Patent Publication Number: US-11657355-B2

Title: Systems and methods to recommend price of benefit items offered through a membership platform

Description:
FIELD 
     The disclosure relates to systems and methods to recommend price of benefit items offered through a membership platform. 
     BACKGROUND 
     Different platforms may be utilized by entities seeking contributions from the general public to obtain a needed service(s) and/or resource(s). Some of these platforms facilitate raising resources (i.e., funds) from the users through monetary contributions or donations to support a project. Oftentimes, supporters of a project are given rewards or special perks, where the size and/or exclusivity of the rewards or special perks may depend on the amount contributed. 
     SUMMARY 
     A membership platform may be comprised of users including one or more of content creators, subscribers, and/or other users. Content creators may be users of the membership platform who offer content (also referred to as “benefit items”) to subscribers in exchange for consideration. A “benefit item” may refer to a good and/or service. A good may comprise a physical good and/or a digital good. In some implementations, subscribers may donate funds to a content creator such that the benefit item may be the altruism in supporting the content creator. Subscribers may be users of the membership platform who subscribe, through payment of a one-time and/or recurring (e.g., monthly) fee, to one or more content creators. A subscriber of an individual content creator may obtain access to benefit items offered through the membership platform by virtue of being a subscriber to the individual content creator. A subscriber of an individual content creator may obtain preferential access to benefit items offered through the membership platform by virtue of being a subscriber to the individual content creator. Preferential access may refer to subscriber-only access to benefit items and/or other content. Preferential access may refer to tiered levels of access to benefit items and/or other content. Different levels of access may offer different quantities, content types, and/or combinations of benefit items. Different levels may correspond to different amounts of consideration paid by the given subscriber. In some implementations, other users of the membership platform may obtain limited access to benefit items. In some implementations, other users may be non-paying users and/or one-time visitors to the membership platform. 
     Within a membership platform, content creators may not know how to price their benefit items in order to obtain more paying subscribers. The content creators would greatly benefit from a determination of optimal pricing for the different benefit items and/or types of benefit items. One or more implementations described herein may use machine learning, heuristics, and/or other techniques to determine optimal pricing. 
     One aspect of the present disclosure relates to a system configured for recommending price of benefit items offered through a membership platform. The system may include one or more hardware processors configured by machine-readable instructions. The computer components may include one or more of a benefit component, a consumption component, a model training component, recommendation component, and/or other computer components. 
     The benefit component may be configured to obtain benefit information for content creators of a membership platform, and/or other information. The benefit information may characterize benefit items offered by the content creators to subscribers of the membership platform in exchange for requested amounts of consideration. By way of non-limiting illustration, the benefit information may characterize a first benefit item offered by a first content creator in exchange for a first requested amount of consideration. 
     The consumption component may be configured to obtain consumption information and/or other information. The consumption information may describe acceptance of offers for the benefit items at the requested amounts by the subscribers of the content creators. The acceptance of the offers for the benefit items by the subscribers may be described based on one or more of quantity of subscribers who have accepted the offers at the requested amounts, amount of revenue received from the acceptance of the offers at the requested amounts, and/or other measure. 
     The model training component may be configured to train a machine learning model to generate recommendations for recommended amounts of consideration for the benefit items that correspond to greater acceptance. The machine learning model may be trained on input/output pairs to generate a trained machine learning model. The individual input/output pairs may include training input information, training output information, and/or other information. The training input information may include the benefit information and/or other information. The training output information may include the consumption information and/or other information. The model training component may be configured to store the trained machine learning model. 
     The recommendation component may be configured to determine, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance. By way of non-limiting example, a first recommended amount may be determined for the first benefit item. The first recommended amount may be different from the first requested amount. 
     The recommendation component may be configured to generate recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators. By way of non-limiting example, a first recommendation conveying the first recommended amount may be generated for the first content creator. 
     In some implementations, the recommendation component may be configured to use the trained machine learning model to determine recommended amounts of consideration for benefit items yet to be offered to subscribers via the membership platform. By way of non-limiting illustration, the recommendation component may be configured to provide the trained machine learning model with benefit information characterizing a second benefit item of a second content creator. The trained machine learning model may be configured to output recommended amounts of consideration for the benefit items that correspond to greater acceptance. By way of non-limiting illustration, the model may output a second recommended amount for the second benefit item. 
