data may include revenue dataassociated with the organic competing products and the target product (if available). For example, where a click-rate of any given product (e.g., target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module. The recommendation module may then provide a recommendation descriptive of the ability (or inability) of the target product to compete with the at least one organic competing product. In some embodiments, a threshold competitivity score may be set such that the report provided by the recommendation module indicates to the seller of the target product whether to proceed to sell that product on the digital marketplace. Alternatively, where the competitivity score has not met the threshold the competitivity score generating module may not forward the competitivity score onto a recommendation module to generate the actionable report. When the threshold competitivity score is not reached, the recommendation module simply provides an indication to the seller that it is not recommended that the seller initiate or continue to sell the target product on the digital marketplace. At this point, the method 700 may end.

[0101] FIG. 8 is a schematic block diagram illustrating computing device 822 and a server 852 in operating a digital marketplace 882, which may cooperate to enable practice of the disclosure with client / server architecture. In addition to providing an actionable report (FIG. 3, 337) regarding the competitivity of the target in the digital marketplace 882 as described in connection with FIGS. 1-7, the present computing device 822 may further describe an actionable report 837 that describes sustainable and feasible grow th over time on an ecommerce platform (e.g., the digital marketplace 882) on a product level as well as provide a winnability report 804 descriptive of a probability' of winning each search term (e.g., having the target product associated with the search term) at any given point in time along with the estimated costs to win those search terms. The actionable report 837 and winnability report 804 may, in some embodiments, provide a user with an indication as to how to optimize advertising and search engine implementation to increase revenue.

[0102] As described herein, the computing device 822 may include a processor 810, a memory 820. user inputs 860, user outputs 870 and a data store 830 that operatesimilar to those similar elements described in connection with FIGS. 2 A and 2B, for example. The data store 830 may include those modules described herein including a comparison module 834. and a revenue module 899.

[0103] The computing device 822 described may include any module, data store 830, or data maintained on the computer as those described in connection with FIG. 3 herein. In the embodiments described herein, an actionable report 837 may be provided using a comparison module 834 similar to the comparison module 334 described in connection with FIG. 3. Although these modules (e.g., comparison module 834) may be similar to those described in FIG. 3, the modules in FIG. 8 may perform additional and different processes as described herein in order to provide an actionable report 837 indicating optimized advertising and search engine implementation.

[0104] In some embodiments, the computing device 822 may initially determine any competitive products that, at any point in time, compete with the target product. The computing device 822 may do this by accessing a search engine 894 associated with a digital marketplace 882 via the processor 810 and NID 880 of the computing device 822. Upon accessing the search engine 894, the processor 810 may retrieve data descriptive of the frequency of appearance of one or more search terms associated with the target product. Additionally, the processor 810 may obtain data related to the ranking of those search terms. This data may be descriptive of the coincidence that the target product and any competitive product are associated with the same search terms. Still further, this data may be descriptive of how the search terms associated with the target product and each competitive product are similar in their rankings. For example, where the target product is an athletic shoe, some pertinent search terms may include running, hiking, basketball, tennis, sole, laces, and marathon among other potential terms associated with the target product athletic shoe. The data may also include which competing products also rank similarly with these terms. For example, a competing product that matches 9 out of 10 search terms with the target product is "higher ranked" as compared to a competing product that matches 4 out of 10 search terms.

[0105] In a specific embodiment, the processor 810 may access this data using, for example, a search query website such as Google ® Trends ®. These types of websites may be used by the processor 810 to access a number of search queries for specific terms associated with any of the target product and any number of competitive products. The search quay' websites may be accessed by the processor 810 to automatically access search query inquiries in order to obtain the data used herein by the computing device 822. Although specific search query’ websites are contemplated herein, the present specification also contemplates that other search query databases may be accessed by the processor 810 whether those databases are accessible by a user via a website or not.

[0106] The computing device 822 also includes a machine learning module 896 (e.g., which may be referred to herein as the machine learning model 896). The machine learning module 896 may build a number of mathematical models that provide a competitive set report 898 describing a competitive set of products that compete with the target product. As with each machine learning module 896, the machine learning module 896 may be "taught" by using, as input, a plurality of sets of target product search terms and rankings as well as a plurality’ of sets of competing product search terms and rankings. Again, the plurality of sets of target product search terms and rankings as well as a plurality of sets of competing product search terms and rankings may be accessible by the processor 810 either via a specific search query website or database.

