Patent Publication Number: US-2023146271-A1

Title: Systems and methods for forecasting inbound telecommunications associated with an electronic transactions subscription platform

Description:
TECHNICAL FIELD 
     Various embodiments of the present disclosure generally relate to telecommunications forecasting, and more particularly, to analyzing real-time and historical call center data, and applying a forecasting model to said data, in order to predict inbound calls and the human and technological resources that will be necessary to service those calls. 
     BACKGROUND 
     Call centers and customer service departments often struggle with the problem of optimal resourcing based on workload demand (customer outreach using via one or more communication channels) while also adhering to service level agreements reached with business partners. As such, call centers and customer service departments require accurate data regarding incoming and outgoing calls, as well as insight into the needs and types of customers that are being communicated with. 
     Call centers employ various conventional techniques for forecasting inbound and outbound calls. For example, queuing equation-based methods are commonly used in call center management systems, and are by far the most commonly used method for call center forecasting and data analysis. Another commonly used tool, for example, are call center agent management systems, which are primarily employed for short-term personnel scheduling and management. Additionally, call center agent management systems provide tools for forecasting agent workloads as well as automatic and manual methods for assigning agents to work-shifts in order to meet metrics agreed to in service level agreements. In these systems, queuing methods are used to predict the number of agents required to service a given workload, for a specific period of time (e.g. a shift or work day). 
     However, queuing equation-based methods and call center agent management systems are deficient when used for forecasting purposes for two main reasons. First, queuing equation-based methods require over simplifying agent workflow metrics, which results in inaccurate forecasts for call center needs. Second, call center agent management systems are not designed for forecasting, planning, and analysis, and therefore, relying on said systems for customer call forecasting results in system inefficiencies. 
     Moreover, existing techniques are unable to account for significant or irregular changes in the amount of business (i.e., number of subscribing customers) being serviced by a given call center. For example, classical approaches such as ARIMA, Exponential smoothing etc., are unable to take into consideration users added through any of the future marketing activities or non-regular activities that a business may use to increase subscribers from day-to-day or month-to-month. 
     Thus, there is a need for systems and methods that not only take existing users into account but also users to be acquired in future to estimate call volumes. Moreover, there is a need for an approach that utilizes additional metrics and tools to overcome some of the challenges described above. 
     The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. 
     SUMMARY OF THE DISCLOSURE 
     According to certain aspects of the disclosure, systems and methods are disclosed for telecommunications forecasting to overcome the problems with conventional methods noted above. 
     In one embodiment, a computer-implemented method for forecasting inbound telecommunications comprising: estimating a number of expected daily retained users within a specific window of time; estimating a number of acquired new users added each day over the upcoming specific window of time via a decay function; calculating an expected daily user count based on the estimated number of expected daily retained users and acquired new users; calculating a total user call rate based on a historic cancellation rate and a historic resolution rate; and calculating a total number of expected calls based on the expected daily user count and total user call rate. 
     In one embodiment, a system for forecasting inbound telecommunications comprising: a storage device that stores a set of instructions; and at least one processor coupled to the storage device, the set of instructions configuring the at least one processor to: estimating a number of expected daily retained users within a specific window of time; estimating a number of acquired new users added each day over the upcoming specific window of time via a decay function; calculating an expected daily user count based on the estimated number of expected daily retained users and acquired new users; calculating a total user call rate based on a historic cancellation rate and a historic resolution rate; and calculating a total number of expected calls based on the expected daily user count and total user call rate. 
     In one embodiment, a non-transitory computer-readable medium storing instructions for forecasting inbound telecommunications, the instructions configured to cause at least one processor to perform operations comprising: estimating a number of expected daily retained users within a specific window of time; estimating a number of acquired new users added each day over the upcoming specific window of time via a decay function; calculating an expected daily user count based on the estimated number of expected daily retained users and acquired new users; calculating a total user call rate based on a historic cancellation rate and a historic resolution rate; and calculating a total number of expected calls based on the expected daily user count and total user call rate. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and together with the description, serve to explain the principles of the disclosed embodiments. 
         FIG.  1    depicts an example environment in which methods, systems, and other aspects of the present disclosure may be implemented. 
         FIG.  2    depicts an exemplary high-level process for forecasting inbound telecommunications, according to some embodiments. 
         FIG.  3    depicts an exemplary sub-process for forecasting expected daily users of an electronic subscriptions platform, according to some embodiments. 
         FIG.  4    depicts an exemplary sub-process for forecasting inbound telecommunications associated with the electronic subscriptions platform given a calculated expected daily user count, according to some embodiments. 
         FIG.  5    depicts an exemplary process for telecommunications forecasting, according to some embodiments. 