     As used herein, any association (or relation, or reflection, or indication, or correspondence) involving servers, processors, client computing platforms, and/or another entity or object that interacts with any part of the system and/or plays a part in the operation of the system, may be a one-to-one association, a one-to-many association, a many-to-one association, and/or a many-to-many association or N-to-M association (note that N and M may be different numbers greater than 1). 
     As used herein, the term “obtain” (and derivatives thereof) may include active and/or passive retrieval, determination, derivation, transfer, upload, download, submission, and/or exchange of information, and/or any combination thereof. As used herein, the term “effectuate” (and derivatives thereof) may include active and/or passive causation of any effect, both local and remote. As used herein, the term “determine” (and derivatives thereof) may include measure, calculate, compute, estimate, approximate, generate, and/or otherwise derive, and/or any combination thereof. 
     These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example membership system. 
         FIG.  2    illustrates a system configured to recommend price of benefit items offered through a membership platform, in accordance with one or more implementations. 
         FIG.  3    illustrates an example database. 
         FIG.  4    illustrates a method to recommend price of benefit items offered through a membership platform, in accordance with one or more implementations. 
         FIG.  5    illustrates a user interface, in accordance with one or more implementations. 
         FIG.  6   . illustrates a user interface, in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
     Some entities may seek to obtain funds through subscriptions. Such entities may utilize online membership platforms that allow consumers to sign up for ongoing payments in exchange for rewards or other membership benefits. Entities seeking funding may be content creators, for example, artists, musicians, educators, etc. Content creators may create content, which may refer to one or more of information, experience, products, and/or other content provided to an audience or end-user, whether it be digital, analog, virtual, and/or other form. For example, types of content may include but is not limited to video content, podcasts, photographic art, webcomics, do-it-yourself crafts, digital music, performance art, and/or other types of content. Content creators may utilize membership platforms that allow consumers to become subscribers of the content creator. As subscribers, consumers may contribute or donate money to a content creator on a recurring (e.g., weekly or monthly) basis and/or per piece of content created by the content creator. Content creators may interact with subscribers and/or prospective subscribers (e.g., consumers that show interest in the content created by content creators) in a variety of ways. Understanding the price of benefit items which lead to greater acceptance by subscribers is important in order to drive the content creators to greater growth. 
       FIG.  1    illustrates an example subscriber-based membership system  10  (sometimes referred to herein as a “membership platform”). A content creator  12  may register and set up a creator account with subscription platform  16 . Content creator  12  may create a page on a website hosted by server  22  of subscription platform  16  and input relevant information. Content creator  12  may input information associated with and/or relevant to content creator  12  via subscription component  18 , such as creation information, content information, information specifying desired and/or initial subscription levels, preferred revenue source information (e.g., preferred currency, currency source, and/or other information), identification information (e.g., identification of applicable tax jurisdiction and/or other information), and/or other information. A page created by content creator  12  may be built using such information to make potential consumers aware of how content creator  12  may wish to be supported/receive support for his/her content creation in addition to subscribership revenue. Content creator  12  may set up a content creator account with subscription platform  16  through subscription component  18  or another appropriate component allowing content creator  12  to register with subscription platform  16 . Various types of information regarding content creator  12  may be input into subscription platform  16 , some of which may be information identifying content creator  12 . 
     Consumer  14  (also referred to as a “subscriber”) may set up a subscriber account with subscription platform  16 . In setting up the subscriber account, consumer  14  may input demographic information relevant to consumer  14  (e.g., age, income, job, etc.). Information identifying consumer  14  (e.g., name, a picture, a phone number, etc.) may be input by consumer  14  when setting up the subscriber account. Through the page created by content creator  12 , a consumer  14  may pledge to donate a given amount of money to content creator  12  every time content creator  12  creates content. For example, if content creator  12  is an artist, consumer  14  may pledge to donate ten dollars each time content creator  12  creates a piece of art. 