[0107] The machine learning module 896. in some embodiments, may. upon execution by the processor 810, determine such correlations, in some embodiments, based on any machine learning or neural network methodology known in the art or developed in the future. In a specific embodiment, the machine learning module 896 may implement an unsupervised learning clustering technique. For example, the machine learning module, in some embodiments, may model the relationships between each plurality of sets of target product search terms and rankings as well as a plurality’ of sets of competing product search terms and rankings using a layered neural network topology. Such a neural network, in some embodiments, may include an input layer (e.g., plurality of sets of target product search terms and rankings aswell as a plurality' of sets of competing product search terms and rankings) including a known, recorded set of values for each of these parameters, settings, indicators, and usage data metrics, and an output layer including a projected optimal competitive set report 898, based on the known, recorded set of values in the input layer. The machine learning module 896, in some embodiments, may propagate input through the layers of the neural network to project or predict optimal competitive set report 898 based on the known and recorded search term metrics, and compare these projected values to optimal search terms to be presented in the competitive set report 898. Using a back-propagation method, the machine learning module 896, in some embodiments, may then use the difference between the projected values and the known optimal values to adjust weight matrices of the neural network describing the ways in which changes in each of the search term data metrics are likely to affect the optimal search terms to be presented in the competitive set report 898.

[0108] With the output layer, the computing device 822 may provide learned competitive search terms that are determined to be the optimal search terms if any have been designated and based upon the similar and frequent search terms detected at the search engine 894 of the digital marketplace 882 during use of the computing device 822. These resulting learned optimal search terms may be suggested to a user or automatically implemented. Suggestion may come with an indicator and may be shown in a graph at a user interface (e.g., such as a graphical user interface) for, in some embodiments, approval by the user before implementation of the other processes executed by the processor 810 of the computing device 822.

[0109] An example representation of the graph is shown in FIG. 9. This example graph may indicate positions of each search term of a competitive product relative to the target product based on the frequency. Each point (e.g. circle) on the graph represented in FIG. 9 is representative of a search term. Each representative search term is arranged on the graph in FIG. 9 at a point that defines that terms frequency in appearing together with a search term of the target product and at a position where the search term is similar or not relative to the search terms associated with the target product. In this example graph, the further to the right any given search term is, the more similar the search terms of a competitive product are similar to the search termsof the target product. Additionally, the further to the left any given search term is, the less similar the search terms of the competitive product are similar to the search terms of the target product. Further, the closer to the top of the graph any given search term is, the more general the search term is compared to the target product while the closer to the bottom of the graph any given search term is, the more niche the search term is compared to the target product. In some embodiments, it may be most desirable to have a target product that has associated search terms relative to the search terms of a competitive product that is more general and similar. This indicates that the target product is competing with relatively well-known competing products. The processes described herein, may help to provide a report to a user indicative of how to adjust advertisement revenue to focus on more general and similar search terms as the competitive products.

[0110] In some embodiments, the machine learning module 896 may perform a forward propagation and backward propagation, using different input node values repeatedly to finely tune any matrices either weighted or not. In such a way, the machine learning module 896, in some embodiments, may adaptively leam how changes in the plurality of sets of target product search terms and rankings as well as a plurality of sets of competing product search terms and rankings may affect the data reflected in the competitive set report 898. The weight matrices associated with the layers of the neural network model in such an embodiment may describe, mathematically, these correlations for an individual target product. The neural network model (including designation of the node values in the input layer, and number of layers), along with the weight matrices associated with each layer, in some embodiments, may form a trained machine learning classifier, algorithm, or mathematical model to be used in generating any competitive set report 898 as described herein.