         FIG.  6 A  depicts an exemplary call center dashboard of a graphical user interface for forecasting expected daily users and forecasting inbound telecommunications associated with the electronic subscriptions platform, according to some embodiments. 
         FIG.  6 B  depicts another exemplary call center dashboard of a graphical user interface for forecasting expected daily users and forecasting inbound telecommunications associated with the electronic subscriptions platform, according to some embodiments. 
         FIG.  7 A  depicts an exemplary call center dashboard comprising a graphical user interface, according to some embodiments. 
         FIG.  7 B  depicts an exemplary call center dashboard report displayed via a graphical user interface, according to some embodiments. 
         FIG.  8    depicts an exemplary computer device or system, in which embodiments of the present disclosure, or portions thereof, may be implemented. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     As will be described further herein, there is a need for a telecommunications forecasting model that takes into consideration consumers added through various channels (e.g. future consumer acquisition activities or atypical consumer acquisition activities) which typically are not considered in conventional approaches such as ARIMA (i.e., Auto-Regressive Integrated Moving Average), exponential smoothing etc. Therefore, the embodiments disclosed herein provide forecasting models, forecasting tools, and solutions, which can enable call centers to more accurately predict expected inbound telecommunication volumes. 
     In some embodiments the present disclosure describes an inbound call forecasting method which predicts expected call counts. Call centers often provide telecommunication services for a plurality of business customers/partners, hereinafter referred to in some cases as “tenants.” In the embodiment in which a call center is associated with a clothing subscription platform, a “tenant” may be a third-party clothing brand or retailer for which the clothing subscription platform provides the back office services, warehousing, shipping, logistics, web/mobile applications, etc. Providing telecommunication services for such tenants or other business customers often entails responding to communications from consumers that may be subscribers or customers of tenants according to quality and metric guidelines agreed upon in a service level agreement (SLA). However, predicting the requisite call center resources that are necessary for servicing tenant customers presents a challenge in that accurate forecasting models typically need two-to-three years of historical data, which is typically not available for tenants that are new business customers of the subscription platform and/or its associated call center. In addition to the lack of historical tenant customer data, the lack of tenant customer acquisition data and present/future tenant customer acquisition initiative data may also have negative implications on a call center&#39;s ability to accurately forecast expected telecommunications volume and call center resource needs. 
     Accordingly, the embodiments disclosed herein may be applied to technological environments and interactions between a call center system, call center employees, tenant systems, and tenant customer devices. That said, while the exemplary system architecture as described in the present disclosure relates to a call center system and call center dashboard for forecasting tenant customer telecommunication volume, various methods and implementations disclosed herein may effectively serve systems and ancillary methods for managing, subscribing to, purchasing, or renting wearable items (e.g., clothing-as-a-service (CaaS) or Try-Then-Buy (TTB) service), or other online transaction platforms in the context of any other subscription, purchase, rental, or retail services without departing from the scope of the disclosure. 
     As used in the present disclosure, the term “CaaS” (i.e., clothing-as-a-service) may collectively refer to computer-implemented services and functions associated with subscription, purchase, and/or rental services for users (e.g., periodic subscription for receiving wearable items, apparel rental or purchase order, distribution, return processing, TTB services, account management, marketing, customer service, warehouse operations, etc.). As used in the present disclosure, the term “wearable item” may refer to any article of clothing, apparel, jewelry, hat, wearable electronics, smart clothing, accessories, or other product which may be worn by a person, an animal, or a thing, or be used as an ornament for a person, an animal, or a thing. 
     In accordance with the present disclosure, user interfaces, periodically executed computer-implemented services, ad hoc services, and automations being integrated together in a connected platform may be achieved by a uniquely configured system architecture, job execution cluster configuring one or more processors to perform both storefront and back office tasks, and various user interfaces providing specialized or customized access to users of different roles. The ordered combination of various ad hoc and automated tasks in the presently disclosed platform necessarily achieve technological improvements through the specific processes described more in detail below. In addition, the unconventional and unique aspects of these specific automation processes represent a sharp contrast to merely providing a well-known or routine environment for performing a manual or mental task. 
     The subject matter of the present description will now be described more fully hereinafter with reference to the accompanying drawings, which form a part thereof, and which show, by way of illustration, specific exemplary embodiments. An embodiment or implementation described herein as “exemplary” is not to be construed as preferred or advantageous, for example, over other embodiments or implementations; rather, it is intended to reflect or indicate that the embodiment(s) is/are “example” embodiment(s). Subject matter can be embodied in a variety of different forms and, therefore, covered or claimed subject matter is intended to be construed as not being limited to any exemplary embodiments set forth herein; exemplary embodiments are provided merely to be illustrative. Likewise, a reasonably broad scope for claimed or covered subject matter is intended. Among other things, for example, subject matter may be embodied as methods, devices, components, or systems. Accordingly, embodiments may, for example, take the form of hardware, software, firmware, or any combination thereof (other than software per se). The following detailed description is, therefore, not intended to be taken in a limiting sense. 
     Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter include combinations of exemplary embodiments in whole or in part. 
     The terminology used below may be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific examples of the present disclosure. Indeed, certain terms may even be emphasized below; however, any terminology intended to be interpreted in any restricted manner will be overtly and specifically defined as such in this Detailed Description section. Both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the features, as claimed. 
     In this disclosure, the term “based on” means “based at least in part on.” The singular forms “a,” “an,” and “the” include plural referents unless the context dictates otherwise. The term “exemplary” is used in the sense of “example” rather than “ideal.” The term “or” is meant to be inclusive and means either, any, several, or all of the listed items. The terms “comprises,” “comprising,” “includes,” “including,” or other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, or product that comprises a list of elements does not necessarily include only those elements, but may include other elements not expressly listed or inherent to such a process, method, article, or apparatus. Relative terms, such as, “substantially” and “generally,” are used to indicate a possible variation of ±10% of a stated or understood value. 
     Referring now to the appended drawings,  FIG.  1    shows an example environment  100 , according to one or more embodiments of the present disclosure. As shown, the example environment  100  may include one or more networks  101  that interconnect a server system  102 , user devices  112 , employees  114  or employee devices  116 , tenant devices  120 , external systems  122 , call center(s)  124 , and Public Switched Telephone Network (PSTN)  128 . The one or more networks  101  and/or PSTN  128  may together or separately include, for example, one or more of a cellular network, a public land mobile network, a local area network, a wide area network, a metropolitan area network, a telephone network, a private network, an ad hoc network, an intranet, the Internet, a fiber optic based network, a cloud computing network, etc. User devices  112  may be accessed by users (e.g. tenant customers)  108 , employee devices  116  may be accessed by authorized employees (e.g. call center employees)  114 , and tenant devices  120  may be accessed by employees of tenant entities  118 . In some implementations, employee devices  116  may be used to perform the functions of the tenant devices  120  and/or the user devices  112 . Server system  102  may comprise one or more servers  104  and one or more databases  106 , which may be configured to store and/or process a plurality of data, microservices, and service components, and/or associated functions thereof. 
     Users  108  may access the server system  102  through the one or more networks  101  using user devices  112 . Each device among the user devices  112  may be any type of computing device (e.g., personal computing device, mobile computing devices, etc.) which allows users  108  to display a web browser or an application for accessing the server system  102  through the network  101 . The user devices  112  may, for example, be configured to display a web browser, a web based application, or any other user interface (e.g., one or more mobile applications) for allowing users  108  to exchange information with other device(s) or system(s) in the environment  100  over the one or more networks  101 . For example, a device among the user devices  110  may load an application with a graphical user interface (GUI), and the application may display on the GUI one or more apparel recommendations for closeting by the user. Users  108  accessing user devices  112  may be, for example, users and/or potential users of apparel made available for subscription based distribution via electronic transactions and physical shipment. Additionally, or alternatively, users  108  may use user devices  112  to communicate with phone devices  126  of call center  124  to manage one or more user accounts, cancel a subscription, create a new account, view catalogs, configure one or more user profiles, engage in customer service communications, make purchase orders, track shipments, generate shipments, monitor order fulfillment processes, initiate or process returns, order apparel for purchase, provide feedback, refer other users, navigate through various features, such as a closet assistant, size advisor, perform personalized discovery, and/or make recommendations. It should be appreciated that users  108  may alternatively or additionally perform any of these tasks by accessing server system  102 , whether via network  101  or PSTN  128  with their user devices  112 . 
     Employee devices  116  may be configured to be accessed by one or more employees  114 , including, for example, customer service employees, call center managers, call center employees, marketer employees, warehouse employees, analytics employees, or any other employees who are authorized and/or authenticated to perform tasks, operations, and/or transactions associated with the server system  102 , external systems  122 , and/or the call center  124 . Employee devices  116  may be the same as or different from phone devices  126  of call center  124 . In one embodiment, employee devices  116  are owned and operated by the same entity or at least an affiliate of the entity operating the e-commerce (e.g., CaaS) business hosted on server systems  102 . Each device among the employee devices  116  may be any type of computing device (e.g., personal computing device, mobile computing devices, etc.). The employee devices  116  may allow employees  114  to view a web browser or an application for accessing the server system  102  and/or the external systems  122 , through the one or more networks  101 . For example, a device among the one or more of the employee devices  116  may load an application with graphical user interface (GUI) (e.g. a call center dashboard), and the application may display on the GUI one or more call center operations associated with providing CaaS to users  108 . In some implementations, the employee devices  116  may communicate directly with the server system  102  and call center  124  via a communications link bypassing public networks  101 . Additionally, or alternatively, the employee devices  116  may communicate with the server system  102  via network  101  (e.g., access by web browsers or applications). 
     Tenant devices  120  may be configured to be accessed by one or more tenants  118 . Each device among the tenant devices  120  may be any type of computing device (e.g., personal computing device, mobile computing devices, etc.). As used herein, each tenant, among one or more tenants  118 , may refer to an entity that allocates and/or supplies one or more specific collections of apparel for the CaaS inventory. For example, each of the one or more tenants  118  may be a retailer, a designer, a manufacturer, a merchandiser, or a brand owner entity that supplies one or more collections of wearable items to the CaaS inventory managed and/or accessed by the server system  102 . Tenants  118  may use one or more electronic tenant interfaces (e.g., a customer management system associated with each tenant) to provide the server system  102  or call center  124  with tenant customer data (e.g., number of subscribers, subscriber call data, subscriber account information) that may transmitted to and/or stored on server system  102 . For example, tenant data for each of the one or more tenants  118  may be generated and/or updated at the server system  102  or call center  124  dynamically and/or periodically. Tenant devices  120  may serve as access terminals for the tenants  118 , for communicating with the electronic tenant interfaces and/or other subsystems hosted at the server system  102 . The tenant devices  120  may, for example, be configured to display a dashboard, a web browser, an application, or any other user interface for allowing tenants  118  to load the electronic tenant interfaces and/or exchange data with other device(s) or system(s) in the environment  100  over the one or more networks  101 . 
     External systems  122  may be, for example, one or more third party and/or auxiliary systems that integrate and/or communicate with the server system  102  in performing various CaaS tasks. External systems  122  may be in communication with other device(s) or system(s) in the environment  100  over the one or more networks  101 . For example, external systems  122  may communicate with the server system  102  via API (application programming interface) access over the one or more networks  101 , and also communicate with the employee devices  116  via web browser access over the one or more networks  101 . 
     Call center  124  may be one or more physical buildings or virtual (remote) networks of telephony systems including, for example, one or more servers, routers, switches, telephony components, phone devices  126  (which may comprise of various call center technology, such as: predictive dialer, a VOIP system, a data handler, etc.), and/or dashboards, that integrate with or provide functionality and telephony services to the employee devices  116 . Call center  124  may be in communication with other device(s) or system(s) in the environment  100  over the one or more networks  101 . For example, call center  124  may communicate with the server system  102  via API (application programming interface) access and/or one or more internet protocols, over the one or more networks  101 , and also communicate with the employee devices  116  via a dashboard, and/or internet protocols, over the one or more networks  101 . 
     Public Switched Telephone Network (PSTN)  128  may be, for example, one or more servers, routers, switches, telephony components, customer premises equipment (CPE) (e.g., routers, switches, real property gateways, set-top boxes, fixed mobile convergence products, networking adapters and Internet access gateways), switching equipment (e.g. authentication nodes, call control/switching nodes, charging nodes), and call switching components. PSTN  128  may be in communication with other device(s) or system(s) in the environment  100  over the one or more networks  101 . For example, PSTN  128  may communicate with call center  124  via one or more telephony protocols. PSTN  128  may communicate with server system  102  via API (application programming interface) access and/or one or more telephony or internet protocols, over the one or more networks  101 , and also communicate with the employee devices  116  and tenant devices  120  via a dashboard, and/or internet protocols, over the one or more networks  101 . As indicated above,  FIG.  1    is provided merely as an example. Other examples that differ from the example environment  100  of  FIG.  1    are contemplated within the scope of the present embodiments. In addition, the number and arrangement of devices and networks shown in environment  100  are provided as an example. In practice, there may be additional devices, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in environment  100 . Furthermore, two or more devices shown in  FIG.  1    may be implemented within a single device, or a single device shown in  FIG.  1    may be implemented as multiple, distributed devices. Additionally, or alternatively, one or more devices may perform one or more functions of other devices in the example environment  100 . For example, employee devices  116  may be configured to perform one or more functions of tenant devices  120 , in addition to their own functions. 
       FIG.  2    depicts an exemplary high-level process for forecasting inbound telecommunications, according to some embodiments. As shown in  FIG.  2   , steps for forecasting inbound telecommunications may include estimating expected daily user count retained or added in an electronic transactions subscription service over an upcoming specific window of time (Step  202 ) and estimating daily call volume over the upcoming specific window of time based on industry-specific inputs (e.g., calls/user, cancellation call rate, weekday call multiplier trends, cyclicity, seasonality, and random variations, etc.) (from Step  206 ) applied to the estimated expected daily user count (Step  204 ). 
     A trend relevant to calculating expected daily user count and/or daily call volume may be derived from historic data known about a user and future projections based on recently acquired users. Cyclicity and seasonality may be derived from aggregating data over long periods of time (e.g. 5-20 years). Specifically, cyclicity and seasonality trends may provide data for specific time periods within a year regarding user peak or non-peak demand levels. In instances where new tenants (e.g., clothiers/business partners) are established, there may otherwise be very little historical data regarding the tenant&#39;s users (i.e., customers/subscribers). Therefore current call volumes for current users over a recent period of time (e.g., the trailing three months or four weeks), may be utilized to forecast expected daily user count. Regarding random variations, classical forecasting techniques might traditionally be insufficient to account for random or abrupt variations in input data (e.g., user trends and/or call volume data, user retention rate, etc.). By contrast, the use of inputs (Step  206 ) into the instant forecasting model allows for flexible data inputs, such as call rate, decay multiplier, weekday multiplier etc., which can be factored into the forecasting model in real-time to reflect changing business parameters without re-running the model as would be the case with classical approaches. Hence, the disclosed model provides much more flexibility to adjust to the current business scenarios. Expected daily user count can also used to identify a number of users that are expected to lost during specific period of time (e.g., a month) and the expected daily user count can be linearly decayed to account for different users (e.g., customers/subscribers) having different renewal billing dates every month. Both steps  202  and  204  further comprise sub-steps, that will be described in  FIG.  3    and  FIG.  4    respectively. The decay function may be comprised of one or more sub-functions for factoring in a user call-in type (e.g. cancellations) over specific periods of time (e.g. cancellations within the last 24 hours, 2-7 days cancellations, and 8-30 days cancellations). 
       FIG.  3    depicts an exemplary sub-process for forecasting expected daily users of an electronic subscriptions platform, according to some embodiments. In an exemplary embodiment,  FIG.  3    depicts exemplary sub-steps of Step  202  from  FIG.  2   . For example, estimating expected daily user count over an upcoming specific window of time may comprise both (i) estimating expected daily retained user count over an upcoming specific window of time (Step  302 ) as well as (ii) estimating acquired new users added each day over the upcoming specific window of time (Step  304 ). Here, in addition to estimating the daily expected user count, server system  102  (or the server system  102  in conjunction with other systems, for example the call center  124  on the network  101 ) may estimate the number of new users expected to be acquired over an upcoming period of time (e.g. 30 days, one month, etc.). Users may be acquired both organically and through marketing initiatives. 
     Regarding users added through marketing initiatives, server system  102  (or the server system  102  in conjunction with other systems, for example the call center  124  on the network  101 ) may calculate a number of users acquired via marketing initiatives by receiving input from a look-up table or employees  114 , or predicting a number of acquired users via a machine learning trained on a number of new users expected to be added via marketing initiatives. Once the number of new users expected to be added via marketing initiatives is predicted, an algorithm implemented by the server system  120  or call center  124  may decay this number to calculate the expected a number of users expected to remain customers at the end of a specific period of time (e.g., a month, a quarter, a year, etc.). The algorithm decay function may linearly distribute cancelled users and remaining users (i.e., customers) in order to account for exponential cancels and retained users. 
     Regarding organically acquired users (i.e., users that are customers of tenants), server system  102  (or the server system  102  in conjunction with other systems, for example the call center  124  on the network  101 ) may calculate a number of organically acquired users by receiving an average number of daily acquired users as inputs from employees  114  or accessing a storage device (e.g., self-referential database) to retrieve data representative of the actual number of new users that have been added organically. Once the average number of new users that have been added organically is identified, an algorithm implemented by the server system  120  or call center  124  may similarly decay this number to calculate the expected a number of users expected to cancel and/or to remain customers at the end of a specific period of time (e.g., a month, a quarter, a year, etc.). The algorithm decay function may linearly distribute cancelled users and remaining users (i.e. customers) in order to account for exponential cancels and retained users. The estimated outcomes of steps  302  and  304  are then summed in implementing step  306 . Specifically, method  300  may then comprise calculating a forecast for expected daily user count by summing the estimated daily user count and estimated acquired new users over the upcoming specific window of time (Step  306 ), on a day-by-day basis. For example, an algorithm may be applied to the estimated expected daily user count and the estimated number of newly acquired users, wherein the algorithm at least sums the estimated expected daily user count and the estimated number of newly acquired users in order to forecast an expected user count. It should be appreciated that user count may be calculated on any other desired interval, such as minute-by-minute, hour-by-hour, or week-by-week. 
       FIG.  4    depicts an exemplary sub-process for forecasting inbound telecommunications associated with the electronic subscriptions platform given a calculated expected daily user count, according to some embodiments. In one embodiment,  FIG.  4    depicts the sub-steps of Step  204  from  FIG.  2   . For example, estimating daily call volume over a specific window of time by superimposing inputs and call trends on the estimated expected daily user (Step  204 ) may include several sub-steps of method  400  of  FIG.  4   , that is, steps  404 - 410 . Although the following steps may be performed in a different order, in an exemplary embodiment, estimating daily call volume over a specific window of time by superimposing inputs and call trends on the estimated expected daily user (Step  402 ) may comprise (i) estimating a number of potential user cancellation calls (Step  404 ), and then (ii) estimating a number of potential user issue resolution calls (Step  406 ). For example, one or more systems (e.g. server system  102  and/or call center  124 ) in environment  100  may estimate a number of users that may be expected to cancel their subscription over a specific period of time. Superimposed inputs may be received via employee devices  116 , predicted via machine learning techniques, and/or generated automatically by server system  120  and/or call center  124 . Next, one or more systems in environment  100  may perform a step for estimating a number of potential user issue resolution calls (Step  406 ). For example, the server system  102  or call center  124 , independently or in conjunction, may estimate the number of potential calls wherein users need an issue resolved in a specific time period (e.g. one month, or three months, one year, etc.). In one embodiment, the estimated number of cancellation calls and/or issue resolution calls may be estimated based on a number of factors as described in more detail below, including number of subscribers/users, trends, seasonality, cyclicity, etc. 
     The estimated values of steps  404 - 406  may then be used in an algorithm for calculating total call count, for example, in one embodiment by summing the output of the estimated number of potential user cancellation calls and the estimated number of potential user issue resolution calls (Step  408 ). For example, an algorithm may at the very least, sum the estimated number of potential user cancellation calls and the estimated number of potential user issue resolution calls, in order to calculate an expected total call count. The one or more systems in environment  100  may then implement a process for modifying the calculated total call count by multiplying the calculated total call count by an index value based on weekday (Step  410 ). For example, the server system  102  and/or the call center  124  may then modify the calculated total call count by multiplying the calculated total call count by an index value (i.e., a load factor, which is a value that represents in user call volume, for a given category. For example, if calls increase by 10%, the load factor will be 1.1.). 
       FIG.  5    depicts an exemplary process for telecommunications forecasting, according to some embodiments. Although the following steps may be performed in a different order, in an exemplary embodiment, one or more systems in environment  100  may initiate the process for forecasting telecommunications by estimating a number of expected daily retained users within a specific window of time (Step  502 ). For example, server system  102  and/or call center  124  may estimate a number of retained subscribers (i.e., the number of subscribers that maintain their subscription) for a specific window of time (e.g., thirty days, one month, one year, etc.). One or more systems (e.g. server system  102  and/or call center  124 ) in environment  100  may then implement a step for estimating a number of acquired new users added each day over the upcoming specific window of time via a decay function (Step  504 ). For example, an algorithm implemented by the server system  102  may estimate an expected number of new users for specific window of time (e.g., thirty days, one month, one year, etc.). The one or more systems may then implement a step for calculating an expected daily user count based on the estimated number of expected daily retained users and acquired new users (Step  506 ). For example, an algorithm may be applied to the estimated expected daily user count and the estimated number of newly acquired users, wherein the algorithm at least sums the estimated expected daily user count and the estimated number of newly acquired users in order to forecast an expected user count. 
     The one or more systems may then implement a step for calculating a total user call rate based on a historic cancellation rate and a historic resolution rate (Step  508 ). Here, for example, an algorithm may retrieve data pertaining to historic cancellation rate and historic resolution rate, and some instances sum these retrieved figures in order to calculate a total user call rate. Calculating a total number of expected calls based on the expected daily user count and total user call rate (Step  510 ). For example, the one or more systems (e.g. server system  102  and/or call center  124 ) may calculate the total number of expected calls, in one instance, by multiplying the expected daily user count by the total user call rate. Alternatively, the expected daily user count and total user call rate may just be one of several variables in an algorithm that calculates the total number of expected calls. 
       FIG.  6 A  depicts an exemplary call center dashboard  602  of a graphical user interface for forecasting expected daily users and forecasting inbound telecommunications associated with the electronic subscriptions platform, according to some embodiments. According, to one embodiment, a call center dashboard may include an interface for establishing parameters and inputs in order to forecast inbound communications. Dashboard  602  may be made available to employees  116  in order the employees  116  to forecast inbound communications by allowing the employees  116  to define which parameters and inputs should be utilized in forecasting said inbound communications. Specially,  FIG.  6 A  depicts a dashboard for employees  116  to manipulate parameters and inputs for brands owned by an entity which also owns or operates call center  124 . As depicted in regions  604 ,  606 , and  608 , once parameters (e.g., customer conversion/retention rate, time, customer cancellations, acquired new users, load factor, etc.) and each parameters associated inputs are factored in, a forecast for inbound telecommunications can be generated. Specifically, region  604  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e., customer/subscriber) conversion/retention rate. Region  606  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e., customer/subscriber) cancellation/postponement rate, contract rate, marketing conversion rate, and load factor. Region  608  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e., customer/subscriber) acquired users and change in the number of users due to marketing campaigns, as a function of time (e.g., a week, month, year, etc.). 
       FIG.  6 B  depicts another exemplary call center dashboard  602  of a graphical user interface for forecasting expected daily users and forecasting inbound telecommunications associated with the electronic subscriptions platform, according to some embodiments. According, to one embodiment, a call center dashboard may include an interface for establishing parameters and inputs in order to forecast inbound communications. Dashboard  602  may be made available to employees  116  in order the employees  116  to forecast inbound communications by allowing the employees  116  to define which parameters and inputs should be utilized in forecasting said inbound communications. Specially,  FIG.  6 B  depicts a dashboard for employees  116  to manipulate parameters and inputs for clothiers owned by one or more tenants  118 . As depicted in regions  610 ,  612 , and  614 , once parameters (e.g., customer conversion/retention rate, time, customer cancellations, acquired new users, load factor, etc.) and each parameters associated inputs are factored in, a forecast for inbound telecommunications can be generated. Specifically, region  604  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e., customer/subscriber) conversion/retention rate. Region  606  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e., customer/subscriber) cancellation/postponement rate, contract rate, marketing conversion rate, and load factor. Region  608  is at least comprised of fields, widgets, menus, and data, that can be manipulated in order to generate parameters associated with user (i.e. customer/subscriber) acquired users and change in the number of users due to marketing campaigns, as a function of time (e.g., a week, month, year, etc.). 
       FIG.  7 A  depicts an exemplary call center dashboard  702  comprising a graphical user interface, according to some embodiments. According to one embodiment, a call center dashboard may include: a home dashboard user interface, a communication routing interface, a simulation interface, an employee user interface, and/or a tenant user interface hosted by call center  124  and/or server system  102 . The employee user interface may include, but is not limited to, an employee team interface and widget, and an employee profile interface. A simulation interface may include, but is not limited to, an interface for entering multiple variables and running simulations capable of projecting forecasted outcomes. The routing dashboard user interface may include, but is not limited to, a call routing selection interface. The reporting user interface may include, but is not limited to, a main reporting dashboard, a report details dashboard, and one or more menu options/widgets for generating custom reports. The reporting user interface may be used, for example, by an employee or manager managing the employees for a call center. According to this embodiment, various performance visualization tools can be generated. For example, employees  114  may be able to generate a call volume tool  604  capable of depicting the number of calls received, number of calls placed, and/or the type of call for one or more employees. Employees  114  performance may be depicted in different formats. For example, an employees  114  performance may be depicted as a function of time call volume range tool  706 . The call volume range tool may further comprise heat map features capable of emphasizing and distinguishing between data points. Dashboard  702  may additionally include a subscriber tool  708 , capable of visualizing metrics data corresponding to subscribers. For example, subscriber tool  608  may be manipulated by employees  114  to convey metrics pertaining to subscriber conversion rates, subscriber retention rates, subscriber tenure information, subscriber cancellations, and subscriber information on a per tenant basis. According to one embodiment, security code and protocols running on the server system  102 , employee devices  116 , tenant devices  120 , external systems  122 , and/or call center  124 , implement one or more APIs to manage to access to the dashboard. For example, employee device  114  and/or tenant device  120  may perform a handshake with server system  102  and/or call center  124  in order to access call data and/or subscriber data. 
       FIG.  7 B  depicts an exemplary call center dashboard report  710  displayed via a graphical user interface, according to some embodiments. As disclosed in relation to  FIG.  7 A , call center dashboard  702  may further include an interface configured to generate reports (e.g. graphs, meat maps, histograms, and icons) to visual depict collected and forecasted data regarding call data and user data. Specifically, call center dashboard report  710  is graph representing actual inbound calls and predicted inbound calls. Such reports are beneficial in that they visually represent the output accuracy of forecasting models implemented by employees  114  and other entities with access to call center dashboard  702 . 
       FIG.  8    depicts an exemplary computer device or system, in which embodiments of the present disclosure, or portions thereof, may be implemented. As shown in  FIG.  8   , a device  800  used for performing the various embodiments of the present disclosure (e.g., the server system  102 , the user devices  112 , the employee devices  116 , the tenant devices  120 , and/or any other computer system or user terminal for performing the various embodiments of the present disclosure) may include a central processing unit (CPU)  820 . CPU  820  may be any type of processor device including, for example, any type of special purpose or a general-purpose microprocessor device. As will be appreciated by persons skilled in the relevant art, CPU  820  also may be a single processor in a multi-core/multiprocessor system, such system operating alone, or in a cluster of computing devices operating in a cluster or server farm. CPU  820  may be connected to a data communication infrastructure  810 , for example, a bus, message queue, network, or multi-core message-passing scheme. 
     A device  800  (e.g., the server system  102 , the user devices  112 , the employee devices  116 , the tenant devices  120 , and/or any other computer system or user terminal for performing the various embodiments of the present disclosure) may also include a main memory  840 , for example, random access memory (RAM), and may also include a secondary memory  830 . Secondary memory, e.g., a read-only memory (ROM), may be, for example, a hard disk drive or a removable storage drive. Such a removable storage drive may comprise, for example, a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, or the like. The removable storage drive in this example reads from and/or writes to a removable storage unit in a well-known manner. The removable storage unit may comprise a floppy disk, magnetic tape, optical disk, etc., which is read by and written to by the removable storage drive. As will be appreciated by persons skilled in the relevant art, such a removable storage unit generally includes a computer usable storage medium having stored therein computer software and/or data. 
     In alternative implementations, secondary memory  830  may include other similar means for allowing computer programs or other instructions to be loaded into device  800 . Examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units and interfaces, which allow software and data to be transferred from a removable storage unit to device  800 . 
     A device  800  may also include a communications interface (“COM”)  760 . Communications interface  860  allows software and data to be transferred between device  700  and external devices. Communications interface  860  may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, or the like. Software and data transferred via communications interface may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface  860 . These signals may be provided to communications interface  860  via a communications path of device  800 , which may be implemented using, for example, wire or cable, fiber optics, a phone line, a cellular phone link, an RF link or other communications channels. 
     The hardware elements, operating systems, and programming languages of such equipment are conventional in nature, and it is presumed that those skilled in the art are adequately familiar therewith. A device  800  also may include input and output ports  850  to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various server functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the servers may be implemented by appropriate programming of one computer hardware platform. 
     The systems, apparatuses, devices, and methods disclosed herein are described in detail by way of examples and with reference to the figures. The examples discussed herein are examples only and are provided to assist in the explanation of the apparatuses, devices, systems, and methods described herein. None of the features or components shown in the drawings or discussed below should be taken as mandatory for any specific implementation of any of these the apparatuses, devices, systems, or methods unless specifically designated as mandatory. For ease of reading and clarity, certain components, modules, or methods may be described solely in connection with a specific figure. In this disclosure, any identification of specific techniques, arrangements, etc. are either related to a specific example presented or are merely a general description of such a technique, arrangement, etc. Identifications of specific details or examples are not intended to be, and should not be, construed as mandatory or limiting unless specifically designated as such. Any failure to specifically describe a combination or sub-combination of components should not be understood as an indication that any combination or sub-combination is not possible. It will be appreciated that modifications to disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices, systems, methods, etc. can be made and may be desired for a specific application. Also, for any methods described, regardless of whether the method is described in conjunction with a flow diagram, it should be understood that unless otherwise specified or required by context, any explicit or implicit ordering of steps performed in the execution of a method does not imply that those steps must be performed in the order presented but instead may be performed in a different order or in parallel. 
     Throughout this disclosure, references to components or modules generally refer to items that logically can be grouped together to perform a function or group of related functions. Like reference numerals are generally intended to refer to the same or similar components. Components and modules can be implemented in software, hardware, or a combination of software and hardware. The term “software” is used expansively to include not only executable code, for example machine-executable or machine-interpretable instructions, but also data structures, data stores and computing instructions stored in any suitable electronic format, including firmware, and embedded software. The terms “information” and “data” are used expansively and includes a wide variety of electronic information, including executable code; content such as text, video data, and audio data, among others; and various codes or flags. The terms “information,” “data,” and “content” are sometimes used interchangeably when permitted by context. 
     It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.