     In order to remit payment to content creator  12 , consumer  14  may set up a payment mechanism through subscription platform  16  as part of setting up his/her subscriber account. When subscription platform  16  is notified or determines that content creator  12  has created content, subscription platform  16  may access payment network  26  to obtain and/or transfer the pledged amount from consumer bank  28  to content creator bank  30 . Alternatively (or in addition to per content pledge donations), consumer  14  may pledge to donate a given amount to content creator  12  on a recurring basis through subscription platform  16 . For example, consumer  14  may pledge to donate five dollars each month to content creator  12 , where each month, subscription platform  16  may access payment network  26  to obtain and transfer the pledged amount from consumer bank  28  to content creator bank  30 . It should be understood that consumer  14  may have an established relationship with consumer bank  28 , and that content creator  12  may have an established relationship with content creator bank  30 . It should be noted that subscription platform  16  may retain a portion, such as some percentage, of the pledged amount, as a fee for hosting the page created by content creator  12 , providing payment services, etc. 
     As consideration for the pledged donations, content creator  12  may provide some type of preferential access to consumer  14  in the form of, e.g., special perks or rewards. Content creator  12  may specify tiers of preferential access based upon the amount of money consumer  14  pledges to donate and/or depending on whether the pledged donation is a recurring donation or a per content donation. The amounts and/or types of pledged donations that may be made by consumer  14  to back content creator  12  may be referred to as subscription levels. 
     For example, in return for a monthly, recurring dollar amount of donation, content creator  12  may provide a high-resolution digital image of the artwork created during that month to consumer  14 . In exchange for a weekly, recurring dollar amount of donation, content creator  12  may provide a high-resolution digital image of the artwork created during that month as well as a time-lapse video of content creator  12  creating the artwork. In exchange for another dollar amount per content donation, content creator  12  may provide a low-resolution digital image of the artwork. For another dollar amount per content donation, content creator  12  may engage in a live webchat or live meet-and-greet with consumer  14 . Various types of preferential access may be provided by content creator  12  to consumer  14 , and content creator  12  may specify the subscription level to preferential access correlation. 
     The preferential access may be provided to consumer  14  from content creator  12 . For example, content creator  12  may email digital copies of artwork to consumer  14  over a communications network, such as a local area network (LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile communication network, a satellite network, the Internet, fiber optic, coaxial cable, infrared, radio frequency (RF) or any other suitable network. The preferential access may be provided to consumer  14  from content creator  12  via subscriber platform  16 . For example, the live webchat between content creator  12  and consumer  14  may be provided through some chat functionality of the page of content creator  12  hosted on server  22  of subscription platform  16 , which may reside on communications network  32  or on another network (not shown). 
     It should be noted that not all subscription levels are necessarily associated with preferential access. Some consumers may be driven to subscribe to content creator  12  on the basis of created content rather than any special perks or rewards. 
     The specification and management of subscriptions on behalf of content creator  12  may be handled by subscription component  18  alone or in conjunction with database  24 . For example, a user interface may be provided via subscription component  18  allowing content creator  12  to specify his/her desired subscription levels and corresponding preferential access, as well as his/her preferred sources of revenue. Subscription component  18  may receive the information input by content creator  12  and transmit the information for storage as one or more records, matrices, or other data structures in database  24  or within memory local to subscription component  18 . Database  24  or the local memory of subscription component  18  may be configured in a suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and database storage types will be apparent to persons having skill in the relevant art. 
     Content creator  12  may add subscribership information, update and/or delete existing subscribership information, add creation information, as well as update and/or delete creation information, add, update, and/or delete preferential access information and/or its correspondence to subscription levels, etc. Such changes may be input via subscription component  18  and reflected in its local memory and/or database  24 . It should be understood that content creator  12  and/or consumer  14  may be an individual or some entity representative of an individual or group of individuals. 
     Apart from providing preferential access to consumer  14 , content creator may engage with consumer  14  by interacting in a variety of ways. For example, content creator  12  may communicate with consumer  14  over email, one or more social media platforms, a messaging platform or other appropriate communication mechanism or method. It should be understood that such communication platforms or mechanisms may be embodied in communications network  32  allowing content creator  12  and consumer  14  to communicate outside of subscription platform  16 . It should be understood that communication platforms or mechanisms may operate in conjunction with subscription platform  16  such that one or more of their respective functionalities may be utilized through subscription platform  16 . For example, social media hyperlinks allowing information from content creator  12 &#39;s page may be provided on the webpage allowing content creator  12  to share content creation progress updates with consumer  14 . For example, content creator  12  may respond to a communication from consumer  14  posted on a comment section provided on content creator  12 &#39;s page in a private message or as part of the comment thread. It should be noted that content creator  12  may engage a single consumer, e.g., consumer  14 , one-on-one and/or may engage a group of consumers. For example, content creator  12  may post a “public” comment on his/her webpage that may be seen by any consumer that is a subscriber to content creator  12  and/or any consumer that may be a potential subscriber. 