[0111] As described herein, the output from the, now trained, machine learning module 896 is a competitive set report 898. With the competitive set report 898 the computing device 822 may, with the processor 810 and NID 880, determine a current performance on the search terms related to the target product that are most relevant to the competitive set defined in the competitive set report 898. In this process, the twovariables that are discovered are how often a term appears in a search generally (e.g., a general search term volume, or how many times people search the term per day) and how often the term appears in searches associated with the competitive set report 898. More specifically, in some embodiments, those search terms found to be most general and similar among the target product and each competitive product are provided to the comparison module 834 which searches, via execution of the processor 810 at the search engine 894, those search terms defined in the competitive set report 898. During this process, the processor 810 may access the search engine 894 at the digital marketplace 882 or any other search engine and obtain search term metadata that describes the current performance of each of the search terms related to the target product that are most relevant to the competitive set defined in the competitive set report 898. The comparison module 834 may compare these most relevant search terms from the competitive set report 898 and provide that data to the user in the form of an actionable report 837. In some embodiments, the data descriptive of the search terms related to the target product that are most relevant to the competitive set in the actionable report 837 may be provided to the user via a graphical representation.

[0112] An example graphical representation of this current performance on the search terms related to the target product is shown in FIG. 10. As shown in FIG. 10, the further to the right of the graph any search term (e.g., represented by a circle) is, the search term has a higher volume or appears more often than the other search terms indicating a relatively higher relevance to competing products. Additionally, the further to the left of the graph any search term is, the search term has a lower volume or appears less often than the other search terms indicating a relatively lower relevance to competing products. Also, the further to the top of the graph any search term is, the search term has a higher relevance than the other search terms indicating a relatively higher relevance to competing products. Further as the search term is placed lower on the graph, the search term has a lower relevance than the other search terms indicating a relatively lower relevance to competing products. The most frequently searched and relevant terms may be provided to the comparison module 834 as well and used to further define the sustainability and feasible growth over time of the target product on, for example, the digital marketplace 882.

[0113] With those most relevant and most frequent search terms as indicated in FIG. 10 being discovered and presented in the actionable report 837, the computing device 822 may also quantify an opportunity of those search terms that, when associated with the target product, would increase the revenue and profit margins in selling the target product. In some embodiments, the processor 810 may execute a revenue module 899 to receive those relevant and most frequent search terms from the actionable report 837 and provide output to a user in the form of an increased revenue metric. The increase revenue may be calculated by the revenue module by, upon execution of the processor 810, the following formula:Increased Revenue =Impressions* Click Rate* Conversion Rate* Basket Size* PriceEquation 1

[0114] In the context of Equation 1, the impressions may be defined as the search volume of each of those most relevant and most frequent search terms, in some embodiments. In some embodiments, the quantify of impressions may be measured by a number of times an ad associated with the target product is presented to any given user during or after those most relevant and most frequent search terms are entered into a search engine 894. This data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website.

[0115] In some embodiments, the click rate of Equation 1 may be defined as an estimation along a curve of the probabilities of receiving clicks associated with the rank for each of the most relevant and most frequent search terms provided by the actionable report 837. For example, a ranking may be set to include a first place click rate (e.g.. 20% of clicks), second place click rate (14% of clicks), up until a 10th place click rate (6% of clicks) and beyond to any number of ranked most relevant and most frequent search terms. This data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website.

[0116] The conversion rate in Equation 1 may, in some embodiments, be defined as percentage of those most relevant and most frequent search terms that were clickedand associated with the target product and converted into a sale (e.g., resulted in a sale of the target product). This data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website.

[0117] In some embodiments, the basket size may be defined as the number of units purchased with each conversion. This number may be averaged over a plurality of purchases, in some embodiments. For example, where a number of conversions have been detected, the processor 810 may calculate how many units of the target product were purchased at any one time (e.g., units placed in a "shopping cart" for purchase at the digital marketplace 882). This value may at least be equal to 1 or more. Again, this data may be retrieved by the processor 810 by accessing a particular database or. as described herein, accessing a search query website.

[0118] The price of the target product may be, in some embodiments, a suggested retail price by the manufacturer. In some embodiments, the quantitative value of the price in Equation 1 is an average price of the target product, or other brand products resulting from the purchase, across any plurality of digital marketplaces 882 net of any discounts or promotions associated with those sales. This data may be retrieved by the processor 810 by accessing a particular database, accessing a search query website as described herein, or accessing sales data from a database maintained by the manufacturer of the target product.

