Patent Publication Number: US-2023153840-A1

Title: Generation of subscription recommendations for content creators

Description:
FIELD 
     The disclosure relates to membership platforms, and in particular, to subscription-based funding in exchange for content creator consideration. Subscription recommendations, e.g., recommendations for creating, updating, and/or providing levels of funding to which users can subscribe, may be provided to a content creator to allow the content creator to fine-tune subscription offerings. 
     BACKGROUND 
     Different platforms can 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 general public 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 can depend on the amount contributed. 
     SUMMARY 
     The disclosure describes systems and methods for generating subscription recommendations suggesting ways that one or more creators of content may increase subscribership and/or prominence of the one or more creators. Subscription recommendations can involve changing and/or updating subscription levels offered by a content creator to potential, existing, or previous subscribers of the content creator. Current and/or historical performance data associated with the content creator, other content creators, and/or the consumers making up a community of subscribers may be analyzed in order to generate subscription recommendations. 
     One aspect of the disclosure relates to a system configured to analyze a subscription-based enterprise driven by a content creator creating recurring content and generate subscription recommendations for the content creator. The system may comprise one or more physical processors configured by machine-readable instructions to obtain performance data associated with the content creator. Such performance data may include creation data and community data. Creation data may characterize at least one of content made available by the content creator, the content creator, and/or content creator activity to which one or more of consumers of content generated by the content creator are granted preferential access. Community data may characterize one or more attributes of the consumers. 
     The system may obtain a set of subscription levels for the content creator. Different subscription levels of the set of subscription levels can correspond to different rates of consideration payment payable by the consumers. The system may generate the subscription recommendations based on the performance data and the set of subscription levels, where the subscription recommendations can indicate specific creator consideration in exchange for the different subscription levels. 
     Creator consideration may comprise at least one of an amount of content created by the content creator and/or an amount of content creator activity commensurate with the different rates of consideration payment. The creator consideration may comprise at least one of a frequency at which content is created by the content creator and/or a frequency of content creator activity commensurate with the different rates of consideration payment. The creator consideration may comprise at least one of a type of content created by the content creator and/or a type of content creator activity commensurate with the different rates of consideration payment. 
     The system may be configured by machine-readable instructions to obtain secondary performance data. The secondary performance data may characterize at least one of secondary creation data and secondary community data associated with at least a second content creator. The secondary creation data may characterize at least one of secondary content created by the secondary content creator, the secondary content creator, and/or secondary content creator activity to which one or more of consumers of the secondary content are granted preferential access. The secondary community data may characterize one or more attributes of the consumers of the secondary content; and generate the subscription recommendations based on the performance data, the secondary performance data, and the set of subscription levels. 
     The one or more physical processors may be further configured by machine-readable instructions to obtain a set of preferred subscription levels specified by the content creator. The one or more physical processors may be further configured by machine-readable instructions to customize the generated subscription recommendations based on the set of preferred subscription levels. The one or more physical processors may be further configured by machine-readable instructions to obtain at least one of content and/or content creator activity preferences specified by the content creator. The one or more physical processors may be further configured by machine-readable instructions to customize the generated subscription recommendations based on the at least one of content and/or content creator activity preferences. 
     The at least one of content and/or content creator activity preferences may comprise one or more of preferred amounts of content to be created and/or content creator activity to be engaged in, preferred frequencies at which content is to be created and/or content creator activity to be engaged in, and/or preferred types of content to be created and/or content creator activity to be engaged in specified by the content creator. The subscription recommendations may comprise a recommendation for adjusting a current subscription level of at least one consumer to a new subscription level based upon an observed trend in the performance data, wherein the observed trend in the performance data comprises at least one of a churn rate and a cycling trend associated with the consumers of the content. 
     One aspect of the disclosure relates to a method of generating subscription recommendations. The method may be implemented in a computer system comprising one or more physical processors and non-transitory electronic storage media storing machine-readable instructions. The method may comprise monitoring performance data associated with one or more content creators utilizing a recurring content creation site. The performance data may include creation data characterizing at least one of content created by the one or more content creators, the one or more content creators, and/or content creator activity undertaken by the one or more content creators to which preferential access is granted to one or more consumers of the content. The performance data may include community data characterizing one or more attributes of consumers of content generated by the one or more content creators. The method may comprise generating the subscription recommendations based on correlations between the monitored performance data and subscription levels corresponding to different rates of consideration payment payable by consumers in exchange for specific creator consideration from the one or more content creators for each of the subscription levels. 
     The specific creator consideration may comprise at least one of amounts of content created by the one or more content creators and/or amounts of content creator activity undertaken by the one or more content creators commensurate with the different rates of consideration payment. The specific creator consideration may comprise at least one of frequencies at which content is created by the one or more content creators and/or frequencies of content creator activity undertaken by the one or more content creators commensurate with the different rates of consideration payment. The specific creator consideration may comprise at least one of types of content created by the one or more content creators and/or types of content creator activity to be undertaken by the one or more content creators commensurate with the different rates of consideration payment. 
     The method may further comprise obtaining one or more of preferred subscription levels, preferred content to be created, and/or preferred content creator activity to be undertaken specified by the one or more content creators. The generation of subscription recommendations may be further based upon the one or more of the preferred subscription levels, the preferred content to be created, and/or the preferred content creator activity to be undertaken. 
     The subscription recommendations may comprise a recommendation for adjusting a current subscription level of at least one consumer to a new subscription level based upon an observed trend in the performance data, wherein the observed trend in the performance data may comprise at least one of a churn rate and a cycling trend associated with the consumers of the content. 
     The subscription recommendations may comprise a recommendation for engaging in a partnership with a third-party undertaking a promotional activity on behalf of the one or more content creators. The subscription recommendations may comprise a recommendation for increasing or discounting the different rates of consideration payment payable by the consumers. 
     These and other objects, features, and characteristics of the system and/or method disclosed herein, 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    is an example computing component that may be used to implement various features described in the disclosure. 
         FIG.  3    illustrates an example subscription recommendation system. 
         FIG.  4    is an example flow diagram illustrating example processes that may be performed for providing subscription recommendations. 
         FIG.  5    illustrates example data structures utilized in providing subscription recommendations. 
         FIG.  6    illustrates an example content creator user interface through which subscriber recommendations may be presented and implemented. 
     
    
    
     DETAILED DESCRIPTION 
     Some entities may seek to fund their business 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. 
     Some entities seeking funding may be content creators, for example, artists, musicians, educators, etc. Content creators may create content, which can refer to any type of information, experience, or product provided to an audience or end-user, whether it be digital, analog, or virtual. For example, content may include but is not limited to video content, podcasts, photographic art, webcomics, do-it-yourself crafts, digital music, performance art, and 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 or per piece of content created by the content creator. It should be noted that although the disclosure may describe embodiments in the context of a subscriber-based membership platform such as a recurring content website, subscription recommendations can be provided to content creators irrespective of how content may be distributed and/or any particular membership platform utilized by the content creator. 
     In order to maximize revenue received from subscribers, maximize the number of subscribers, and/or gain prominence, content creators can be provided with subscription recommendations. As described above, a subscription recommendation can be some form of a proposal, advice, or suggestion for creating, updating, and/or providing levels of funding to which users can subscribe that may allow the content creator to fine-tune subscription offerings. For example, subscription recommendations can suggest how much content a content creator might generate, how frequently the content creator might generate content, what type of content or special perks the content creator might offer to consumers, etc. in order to increase the number of his/her subscribers. For example, subscription recommendations can suggest how a content creator might entice a consumer contributing at a particular subscription level to increase his/her contributions by subscribing at a higher subscription level. Subscription recommendations may include suggestions that a content creator partner with a third party to provide promotional merchandise or engage in joint-promotional activity. 
     Generating subscription recommendations can be based on current performance data of the content creator, performance data relevant to the community of consumers that subscribe to the content creator, as well as historical performance data associated with the content creator and/or community of consumers. Subscription recommendations can be based on the preferences of the content creator, as well as performance data associated with other content creators creating similar content or different content. Subscription recommendations may provide suggestions for scaling back content generation and/or the offering of special perks when data indicates that a content creator can at least maintain subscribers with less offerings. Other types and/or forms of subscription recommendations are contemplated in the disclosure, some of which will be discussed in greater detail below. 
       FIG.  1    illustrates an example subscriber-based membership system  10 . 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 data, content data, information specifying desired and/or initial subscription levels, and/or preferential access information which will be discussed in greater detail below. The 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. 
     Consumer  14  may set up a subscriber account with subscription platform  16 . 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 . 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 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  can 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 can be made by consumer  14  to support content creator  12  can be referred to as subscription levels. 
     For example, in return for a monthly, recurring ten dollar 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 ten dollar 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 a five dollar per content donation, content creator  12  may provide a low-resolution digital image of the artwork. For a one hundred dollar per content donation, content creator  12  may engage in a live webchat with consumer  14 . Various types of preferential access can 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 . 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 . 
     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. Subscription component  18  may receive the information input by content creator  12  regarding desired subscription levels and corresponding preferential access. Such information may be stored as a matrix or other data structure in database  24  or within memory local to subscription component  18 . 
     Content creator  12  may add subscription levels, update and/or delete existing subscription levels, as well as add, update, and/or delete preferential access information and/or its correspondence to subscription levels. Such changes can be input via subscription component  18  and reflected in its local memory and/or database  24 . Such changes can be based upon the subscription levels provided to content creator  12  via recommendation component  20 . 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. 
       FIG.  2    illustrates an example computing component that may be used to implement various features of the system and methods disclosed herein, for example, subscription platform  1 , server  22 , subscription component  18 , payment network  26 , recommendation component  20 , and/or one or more elements comprising these components. 
     As used herein, the term component might describe a given unit of functionality that can be performed in accordance with one or more embodiments of the present application. As used herein, a component might be implemented utilizing any form of hardware, software, or a combination thereof. For example, one or more processors, controllers, ASICs, PLAs, PALs, CPLDs, FPGAs, logical components, software routines or other mechanisms might be implemented to make up a component. In implementation, the various components described herein might be implemented as discrete components or the functions and features described can be shared in part or in total among one or more components. In other words, as would be apparent to one of ordinary skill in the art after reading this disclosure, the various features and functionality described herein may be implemented in any given application and can be implemented in one or more separate or shared components in various combinations and permutations. Even though various features or elements of functionality may be individually described or claimed as separate components, one of ordinary skill in the art will understand that these features and functionality can be shared among one or more common software and hardware elements, and such description shall not require or imply that separate hardware or software components are used to implement such features or functionality. 
     Where components are implemented in whole or in part using software, in one embodiment, these software elements can be implemented to operate with a computing or processing component capable of carrying out the functionality described with respect thereto. One such example computing component is shown in  FIG.  2   . Various embodiments may be described in terms of this example computing component  32 . After reading this disclosure, it will become apparent to a person skilled in the relevant art how to implement the application using other computing components or architectures. 
     Computing component  32  may represent, for example, computing or processing capabilities found within a desktop, laptop, notebook, and tablet computers; hand-held computing devices (tablets, PDA&#39;s, smart phones, cell phones, palmtops, etc.); 
     workstations or other devices with displays; servers; or any other type of special-purpose or general-purpose computing devices as may be desirable or appropriate for a given application or environment. Computing component  32  might also represent computing capabilities embedded within or otherwise available to a given device. For example, a computing component might be found in other electronic devices such as, for example, portable computing devices, and other electronic devices that might include some form of processing capability. 
     Computing component  32  might include, for example, one or more processors, controllers, control components, or other processing devices, such as a processor  36 . Processor  36  might be implemented using a general-purpose or special-purpose processing engine such as, for example, a microprocessor, controller, or other control logic. In the illustrated example, processor  36  is connected to a bus  34 , although any communication medium can be used to facilitate interaction with other components of computing component  32  or to communicate externally. 
     Computing component  32  might include one or more memory components, simply referred to herein as memory  38 . For example, preferably random access memory (RAM) or other dynamic memory, might be used for storing information and instructions to be executed by processor  36 . Memory  38  might be used for storing temporary variables or other intermediate information during execution of instructions, such as machine-readable instructions, to be executed by processor  36 . Computing component  32  might include a read only memory (“ROM”) or other static storage device coupled to bus  34  for storing static information and instructions for processor  36 . 
     The computing component  32  might include one or more various forms of information storage mechanisms  40 , which might include, for example, a media drive  42 . The media drive  42  might include a drive or other mechanism to support fixed or removable storage media  44 . For example, a hard disk drive, a solid state drive, a magnetic tape drive, an optical disk drive, a compact disc (CD) or digital video disc (DVD) drive (R or RW), or other removable or fixed media drive might be provided. Accordingly, storage media  44  might include, for example, a hard disk, an integrated circuit assembly, magnetic tape, cartridge, optical disk, a CD or DVD, or other fixed or removable medium that is read by, written to or accessed by media drive  42 . As these examples illustrate, the storage media  44  can include a computer usable storage medium having stored therein computer software or data. 
     Computing component  32  might include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing component  32 . Such instrumentalities might include, for example, a fixed or removable storage unit  48  and an interface  46 . Examples of such storage units  48  and interfaces  46  can include a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory component) and memory slot, a PCMCIA slot and card, and other fixed or removable storage units  48  and interfaces  46  that allow software and data to be transferred from the storage unit  48  to computing component  32 . 
     Computing component  32  might include a communications interface  50 . Communications interface  50  might be used to allow software and data to be transferred between computing component  32  and external devices. Examples of communications interface  50  might include a modem or softmodem, a network interface (such as an Ethernet, network interface card, WiMedia, IEEE 802.XX or other interface), a communications port (such as for example, a USB port, IR port, RS232 port Bluetooth® interface, or other port), or other communications interface. Software and data transferred via communications interface  50  might typically be carried on signals, which can be electronic, electromagnetic (which includes optical) or other signals capable of being exchanged by a given communications interface  50 . These signals might be provided to communications interface  50  via a channel  52 . This channel  52  might carry signals and might be implemented using a wired or wireless communication medium. Some examples of a channel might include a phone line, a cellular link, an RF link, an optical link, a network interface, a local or wide area network, and other wired or wireless communications channels. 
     In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to transitory or non-transitory media such as, for example, memory  38 , storage unit  48 , media  44 , and channel  52 . These and other various forms of computer program media or computer usable media may be involved in carrying one or more sequences of one or more instructions to a processing device for execution. Such instructions embodied on the medium, are generally referred to as “machine-readable code,” “computer program code” or a “computer program product” (which may be grouped in the form of computer programs or other groupings). When executed, such instructions might enable the computing component  32  to perform features or functions of the disclosure as discussed herein. 
       FIG.  3    illustrates elements that may make up recommendation component  20  and database  24 .  FIG.  4    illustrates example data structures that can be used in generating subscription recommendations.  FIGS.  3  and  4    are described in conjunction with each other and in the context of system  10  (see  FIG.  1   ) for ease of explanation. 
     Referring to  FIG.  3   , Database  24  may include a content creator database  24   a , a content database  24   b , a preferential access database  24   c , a subscriber database  24   d , a subscription level database  24   e , and a historical subscription level database  24   f . 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 . 
     As indicated previously, subscription component  18  may transmit information regarding creation data, e.g., content creator data, content data, preferential access information, as well as subscription levels for storage in database  24 . Database  24  may be populated with 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 . Creation data can 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 which consumer  14  is granted preferential access. 
     Content creator data characterizing content creator  12  can be data reflecting the type of creator content creator  12  designates him/herself to be and/or any preferences regarding subscription offerings by content creator  12 . For example, content creator type data can 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 data can reflect subscription levels content creator  12  wishes to offer to consumers and/or subscription level proportions, e.g., whether content creator  12  desires more subscribers pledging some amount of money or less subscribers pledging a greater amount of money. Content creator preference data can reflect, e.g., a desired minimum revenue or a frequency at which content creator  12  seeks to create content, etc. 
     Referring to  FIG.  4   , content creator database  24 a can store content creator data in the form of records that may include, e.g., a content creator identifier (“CC_ID”), a content creator type (“CC_TYPE”), and content creator preferences (“CC_REFS”). 
     Returning to  FIG.  3   , content data characterizing the content that content creator  12  creates can refer to the type of content created, the medium in which the content is created and/or presented, the amount of content created, and/or the frequency at which the content is created. For example, type of content data and/or content medium data may indicate that content creator  12  creates paintings on canvas, video games for a mobile platform, online musical performances, etc. For example, content amount data can reflect that content creator  12  created a series of artwork comprising four paintings. For example, content frequency data can indicate that content creator  12  developed three video games over the course of six months. 
     As illustrated in  FIG.  4   , content data can be stored in content database  24 b in the form of records that may include, e.g., a content identifier (“CONTENT_ID”), a content type (“CONTENT_TYPE”), a content medium or media (“MEDM”), and content parameters which can indicate the amount of content created, and/or the frequency at which the content is created (“CONTENT_PARAMS”). The record may include information indicative of a creation date (“CREATN_DATE”). The record may be linked to content creator database  24 a with the inclusion of a content creator identifier (“CC_ID”) that corresponds to a content creator identifier in content creator database  24 a linking content data to an appropriate content creator. 
     Referring back to  FIG.  3   , preferential access data characterizing activity in which content creator  12  engages can refer to data reflecting the type of activity, the level and/or exclusivity of preferential access to that activity granted to consumer  14 , the amount of activity in which content creator  12  engages, and/or the frequency at which the activity occurs. For example, preferential access data can include data reflecting types of activity including, but are not limited to live webcasts involving content creator  12  in which consumer  14  can participate and/or view, digital copies of artwork, VIP seating at a performance, etc. For example, preferential access data can include data reflecting content creator  12 &#39;s engagement in four live webcasts during the current month, and that the live webcasts occurred every Monday of the current month. 
     As reflected in  FIG.  4   , preferential access data can be stored in preferential access database  24 c in the form of records that may include, e.g., a preferential access data identifier (“PAID”), a preferential access data type (“PA_TYPE”), and preferential access parameters indicative of, e.g., the amount of activities and/or the frequency in which content creator  12  wishes to engage (“PA_PARAMS”). 
     As shown in  FIG.  3   , database  24  may include a subscriber database  24   d . Subscriber data reflecting information characterizing one or more aspects of subscribers may be stored in subscriber database  24   d . Consumer  14  may be a subscriber. Upon registering with subscription platform  16 , consumer  14  may input certain demographic information indicative of economic and/or social characteristics of consumer  14 . For example, subscriber data 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 data can include data regarding the amount of money consumer  14  is currently pledged to donate to one or more content creators. Over time, subscriber data can include data regarding the amount of money consumer  14  has previously donated to one or more content creators. It should be noted that a content creator&#39;s subscribers can be collectively referred to as the content creator&#39;s community. 
     As shown in  FIG.  4   , subscriber database  24 d may store subscriber data in the form of a record that may include, e.g., a subscriber identifier (“SUBSCR_ID”), subscriber demographic data (“SUBSCR_DEMO”), a subscriber&#39;s current pledges (“CURR_PLDGS”), and a subscriber&#39;s past pledged amount (“PAST_PLDGS”). 
     Returning to  FIG.  3   , subscription level data can refer to data characterizing different subscription levels and corresponding preferential access information specified by content creator  12 . For example, subscription level data can reflect that a ten dollar recurring donation is rewarded with a high-resolution digital image of artwork created during that month to consumer  14 . 
     As shown in  FIG.  4   , subscription level database  24 e may store subscription level data in a record that may include, e.g., a subscription level identifier (“SUBLVL_ID”). Subscription level database  24   e  can associate a particular subscription level with a particular subscriber, where the subscriber is identified with a subscriber identifier (“SUBSCR_ID”). Subscription level database  24   e  can link a particular content creator and a particular preferential access activity by including the appropriate content creator identifier and preferential access identifier in the record. 
     Periodically (e.g., every week or month) or aperiodically (e.g., upon instruction by an administrator of subscription platform  16  or upon being instructed by content creator  12 ), the records maintained in subscription level database  24 e (which can be thought of as current data) can be transferred to historical subscription level database  24 f. Historical subscription level database  24   f  can include duration data which indicates the amount of time that consumer  14  has been a subscriber of content creator  12  at a particular subscription level, along with corresponding preferential access information. In  FIG.  4   , it can be seen that this duration data (“DURATN”) can be stored in historical subscription level database  24   f  along with the commensurate content creator identifier, subscriber identifier, subscription level identifier, and preferential access identifier. The duration data can be characterized by including data indicative of a begin date (“BEG_DT”) and end date (“END_DT”). 
     It should be noted that not all the data described above is necessarily required, and that data reflecting additional aspects of, e.g., the content, content creator, preferential access and/or subscriptions levels is contemplated by the disclosure. For example, content creator  12  need not initially specify his/her content creator preferences, and instead rely solely on recommendation component  20  to provide subscription recommendations specifying the amount and/or frequency of content creation. 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. It should be noted that the particular format of records illustrated in  FIG.  4    and described herein can vary. For example, duration data may be reflected by information indicative of the number of months consumer  14  has been a subscriber to content creator  12  rather than begin and end dates. For example, alternatives to relational databases may be used such as could be based on an object-oriented model, and hierarchical model, an entity-attribute-value data model, etc. 
       FIG.  3    illustrates that recommendation component  20  may comprise a subscription performance and prediction engine  20   a  and a reporting engine  20   b  for generating subscription recommendations for and reporting the subscription recommendations and performance data to one or more content creators and/or subscription platform  16 . Subscription performance and prediction engine  20   a  may be configured to determine initial and/or current performance regarding revenue received from subscriptions and/or exposure to subscribers. Subscription performance and prediction engine  20   a  may obtain performance data associated with a content creator, e.g., content creator  12 , from one or more of databases  24   a - e . For example, subscription performance and prediction engine  20   a  may obtain subscription level data from subscription level database  24   e , which can provide all performance data associated with a particular content creator by virtue of the linking of content creator, subscriber, and preferential access identifiers. Subscription performance and prediction engine  20   a  may selectively obtain one or more types of performance data from one or more of the databases  24   a - e . Subscription performance and prediction engine  20   a  can obtain secondary performance data associated with one or more other content creators and/or subscribers. Subscription performance and prediction engine  20   a  can obtain historical performance data associated with a content creator or one or more other content creators. 
     Performance data can refer to data obtained regarding a content creator, the content creator&#39;s content, subscriber data associated with subscribers to content creator  12 , and subscription level to preferential access correspondence that has been parsed and/or analyzed to determine revenue received from subscription and/or subscriber exposure. Subscription performance and prediction engine  20   a  can compare current performance data of a content creator (e.g., performance data from subscription level database  24   e ) with historical performance data of that content creator (e.g., performance data from historical subscription level database  24   f ) to determine whether the performance of that content creator is trending positively or negatively. Such trends can be determined from an overall performance perspective or a more granular perspective, e.g., from an individual subscriber perspective, i.e., whether or not a particular subscriber has shown a history of increasing or decreasing pledged donations, or altogether unsubscribing (which can reflected as a churn rate). For example, historical content information can be correlated to subscription level information. That is, subscription performance and prediction engine  20   a  may determine whether or not the creation of content impacts subscriptions. For example, it can be determined whether the creation of content results in an increase in subscriptions from never-before subscribed consumers. It can be determined whether the creation of content results in the maintenance of existing subscriptions. It can be determined whether the creation of content results in a loss of subscription (e.g., suggesting that the type, amount, and/or frequency of content creation was not enough to satisfy a consumer). 
     Subscription performance and predication engine  20   a  can compare the performance data associated with a particular content creator and compare that with secondary performance data associated with one or more other content creators. It should be noted that subscription performance and prediction engine  20   a  may, in comparing performance data, take into consideration whether or not there is an overlap of data between two or more content creators, e.g., an overlap in content type, subscribers, subscriber levels, etc. Such comparisons can lead to determinations regarding the effectiveness of one content creator&#39;s subscriber level and preferential access pairings relative to that of other content creators. For example, subscription performance and prediction engine  20   a  may compare the performance data of content creators that create the same or similar type of content to determine how the amount and/or frequency of content creation impacts are particular content creator type. For example, subscription performance and prediction engine  20   a  may determine that the webcomic content creators experience greater numbers of new subscribers when content is generated on a weekly basis versus a monthly basis. For example, subscription performance and prediction engine  20   a  may determine that performance musician content creators experience the same or similar subscribership regardless of whether those content creators provide weekly live webchats or monthly live webchats. 
     Subscription performance and prediction engine  20   a  may take into consideration time periods associated with obtained performance data, such as how subscriptions may be impacted by seasonality. Subscription performance and prediction engine  20   a  may take into consideration subscriber demographics and/or subscribers&#39; other subscriptions to determine its impact on performance. For example, subscription performance and prediction engine  20   a  may determine that consumers that subscribe to multiple content creators of a particular type may be more apt to increase the amount of their recurring pledge donations. Subscription performance and prediction engine  20   a  may apply various weighting values to certain performance data to normalize that performance data. For example, subscription performance and prediction engine  20   a  may compare performance data of content creators that create art but in different mediums (e.g., canvas versus digital media) and give more weight to the performance data artistic content creators that create content in the same medium as that of the particular content creator whose performance is being analyzed. 
     Based on one or more aspects of performance data, secondary performance data, and/or historical performance data, subscription performance and prediction engine  20   a  may generate recommendations to increase a content creator&#39;s subscribership and/or subscriber revenue. 
     Subscription recommendations can refer to characterizations or re-characterizations of subscription levels and/or preferential access information correlations with subscription levels. Subscription recommendations may take a variety of forms and can involve suggestions to add, update, or remove subscription level and/or preferential access correlations currently in place for one or more content creators. Subscription recommendations can comprise donation adjustments. For example, the subscription recommendations can provide a suggestion to raise or lower the donation amount needed to gain a particular preferential access activity, a suggestion to increase or decrease the amount and/or frequency of content creation, etc. For example, subscription recommendations can suggest providing pledge donation discounts for consumers that have been subscribers for a certain amount of time 
     Subscription recommendations can comprise subscription tier adjustments. For example, subscription recommendations may suggest adding additional levels of subscriptions to entice more consumers to subscribe that might be dissuaded by a content creator with only a few higher-priced subscription offerings. Subscription recommendations can comprise subscriber adjustments. For example, subscription recommendations may suggest that one or more subscribers be urged or prompted to “cycle up,” e.g., pledge donations commensurate with a higher level subscription. Subscription recommendations may comprise partnership recommendations. For example subscription recommendations can include suggestions for a content creator to engage in one or more promotional or preferential access activities with a third party, such as a third party producing promotional merchandise. 
     It should be noted that only some examples of subscription recommendations are discussed herein, and are not intended to be limiting. Subscription recommendations can involve a suggested characterization or re-characterization any one of or any combination of content, content creator, preferential access, subscriber, and/or subscription level data or information. It should be noted that subscription performance and prediction engine  20   a  can be operative to provide subscription recommendations to a single content creator, or to a group of content creators (e.g., those having some overlapping aspects). 
     Subscription performance and prediction engine  20   a  may compute the aforementioned impacts, make the aforementioned comparisons, and/or determinations through the use of one or more statistical methods. Such statistical methods may include, but are not limited to: correlation analysis; regression analysis; clustering; decision tree analysis; and Chi-squared Automatic Interaction Detection (CHAID), a specific type of decision tree analysis; or a combination of one or more such methods. 
     Subscription performance and prediction engine  20   a  may generate subscription recommendations based upon determinations made regarding the performance of a particular content creator, and may utilize computed values indicative of the impact of various factors on performance. For example, subscription performance and prediction engine  20   a  may apply impact values (learned via analysis of historical performance data and its impact on subscriptions) to current performance data of a particular content creator to predict that content creator&#39;s performance. From that prediction, subscription performance and prediction engine  20   a  may generate subscription recommendations determined to increase revenue and/or prominence. 
     Subscription performance and prediction engine  20   a  may forward the aforementioned subscription recommendations to reporting engine  20   b  to be reported to one or more content creators, and/or subscription platform  16 . Reporting engine  20   b  may format the received subscription recommendations. For example, reporting engine  20   b  may format the received subscription recommendations into a more generalized format suggesting changes to the number of content a content creator should generate over the course of the next year. For example, reporting engine  20   b  may format the received subscription recommendations into a detailed report outlining different sets of changes to one or more of content, subscription level, and preferential access factors that may be implemented gradually throughout the next year. Reporting engine  20   b  may present subscription recommendations as selectable options via some user interface accessible by content creator  12  (as will be discussed in greater detail below). 
       FIG.  5    is a flow chart illustrating operations of an example method  54  that may be performed for generating subscription recommendations. At operation  56 , performance data associated with a content creator is obtained. As described previously, performance data may comprise data characterizing one or more of a content creator, content created by the content creator, content creator activity to which one or more of consumers of content generated by the content creator are granted preferential access, one or more attributes of the consumers, etc. At operation  58 , the performance data associated with one or more content creators may be monitored over some period of time. At operation  60 , a set of subscription levels for the content creator may be obtained. For example, subscription levels that may be applicable to the content creator can be determined from current or historical data. At operation  62 , the monitored performance data and the subscription levels may be correlated. For example, subscription levels can be paired performance data indicative of, e.g., the content frequency or amount, the preferential access granted, etc. to increase revenue. At operation  64 , subscription recommendations can be generated based on the correlations between the monitored performance data and the subscription levels. 
       FIG.  6    illustrates an example user interface  66 . User interface  66  may be presented to one or more content creators, e.g., content creator  12 , that have a creator account on subscription platform  16 . User interface  66  may be implemented as part of a webpage “dashboard” and/or separate application accessible by content creator  12 . In the context of this disclosure, a dashboard can refer to a collation of information about one or more content creators. 
     User interface  66  may present options content creator  12  may select to view one or more aspects of content creator  12 ′s subscription-based business via a statistics reporting menu  68 . For example, user interface  66  may present an option to view content creator  12 &#39;s earnings over some period time, subscribership statistics over some period of time, etc. User interface  66  may present information regarding content creator  12 &#39;s subscriber-based business, e.g., earnings, subscribership, etc. in a graphical format such as a chart  70 . User interface  66  may present information in other formats or combinations of formats as well, such as in the form of a text report. 
     As previously discussed, content creator  12  may input one or more preferences via a preferences interface  72  associated with him/her, his/her content, subscription levels, correlated preferential access information, etc. For example, as illustrated in  FIG.  6   , options regarding content amount, content creation frequency, subscriber levels, preferential access activities, etc. may be presented to content creator  12 . Content creator  12  may interact with preferences interface  72  via drop-down menus (or some other mechanism(s)) to view, enter, and/or select options regarding the above-noted preferences. For example, content creator  12  may select a desired subscriber level, a preferential access activity, and create a correlation between the two. Content creator  12  may enter such preferences upon creation of a creator account with subscription platform  16  and/or anytime during content creator&#39;s use of the membership platform. 
     As previously discussed with respect to  FIG.  3   , reporting engine  20   b  may output subscription recommendations generated by subscription performance and prediction engine  20   a . One or more of these generated subscription recommendations may be presented to content creator  12  via a subscription recommendations drop-down menu  74  as selectable options. For example, each selectable option may correspond to a particular subscription recommendation. Content creator may choose to implement a subscription recommendation by selecting to accept the subscription recommendation. 
     It should be noted that subscription recommendations themselves may be tiered, where subscription recommendations may be generated and presented to content creator  12  in terms of predicted success levels. For example, a first set of subscription recommendations may be generated and presented to content creator  12 , where this first set may be predicted to increase revenue by, e.g., 5 percent. A second set of subscription recommendations may be generated and presented to content creator  12 , where this second set may include more aggressive content creation, higher level preferential access activities, etc. This second set of subscription recommendations may be predicted to increase revenue by, e.g., 20 percent. Depending, for example, on how quickly or aggressively content creator  12  wishes to increase revenue or prominence, content creator  12  may select an appropriate subscription recommendation set. 
     It should be noted that subscription performance and prediction engine  20   a  may, through one or more of the aforementioned analytic methods, or one or more other analytic methods, determine predicted results of implementing selected subscription recommendations. Predicted results can comprise purely forecasted statistics and/or comparative statistics relative to the past. Chart  70  may reflect one or both types of predicted results. For example, as illustrated in chart  70 , selecting to update the five dollar recurring subscription level to correspond to two live webcasts/month can be predicted to have resulted in greater returns amount to at least $200 per month. In predicting such results, subscription performance and prediction engine  20   a  may consider various factors, such as seasonality. For example, chart  70  indicates that during the months of November and December, earnings per month may be even greater as historical performance data may suggest consumers tend to be more generous during the holiday season. 
     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.