       FIG.  2    illustrates a system  100  configured to recommend price of benefit items offered through a membership platform. In some implementations, system  100  may include one or more of server(s)  102 , client computing platform(s)  104 , and/or other components. The terms client computing platform, remote computing platform, and/or computing platform may be used interchangeably herein to refer to individual ones of the client computing platform(s)  104 . Server(s)  102  may be configured to communicate with one or more client computing platforms  104  according to a client/server architecture and/or other architectures via one or more network(s)  122 . In some implementations, one or more network(s)  122  may include the Internet and/or other networks. Client computing platform(s)  104  may be configured to communicate with other client computing platforms via server(s)  102  and/or according to a peer-to-peer architecture, a client-server architecture, and/or other architectures. Users may access system  100  via client computing platform(s)  104 . 
     It is noted the system  100  of  FIG.  2    may be the same as, or included as part of, the system  10  shown in  FIG.  1   . For example, the server(s)  102  may be the same as or included in servers  22 . Network(s)  122  may be the same as or included in network  32 . Individual client computing platforms of one or more client computing platforms  104  may be computing platforms utilized by content creator  12  and/or consumer  14  to access system  10  and/or system  100 . Non-transitory electronic storage  118  may be the same as or included in database  24 . Accordingly, those skilled in the art will recognize that although system  10  and system  100  are shown and described separately, they may comprise a single common system. However, in some implementations, the features and/or functionality of system  100  may be provided remotely as a separate system from system  10 . 
     Server(s)  102  may be configured by machine-readable instructions  106 . Machine-readable instructions  106  may include one or more instruction components. The instruction components may include computer program components. The instruction components may include one or more of a benefit component  108 , a consumption component  110 , a model training component  112 , a recommendation component  114 , and/or other instruction components. 
     Benefit component  108  may be configured to obtain benefit information for content creators of a membership platform and/or other information. The benefit information may characterize benefit items offered by the content creators to subscribers of the membership platform in exchange for requested amounts of consideration. The benefit items may be characterized by different benefit types of the benefit items, and/or other characteristics. The benefit types may characterize the benefit items based on one or more of an amount of consideration requested to access the benefit items (e.g., the subscription level needed to obtain access to a given benefit item), a medium of creation, content of the benefit items, and/or other characteristics. Medium of creation may include one or more of physical, digital, and/or other mediums. The content of the benefit items may include one or more of a painting, a song, music, spoken word audio (e.g., a podcast, a shout out, and/or other audio), a video content, photographic art, webcomics, do-it-yourself crafts, performance art, and/or other content. In some implementations, benefit information may be obtained from individual content creator through entry and/or selection of the benefit information by the content creators into a user interface. 
     By way of non-limiting example, the benefit information may characterize a first benefit item offered by a first content creator in exchange for a first requested amount of consideration. The first benefit item may be characterized by a first benefit type and/or other characteristics. 
     Consumption component  110  may be configured to obtain consumption information and/or other information. The consumption information may describe acceptance of offers for the benefit items at requested amounts by the subscribers. The acceptance of the offers for the benefit items by the subscribers may be described based on one or more of quantity of subscribers who have accepted the offers at the requested amounts (e.g., including one or more of a total, an average, a frequency, a quantity over a certain time period, etc.), an amount of revenue received from subscribers who have accepted the offers at the requested amounts (e.g., including one or more of a total, an average, a frequency, a quantity over a certain time period, etc.), and/or other information. 