[0119] In some embodiments, any of the impression values, click rate values, conversion rate values, basket size values, and price values in Equation 1 may be augmented by a weight value. In this embodiment, the weight value may accentuate or abate the effect of any one of these values in Equation 1 in order to better determine an increased revenue value or opportunity by the seller of the target product to increase that revenue. Because the actual, real-time data is being used in Equation 1, the seller of the target product or user of the computing device 822 may know, in real-time, whether to take advantage of any instance of increased views or sales of a product in order to increase interest in the target product over any competitors' products.

[0120] In some embodiments, the value associated with click rate in Equation 1 may significantly shift a decision by a user of the computing device 822 whether to take an action such as provide more advertising supporting the target product. This click rate associated with improving the search rank from the target product's current position on a search term to a potential rank position of a search phrase may be weighted to accommodate for an increase in importance of this value in some embodiments. For example, for a given search term that may improve an organic search rank for any of the search terms from 20th rank to 5th rank will improve the click rate by an estimated 3 times. Some of the improvement in rank may also originate from increased impressions and especially in situations where having an unranked target product on a search term achieves a search rank 10th among the rankings. In this example, this would improve clicks from zero (due to zero impressions) to the associated estimated clicks of 10th rank on that search term. As output, the processor 810 may, via the revenue module 899, provide an increased revenue report 802 describing how to, if at all. increase the revenue related to the sales of the target product.

[0121] In some instances, some search terms are not applicable to the target product but, if applicable to the target product, may increase revenue. These currently inapplicable search terms may be referred to, in the context of advertisement, as "unattainable." These unattainable search terms may be those search terms that are irrelevant, at least initially, to the target product for some reason or not yet associated with the target product because platform data associated with the digital marketplace 882 lacks data associated with the target product. In some embodiments, the machine learning module 896 may also be trained and used to receive data related to the characteristics of the target product, current competitors of the target product, and the current state of the ecommerce search term algorithm to determine the "winnability" of a search term. The winnability of a search term may be defined as the probability of winning each search term (e.g., having the target product associated with the search term) at any given point in time along with the estimated costs to win those search terms.

[0122] The machine learning module 896 may be trained with winnability7inputs as described herein in order to provide a winnability report 804. Some of the inputs for this model included any number of inputs and the description of certain types of inputs is not meant to limit the breadth of input into the machine learning module 896 in order to obtain a winnability report and the present specification contemplates these additional and different inputs. By way of example, an input may include a current and historical price for both the target product and competitive products. This historical pricing may be retrieved from one or more digital marketplaces 882 via the execution of the processor 810 and NID 880 as described herein. In this specific example, the processor 810 may cause the NID 880 to access the one or more digital marketplaces 882 either via a wired (wired transmitter / receiver 840) or wireless (wireless transmitter / receiver 850) connection, find instances of the target product and competing products being sold, and retrieve their historic pricing values.

[0123] Another input to the machine learning module 896 may include a current and historical review ratings and review counts associated with the target product and competing products. These review ratings and review counts data may be retrieved from one or more digital marketplaces 882 via the execution of the processor 810 and NID 880 as described herein. Digital marketplaces 882 often provide a GUI that allows the consumer of the target product and competing products to enter text descriptive of the consumers' experiences with the target product and competing products as well as a ranked evaluation of those products in the form of a number rating system or star rating system. In this specific example, the processor 810 maycause the NID 880 to access the one or more digital marketplaces 882 either via a wired or wireless connection and find review ratings and review counts associated with the target product and competing products being sold, and provide that review ratings and review counts data to the machine learning module 896.

[0124] Yet another input to the machine learning module 896 may include content similarity7scores of any a search term related to the target product and competing products. These scores may be generated based on the data provided, in some embodiments. In FIG. 9., for example, the further to the right any given search term is on the graph of FIG. 9. the more similar the search terms of a competitive product aresimilar to the search terms of the target product. In a specific example, the x-axis (bottom) of the graph of FIG. 9, or its associated data, may be used to assign this similarity score. As is shown in FIG. 9, the similarity score may be either a positive or a negative score per the number ranking on the x-axis of FIG. 9. In this example, the similarity score may be a positive weight or a negative weight reflected in the winnability report 804 provided by the processor 810 upon execution of the machine learning module 896. In this embodiment, the processor 810 may, again, cause the NID 880 to access the one or more digital marketplaces 882 either via a wired or wireless connection and retrieve the plurality of sets of target product search terms and rankings as well as a plurality of sets of competing product search terms and rankings associated with the target product and competing products being sold. This data is then provided to the machine learning module 896.

[0125] Still further, other input to the machine learning module 896 may include platform specific information such as average best seller rank (BSR) for any given digital marketplaces 882 associated with the target product and any number of competing products. A BSR may vary at any given digital marketplace 882, but these rankings may be averaged over a plurality7of digital marketplaces 882 to get this value. In this embodiment, the processor 810 may, again, cause the NID 880 to access the one or more digital marketplaces 882 either via a wired or wireless connection and retrieve this BSR data. This data is then provided to the machine learning module 896.

[0126] Other input to the machine learning module 896 may include a projected search term volume and click distribution. In connection with this type of data provided to the machine learning module 896, the projected search term volume may be retrieved from the data used to create the graph in FIG. 10. This data describing how often any given search term associate with the target product and competing product appears in searches may be accessed by the processor 810 and provided as input to the machine learning module 896. Additionally, any click distribution describing how many clicks any given search term gets may be accessed by the processor 810 and NID 880 at the search engine 894 of the digital marketplaces 882.

[0127] Yet other input to the machine learning module 896 may include historical variations in search term ranks related to the target product and search phrase products. At any given time, a search engine 894 may have varying fluctuations in what is searched for on the internet. These search terms may be ranked and their historic ranking may change over time based on a number of social, political, environmental, and economic factors. This historical data may be retrieved from the search engine 894 by the processor 810 and NID 880 and provided to the machine learning module 896.

[0128] Another example input to the machine learning module 896 may include targeted advertising spending associated with the search terms associated with the target product. This data may be maintained on any database that is accessible to the processor 810 of the computing device 822. In a specific embodiment, this data descriptive of the targeted advertising spending associated with the search terms associated with the target product may be maintained by the seller of the targeted product on a private database and the user of the computing device 822 may be given secure access to that database. This type of data too may be provided to the machine learning module 896.

[0129] With all of these different types of data obtained by the processor 810 via the NID 880, the machine learning module 896 may build a number of mathematical models that provide a winnability report 804 that describes a probability of winning each search term (e.g., having the target product associated with the search term) at any given point in time along with the estimated costs to win those search terms. As with each machine learning module 896, the machine learning module 896 may be "taught" by using the winnability' factors described herein. In some embodiments, the machine learning module 896 may implement one or more non-parametric and parametric learning techniques. For example, the machine learning module, in some embodiments, may model the relationships between each plurality of sets of winnability factors using a layered neural network topology7. Such a neural network, in some embodiments, may include an input layer (e.g., the winnability factors) including a known, recorded set of values for each of these parameters, settings, indicators, and usage data metrics, and an output layer including a projectedw innability report 804, based on the known, recorded set of values in the input layer. The machine learning module 896, in some embodiments, may propagate input through the layers of the neural network to project or predict an optimal winnabilities of search terms based on the known and recorded search term metrics, and compare these projected values to optimal search terms to be presented in the winnability report 804. Using a back-propagation method, the machine learning module 896, in some embodiments, may then use the difference between the projected values and the know n optimal values to adjust weight matrices of the neural network describing the ways in which changes in each of the search term data metrics are likely to affect the optimal search terms to be presented in the winnability report 804.

[0130] With the output layer, the computing device 822 may provide learned competitive search terms that are determined to be the optimal search terms if any have been designated and based upon the winnable search terms detected at the search engine 894 of the digital marketplace 882 or other database during use of the computing device 822. These resulting learned optimal search terms may be suggested to a user or automatically implemented. Suggestion may come with an indicator and may be shown in a graph at a user interface for, in some embodiments, approval by the user before implementation of the other processes executed by the processor 810 of the computing device 822.