     Model training component  112  may be configured to train a machine learning model to determine recommended amounts of consideration for the benefit items that correspond to greater acceptance. The model training component  112  may be configured to train the machine learning model based on a training corpus and/or other information to generate a trained machine learning mode. The training corpus may include input/output pairs. The individual input/output pairs may include one or more of training input information, training output information, and/or other information. The training input information may include benefit information and/or other information. The training output information may include consumption information and/or other information. Model training component  112  may be configured to store the trained machine learning model. 
     The machine learning model may include one or more of a neural network, a convolutional neural network, and/or other machine-learning framework. In some implementations, the machine learning model may be configured to optimize objective functions. In some implementations, optimizing objective functions may include one or both of maximizing a likelihood of the training set or minimizing a classification error on a held-out set. 
     Recommendation component  114  may be configured to determine, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance. The condition of “greater” may be relative to one or more of other benefit items offered by other content creators (e.g., a system-wide basis), other benefit items offered by individual content creators (e.g., a content creator basis), and/or other information. In some implementations, the recommended amounts of consideration for the benefit items may represent the requested amounts of consideration for the benefit items having the largest quantity of subscribers who have accepted the offers for the benefit items at the requested amounts. In some implementations, the recommended amounts of consideration for the benefit items may represent the requested amounts of consideration for the benefit items having returned the highest revenue from the acceptance of the offers at the requested amounts. 
     In some implementations, the recommended amounts of consideration for the benefit items may be characterized and/or categorized by benefit type. By way of non-limiting example, a first recommended amount may be determined for the first benefit item. By virtue of the first benefit item being the first benefit type, the first recommended amount may be determined for benefit items of the first benefit type. 
     Recommendation component  114  may be configured to generate recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators. By way of non-limiting example, a first recommendation conveying the first recommended amount may be generated for the first content creator. 
     In some implementations, the recommendation component  114  may be configured to provide the trained machine learning model with benefit information characterizing one or more benefit items of one or more content creators not included in the training corpus. By way of non-limiting illustration, the trained machine learning model may be used during registration of user accounts of the content creators with the membership platform in order to initially set prices. The trained machine learning model may be configured to output recommendations for recommended amounts of consideration. By way of non-limiting illustration, the recommendation component  114  may be configured to provide the trained machine learning model benefit information characterizing a second benefit item offered by a second content creator. The benefit information characterizing the second benefit item offered by a second content creator may be information not included in the training corpus. The trained machine learning model may output a second recommendation for a second recommended amount of consideration for the second benefit item. 
     In some implementations, recommendation component  114  may be configured to generate recommendations for individual content creators conveying the recommended amounts for the benefit items of a certain benefit type. The benefit types may characterize the benefit items based on one or more of an amount of consideration requested to access the benefit items (e.g., the subscription level needed to obtain access to a given benefit item), a medium of creation, content of the benefit items, and/or other characteristics. By way of non-limiting illustration, a third benefit item and/or other benefit items may be of the first benefit type. Based on the first benefit item being the first benefit type, the first recommended amount may be determined for the benefit items of the first benefit type. A third recommendation may be generated conveying the first recommended amount for the third benefit item by virtue of the third benefit also being of the first benefit type. 
     In some implementations, recommendation component  114  may be configured to effectuate presentation of the recommendations on a user interface displayed on computing platform(s)  104  associated with the content creators. The user interface may be configured to obtain user entry and/or selection by the content creators to accept the recommendations. In some implementations, the user interface may be part of the membership platform. In some implementations, the user interface may be provided through one or more communication channels external to the membership platform, e.g., email, SMS, etc. Acceptance of the recommendations may cause the requested amounts of consideration for the benefit items to be automatically changed to the recommended amounts of consideration for the benefit items. An instance of a user interface may include one or more user interface elements configured to facilitate user interaction with the user interface. By way of non-limiting illustration, user interface elements may include one or more of text input fields, drop down menus, check boxes, display windows, virtual buttons, and/or other elements configured to facilitate user interaction. 
     In some implementations, recommendation component  114  may be configured to effectuate presentation of one or more recommendations for recommended amounts of consideration for benefit items on a user interface displayed on the computing platform(s)  104  associated with content creators during registration of user accounts within the membership platform. The user interface may be configured to obtain one or more of user entry and/or selection by the content creators of benefit information, user entry and/or selection to accept recommendations, and/or other input. Acceptance of the recommendations may cause amounts of consideration for the benefit items to be automatically set within the membership platform to the recommended amounts of consideration. 
     By way of non-limiting illustration, recommendation component  114  may be configured to effectuate presentation of the second recommendation on a user interface displayed on a computing platform associated with the second content creator during a registration of a user account of the second content creator with the membership platform. The user interface may be configured to obtain user entry and/or selection by the second content creator to accept the second recommendation. Acceptance of the second recommendation may cause an amount of consideration for the second benefit item to be automatically set within the membership platform to the second recommended amount of consideration. 
       FIG.  5    illustrates a user interface  500 , in accordance with one or more implementations. The user interface  500  may display a recommendation  502  conveying a recommended amount of consideration for a benefit item offered by a content creator. The user interface  500  may be configured to obtain user entry and/or selection by the content creator to accept the recommendation  502 . By way of non-limiting illustration, a user interface element  504  may be provided. An acceptance of the recommendation  502  may cause a requested amount of consideration for the benefit item to be automatically changed to the recommended amount of consideration. 
       FIG.  6   . illustrates a user interface  600 , in accordance with one or more implementations. The user interface  600  may display a recommendation  602  conveying a recommended amount of consideration for a benefit item to be offered by a content creator. The user interface  600  may be displayed during a registration of a user account of the content creator with the membership platform. The user interface  600  may be configured to obtain user entry and/or selection by the content creator to accept the recommendation  602 . By way of non-limiting illustration, a user interface element  604  may be provided. An acceptance of the recommendation  602  may cause an amount of consideration for the benefit item to be automatically set to the recommended amount of consideration. 
     In some implementations, server(s)  102 , client computing platform(s)  104 , and/or external resources  116  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s)  102 , client computing platform(s)  104 , and/or external resources  116  may be operatively linked via some other communication media. 
     A given client computing platform  104  may include one or more processors configured to execute one or more computer program components. The computer program components may be configured to enable an expert or user associated with the given client computing platform  104  to interface with system  100 , system  10 , and/or external resources  116 , and/or provide other functionality attributed herein to client computing platform(s)  104 . By way of non-limiting example, the given client computing platform  104  may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a NetBook, a Smartphone, a gaming console, and/or other computing platforms. 
     External resources  116  may include sources of information outside of system  100 , external entities participating with system  100 , and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  116  may be provided by resources included in system  100 . 
     Server(s)  102  may include electronic storage  118 , one or more processors  120 , and/or other components. Server(s)  102  may include communication lines, or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server(s)  102  in  FIG.  2    is not intended to be limiting. Server(s)  102  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server(s)  102 . For example, server(s)  102  may be implemented by a cloud of computing platforms operating together as server(s)  102 . 
     Electronic storage  118  may comprise non-transitory storage media that electronically stores information. The electronic storage media of electronic storage  118  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server(s)  102  and/or removable storage that is removably communicable with server(s)  102  via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage  118  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage  118  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  118  may store software algorithms, information determined by processor(s)  120 , information received from server(s)  102 , information received from client computing platform(s)  104 , and/or other information that enables server(s)  102  to function as described herein. 
     Processor(s)  120  may be configured to provide information processing capabilities in server(s)  102 . As such, processor(s)  120  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor(s)  120  is shown in  FIG.  2    as a single entity, this is for illustrative purposes only. In some implementations, processor(s)  120  may include a plurality of processing units. These processing units may be physically located within the same device, or processor(s)  120  may represent processing functionality of a plurality of devices operating in coordination. Processor(s)  120  may be configured to execute components  108 ,  110 ,  112  and/or  114 , and/or other components. Processor(s)  120  may be configured to execute components  108 ,  110 ,  112  and/or  114 , and/or other components by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor(s)  120 . As used herein, the term “component” may refer to any component or set of components that perform the functionality attributed to the component. This may include one or more physical processors during execution of processor readable instructions, the processor readable instructions, circuitry, hardware, storage media, or any other components. 
     It should be appreciated that although components  108 ,  110 ,  112  and/or  114  are illustrated in  FIG.  2    as being implemented within a single processing unit, in implementations in which processor(s)  120  includes multiple processing units, one or more of components  108 ,  110 ,  112  and/or  114  may be implemented remotely from the other components. The description of the functionality provided by the different components  108 ,  110 ,  112  and/or  114  described below is for illustrative purposes, and is not intended to be limiting, as any of components  108 ,  110 ,  112  and/or  114  may provide more or less functionality than is described. For example, one or more of components  108 ,  110 ,  112  and/or  114  may be eliminated, and some or all of its functionality may be provided by other ones of components  108 ,  110 ,  112  and/or  114 . As another example, processor(s)  120  may be configured to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  108 ,  110 ,  112  and/or  114 . 
       FIG.  3    illustrates elements that may make up database  24 . As indicated previously, subscription component  18  of  FIG.  1    may transmit information input by content creator  12  and/or consumer  14  regarding creation and/or subscribership information to database  24 . Subscription platform  16 , via server  22 , for example, may monitor and obtain creation and/or subscribership information for storage in database  24 . For example, subscription platform  16  may monitor and store additional content created and/or subscriber demographic information as well as performance-related subscribership information, e.g., engagement activity between content creator  12  and his/her subscribers, one of whom may be consumer  14 . For example, subscription platform  16  may monitor the amount of money being generated and/or lost through the subscribers (e.g., outcome information), as well as content creator  12 &#39;s subscriber retention rate. For example, subscription platform  16  may monitor and store performance-related creation information, such as the amount of content that content creator  12  is creating, how often and/or how quickly content creator  12  reacts to subscriber engagement activity, etc. 
     Database  24  may include one or more databases or partitions in which information relating to content creator  12 , and/or subscribership relevant to content creator  12 . For example, database  24  may include a content creator database  24   a , a content database  24   b , a subscriber database  24   c , and a subscription database  24   d . It should be noted that the elements and/or functionality of database  24  may be implemented in local memory resident in subscription component  18  or shared between database  24  and the local memory of subscription component  18  rather than solely in database  24 . 
     Database  24  may be populated with one or more of benefit information, consumption information, recommendations, creation data and/or subscription level information monitored or obtained from and/or associated with existing content creator and/or subscriber accounts established in subscription platform  16 , and/or other information. Creation data may refer to information that characterizes one or more of content creator  12 , the content that content creator  12  creates, and activity engaged in by content creator  12  to interact with one or more subscribers and/or to which consumer  14  is granted preferential access. 
     Content creator information characterizing content creator  12  may be information reflecting the type of creator that content creator  12  designates him/herself to be and/or other self-identified preferences regarding subscription offerings by content creator  12 . For example, content creator type information may reflect that content creator  12  may be a paint artist, a digital artist, a sculptor, a video game developer, a writer, a performance artist, etc. Content creator preference information may reflect subscription levels content creator  12  wishes to offer to subscribers. Content creator preference information may reflect, e.g., a desired minimum revenue, preferred sources of revenue, subscription level proportions, etc. For example, content creator preference information may include information indicating content creator  12 &#39;s desire for more subscribers pledging some amount of money or less subscribers pledging a greater amount of money. For example, content creator preference information may include information specifying that content creator  12  wishes to supplement his/her subscription-generated revenue with revenue generated from the sale of promotional merchandise. Such information may be stored in a content creator database  24   a.    
     In addition to content creation-related information, and upon registering with subscription platform  16  as a content creator, content creator  12  may input information characterizing the identity of content creator  12 . For example, content creator  12  may input or upload contact information, a telephone number associated with a personal user device, such as a smartphone, an email address, a photograph, and/or other identifying information. Such identifying information may be used by subscription platform  16  in a variety of ways to associate content creator  12  with particular content, his/her webpage, payment of subscription donations, and/or other information. 
     Content information characterizing the content that content creator  12  creates may refer to one or more of the type of content created, the medium in which the content is created and/or presented (based on self-identified preference, commonality, and/or other measure), the amount of content created, and/or the frequency at which the content is created. For example, type of content information and/or content medium information may indicate that content creator  12  prefers to create paintings on canvas, develops video games for a mobile platform, performs in online musical performances, and/or other information. For example, content amount information may reflect that content creator  12  created a series of artwork comprising four paintings. For example, content frequency information may indicate that content creator  12  developed three video games over the course of six months. Such information may be stored in content database  24   b.    
     Consumer  14  may subscribe to content creator  12  by registering with subscription platform  16 . During registration, consumer  14  may input certain subscriber demographic information indicative of economic and/or social characteristics of consumer  14 . Subscriber demographic information may reflect the yearly income of consumer  14 , a geographic area in which consumer  14  resides, the age of consumer  14 , interests of consumer  14 , etc. Subscriber information may include data regarding the amount of money consumer  14  is currently pledged to donate to one or more content creators. Over time, as monitored and collected by subscription platform  16 , subscriber information may include information regarding the amount of money consumer  14  has previously donated to one or more content creators, including content creator  12 . Subscriber information, as monitored and obtained by subscription platform  16  may include an Internet Protocol (IP) address indicative of a current location of consumer  14  and/or an IP address indicating a payment source. Such information may be stored in subscriber database  24   c.    
     Like content creator  12 , consumer  14  may input or upload other identifying information that may be used by subscription platform  16  in a variety of ways to associate consumer  14  with particular content, a particular content creator, payment of subscription donations, etc. For example, a photograph or phone number of consumer  14  may be used, e.g., as a mechanism for correlating consumer  14 &#39;s attendance at a live event with consumer  14 &#39;s status as a subscriber of content creator  12 , another content creator present at the live event, a subscriber of content similar to that being presented at the live event, etc. Such information may be stored in subscriber database  24   c . Subscription component  18  or another component may be used to provide a user interface that may be used by consumer  14  to input such information. 
     Subscription level information may refer to information characterizing different subscription levels and corresponding preferential access information specified by content creator  12 . For example, subscription level information may reflect that a ten dollar recurring donation is rewarded with a high-resolution digital image of artwork created during that month to consumer  14 . Such subscriber level information may be stored in subscription database  24   d.    
     It should be noted that other databases or partitions may make up database  24 . For example, database  24  may include one or more databases or partitions for storing information including, but not limited to the following: preferential access information characterizing activity in which content creator  12  engagements may refer to data reflecting the type of activity, the level and/or exclusivity of preferential access to that activity granted to consumer  14 ; subscriber and/or content creator engagement information characterizing interactions, the type and/or frequency of interactions between subscribers and content creators, and/or the medium over which interactions may occur; and historical subscription level and/or engagement information reflecting subscription level and/or engagement information monitored and gathered over one or more periods of time. 
     It should be noted that some of the information described above may not necessarily be required. It should be noted that information reflecting additional aspects of, e.g., the content, content creator, content creator preferences, and/or subscribership, is contemplated by the disclosure. For example, preferential access need not necessarily be offered for each subscription level. For example, subscriber data may include data reflecting particular content creators to which a subscriber pledges donations. 
       FIG.  4    illustrates a method  400  to recommend price of benefit items offered through a membership platform, in accordance with one or more implementations. The operations of method  400  presented below are intended to be illustrative. In some implementations, method  400  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  400  are illustrated in  FIG.  4    and described below is not intended to be limiting. 
     In some implementations, method  400  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  400  in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  400 . 
     An operation  402  may include obtaining benefit information for content creators of a membership platform. The benefit information characterizing benefit items offered by the content creators to subscribers of the membership platform in exchange for requested amounts of consideration. Operation  402  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to benefit component  108 , in accordance with one or more implementations. 
     An operation  404  may include obtaining consumption information. The consumption information may describe acceptance of offers for the benefit items at the requested amounts by the subscribers of the content creators. Operation  404  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to consumption component  110 , in accordance with one or more implementations. 
     An operation  406  may include training a machine learning model on input/output pairs to generate a trained machine learning model. The individual input/output pairs including training input information and training output information. Operation  406  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to model training component  112 , in accordance with one or more implementations. 
     An operation  408  may include storing the trained machine learning model. Operation  408  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to model training component  112 , in accordance with one or more implementations. 
     An operation  410  may include determining, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance. Operation  410  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to recommendation component  114 , in accordance with one or more implementations. 
     An operation  412  may include generating recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators. Operation  412  may be performed by one or more hardware processors configured by machine-readable instructions including a component that is the same as or similar to recommendation component  114 , in accordance with one or more implementations. 
     Although the system(s) and/or method(s) of this disclosure have been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.