[0131] In some embodiments, the machine learning module 896 may perform a forward propagation and backward propagation, using different input node values repeatedly to finely tune any matrices either weighted or not. In such a way, the machine learning module 896, in some embodiments, may adaptively learn how changes in the winnability factors may affect the data reflected in the winnability report 804. The w eight matrices associated with the layers of the neural network model in such an embodiment may describe, mathematically, these correlations for an individual target product. The neural network model (including designation of the node values in the input layer, and number of layers), along with the weight matrices associated with each layer, in some embodiments, may form a trained machine learning classifier, algorithm, or mathematical model to be used in generating any winnability report 804 as described herein.

[0132] As described herein, the output from the, now trained, machine learning module 896 is a winnability report 804. With the winnability report 804 the computing device 822 may, with the processor 810 and NID 880. determine a probability of attaining the desired change in revenue based on a required investment. In some embodiments, the required investment may be calculated by the following equation:Required Investment = Projected Bid* (Impressions* Clickthrough Rate)Equation 2

[0133] A return on investment (ROI) may then be calculated using the following equation:ROI = Increased Revenue *(Projected Time to Remain at Required Investment)Equation 3

[0134] With Equations 2 and 3 those target products with search terms with high returns on investment can then be prioritized for both advertising and search engine optimization actions by the user. In this manner, the computing device 822 may execute the machine learning module 896 for a second purpose of determining the "winnability" of a search term where additional funds are applied to advertisements and search engine optimization.

[0135] In some embodiments, the ad spend margin, ad spend potential and revenue potential calculations by the processor 810 may also be conducted to specifically determine how much additional advertising funds to apply to the target product. Again, the ad spend margin may be calculated by multiplying a target ACoS by the price of the target product. A target ACoS may be determined and set by the seller based on available capital or may be set by the seller based on the fraction of the revenue received thus far from the sale of the target product on the digital marketplace 382 and costs of manufacturing. Ad spend potential may then be calculated by multiplying monthly opportunity' units (OU) by the spend margin. The monthly OUs may be calculated as a result of the conversion rate of clicks to the target product thatis the result of sales of the target product after a purchaser has viewed the product. The revenue potential may then be calculated by multiplying the OU with the price of the target product. This revenue potential of each of the target products may be ranked to determine the placement of the target product within the digital marketplace 882. The search terms presented in the winnability report 804 may be sorted by revenue potential to determine the target product's best opportunities for revenue growth. In order to refine a recommendation, the process may continue with inputting estimated bid amounts from the digital marketplaces 882 required to win advertising slots for these keywords. In this manner, the execution of the processor 810 may initiate these calculations in order to predict a number of clicks and a cost necessary to achieve the potential growth. The equation to make this calculation is found in connection with Equation 2 herein.

[0136] An ROI may further be calculated by the following equation:ROI = Ad Spend Potential* (Investment Payoff Term - Investment Needed)Equation 4

[0137] As highly winnable terms are targeted in this process with both advertising and search engine optimization techniques, increasing the associated impressions, clicks, and conversions, the processing applied to the target product may continually adapt. As a target product succeeds on new search terms the competitive products set defined in the competitive set report 898 will shift to be compared to larger and less niche competing products. As the competitive products set defined in the competitive set report 898 shifts, the competitive terms set w ill shift as well. As reviews, terms, seller ranks, and other attributes shift, the winnability and associated required investment of each term also shifts. With the shift in winnability, new terms are prioritized and the cycle continues iteratively to cause the revenue associated with the targeted product to increase proportionally.

[0138] In some embodiments, the computing device 882 (e.g., and / or the system 100, the computing device 420, the computing device 520, and / or the computing device 322) may be configured to generate an optimal cost per click real-time bidding systembalancing target spend and efficiency. For example, the computing device 882 may, for bidding campaigns that may otherwise run out of budget or overshoot desired spend, lower bids. Additionally, or alternatively, the computing device 882 may. for campaigns that underspend but have room given efficiency targets, increase bids.

[0139] The computing device 882 may adjust the bids to achieve a return on advertising spend over a period of time. Campaigns that run out of budget or exceed spending targets, before a period ends, may not realize all potential revenue due to spending more than necessary over an undesirably short period of time. Alternatively, campaigns that do not spend enough budget, sacrifice sales volume. As such, the computing device 882 may provide automated computations that adjust bids to maximize efficiency while maintaining target spend. The computing device 882 may use one or more critical models and / or data processes to automatically adjust bids. The one or more critical models and / or data processes may include and / or use efficiency targets, estimated conversion rates, estimated order values, diminishing returns on incremental spend, and / or other suitable rates, values, data, and / or the like.

[0140] In some embodiments, the computing device 882 uses one or more of: a minimum efficiency and strategic requirements provided by a user; and desired spend within strategic requirements and minimum efficiency levels provided by the user. The computing device 882 may generate one or more models for individual targets including: a model to estimate conversion rate; a model to estimate order values; a model to estimate cost per click; and / or one or more other suitable models.

[0141] The computing device 882 may, using output from the one or more models, generate another model to predict tradeoff between bid, cost per click, and revenue to generate incremental revenue and incremental cost tradeoff. The computing device 882 may use a data stream to ingest data and predict, using the models, target bidding on a near real-time basis. The computing device 882 may receive user preferences, constraints, and a spending target, and may optimally adjust bids according to model outputs to achieve the desired spend, such that the desired spend is achievable given the constraints.

[0142] In some embodiments, the computing device 882 may receive user input data. In some embodiments, the user input data includes a desired total bid value and a bid period. In some embodiments, the user input data includes information associated the at least one product. In some embodiments, the user input includes information associated with the at least one marketplace.

[0143] The computing device 882 may generate a first machine learning model that generates a predicted expected performance based on the user input data. The first machine learning model may include a tree-based machine learning model, a Bayesian machine learning model, or other suitable machine learning model.

[0144] The computing device 882 may adjust at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0145] In some embodiments, the computing device 882 may generate a second machine learning model that generates a predicted expected cost based on the user input. The second machine learning model may include a tree-based machine learning model, a Bayesian machine learning model, or other suitable machine learning model. In some embodiments, the computing device 882 may adjust the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost.

[0146] FIG. 12 is a flow diagram illustrating a real-time bidding method 1200. according to the principles of the present disclosure. At 1202, the method 1200 receives user input data.

[0147] At 1204, the method 1200 generates a first machine learning model that generates a predicted expected performance based on the user input data.

[0148] At 1206, the method 1200 adjusts at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0149] In some embodiments, a real-time bidding system includes a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive user input data; generate a first machine learning model that generates a predicted expected performance based on the user input data; and adjust at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0150] In some embodiments, the user input data includes a desired total bid value and a bid period. In some embodiments, the user input data includes information associated the at least one product. In some embodiments, the user input includes information associated with the at least one marketplace. In some embodiments, the first machine learning model includes a tree-based machine learning model. In some embodiments, the first machine learning model includes a Bayesian machine learning model. In some embodiments, the instructions further cause the processor to generate a second machine learning model that generates a predicted expected cost based on the user input. In some embodiments, the second machine learning model includes a tree-based machine learning model. In some embodiments, the second machine learning model includes a Bayesian machine learning model. In some embodiments, the instructions further cause the processor to adjust the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost.

[0151] In some embodiments, a real-time bidding method includes receiving user input data, generating a first machine learning model that generates a predicted expected performance based on the user input data, and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

[0152] In some embodiments, the user input data includes a desired total bid value and a bid period. In some embodiments, the user input data includes information associated the at least one product. In some embodiments, the user input includes information associated with the at least one marketplace. In some embodiments, thefirst machine learning model includes a tree-based machine learning model. In some embodiments, the first machine learning model includes a Bayesian machine learning model. In some embodiments, the method also includes generating a second machine learning model that generates a predicted expected cost based on the user input. In some embodiments, the second machine learning model includes a tree-based machine learning model. In some embodiments, the second machine learning model includes a Bayesian machine learning model. In some embodiments, the method also includes adjusting the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost.

[0153] Any methods disclosed herein comprise one or more steps or actions for performing the described method. The method steps and / or actions may be interchanged with one another. In other words, unless a specific order of steps or actions is required for proper operation of the embodiment, the order and / or use of specific steps and / or actions may be modified.

[0154] Recitation in the claims of the term "first" with respect to a feature or element does not necessarily imply the existence of a second or additional such feature or element. Elements recited in means-plus-function format are intended to be construed in accordance with 35 U.S.C. §1 12 Para. 6. It will be apparent to those having skill in the art that changes may be made to the details of the above-described embodiments without departing from the underlying principles of the disclosure.

[0155] The above discussion is meant to be illustrative of the principles and various embodiments of the present disclosure. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

[0156] The word “example” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather,use of the word “example’' is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X includes A or B” is intended to mean any of the natural inclusive permutations. That is, if X includes A; X includes B; or X includes both A and B, then “X includes A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Moreover, use of the term “an implementation” or “one implementation” throughout is not intended to mean the same embodiment or implementation unless described as such.

[0157] Implementations of the systems, algorithms, methods, instructions, etc., described herein can be realized in hardware, software, or any combination thereof. The hardware can 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 circuit. In the claims, the term “processor” should be understood as encompassing any of the foregoing hardware, either singly or in combination. The terms “signal” and “data” are used interchangeably.

[0158] As used herein, the term module can include a packaged functional hardware unit designed for use with other components, a set of instructions executable by a controller (e g., a processor executing software or firmware), processing circuitry configured to perform a particular function, and a self-contained hardware or software component that interfaces with a larger system. For example, a module can include an application specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA), a circuit, digital logic circuit, an analog circuit, a combination of discrete circuits, gates, and other types of hardware or combination thereof. In other embodiments, a module can include memory that stores instructions executable by a controller to implement a feature of the module.

[0159] Further, in one aspect, for example, systems described herein can be implemented using a general-purpose computer or general-purpose processor with a computer program that, when executed, carries out any of the respective methods, algorithms, and / or instructions described herein. In addition, or alternatively, for example, a special purpose computer / processor can be utilized which can contain other hardware for carrying out any of the methods, algorithms, or instructions described herein.

[0160] Further, all or a portion of implementations of the present disclosure can 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, communicate, or transport the program for use by or in connection with any processor. The medium can be, for example, an electronic, magnetic, optical, electromagnetic, or a semiconductor device. Other suitable mediums are also available.

[0161] The above-described embodiments, implementations, and aspects have been described in order to allow easy understanding of the present disclosure and do not limit the present disclosure. On the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation to encompass all such modifications and equivalent structure as is permitted under the law.

Claims

CLAIMSWhat is claimed is:

1. A real-time bidding system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to: receive user input data; generate a first machine learning model that generates a predicted expected performance based on the user input data; and adjust at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

2. The system of claim 1, wherein the user input data includes a desired total bid value and a bid period.

3. The system of claim 1, wherein the user input data includes information associated the at least one product.

4. The system of claim 1, wherein the user input includes information associated with the at least one marketplace.

5. The system of claim 1, wherein the first machine learning model includes a tree-based machine learning model.

6. The system of claim 1, wherein the first machine learning model includes a Bayesian machine learning model.

7. The system of claim 1, wherein the instructions further cause the processor to generate a second machine learning model that generates a predicted expected cost based on the user input.

8. The system of claim 7, wherein the second machine learning model includes a tree-based machine learning model.

9. The system of claim 7, wherein the second machine learning model includes a Bayesian machine learning model.

10. The system of claim 7, wherein the instmctions further cause the processor to adjust the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost.

11. A real-time bidding method comprising: receiving user input data; generating a first machine learning model that generates a predicted expected performance based on the user input data; and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.

12. The method of claim 11, wherein the user input data includes a desired total bid value and a bid period.

13. The method of claim 11, wherein the user input data includes information associated the at least one product.

14. The method of claim 11, wherein the user input includes information associated with the at least one marketplace.

15. The method of claim 11, wherein the first machine learning model includes a tree-based machine learning model.

16. The method of claim 11, wherein the first machine learning model includes a Bayesian machine learning model.

17. The method of claim 11, further comprising generating a second machine learning model that generates a predicted expected cost based on the user input.

18. The method of claim 17, wherein the second machine learning model includes a tree-based machine learning model.

19. The method of claim 17, wherein the second machine learning model includes a Bayesian machine learning model.

20. The method of claim 17, further comprising adjusting the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost.