JP2015524130A - Inline benchmarking and comparative analysis of recurring revenue assets - Google Patents

Abstract

A recurring revenue benchmarking system and method is disclosed. A repository that stores aggregate information about service revenue updates is accessed. Aggregation information includes historical data generated by one or more commercial entities, which is defined according to one or more metrics associated with the update of one or more service assets by one or more commercial entities. Is done. The prediction model is generated based on the information of the set accessed from the repository. A set of parameters representing at least one proposal for renewal of service assets within the sales period is defined. The proposal relates to at least one recurring revenue asset managed by the first commercial entity using a recurring revenue management system. The predicted outcome for the proposal is calculated using the proposed set of parameters as input to the prediction model.

Description

  The subject matter described herein relates to data processing and management, and more specifically, supports recurring revenue asset information, including performance benchmarking, for example, supporting sales execution and opportunity management for recurring revenue streams. Related to managing.

  To increase the amount of market share controlled by a company or other commercial entity, innovating in products or marketing, introducing new or improved products, selling to new customers or further selling to existing customers, and Other approaches have historically been a major driver for revenue growth. However, as the market grows and establishes, it is used to refer to commercial entities (broadly referred to herein as companies, corporations, organizations, persons, groups of persons, etc. that engage in commercial activities. The greater rate of return realized by selling, and / or service to customers is derived from recurring revenue asset renewals. As used herein, recurring revenue assets generally refer to maintenance and / or support contracts for software or hardware, products that include services, service contracts, recruitment contracts, guarantees, renewable energy, etc. Can do.

  Many commercial entities fail, at least in part, to maximizing the realization of revenue from recurring revenue assets. This is because the management of such assets is not well supported by currently available approaches. Currently available tools for managing recurring revenue range from customer relationship management (CRM) systems, partner relationship management (PRM) systems, data warehouses, entitlement systems, billing systems, and spreadsheet programs. Is popular. However, recurring revenue management offers many challenges that differ from the typical focus of a commercial entity sales staff in a substantial and important perspective.

  A typical sales process involves closing (closing) a sale or transaction and moving to the next customer. In contrast, recurring revenue relationships are by repetitive rules and are not effectively tracked or managed by tools designed to support initial sales. Also, existing investments of sales entities, sales processes, and commercial entities in information systems typically target the appropriate technology to achieve maximum revenue performance in the goods or services sales business, and this technology Optimized and supported. These are not done to manage the recurring revenue streams and sales activities necessary to ensure that the recurring revenue assets are managed to further increase value. Optimizing recurring revenue for service contracts, other service assets, etc. requires solving issues across multiple areas of a company's business. In today's ever-changing market environment, service revenue is generally related to the success of commercial entities. The problem, however, is that it encounters many challenges that it does not provide, as commercial entities are beginning to recognize the need for this latest focus.

  For example, companies often need to handle fragmented data from different sources, and this data can be inaccurate and incomplete, especially when selling in or using sales channels. Yes, and sometimes overlap. Furthermore, in the case of mergers or acquisitions, various legacy systems introduce complexity. A further challenge is the lack of specialization for service contracts or service renewals. Much of the company data is concentrated on the products that the company sells, rather than the services contracted after the sale of the products that the company sells. The lack of industry focus on recurring service revenues has led to a lack of standard metrology and has proven best practice.

  Traditionally available approaches for recurring revenue management do not include an integrated repository, but rather collect and organize these types of information that are important for optimizing recurring revenue sales. It does not include any other means of integrating and integrating data from multiple disparate external client systems in support of an asset data model that can solve and solve critical problems. Here, the important issues are what was purchased (eg, service, serviceable product, etc.), who (eg, which commercial entity) made one or more purchases, and who was purchased Whether you are an end user of a service or serviceable product, where a purchased service or serviceable product is used, how a purchased service or serviceable product is used, and how The end user (and possibly others in the commercial entity) is satisfied with the purchased service or serviceable product, or to the commercial entity as a result of selling the purchased service or serviceable product There are other opportunities to sell additional or unrelated services. In addition, software systems for automating and optimizing the recurring revenue market generally acquire and collect recurring revenue assets, well-developed key performance indicators (KPIs) for managing such assets, and data streams. Limited to interest in related analysis dedicated to capabilities and other useful features that support the optimization of recurring revenue sales and renewals.

  Consistent with one or more aspects of the present inventive subject matter, a recurring revenue repository is obtained from one or more external data sources and is currently ongoing sales activities (eg, user interaction and database updates). ) Can be maintained using current revenue information obtained from. Some aspects of the present inventive subject matter can include the use of a recurring revenue asset data model generated from at least a portion of this recurring revenue information. The recurring revenue asset data model includes recurring revenue assets and organizations, products sold prior to recurring revenue earning assets, recurring revenue assets and people and commodities associated with products sold prior to restructuring, and the like The relationship between things can be expressed. Many analysis and business intelligence processes can optionally be performed on the recurring revenue asset data model and on recurring revenue information in the recurring revenue repository.

  In some aspects of the present inventive subject matter, the method includes accessing a repository that stores a set of information regarding service revenue updates. Aggregation information includes historical data generated by one or more commercial entities, and according to one or more metrics associated with the update of one or more service assets by the one or more commercial entities. It is prescribed. The method further includes generating a predictive model based on the set information accessed from the repository and defining a set of parameters representative of at least one proposal for updating service assets within a sales period. Including. At least one proposal is associated with at least one recurring revenue asset managed by the first commercial entity using a recurring revenue management system. The method further includes calculating a predicted outcome for the at least one proposal using the set of parameters of the at least one proposal as an input to the prediction model. The predicted outcome represents a potential outcome of at least one proposal according to a selected one or more metrics associated with the service asset renewal within the sales period.

  Implementation of the subject matter of the present invention is an article comprising an explicitly implemented machine-readable medium operable to cause one or more machines (e.g., a computer, etc.) to perform operations compatible with these features. Of course, systems and methods including one or more features as described herein can be included, but are not limited to such. Similarly, a computer system is also described, which may include one or more processors and one or more memories connected to the one or more processors. The memory can include a computer-readable storage medium, wherein the memory is one or more that causes one or more processors to perform one or more of the operations described herein. A program may be included, encoded, stored, or the like. Computer-implemented methods compatible with one or more implementations of the present inventive subject matter can be implemented by one or more data processors residing in a single computer system or multiple computer systems. Such computer systems can be connected and can exchange data and / or commands, other instructions, etc. via one or more connections. Here, the one or more connections are connections on a network (eg, the Internet, a wireless wide area network, a local area network, a direct connection between one or more of a plurality of computer systems, etc. Wide area network, wired network, etc.), but not limited thereto.

  The details of one or more variations of the subject matter described in this specification are set forth in the accompanying drawings and the detailed description below. Other features and advantages of the subject matter described in this specification will be apparent from the description, drawings, and claims. While certain features of the presently described subject matter are described for purposes of illustration in connection with enterprise resource software systems or other business software solutions or architectures, it is readily understood that such features are not intended to be limiting. Should. The claims following this disclosure are intended to define the scope of the protected subject matter.

FIG. 4 illustrates a data flow in a recurring revenue management framework compatible with implementation of the present subject matter. FIG. 6 illustrates data exchange between a recurring revenue management system compatible with an implementation of the present subject matter and an example external client system. FIG. 3 illustrates the characteristics of an asset data model that is compatible with implementations of the present subject matter. FIG. 4 illustrates relationships between components of a recurring revenue management framework compatible with implementation of the present subject matter. Figure 5 is a process flow diagram illustrating features of a method compatible with implementation of the present subject matter. FIG. 4 is a screenshot view of analysis detail information display compatible with implementation of the present subject matter. FIG. 4 is a screenshot view of analysis detail information display compatible with implementation of the present subject matter. FIG. 4 is a screenshot view of analysis detail information display compatible with implementation of the present subject matter. FIG. 4 is a screenshot view of analysis detail information display compatible with implementation of the present subject matter. Figure 5 is a process flow diagram illustrating features of a method compatible with implementation of the present subject matter. FIG. 6 illustrates the characteristics of a sales process compatible with the implementation of the subject matter of the present invention. FIG. 6 illustrates an annotated user interface view showing features associated with a sales team responsibility branch. FIG. 6 shows a screenshot view of a focused view that can be displayed to a sales representative compatible with implementation of the inventive subject matter. FIG. 6 shows a screenshot view of a focused view that can be displayed to sales activity team members compatible with implementation of the inventive subject matter. FIG. 6 shows a screenshot view of a focused view that can be displayed to a manager compatible with implementations of the present subject matter. FIG. 6 shows a screenshot view of an analysis screen associated with various metrics or predictions compatible with implementation of the inventive subject matter. FIG. 6 shows a screenshot view of an analysis screen associated with various metrics or predictions compatible with implementation of the inventive subject matter. FIG. 6 shows a screenshot view of an analysis screen associated with various metrics or predictions compatible with implementation of the inventive subject matter. FIG. 6 shows a screenshot view of an analysis screen associated with various metrics or predictions compatible with implementation of the inventive subject matter. FIG. 7 illustrates an example of a computing overview having one or more features that can be included in the implementation of the present subject matter.

Cross-reference to related applications This application is a partially pending application of the following co-pending patent applications: US patent application Ser. No. 13 / filed Mar. 15, 2013 entitled “Asset Data Model for Ordinary Revenue Asset Management”. US Patent Application No. 8416881, US Patent Application No. 13/844306, filed March 15, 2013 entitled “Predictive Model for Revenue Profit Opportunity”, US Patent, filed March 15, 2013, entitled “Generation of Revenue Revenue Asset Sales Opportunities” Application No. 13 / 844,002, U.S. Patent Application No. 13/842035, filed March 15, 2013 entitled "Provenance Tracking and Quality Analysis of Revenue Asset Management Data", and "Inbound Data for Ordinary Revenue Asset Management" US patent application Ser. No. 13 / 842,398, filed Mar. 15, 2013, entitled “Handling Outbound Data”. All five of these applications are filed under US Provisional Patent Application No. 61 / 661,299, filed June 18, 2012 entitled “Ordinary Revenue Asset Management System and Method” under 35 USC 119 (e). Claim priority. This application is also related to the following co-pending co-owned application: US patent application Ser. No. 13 / 895,276, filed May 15, 2013, entitled “Multilayer Channel Partner Management for Recurring Revenue Sales”. , US Patent Application No. 13/895302, filed May 15, 2013, entitled “Visual Representation and Prediction of Current Revenue Management System Data”, dated May 15, 2013, entitled “Recurring Revenue Management Benchmarking” Filed U.S. Patent Application No. 13/895294. The disclosure of each document identified in this paragraph is hereby incorporated by reference in its entirety.

  The accompanying drawings are incorporated and constitute part of this specification, illustrate certain aspects of the subject matter disclosed herein, and assist some explanation of the principles associated with the implementations disclosed with the specification. It becomes.

  Substantially similar reference numbers indicate similar configurations, features, or components.

  This specification describes a recurring revenue management system and method, techniques, etc. related to operations and functions that support recurring revenue management. Consistent with the implementation of the present subject matter, the recurring revenue management system can include a recurring revenue asset repository that stores data relating to recurring revenue assets, which are described in further detail below. An integrated data store model can be presented to manage the use of both transactions and analytics on data. In some implementations of the present inventive subject matter, the use of in-memory data structures (including but not limited to data cubes and the like) that are dynamically generated and updated can enrich data in near real time. Analysis analysis can be facilitated, which allows adaptation to customer specific features and metrics. In some implementations of the present subject matter, the analysis framework supports role-specific dashboards, and a plug-in model using a rich representation state transition (REST) application programming interface (API) is provided by third-party ad hoc. Can be included to support the use of various analytical products. Also, consistent with the implementation of the subject matter of the present invention, the data held in the recurring revenue management system is organized according to an asset data model that includes a plurality of data objects and relationships defined or derived between these data objects. Is possible. Data objects include products sold by commercial entities, recurring revenue assets of such commercial entities, opportunities to sell or renew new or existing recurring revenue assets, merchandise, recurring revenue assets, opportunities, and other And contacts associated with one or more of the contacts.

  FIG. 1 shows a diagram 100 illustrating the data flow that appears in a recurring revenue management system consistent with the implementation of the present subject matter. As shown in FIG. 1, a number of data sources can contribute to data contained within the asset data model, such as the recurring revenue asset information in the recurring revenue asset repository 102, the opportunity in the opportunity repository 104. Information and / or proposal information, quote information in the quote repository 106, reservation information in the reservation repository 110, contact information in the contact repository 112, and the like may be included. It should be noted that although the various repositories of FIG. 1 are depicted as being physically separate from one another, any data storage configuration and arrangement of one or more repositories is within the scope of the present subject matter. That's what it means. For example, all or part of the repositories 102, 104, 106, 110, 112 of FIG. 1 may be included in a single repository configuration that is hosted on a single storage device. Or hosted in a distributed storage (eg, in the “cloud”).

  Referring back to FIG. 1, the recurring revenue asset repository 102 is a physical item or software that has already been sold and can be sold by the commercial entity to one or more customers of the commercial entity. Products such as licenses, which can also include information about instances of the asset. These target assets form the basis for recurring revenue assets. Because they constitute an existing instance of the seller-buyer relationship between the commercial entity and the customer who purchased the target asset. Recurring revenue assets (service assets) themselves are service contracts, warranties, sales, etc. sold to customers by commercial entities that are related to or provide, among other assets, or ongoing service arrangements for the subject asset Maintenance contracts can be included.

  Since it relates to an existing and ongoing relationship with the customer, the information about the products and their associated recurring assets contained within the recurring revenue asset repository 102 is one for updating existing recurring revenue assets. Or can be used to generate multiple opportunities (and thereby maintain a recurring revenue stream), and in some cases (for example, by selling more expensive agreements, service contracts, etc. to customers) ) Can be used to increase the value of recurring revenue assets. As used herein, an opportunity is a grouping of proposals targeted for execution of sales to customers, and proposals are individual instances of recurring revenue assets that are available for renewal. . Proposal groupings in opportunities can include multiple proposal instances for the same customer. In one example, all proposals grouped in one opportunity can have due dates between sales periods (eg, one month, quarter, one year, etc.). Opportunities can be collected in the opportunity repository 104.

  Once the sales staff contacts the appropriate decision-makers on the customer side, the opportunity is moved to the quote stage, which is the period during which quotes related to the renewal, increase, etc. of existing recurring assets can be generated. Can. Quotes including transaction proposals for the set of proposals included in the opportunity can be stored in the quote repository 106. Once the quote is transferred to an established transaction, a reservation indicating the set of proposals accepted by the customer can be generated and stored in the reservation repository 110. The resolution of the reservation results in the creation of a new recurring revenue asset that can be maintained in the recurring revenue asset repository 102 for use in generating future proposals and opportunities. For example, when a resolved service contract, etc. approaches its next deadline, a new proposal for renewal of that service contract can be generated, and optionally a deadline date determined in the same way. Along with other proposals for the same customer that accompanies, it can be grouped into new opportunities. Further data that affects the creation of opportunities and the execution of those opportunities is the basis for decision makers and other important contacts (such as individuals and companies included within the sales process) that sales staff need to contact. For contact information. This contact information can be stored in the contact repository 112.

  An efficient structured assembly for the information contained in the repositories 102, 104, 106, 110, 112 shown in FIG. 1 is an important form of a recurring revenue management approach that is compatible with the implementation of the present subject matter. obtain. However, much of this information can be found in a data management system (referred to herein as an external client system 114) other than a recurring revenue management system, as described in more detail below with reference to FIG. Usually included (or at least derived). The term “client” is used herein to refer to a commercial entity that utilizes the characteristics or functionality of a current revenue management system provided or supported by a current revenue management system vendor or service provider. In practice, as used herein, a client is a commercial entity that is a customer of a recurring revenue management vendor or service provider. The client commercial entity now has a customer that the client has sold and / or wants to sell or renew a recurring revenue asset such as those described elsewhere herein. Client commercial entity customers typically have an installed base of products for which they can purchase various service agreements, contracts, and the like. In practice, merchandise on an installed basis of merchandise can be considered as a target asset for which a commercial entity can sell one or more service contracts, agreements, etc. to a customer. From the perspective of the client commercial entity, these service agreements, agreements, etc. can be considered service assets or recurring revenue assets.

  A recurring revenue management approach that is compatible with the implementation of the present subject matter is configurable for storing, retrieving, retrieving, and analyzing a wide range of recurring revenue information. Data related to service assets or other recurring revenue assets, referred to herein as “recurring revenue data,” such as services, goods, etc. (eg, subject assets) that the related services can be sold for Information related to what the commercial entity sold. Ordinary revenue data can also include information about the purchaser, user, location of use, mode of use, etc., such as those services, serviceable products, etc. In other words, recurring revenue data provides information about what recurring revenue assets an entity has, who are customers and users of recurring revenue assets, and where and how recurring revenue assets are used. Can be included. Recurring revenue data further includes information about customer or user satisfaction with recurring revenue assets and potential opportunities for the sale of service contracts (eg, serviceable products that will soon be warranted). Can be included.

  The recurring revenue management system can import different information in various formats from the external client system 114 into the common asset data model. The asset data model can facilitate a wide range of recurring revenue management capabilities and can also provide automatic assistance for entity resolution (transforming external entities to pre-existing entities in the system). . The recurring revenue management system can also optionally identify any source of information (eg, all recurring revenue data from each of one or more data sources) all loaded into the system. Log information for information can be retained.

  With reference to FIG. 1 as described above, a recurring revenue management system compatible with the implementation of the present subject matter can receive information from various external client systems 114 and assign available structures. . FIG. 2 shows a diagram 200 showing potential sources of data or other information that can be obtained or received at the recurring revenue management system 202. Among other possible data sources, the external client system 114 shown in FIG. 1 includes a rights granting system 204, an order management system 206, a pricing system 210, a merchandise master database 212, a sales force automation system 214, a customer master database 216, And contract management system 220, user recruitment and usage data source 222, customer service database 224, and the like. It will be appreciated that the individual external client systems shown in FIG. 2 may be one or more systems that support more than one of the characteristics of those external client systems 114 in some implementations of the present subject matter. Can be provided. For example, external client systems include customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, business intelligence (BI) systems, calendar and contact systems or databases, customer service systems, inventory systems, accounts receivable systems, customer use And sources of delegation information, etc. In general, commercial entities obtain these various systems, databases, etc. from more than one different software or computer system provider, each of which is typically for a specific task performed by the product. Use a unique or proprietary database configuration, data format, report configuration, application programming interface (API), etc. that is optimized. An assembly of information from a group of data sources, with a large number of potentially different database configurations and data formats, can indicate the challenges in any system that attempts to collect the information and formats in which it can be used Can support detailed analysis based on data. A further challenge can be indicated by the need to authenticate potentially conflicting information received from more than one source. Moreover, the information collected in a recurring revenue management system that is compatible with the implementation of the present subject matter is typically not static. Rather, users who interact with the system tend to update or correct information based on communications with contacts and the like. The execution of the task shown in FIG. 1 can also change the “correction” version of some of the information received within the recurring revenue management system.

  Approaches to this problem typically involve receiving data from the source system at the receiving system, creating a customized mapping between the individual source system data structures and the receiving system data structures (or pre-generated). Mapping application) and conversion of data from the source system data structure to the receiving system data structure according to the mapping. Following the import of data from the data source, but prior to making the imported data available for productive use by the end user in the receiving system, one or more quality control operations are generated and imported. Data consistency, identify double potentially conflicting records, and alert one or more users with an appropriate tolerance level for the presence of errors, partial imports, missing data, etc. A similar process is performed on the reversely performed mapping for exporting data back from the receiving system to the source system.

  For active recurring revenue management systems, in particular, a large number of sales staff, a large number of recurring revenue assets, and a complex information landscape where many systems maintain data records related to supporting recurring revenue management activities For recurring revenue management systems that support recurring revenue management for large commercial entities, a quality control process that interrupts productive use of the system following data import is undesirable. For example, in a large company with many data streams, quality control is very time consuming. Furthermore, frequent data imports can be beneficial in the benefit of making up-to-date information available for use by the recurring revenue management system. However, if those individual imports require substantial downtime for the import and conversion processes to be completed and the quality assurance routine to be performed, the characteristics supported by the current revenue management system are available. Sex is negatively affected.

  A recurring revenue management approach that is compatible with the implementation of the present subject matter can include data synchronization and integration with a variety of different external client systems. For example, a recurring revenue management system can access data records and formatted data from systems such as entitlement systems, order management systems, customer master systems, merchandise master systems, and service provision systems. Data from these external client systems is usually not static. Thus, it is an advantage that the recurring revenue management system can receive data feeds from them at a high throughput rate. Furthermore, the received data is available either upon receipt at the recurring revenue management system or at least within a very short processing time range.

  FIG. 3 illustrates a feature of an asset data model 300 that can include an interrelated data object 301 that supports the recurring revenue management features and functions described elsewhere herein. The recurring revenue management system 302 is an asset data model 300 as a flexible mechanism for representing complex relationships between assets, organizations, people, and products, and is capable of performing numerous analyzes and business intelligence processes. An asset data model 300 can be supported. The recurring revenue management system can provide one or more application programming interfaces (APIs) or other standardized interfaces for accessing service update assets in the recurring revenue repository, and the recurring revenue repository information model used by the repository. Provides a uniform means of analyzing, searching and manipulating recurring revenue information data.

  Consistent with the implementation of the present subject matter, the recurring revenue management system 202 and contained asset data model 300 includes a product data object 302, a recurring revenue asset data object 304, an opportunity data object 306, and a contact data object 310. be able to. The merchandise data object 302 represents a merchandise (e.g., target asset), and the merchandise data object 302 includes items sold to the customer as described above with reference to FIG. Can be considered as part of the installed base. The framework of the data object 301 can include a common set of relationships for recurring revenue management, and these relationships can include product relationships 312, asset relationships 314, opportunity relationships 316, and contact relationships 320. Merchandise is an asset data object 304 via one or more merchandise relationships 312 between the merchandise represented by the merchandise data object 302 and an instance of the asset data object 304 representing a recurring revenue asset or assets. Can provide a basis for recurring revenue assets represented by instances of. The merchandise relationship 312 also optionally includes the merchandise represented by the merchandise data object 302, the recurring revenue asset represented by the instance of the asset data object 304, the opportunity represented by the instance of the opportunity data object 306, and the instance of the constant object 310. Can exist between one or more of the constants represented by, other products represented by other instances of the product data object 302, and the like.

  Still referring to FIG. 3, the asset relationship 314 includes a recurring revenue asset represented by an instance of an asset data object 304 and a product represented by an instance of a product data object 302 (eg, a person associated with a recurring revenue asset). A contact represented by an instance of the contact data object 310 (eg, an organization, etc.) and a bundle of proposals related to, for example, updating an existing recurring revenue stream and / or establishing a new or increased recurring revenue stream, etc. ) Between an opportunity represented by an instance of the opportunity data object 306 and one or more of another recurring revenue asset, etc. represented by another instance of the asset data object 304.

  The opportunity relationship is represented by the opportunity represented by the instance of the opportunity data object 306, the specific recurring revenue asset represented by the instance of the asset data object 304, and the instance of the product data object 302 that forms the basis for the recurring revenue asset. There may be between one or more of a product, a contact represented by another instance of contact data object 310, another opportunity represented by an instance of opportunity data object 306, and so forth. The contact relationship is based on the contact represented by the instance of the contact data object 310 and (eg, based on communication associated with the initial sale of the recurring revenue asset 304 and / or one or more updates of the recurring revenue asset 304). Represented by one or more of the recurring revenue assets represented by an instance of the asset data object 304, a product represented by an instance of the merchandise data object 302 underlying the recurring revenue asset, and an instance of the opportunity data object 306 There can be between one or more of an opportunity, another contact represented by another instance of the contact data object 310, and so on.

  The aforementioned data object 108 may be extended, for example, via a customer specific extension field. Additional custom objects can be added to the customer's configuration. Furthermore, the set of underlying relationships can be expanded to meet the specific requirements of commercial entities using the recurring revenue management system 202. The relationship between the instances of the product data object 302, asset data object 304, opportunity data object, and contact data object includes the data received from the input data stream, existing data in the recurring revenue management system 202, user input, etc. Can be established based on one or more of. The multiple instances of the data object 301 and the defined relationships between these instances together make up the asset data model 300.

  The asset data model 300 as described herein provides a useful means for exploring and understanding the relationships that exist between product objects, asset objects, organization objects, contact objects, and opportunity objects. can do. Many of these relationships are derived directly from the information present in the source client system, while others require inferences about parts of the system and others as part of object creation. Others are generated by the system, others are formed as side effects of system operation, and others are configured as a result of operation by the system user. In an illustrative and non-limiting example of a reasoning approach for creating relationships between objects, relationship reasoning is (for example, if a is related to b and b is related to c, the relationship between a and c The use of transitivity (such as can be shown) and automatic presentation of inverse relations (if a is related to b, there is an inverse relation from b to a) Including.

  A tailored approach to managing opportunities, quotes, and booking activities can be provided through one or more characteristics supported by the asset data model 300 that is compatible with the implementation of the present subject matter. These characteristics can include, but are not limited to, detailed reports, pipeline dashboards, opportunity details, area management, and the like. Detailed reports allow sales representatives to actively identify and address risky opportunities, while pipeline dashboards allow managers to view the pace of sales activity by time period and offer metrics. Opportunity details allow sales representatives, sales activity assistants, managers, and other staff to view transaction progress and recorded activities. Zone management allows managers to manage and drive salesperson behavior by assigning tasks to opportunities based on existing priorities. A zone can indicate a division of available opportunities based on one or more factors, which can include but is not limited to geography, customer grouping, subject matter, and the like.

  Installation of products, services, etc. previously sold to commercial entity customers using asset data model 300, which provides a basis for the sale of new and updated recurring revenue assets A clearer view can be provided to base users (eg, sales staff, sales support staff, managers, executives, etc.). As described elsewhere herein, a clearer view can be provided to the user via one or more user interface displays, computer generated interfaces, and the like. This install-based management using the asset data model 300 can improve accuracy and insight into the customer base and enables the use of install-based data to maximize the recurring revenue stream. Increased sales can be achieved through improved quote accuracy, integration, identification of cross-sell and up-sell opportunities, better handling of update contacts and processes, and the like. Through improved entitlement, shipping, and staff resource planning, service delivery efficiency and customer satisfaction can be increased. Customer behavior predictions can also be improved, which facilitates optimization of sales staff activities.

  Among other information that can be received in an inbound data stream and that can be incorporated into an appropriate instance of the data object 301 in the asset data model 300, the asset data model is a deadline information for an existing recurring asset. Product information (eg, who, what, where, etc., including installed base features), relationships (eg, predecessors, successors, related opportunities, target products, customers, partners, location, etc.), expected value Analysis (eg, predicted future revenue, product margin analysis, etc.), etc. can be stored and organized.

  A recurring revenue management system that includes one or more characteristics described herein can provide scenario-based forecasts from recurring revenue repositories of service asset data and other recurring revenue data. In some implementations, the recurring revenue management system generates a user interface that displays a forecast of revenue over time from a set of assets based on due dates, product information, predicted sales timing, and estimated sales costs.

  A recurring revenue management system that includes one or more features as described herein may also receive input from a user to define a pattern. Here, the pattern, when applied to the data in the recurring revenue repository, gives rise to one or more sets of opportunities that are generated for sales execution. The recurring revenue management system logic identifies different types of opportunities (eg, updates, cross-sells, up-sells, etc.) and optimizes proposal grouping within the opportunities to improve sales productivity, Includes applications that perform multiple methods. The recurring revenue management system can encode and automate computer-based processing for automated guidance of team-based sales of services (recurring revenue sales).

  In various aspects consistent with one or more implementations of the subject matter of the present invention, the implementation of recurring revenue sales includes an opportunity generation and handling approach, a forecasting and targeting approach, a sales stage management approach, and an integrated sale. It can be supported by one or more of an activity approach, a channel partner management approach, and a sales automation approach. In other aspects, one or more sales activity functions may be provided, such as, for example, a quote process, a reservation process, and a data service. The various activities that support such features can optionally be based at least in part on the use of the asset data model 300 of the recurring revenue management system 202 as described above. While the approaches described herein describe various features and functions of the asset data model 300, it is understood that the use of such models is not essential unless specifically stated otherwise. Let's go. The following description and the accompanying drawings relate to features of use having various advantages associated with these aspects and technical features through which such features of use can be provided.

  FIG. 4 shows a diagram 400 illustrating components of a recurring revenue management approach that can be based on one or more features of recurring revenue management system 202 and asset data model 300 as described above. Diagram 400 illustrates the interaction of various components within the context of the sales process for service asset 402. Service asset 402 is associated with target asset 404 that was previously sold, leased, etc. to a customer by a commercial entity (in this example, the commercial entity is a user of recurring revenue management system 202). Existing service assets 402 can have a due date. As a due date data approach, the recurring revenue management system 202 can generate the proposal 406 based at least in part on the data stored and organized in the asset data model 300 that defines the relationships between the data objects 301. The data object 301 is a data object related to a product (for example, the target asset 404), an opportunity, a contact, a service asset, or the like. Proposal 406 is optionally of a group of two or more proposals grouped into an opportunity based on proximity to the due date of two or more service assets associated with the same customer Can be one of the following. Proposal grouping into opportunities is based on the asset data model 300 and the relationships defined therein to suggest that the opportunity group should be directed to a common decision maker on the side of a single customer. And can be executed intelligently. Proposals can be initially classified as “no contact” 410 during the limited phase of opportunity processing, optionally as part of an opportunity. Opportunities can be reclassified as “contacted” once the sales rep makes initial contact with the purchaser or customer decision-maker.

  When a salesperson identifies that he or she has successfully contacted the purchaser or other decision-makers on the customer's side and that some of the purchasers or decision-makers want to consider updating the service asset 402, Salesperson 412 can request a quote 414, which can be recorded in the recurring revenue management system 202. Estimate record 416 may be generated and sent to sales activity team member 420. When the sales activity team member 420 completes the quote (and optionally records it in the recurring revenue management system 202), the recurring revenue management system 202 confirms that the generated quote can be sent to the buyer. The sales representative 412 can be notified. In any variation, the recurring revenue management system 202 can automatically detect when the quote is complete and can provide a notification to the sales representative 412 that this has occurred.

  The sales representative 420 can deliver a quote to the purchaser or other decision maker 422, which event can also be recorded by the recurring revenue management system 202 as further tracking of the sales process. When the salesperson 412 records in the recurring revenue management system 202 that a purchase order has been received, the sales process status can be updated accordingly (424). The recurring revenue management system 202 then generates a reservation record 426. When the sales activity team member 420 records that the sales are complete (430), the sales phase proceeds to a successful sales. Next generation service assets may be generated, for example, with further instances of opportunity objects 306 having a relationship with the object object instance associated with the target asset 404. This next generation service asset provides the starting point for the next proposal to update the service asset 402 as the next generation service asset approaches its due date.

  The asset data model 300 also provides service assets, installed base, total asset value, projected revenue, and data quality, as well as sales programs to drive opportunity generation (eg, evaluate, classify and prioritize subscriptions). Ranking), subscription planning (eg, creating opportunities based on installed base or subscription planning outcomes), real-time (or at least near real-time or approximately real-time) reporting and interpretation. Thus, the asset data model 300 is used to generate proposals and opportunities based on, for example, relationships between data objects and information that can be included in or derived from the asset data model 300. Can do. This additional information includes exclusion rules, deadline window filters, merchandise, customer or region filters, target sales periods, upsell and cross-sell opportunities, equipment updates, and analysis of expected outcomes. And the like, but is not limited to

  An approach to creating and handling opportunities that is compatible with the implementation of the present subject matter can be better understood with reference to FIG. At any particular time, a user or automated process can select a filtered set of service assets to be used as a source input for opportunity generation. Typically, this filter may include a range of service contract expiration dates that correspond to the target sales period (eg, the time period during which a new contract or renewal sale is expected to be established). The due date ranges generally include the due date before the sale period (contracts that have expired or carried forward), the due date between the sale periods (during the period), and the due date after the sale period (future) And a day corresponding to one of them. Opportunity generation can be an automated process that operates on a selected set of service assets. An example of an opportunity generation approach can be better understood with reference to the process flowchart 500. Process flowchart 500 illustrates method features consistent with one or more implementations of the present subject matter.

  At 502, a filtered set of service assets as described above is selected. In various implementations of the subject matter of the present invention, selection of a filtered set of service assets includes, for example, an asset data object associated with a service asset that provides a recurring revenue stream to a commercial entity using one of the filters described above. Can be generated automatically using a computer system applied to an asset data model that includes a collection of data objects including At 504, new service proposals based on one or more specific rules defined for the commercial entity are generated for service assets in the selected set. Generation can optionally include application of one or more proposal generation parameters. Non-limiting examples of proposal generation parameters include the type of update to be proposed (eg, “like-for-like” service, also called per update, upgrade in service, etc.). Generation is also about to expire, to determine the appropriate updated inventory management unit (SKU) or other reference information for the asset that is about to expire and other reference information for the chosen service level Duplicating relevant information from service assets (eg, contact information, etc.) can be included. In some implementations, the related information is an asset data object associated with a service asset that is about to expire or a contact data object that has a relationship with an asset data object associated with a service asset that is about to expire Or the like. A replica is generated by an update by sending data from the asset data object associated with the service asset that is about to expire to the new asset data object associated with the new service asset that is generated by the update. Establishing a relationship between the contact data object having a relationship with the new asset data object associated with the new service asset, or the like.

  At 506, the set of generated proposals can optionally be grouped into two or more smaller sets of opportunities. Grouping proposals into smaller sets of opportunities can be implemented to optimize sales efficiency. Grouping can include, in some examples, associating proposals with the same target customer, the same resale organization, and the like. In further or alternative variations, the grouping can be based on other considerations such as, for example, the same or related product types. At 510, suggestions can be assigned to one or more sales teams, either as a set of opportunities or as individual opportunities. The assignment can be based on data in an opportunity data object included within the opportunity, eg, associated with the opportunity. In further implementations of the subject matter of the present invention, opportunity generation optionally includes more complex logic to automatically add appropriate additional services to the proposal or to generate proposals corresponding to cross-sell opportunities. Can be included.

  Some implementations of the subject matter of the present invention can support understanding the distribution of assets that are about to expire over time, for example, as part of a long-term plan for potential future revenue. This functionality need not include the creation of opportunity objects. FIG. 6 shows an exemplary screenshot view 600 that shows a display of various analysis information that can be provided to a user based on information organized in the asset data model 300. The displayed data is optionally dynamically updated to provide an installed base 602, data quality 604, data import statistics 606, current revenue asset stream values (eg, their existing value 610 and some amount of upsell 612). (Assuming both contract renewals), (e.g., "What-if" based on benchmark quantity 614, historical trend 616, and one or more user-configurable parameters 620) or At least a near real-time view of expected service asset revenue status (using virtual forecasts) can be reflected. Dynamic data updates can be provided with an in-memory database backend with high performance in some implementations of the present subject matter.

  Consistent with the implementation of the subject matter of the present invention, a combined top-down, bottom-up approach for forecasting recurring revenue from service assets enables continuous use of large streams of inbound data received from external client systems 114 Can be supported by one or more of gender, the ability to track, predict and analyze these data, and the like. In this approach, management can set sales targets for sales periods (eg, one month, quarter, etc.) and sales activity team members and / or sales representatives (discussed in more detail below) For example, an updated forecast can be generated on a time scale that is shorter than the sales period (eg, one day, one week, etc.). As used herein, the term “target” is generated through a top-down approach, such as a desired target set by a manager, supervisor, business vice president, etc. for a given sales period. Can be considered as a business goal. In contrast, a “forecast” as used herein is generated by a sales employee to indicate where the sales person expects the number of sales that the sales person should have until the end of the sales period. It can be a prediction that takes the future into account. FIG. 7 shows a chart 700 representing the relationship between goals, forecasts, and “actual” represented by the current successful sales for a given sales period. In the example of FIG. 7, target line 702 represents a “top-down” prediction or expectation set by one or more managers or management / executive staff, while the prediction line is input from actual sales staff. Represents the “bottom-up” estimate generated by. A “currently closed sales” line 706 represents (at least approximately) the actual sales results updated at the current date and / or time 710. Comparison of these various metrics at various times, such as at the end of the sales period, for example over the course of the sales period, can be useful for many analytical evaluations related to sales performance. Examples of analytical evaluations that can be supported based on data collected, generated, etc. using an approach consistent with the subject matter of the present invention are one or more predetermined metrics, other recurring revenue sales activities Comparison of sales performance against benchmarking etc. against the background of data on Benchmarking and benchmarking data can be derived based on historical data or a predictive model applied to the collected current data and presented simultaneously with the predicted metric for a particular proposal.

  As will be described in more detail below, benchmarking can occur in the context of commercial entity internal data, using commercial entity historical data collected in, for example, asset data models. Alternatively or in addition, external benchmarking is also within the scope of the present subject matter.

  Additional features of a recurring revenue management system compatible with the implementation of the present subject matter can include support for timely and accurate analysis of how sales history can drive future sales performance. Tools that help with this effort are rich queries that can support dashboards and visualizations of analytical content such as graphs and charts that appear immediately upon login, and configuration operations, aggregation, background processing, and realized collections. Interfaces, standard reports and / or client specific reports, and external callable REST APIs that facilitate the use of third party data exploration and reporting tools can be included. FIG. 8 shows a screenshot view 800 illustrating an example of real-time performance statistics reported by an analysis tool embodied in a dashboard such as that described herein. The dashboard shown in view 800 can be a summary tab or page, as well as various tabs or pages linked from the summary that allow you to go deeper into the collected sales data, thereby enabling the system Users have access to data on one or more metrics or sales data based on the user's role. For example, a manager's view of data, metrics, etc. can be different from that of a salesperson or sales activity team member. These details are described in more detail below.

  The available opportunities during a given sales period are generally defined as all opportunities that are about to expire in the current sales period, and any remaining open opportunities from the previous sales period. Another aspect of recurring revenue management that can be supported using an approach consistent with the subject matter of the present invention is the dynamic calculation of key performance indicator (KPI) metrics related to sales performance and recurring revenue stream optimization, And real-time or near real-time updates. Among other challenges, what sales team managers in commercial entities, other executives, etc. want to answer is how sales teams can succeed in covering their customer base. How can the team succeed in turning the business into success, how can the team succeed in up-sell and cross-sell, and so on. Upsell generally involves establishing an opportunity with increased revenue points compared to a contract or other recurring revenue stream that has expired. This includes selling higher level services, maintenance or insurance plans with lower deductions, etc.

  FIG. 9 shows a diagram 900 illustrating aspects of key performance indicator calculations that can be used consistent with implementations of the present subject matter. The existing recurring revenue stream can be protected and advantageously can be enhanced by the upsell of new recurring revenue assets sold to existing customers or the like. Mapping is expected so that an appreciable “bucket” or set of resulting updates, upsells, and potential new sales opportunities can be collected to facilitate the prediction and evaluation of sales team performance. And the customer's purchasing behavior is observed.

  The information supplied to the KPI calculation is resolved with a resolution rate that evaluates the ratio of the opportunity to move to the final state within the transaction period (eg, established sales + non-service) divided by the opportunity available during the sales period. Is the ratio of successful (successful) sales to the total amount of opportunities (eg success + loss), and the success rate (contract rate) that evaluates the percentage of resolved opportunities that were resolved as success "Service asset value" divided by the targeted value, and thus can include a conversion rate that evaluates the actual transaction value of the established sales against the targeted value.

  The top-down approach described above may generally rely on KPIs and other historical performance metrics applied to opportunities. For example, multiplying the available opportunity by the resolution rate, multiplying by the establishment rate, and multiplying by the conversion rate gives a prediction for the sales period. As an example, a sales period with an available opportunity of $ 1.2 million, an expected resolution of 80%, an establishment rate of 80%, and a conversion rate of 105% would be $ 806,000 (e.g., 1.2 million x 80 % X 80% x 105% = $ 806000).

  A bottom-up approach that is compatible with the implementation of the present subject matter can use a “commit level” (red, yellow, green) approach, which is a specific forecast percentage applied to each transaction. In this way, sales team members (representatives or activities, depending on the structure of the sales team) can “per opportunity” of the likelihood (probability) of such opportunities (successes) to be resolved successfully. You can enter a quote. Simplified commit specification schemes (eg, red / yellow / green) with a limited number of categories, each indicating certain defined possibilities of successful establishment, are relatively straightforward and sales It does not take time such that the member is likely to pay the necessary attention to enter his or her predictions on a regular schedule (eg once a week). Continuing the bottom-up approach, all targeted opportunities can be assigned commit levels that are representative of the expected outcome potential. The expected date of establishment for each opportunity can also be entered or predicted. Opportunity weights can be all available opportunity values, such opportunities multiplied by the commit level (probability of success). The forecast of total revenue for a sales period can be the sum of the weighted values of opportunities that are expected to hold in such a sales period.

  Implementation of the inventive subject matter can also support an understanding of the distribution of associated due dates for a given set of suggestions or opportunities within the target sales period. Proposals within the sales period can be distinguished by the due date of the service asset associated with each proposal. This due date can be used in conjunction with historical comparison data, and the probability that such a proposal will be resolved, and that such a proposal is within a more specific date range in the sales period (eg within a particular month in a quarter) Can help predict the probability of being established. The recurring revenue management system 202 can support the ability to consistently constrain the forecasting of outcomes so that the forecasting of early outcomes of the sales period appropriately constrains the forecasts of later periods of the sales period. These constraints can be updated over time in the quarter and as actual results are obtained. Opportunity generation can include batching existing recurring revenue assets and potential recurring revenue assets according to their due dates. In some examples, a sales period opportunity metric can be generated based on a calculation of the value of a renewal contract that can be completed during the sales period. The sales period can be a time period different from the quarter (eg, one month, one week, one year or more, one day or less, etc.).

  An example predictive model can be better understood with reference to the process flowchart 1000 shown in FIG. 10, which illustrates features of the method that are compatible with one or more implementations of the present subject matter. At 1002, a set of proposals for service asset updates are distinguished within a sales period. The distinction can be based at least in part on the due date of the service asset associated with each proposal in the set of proposals. At 1004, the set of parameters representing each proposal in the set of proposals is analyzed. The analysis can include applying a predictive model based on the outcome of other proposals for updating service assets. The set of parameters includes at least one user-provided constraint on the predicted outcome of each proposal. For example, the set of parameters may include a predicted commit level that is assigned to each proposal by the person responsible for each proposal. The commit level is described in more detail below. At 1006, a predicted outcome for the set of suggestions is calculated based on an applied prediction model using the set of parameters as an input. At 1010, at least one constraint for at least one proposal in the set of proposals is updated based on at least one sales outcome that occurs during the sales period. At 1012, the predicted outcome for the set of suggestions is recalculated based on the set of parameters including the updated at least one constraint.

  Implementation of the subject matter of the present invention can provide tools that help managers and sales staff at commercial entities to align their sales approach with the purchasing behavior of commercial entity customers. Such behavior can affect the success of one or more opportunities. Features consistent with the subject matter of the present invention utilize the features and capabilities of the recurring revenue management system 202 and asset data model 300 described herein to track potential opportunities and predict sales results. , Sales staff efforts and attention can be directed to where and when it may be most beneficial.

  In general, opportunities based on customer contracts that are about to expire, other service arrangements, etc. can be assigned to one or more different states or performance indicators. The term “contract”, used to refer generally to recurring revenue assets described elsewhere in this specification, is, without limitation, an existing contract or agreement, a “free” or low cost warranty or service contract. (For example, it may be included in the initial purchase of the product). Contracts that are about to expire can provide the basis for generating recurring revenue renewal opportunities. The first indicator associated with the opportunity (eg, metric, instruction, label, etc.) can include a resolution state. The resolution state is a designation or indicator regarding whether the opportunity remains “unopened”, “resolved (resolved)”, or “established”. An “unopened” resolution state indicates that the opportunity has not yet been resolved. The sales process has not failed, but rather continues to have the potential to be resolved advantageously.

  The second indicator, metric, etc. associated with the opportunity is the outcome state. The outcome state can be success or loss. As used herein, the term “successful” or “successful” refers to success in securing a service that is related to performance status and updated or paid, eg, based on an opportunity or group of opportunities. In addition, it refers to the successful establishment of renewal of contracts, other service arrangements, etc. An outcome state such as “lost (loss)” or “non-service” refers to an outcome state in which no service was established for a given opportunity or group of opportunities. For example, lost opportunities based on contracts that are about to expire, any revenue should be received by the commercial entity, and any service expires an existing agreement, service, arrangement, etc. It is provided by the commercial entity for payment of the contract at termination and indicates that no renewal or continuation of the service is paid by the customer.

  The third indicator associated with a contract that is about to expire or a group of contracts that is about to expire is the current resolution of opportunities related to existing contract expiration dates or other relationships that underlie opportunities It is a timing state related to the state. For example, an opportunity to have an unresolved resolution state for a date after the due date of an underlying contract, other service arrangement, etc. may have a late timing state or be assigned. Opportunities that have a “resolved” resolution state and have reached a state before the due date of the underlying contract, other service arrangements, etc. have or assign an on-time or arbitrarily early timing state Can be done. Consistent with some implementations of the subject matter of the present invention, if the date of establishment occurs prior to the sales period including the due date of the underlying contract, other service arrangements, etc., the established opportunity is an early timing state Or can be assigned. If the date of establishment occurs in the same sales period including the due date of the contract on which the date of establishment is based, other service arrangements, etc., the established opportunity has or can be assigned a timed timing state.

  It is common for customers' purchase behavior to be strongly dependent on the timing of proposals regarding due dates for contracts and other service arrangements on which customer decision makers or other contacts are based. Observed. For example, if an opportunity for renewal is presented too early (eg one or more months before the due date for underlying contracts, other service arrangements, etc.), the tendency to prioritize more immediate decisions The urgency of establishment may be reduced. Opportunities that remain open after the due date of underlying contracts, other service arrangements, etc. may also be risky because the customer may become accustomed to proceed without service. The urgency of renewing or resuming services may be reduced, and such opportunities may ultimately increase the risk of being settled as a loss.

  Consistent with the implementation of the subject matter of the present invention, the above issues relating to the timing of sales efforts related to due dates such as contracts that underlie opportunity, other service arrangements, etc. It can be dealt with by grouping into sets. Here, the three sets for the given sales period are carryover opportunities, current opportunities, and future opportunities. Carry-over opportunities may include contracts or other renewal opportunities that have expired in the previous sales period but have not been resolved in the previous sales period and therefore remain likely to be established in the current sales period. it can. As noted above, renewal contracts tend to quickly become obsolete due to typical customer behavior. That means that contracts that have not been renewed in a relatively short time after their expiration have a low probability of doing so. A current contract is a contract that expires during the current sales period. Typical customer behavior generally results in renewal purchases occurring most frequently within approximately 90 days of expiration. As such, current contracts can be most important for the sales activities of many recurring assets. Future contracts include contracts that expire after the end of the current sales period. Such contracts may be resolved during the current sales period. Early contract creation can help protect the value of the recurring revenue stream. However, establishing a contract early at the expense of other contracts that may be expiring sooner may reduce profits. It is desirable to prioritize current and carry-over opportunities to avoid or reduce the occurrence of potential breaks in the revenue stream.

  FIG. 11 shows a diagram 1100 illustrating the gist of a guided sales process 1102 that includes a set of sales phases 1104 that each include one or more sales stages 1106. The sales phase can include limitations 1110, resolution 1112, and establishment 1114. The sales stage includes a contactless 1116 and contacted 1120 in the limited sales phase 1110, a quote request 1122A, a quote completed 1122B and a quote delivery 1124 in the resolved sales phase 1112, and a purchase order in the established sales phase 1114 ( purchase order) receiver 1126A, customer commitment 1126B, and established sales 1130.

  Implementation of the present inventive subject matter keeps track of the processing key control points (eg, which can define the sales stages within the sales phase), information collection requests, and the sales process at each sales phase 1104. Advance triggers that can be included. For example, the limited sales phase 1110 defines a contactless sales stage and a contacted sales stage based on whether the opportunity is being addressed by sales staff and whether a limited buyer (eg, a decision maker) is contacted, respectively. One or more processing key control points may be included. The information collected during the limited sales phase 1110 is optionally customer / limited purchaser, such as notes, dates, contact types and methods related to contact events recorded in processing key control points, task scheduling, etc. One or more records of the contacts may be included. Advance triggers for limited sales phase 1110 can include a record of successful customer contacts that can transition the sales stage from contactless 1116 to contacted 1120. In some examples, states / phases and monitors can be managed for triggering actions that change states. Triggered actions can include actions performed by the user in the context of an opportunity (eg, click “Record Customer Contact” to execute successfully), or any data in an opportunity that fits a predetermined pattern Changes can be included. The state set, trigger action, and state transition can be pre-defined as part of the system configuration and can be changed later.

  For the solution sales phase 1112, the processing key control points can include definitions of a quote request stage 1122A, a quote complete sales stage 1122B, and a quote delivery stage 1124 as follows: the quote has reached a sales solution. (E.g., when a basic agreement on opportunity renewal has been reached between the sales rep and customer decision maker) and when it is ready to deliver a quote, Once delivered to the purchaser, decision maker, etc., it can be registered. The information collected during the Resolution Sales Phase 1112 is optionally the deadlines, service levels and discounts requested by the customer, revision history and records, the financial aspects of the quote, the date of delivery of the quote and the intended recipient. Etc. can be included. The advance trigger for the resolution sales phase 1112 is that the sales representative is requesting a quote (eg, using functionality supported in the user interface of the recurring revenue management system), and the sales activity team member Detection may include detection of being recorded as completed, sales activity team members recording as having delivered an estimate, and the like.

  For the established sales phase 1114, the processing main control points include the provision of the purchase order (Purchase Order) sales stage 1126A, the customer commitment sales stage 1126B, and the established sales stage 1130 as follows. Yes: customer commitment can be indicated by buyer commitment against payment date, purchase order receipt status can be indicated by payment being sent for processing, and successful sales are fully processed Can be indicated by payment. The information collected in the established sales phase 1114 is optionally changed from previous contracts where payment financial information, payment and order dates, order revision history (eg, deadlines and / or service levels have been updated). Such as deadlines and service levels to be applied to the contract being renewed. Advance triggers for established sales phase 1114 are sales representatives recording customer commitments (eg, to move processing to customer commitment stage 1126A), (eg, to move processing to purchase order receipt stage 1126B). ) A sales representative requesting a reservation, a sales activity team member completing the reservation, etc. (e.g., to move processing to a successful sales stage 1130).

  In events where the sales process 1102 does not result in a successful establishment, the opportunity is classified as “lost” as described above. A recurring revenue management system 202 compatible with the implementation of the present subject matter can also include automated data collection and processing related to losses. Such data and other information can help to access the factors that have resulted in unsuccessful sales efforts against the opportunity, and the sales team's approach to well-defined metrics. Can support analysis. Processing key controls for lost opportunities include indicators that the customer is not interested in purchasing (eg, “non-service” situations) and that the opportunity should be identified as invalid (eg, “house account”). Can include receiving. Information collected for lost opportunities is the identification of one or more persons who refused to purchase, one or more reasons given by such one or more persons, or Any other information obtained by the salesperson or sales team member related to the reason for the lost opportunity, any record by the salesperson or other sales team member related to or explaining the loss, etc. Can be included. Advance triggers for lost sales can include sales representatives who resolve opportunities in the system as losses.

  As briefly described above, an asset data model 300 that is compatible with the implementation of the subject matter of the present invention is a reason, explanation, etc. related to the resolution of an opportunity, a group of opportunities, a single suggestion, etc. The “reason” for success or loss outcomes for a given opportunity or proposal) can be tracked. The outcome can be a successful (successful) sale, a “house” account (for example, an invalid opportunity, or possibly related to a basic product or service that the customer once had and then disposed of), or the customer has a service agreement Or it may include “non-service” outcomes that simply choose not to purchase a subscription or allow existing agreements to expire without renewal. One or more reason codes can be included and tracked as part of the asset data model for each type of outcome. These codes can serve to provide feedback to the service salesperson and can serve as additional input data for further analysis and business intelligence processes. For successful sales, the performance code is, without limitation, sales made on the previous date, long-term contract sales, short-term contract sales, discount sales, multi-year advance sales, pricing change sales, recertification costs , Sales renewal, service downgrade sales, service upgrade sales, non-target sales, and the like. For house account results, the result code includes, but is not limited to, bad data status, canceled contract or agreement, eligible service, duplicate service, termination of support conditions, automatic renewal (evergreen) billing status, international One or more of a transaction, a lease status, an OEM customer, an “other” description, a merchandise return, a reduction in sales by the customer, etc. can be indicated. For non-service outcomes, the outcome code includes, but is not limited to, customer exchange of products, competitive distribution value-added resellers (DVAR), competitive product exchanges, further explanation of competitive service losses, customers Cost-effectiveness determination, customer no longer exists, customer satisfaction driven by refusal to purchase or renew, target product or service is about to end its service life, some other data management One of the following: problem, closed service product or otherwise removed from service product, third party maintenance contract, unresponsive end user, unresponsive value added reseller (VAR), non-service VAR, etc. Or more than one can be indicated.

  The sales process and salesperson description herein refers to salespersons and sales activity team members in some examples. One aspect of the subject matter of the present invention can include an integrated sales activity approach, where activities related to the sales process for renewal can be branched between sales activities and back office sales support activities. it can. The sales activity in this example typically presents attractive and valuable proposals that motivate the purchasing party to choose to complete the purchase, particularly by contacting the customer's purchaser, decision maker, etc. Refers to what is considered an important salesperson skill. The skill sets associated with such activities are beneficial to commercial entities, so a recurring revenue management approach that maximizes the use of these skills can be very advantageous. Thus, the user interface views provided by the recurring revenue management system 202 that are compatible with the implementation of the present subject matter can support different views for different users.

  FIG. 12 shows an annotated screenshot view 1200 showing the division of roles among salesperson 412, sales activity team member 420, and sales manager 1202. As shown in the example screenshot view 1300 of FIG. 13, the salesperson 412 supports one or more of opportunity surveys, customer contacts, generation of quote and reservation requests, pipeline management, etc. A focused view can be shown. As shown in the example screenshot view 1400 of FIG. 14, the sales activity team member 420 can complete the request for quotations and reservations, search for data related to opportunities, assets, etc., service level agreement (SLA) and A focused view can be shown that supports one or more of quality control and the like. As shown in the exemplary screenshot view 1500 of FIG. 15, the sales manager 1202 can monitor one of sales and sales activities, manager level authorization for escalation, task and opportunity assignment, goal and forecast management, etc. A focused view that supports one or more can be shown.

  Using the recurring revenue management system 202 that supports these focused views on the user interface tailored to a specific role in the sales team, the recurring revenue management system 202 is generated through contact with buyers and sales activities. In addition, it is possible to establish a close relationship between estimate generation, reservation generation, and the like in response to a request from the sales representative 412. Also, the underlying asset data model 300, which can be part of the recurring revenue management system 202 that is consistent with the meaning of the subject matter of the present invention, supports the close association of the sales process with the underlying data service. it can. In this way, the data management and data quality that underlies the sales process and the update process can be optimized. Further, data that is enhanced or added as part of the sales process can optionally be propagated back to one or more external client systems 114.

  Management, evaluation, and monitoring of service sales activities are supported by one or more implementations of the subject matter of the present invention to ensure a consistent approach to managing opportunities, quote activities and booking activities. Can do. A recurring revenue management system is a detailed report that allows sales staff members (eg, sales representatives) to actively identify and address risky opportunities, and managers to measure time periods and products. Pipeline dashboards that allow you to see the pace, details of opportunities that allow you to see the progress of transactions and recorded activities, managers, etc. by assigning tasks to opportunities based on existing priorities One or more features may be included, such as area management, and the like, that allow sales staff actions to be driven. FIG. 16 shows an exemplary screenshot view 1600 that illustrates features of an analysis view that displays metrics related to established opportunity values. FIG. 17 illustrates an exemplary screenshot view 1700 that illustrates features of an analysis view that displays metrics related to non-service (eg, “loss”) rates for established opportunities. FIG. 18 shows an exemplary screenshot view 1800 showing the features of an analysis view that displays a prediction dashboard for displaying prediction information. FIG. 19 shows an exemplary screenshot view 1900 that illustrates features of an analysis view that displays metrics associated with an opportunity pipeline.

  A recurring revenue management approach that is compatible with one or more implementations of the subject matter of the present invention is based on one or more frameworks and the above roles, and provides dynamic feedback and recurring feedback to users of the recurring revenue management system. The designated tasker's vigation can be generated. In some examples, directed tasker vigations can be designed to maximize the amount of time sales representatives can spend on sales-related activities such that their skill sets are advantageously matched. Based on the ability to identify sales or activity issues discovered in a role-based dashboard by using or identifying data from a survey aspect, a user or manager can determine a set of opportunities that meet specific characteristics, Can be grouped logically. For example, the user or manager may have a due date (eg, before 2014) before the target date or date range and a “no contact” sales stage where the “current reseller” is a specific company (eg, ABC). And you can easily find all the opportunities that you have. These opportunities can therefore be assigned to specific task types, such as generating quotes, requesting appointments, contacting customers, or resolving data matters, and assigned to specific roles or users. Is possible. The progress or evolution of the set of activities associated with such a logical grouping of work can thus be monitored over time as the activities are completed or resolved.

  FIG. 13 shows an annotated screenshot view 1300 of a user interface illustrating features that can be included consistently with implementations of the present subject matter. As shown, the user interface can include multiple items, which can optionally be accessed via a tab bar or other user interface functionality and need not all be displayed on the same screen. . Multiple screens can be associated with various components of the sales process, for example, they have been described above in connection with FIG. For example, the first item of the screen 1302 can provide one or more information related to the target asset 404. Another item on screen 1304 may provide one or more information related to service asset 402 or, alternatively, more than one service asset 402, related to target asset 404. Service asset item 1304 may include information related to proposal 406 related to service asset 402 and information related to an opportunity that includes proposal 406. Via tab bar 1306 or other user interface functionality, the user can characterize customer installed base or other underlying product or contract information 1306, current or last year sales information 1310, transaction information 1312, etc. It is possible to connect (link) to additional screens that provide more detailed information about customer relationship characteristics.

  FIG. 14 is a diagram illustrating a process consistent with the implementation of the present subject matter for generating proposals based on existing service assets 402 and underlying target assets 404. Using the relationship between the data objects 301 in the asset data model 300, the connection (link) is between the product base on the customer side and the target asset 404 that is part of the recurring revenue asset (service asset 402). Can be identified and used. Service asset 402 generally has a current due date 1402, which further includes a due date. Service asset 402 also generally has a current service level 1404 and a current contract value 1406 associated with it. These data are generally part of the asset data model 300, representing the service asset 402, and associated (linked) with a relationship with an instance of the product data object 302 representing the target asset 404. Included in the instance. A proposal 406 is generated as the due date of the due date 1402 of the service asset 402 approaches. The proposal 406 includes a proposal deadline 1410, a proposal service level 1412, and a proposal target amount 1414. Proposal deadline 1410 indicates the time period presented for service asset 402 to be updated. Proposed service level 1412 is generally initialized to current service level 1404 and suggested target amount 1414 is generally initialized to current contract amount 1406.

  A recurring revenue management system 202 compatible with the implementation of the subject matter of the present invention may use a specialized asset data model for selling and updating recurring revenue assets. The asset data model can include fields related to service timing events, service outcomes, relationships to other assets, as well as other context specific fields. For example, supporting new sales of recurring revenue assets (eg, new service contracts, new subscriptions, etc.), recurring revenue management systems can process information received from external client sources, and optionally Can handle updated information generated through sales contacts, ongoing sales and renewal activities, and other functions supported by the Recurring Revenue Management System, local opportunity amounts, local transaction amounts , Oldest existing end date, oldest new start date, latest new end date, commit level (red, yellow, black, green, or other representation), renewal sales stage, target product, service product, Service Product Description, Client Batch Quarter, Success / Loss Results Description, Existing Contracts, Existing Purchase Orders, Existing Distributors, Existing Resellers, New Distributions Who, in one or more fields associated with one or more of such new resellers, inputs the data values.

  Supporting renewal sales of recurring revenue assets (eg renewal of existing contracts, value-added upsells of existing contracts, etc.), recurring revenue management systems process information received from external client sources Can optionally process updated information generated through sales contacts, ongoing sales and renewal activities, and other functions supported by the Recurring Revenue Management System, with no contact / contact One or more related to one or more of the following: status, quote requested status, quote delivered status, customer commitment status, purchase order received status, established sales status, etc. Enter a data value in the field.

  A recurring revenue management system compatible with the implementation of the subject matter of the present invention may include a mechanism to integrate sales teams and individual online and offline activities that best match the expiration and other timing events associated with service sales. it can. These mechanisms can include automated workflows that guide when and how customers are contacted, when activities such as requesting quotes and presenting quotes are performed, and when escalations occur. The sales process can be used to optimize performance and can integrate one or more key metrics associated with service sales, such as resolution rates, completion rates, conversion rates, and the like.

  A recurring revenue management system compatible with the implementation of the subject matter of the present invention can also implement one or more team coordination processes for pipelines and areas that ensure optimization of team performance. The processes, metrics and workflows required to drive sales outcomes are encoded in software and computer program products to enable the recurring revenue management system to perform forecast performance management.

  Automated or semi-automated forecasting of team-based service sales can also be supported by a recurring revenue management system that is compatible with the implementation of the present subject matter. A recurring revenue management system can provide a framework for collecting data from reps based on key sales metrics, which creates recurring (eg, weekly) computer generated reps. The discipline process can be followed, thus providing accurate monthly and quarterly forecasts. The forecasting process can incorporate a dual estimation approach, in which case bottom-up (individual opportunity assessment based) and top-down (deadline grouping based) calculations are employed.

  A user-configured sales automation program can be used to drive special process behavior for a specific set of opportunities and service sales activities. These automated programs can include adaptive quotation and reservation management for service sales. The ability of an adaptive quote and reservation management function that is compatible with one or more implementations of the subject matter of the present invention is a categorization of quote requests (eg, for identifying key attributes of quotes to guide the process). , Estimate request routing (eg, route requests to appropriate resources for human or automation to maximize processing efficiency, eg, predict possible rejection and estimate required processing time) Statistical prediction of the outcome of the request for quotes, SLA management (eg to effectively measure on-time performance and guide the forecast of the request for quotes), Throughput forecasting and management to properly price, CRM view (eg, with external order management system view) Such as semi-automated reservation reconciliation, which enables reconciliation of opportunity-based sales, dynamic (ie, rule-based or algorithmic) performance management engines to optimize work / activity assignments, etc. Can be included.

  The recurring revenue management system can further include a module for managing a complex channel partner network. The capability of the channel partner network management module enables the OEM to efficiently distribute access to opportunities across the channel partner's multi-level network, which opportunity is given to a given channel partner. Responsibility logic to determine visibility, collection and spread prediction information from individual channels, sales incentive programs that can be executed across channels (e.g., managing users and authorizing such partners) One or more of self-service management by channel partners (to enable), estimation process management (eg, dynamically across multiple levels of channels), etc. may be included. For example, for access by channel partners, display a selection of predefined opportunity renewals for these channel partners and be sold, leased, etc. by an original equipment manufacturer (OEM) A user interface can be provided to purchase based on the target asset to do. This or other user interface can include one or more update metrics similar to those described above, and can be based on value to the OEM. Also, data regarding update processing activities occurring on the channel partner side can optionally be synchronized back to the asset data model 300 maintained by the OEM or on behalf of the OEM. The predictions, goals, predictions, etc. described above can be supported for channel partners, for example, in a manner similar to the approaches described elsewhere herein.

  The recurring revenue management system can also support an overall analysis of customer success management (CSM), which includes one or more of service asset analysis, sales execution analysis, sales activity analysis, etc. Can do. These characteristics of the SRM can include analytical capabilities for collecting and displaying benchmark comparisons for all aspects of the SRM.

  One or more service sales-related metrics can be compared to one or more target benchmarks for these metrics as part of a recurring revenue management system and method. Benchmarking can occur at any scale, for example, at the individual salesperson scale, at the sales group scale, at the sales department or sales office scale, or at the scale of the entire sales organization. Historical data, including but not limited to purchase data, update data, etc., can be used as input to one or more forecast models to determine appropriate targets or other benchmarks, while present performance The data can be compared and visualized on a display or electronic dashboard.

  The key data that can be used in the predictive model can be the timing of recurring revenue asset update events. This timing data is expressed as a metric referred to as “days ago” (DIA), which relates to the period of time prior to expiration of the currently active (active) service asset. Time periods are preferably expressed in units of days, but may be expressed in units of other time periods, such as weekly, monthly, quarterly, or any other time period. DIA metrics can be used in predictive models to estimate the optimal timing associated with the expiration of an existing service asset that is most likely to succeed in renewal sales events.

  In some implementations, other metrics are also useful. For example, the metric can be based on the accuracy of the proposal presented initially. More accurate or well-defined proposals generally result in faster renewal sales proposals. The accuracy of the proposal and the relevance of the proposal to the renewal sales customer can be quantified based on a metric that reflects the number of proposal iterations required between the first sale proposal and the last successful establishment . Other metrics may include the rate of conversion of the proposal to sale, the rate of establishment, the time taken to prepare and adjust the quote, the average time to complete the quote request, etc. Any number of these metrics can be used for accurate forecasting and targeting calculations on real-time and near real-time data related to proposals for service asset updates, and for predicting potential outcomes of aggregation. It can be used as an input to a predictive model to generate one or more benchmarks used for model generation.

  A predictive model that is compatible with the implementation of the present subject matter can provide guidance for all levels of people in the organization. For example, managers, executives, sales team members, etc. can output such benchmarking models for improved forecasting and refinement for sales approaches, either at an individual level or at a larger organizational scale. Can be used.

  Part of the process for establishing a useful predictive model can include determining a metric of interest, for example, a metric most relevant to renewal sales success. Sales targets and other key control point targeting or benchmarks can be based on these metrics. In some examples, a target can be developed or improved based on historical data. Historical data can originate from within an organization using a predictive model, or in some examples can include historical data from other organizations that are integrated from external sources. Data from other organizations can be “arranged” or any specific identifying information is stripped to exclude outliers or bias information.

  One or more pattern analysis or other mathematical approaches, such as statistical clustering, for example, can be used to generate predictive model goals and other predictive outputs. The data aggregation machine, in some examples, can perform data mining operations on historical data for a particular organization, or alternatively, on historical data from one or more other organizations involved in selling updated assets. In addition, a data mining process can be executed on the history data of a specific organization.

  The desired output of the predictive model is the success of establishing a renewal sales proposal at any given time and / or at any given current value, as compared to one or more metrics used in the predictive model. The probability of

  In further implementations of the present subject matter, input data for the predictive model can include service level management (SLM) data, such as usage data, social media analysis, and other domains of information, for example. Usage data includes how / who / where / how / how often / underlying assets are used, etc., level of satisfaction with underlying assets by owner / manager / etc, basis Information and / or information on the frequency and significance of suitability for service assets associated with underlying assets and the like.

  Usage context data or other information can also serve as input to the predictive model. For example, in a software license maintenance contract, the type of hardware that the software performs (eg, the underlying asset) can be useful information for making predictions, and improved recurring revenue assets for the software ( For example, it provides suggestions regarding the successful sale of service assets associated with underlying assets. Thus, SLM data, usage context data, and the like are merged with the above data because it is more closely related to the specific purchase of the underlying asset and / or the service asset associated with the underlying asset. Can be done.

  Thus, a prediction of the outcome of a high quality proposal can be provided. Certain models can use data about purchases, renewals, etc. in combination with other data that can affect the likelihood (probability) of renewal, and one or more mathematical pattern analyzes can be updated A very reasonable, timely, and advantageously very accurate prediction of sales performance can be provided.

  As an example of these benchmarking capabilities, the recurring revenue management system 202 is generated by one or more other commercial entities outside the commercial entity whose sales process is automated through the use of the recurring revenue management system 202. Access to aggregate information about service revenue and other recurring revenue stream updates, sales, etc. For example, if the Recurring Revenue Management System 2902 is provided via a Software as a Service (SaaS) framework, the SaaS provider can collect sales process performance data from multiple commercial entities, eg, in a database. And collect these collected data in benchmarks and other metrics that can easily compare sales performance of specific commercial entities, sales teams within commercial entities, individual salespersons, etc. Have the ability to make available.

  As a further feature of the recurring revenue management system 202 that is compatible with some implementations of the subject matter of the present invention, the collected data described above can be applied to improve the prediction and targeting functions described above1. It can serve as input for one or more prediction models. In one example, a set of parameters representative of a particular proposal can be another proposal within a given commercial entity or more than one commercial entity to calculate the expected outcome for such a scheme. Can be analyzed by a predictive model based on the outcome of any of the other proposals over the collected data generated by. The results of this type of modeling can optionally be, for example, various potential sales approaches (for example, the timing of the first contact with respect to the current service asset expiration date, from the first contact by the sales representative to the delivery of the quote. It can be used in an iterative manner, testing the elapsed time in the sales process, etc.) to determine the best set of predicted executions.

  The predictive model can also support collective opportunity outcome prediction, for example, predicting update rate, value, resolution rate, etc. for a group of available proposals or opportunities. The performance prediction can optionally include a prediction of value, such as a set of service assets at a future time, the total expected revenue for the set of service assets, and so on.

  Forecast models based on historical sales performance data can be used to provide one or more of the following types of forecast analysis: specific time based on customer, product, and time interval from expiration Prediction of the probability of a proposal that has been established within the window; prediction of the expected value of the proposal if established within a specific time window based on the customer, product, and time interval from expiration; Prediction of probable upgrade probability when fulfilled within a specific time window based on product and time interval from expiration; etc. Predictive models that are compatible with the implementation of the present subject matter can be constructed using machine learning approaches such as survival analysis, clustering, and regression analysis. The model can be encoded in an application for use in the form of a predictive function that expands the data in the system.

  FIG. 20 shows a diagram of a computer landscape that is compatible with one or more implementations of the present subject matter. Computer system 2002 may perform operations, execute code, or implement current revenue management system 202. The recurring revenue management system 202 can include an asset data model 300 that provides one or more of the characteristics described above. The computer system 2002 may be connected by one or more remote machines 2008 via, for example, a direct connection, a local terminal, or a network 2010 (eg, a local area network, a wide area network, a wireless network, the Internet, etc.). Can be accessed. The external client system 114 can also be accessed via a direct connection and / or via the same network 2010 or one or more other networks. The asset data model 300 and the recurring revenue management system 202 again contact one or more repositories 2016 via a direct connection and / or across the same network 2010 or one or more other networks. be able to. Forms of the inventive subject matter can also be provided via a multi-tenant environment, in which case a number of client organization data records, etc. are maintained in a common repository or set of repositories, at which time a particular data record It is separated from access other than access by the owner.

  Consistent with one or more aspects of the present inventive subject matter, a recurring revenue repository is a recurring revenue derived from one or more external data sources and ongoing sales activities (eg, user interaction and database updates). Can be maintained with information. Some aspects of the present inventive subject matter can include the use of a recurring revenue asset data model generated from at least a portion of this recurring revenue information. The Recurring Revenue Asset data model is based on recurring revenue assets and organizations, recurring revenue assets, previously sold products, recurring revenue assets and people and products associated with previously sold commodities, etc. The relationship between can be expressed. Multiple analysis and business intelligence processes can optionally be performed on the recurring revenue asset data model and on recurring revenue information in the recurring revenue repository.

  In some aspects of the present inventive subject matter, the method distinguishes a set of proposals for updating service assets within a sales period and analyzes a set of parameters that represent each proposal in the set of proposals. Including. The set of parameters includes at least one user-provided constraint on the predicted outcome of each proposal. The method further includes calculating a predicted outcome for the set of suggestions based on an applied forecast model using the set of parameters as input, and based on at least one sales outcome occurring during the sales period. Updating at least one constraint for at least one proposal in the set of proposals, recalculating a predicted outcome for the set of proposals based on the set of parameters including the updated at least one constraint; including.

  As an optional variation, one or more additional features can be included in any possible combination. The distinction may be based at least in part on the due date of the service asset associated with each proposal in the set of proposals. Other proposals can include at least one of other proposals within a given commercial entity or other proposals across the collected data generated by more than one commercial entity. . The method further comprises calculating a plurality of times for testing various possible sales approaches by changing the set of parameters and setting the best implementation based on historical sales data. Repeating the identification. The set of parameters includes the timing of the first contact with the customer by the sales representative for the current service asset expiry date, and the time elapsed in the sales process from the first contact by the sales representative to delivery of the quote. At least one of the following.

  Implementation of the present subject matter is compatible with these features in systems and methods that include one or more features, as described herein, and in one or more machines (eg, a computer, etc.). Can include, but is not limited to, an article with a clearly implemented machine-readable medium that is operable to provide an action to perform. Similarly, a computer system is also described, which may comprise one or more processors and one or more memories connected to the one or more processors. The memory can include a computer-readable storage medium, wherein the memory is one or more that causes one or more processors to perform one or more of the operations described herein. May be included, encoded, stored, or the like. Computer-implemented methods compatible with one or more implementations of the present inventive subject matter can be implemented by one or more data processors residing in a single computer system or multiple computer systems. Such multiple computer systems can be connected via one or more connections, a direct connection between one or more of the multiple computer systems, etc., data and / or commands, or Other commands can be exchanged. Here, the one or more connections include, but are not limited to, connections on a network (for example, the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.).

  Consistent with one or more aspects of the present inventive subject matter, a recurring revenue repository is derived from one or more external data sources and ongoing sales activities (eg, user interactions and database updates). It can be maintained using current revenue information. Some aspects of the present inventive subject matter can include the use of a recurring revenue asset data model generated from at least a portion of this recurring revenue information. The recurring revenue asset data model is based on the recurring revenue assets and organization, the products sold prior to the basis of the recurring revenue assets, the recurring revenue assets and the persons and products associated with the products sold prior to the basis, etc. Can be expressed. Multiple analysis and business intelligence processes can optionally be performed on the recurring revenue asset data model and on recurring revenue information in the recurring revenue repository.

  In one aspect, the method includes specifying a plurality of tiers associated with a recurring revenue asset, each tier of the plurality of tiers having a set of permissions and rules, wherein the hierarchy permissions are determined by a user associated with the tier. Allows access to information in the database on recurring revenue assets, and the rules define the allowed interactions between the user on the recurring revenue asset and the recurring revenue management system. The method further includes receiving data representing one of a plurality of tiers associated with the user and associated with the recurring revenue asset. The method further includes transmitting data associated with the recurring revenue asset from the database to the client computer, wherein the transmitted data is transmitted to the user based on the hierarchy and recurring revenue asset associated with the user. Is allowed. The method further includes managing authorized revenue assets by performing authorized interactions with the user's current revenue management system.

  In another aspect, a recurring revenue management system is provided. The recurring revenue management system is preferably a SaaS system, which is a SaaS system that provides a recurring asset information repository for use by a network of business organizations that cooperate in the sale and use of such assets. . The system manages the relationship information about how the asset relates to the business organization and uses the relationship information to selectively access the current asset information repository (ie, to the organization and to the business organization's agent). To provide). Furthermore, it integrates the activities of users representing different organizations in a way that optimizes revenue (for at least a subset of the organizations in the network).

  Implementation of the present subject matter is compatible with these features in systems and methods that include one or more features as described herein, and one or more machines (eg, a computer, etc.). It can include, but is not limited to, an article with a clearly implemented machine-readable medium that is operable to effect operation. Similarly, a computer system is also described, which may include one or more processors and one or more memories connected to the one or more processors. The memory can include a computer-readable storage medium, wherein the memory is one or more that causes one or more processors to perform one or more of the operations described herein. May be included, encoded, stored, or the like. Computer-implemented methods compatible with one or more implementations of the present inventive subject matter can be implemented by one or more data processors residing in a single computer system or multiple computer systems. Such multiple computer systems are on a network (eg, Internet, wireless wide area network, local area network, wide area network, wired network, etc.) via a direct connection between one or more computer systems, etc. Can be connected.

  Consistent with one or more aspects of the present inventive subject matter, a recurring revenue repository is derived from one or more external data sources and from ongoing sales activities (eg, user interactions and database updates). Can be maintained using recurring revenue information. Some aspects of the present inventive subject matter can include the use of a recurring revenue asset data model generated from at least a portion of this recurring revenue information. The recurring revenue asset data model is based on the recurring revenue assets and organization, the products sold prior to the basis of the recurring revenue assets, the people and products associated with the recurring revenue assets and the products sold prior to the basis, etc. Can be expressed. Many analysis and business intelligence processes can optionally be performed on the recurring revenue asset data model and on recurring revenue information in the recurring revenue repository.

  In some aspects of the present inventive subject matter, the method includes selecting a filtered set of service assets to be used as a source input for opportunity generation. Selecting includes applying the filter to a data set that includes service asset data for a plurality of service assets. Each service asset in the plurality of service assets includes an expiration date. The filter includes a due date range corresponding to the target sales period. The method further includes generating a new service proposal based on at least one specific rule defined for the commercial entity for the service asset in the selected set. Generating includes applying one or more proposal generation parameters, determining reference information for the asset that is about to expire in the data asset model, and a selected service level for the proposal And assigning a new service proposal to at least one sales team.

  As an optional variation, one or more additional features can be included in any possible combination. The filtered set of generated proposals can be grouped into multiple sets of opportunities such that each set of opportunities includes an associated proposal. The range corresponds to one of a due date before the targeted sales period, a due date between the targeted sales periods, and a due date after the targeted sales period. Can include days. A filtered set of generated proposals among multiple sets of opportunities. The grouping may include related proposals for at least one of the same target customer, the same resale organization, the same or related product types. The service asset data in the data set can be part of an asset data model that can include a plurality of asset data objects associated with service assets of the plurality of service assets. One or more algorithms may be applied to identify and add one or more additional services to the proposal or generate a proposal corresponding to the cross-sell opportunity.

  Implementation of the subject matter of the present invention is an article comprising an explicitly implemented machine-readable medium operable to cause one or more machines (e.g., a computer, etc.) to perform operations compatible with these features. Of course, systems and methods including one or more features as described herein can be included, but are not limited to such. Similarly, a computer system is also described, which may include one or more processors and one or more memories connected to the one or more processors. The memory can include a computer-readable storage medium, wherein the memory is one or more that causes one or more processors to perform one or more of the operations described herein. A program may be included, encoded, stored, or the like. Computer-implemented methods compatible with one or more implementations of the present inventive subject matter can be implemented by one or more data processors residing in a single computer system or multiple computer systems. Such multiple computer systems can be connected via one or more connections, a direct connection between one or more of the multiple computer systems, etc., and data and / or commands, Or, other instructions can be exchanged. Here, the one or more connections include, but are not limited to, connections on a network (for example, the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.).

  Consistent with one or more aspects of the present inventive subject matter, a recurring revenue repository is derived from one or more external data sources and from ongoing sales activities (eg, user interactions and database updates). Can be maintained using recurring revenue information. Some aspects of the present inventive subject matter can include the use of a recurring revenue asset data model generated from at least a portion of this recurring revenue information. The recurring revenue asset data model is based on the recurring revenue assets and organization, the products sold prior to the basis of the recurring revenue assets, the people and products associated with the recurring revenue assets and the products sold prior to the basis, etc. Can be expressed. Many analysis and business intelligence processes can optionally be performed on the recurring revenue asset data model and on recurring revenue information in the recurring revenue repository.

  In some aspects of the present inventive subject matter, the method includes generating a user interface for the electronic display device. The user interface includes a graphic representation of one or more recurring assets, the graphic representation of one or more recurring assets according to one or more metrics that characterize each of the one or more recurring assets. Represent each. The method further includes calculating a predicted outcome for at least one of the one or more recurring assets, the calculation being based on a set of historical information collected from the one or more data sources. From the prediction model. The method further includes a graphic of the predicted outcome according to one or more parameters associated with the one or more metrics for current display in a user interface having a graphical representation of the one or more recurring revenue assets. Including generating a representation.

  Implementation of the present inventive subject matter includes systems and methods that include one or more features as described herein, and operations that are compatible with these features in one or more machines (eg, a computer, etc.). Including, but not limited to, an article with a clearly implemented machine-readable medium that is operable to provide Similarly, a computer system is also described, which may include one or more processors and one or more memories connected to the one or more processors. The memory can include a computer-readable storage medium, wherein the memory is one or more that causes one or more processors to perform one or more of the operations described herein. A program may be included, encoded, stored, or the like. Computer-implemented methods compatible with one or more implementations of the present inventive subject matter can be implemented by one or more data processors residing in a single computer system or multiple computer systems. Such multiple computer systems can be connected via one or more connections, a direct connection between one or more of the multiple computer systems, etc., and data and / or commands, Or, other instructions can be exchanged. Here, the one or more connections include, but are not limited to, connections on a network (for example, the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.).

  One or more forms or characteristics relating to the subject matter described herein may include digital electronic circuits, integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software And / or a combination thereof. For example, any citations such as methods, algorithms, calculations, received data, transmitted data, stored data, other data processing tasks, etc. can include at least partially performing those operations by the system, each of the systems Includes at least one programmable processor. These various forms or characteristics may comprise a dedicated or general purpose, at least one programmable processor coupled to receive data and instructions from a storage system and to send data and instructions to the storage system, at least one Implementations in one or more computer programs that are executable and / or interpretable on a programmable system including an input device and at least one output device may be included. A programmable or computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship occurs based on computer programs that run on individual computers and have a client-server relationship with each other.

  These computer programs, sometimes referred to as programs, software, software applications, applications, components, or code, etc., contain machine instructions for programmable processors and are high-level procedural and / or It can be implemented in an object-oriented programming language and / or in an assembly / machine language. As used herein, the term “machine-readable medium” is used to provide machine instructions and / or data to a programmable processor, eg, magnetic disk, optical disk, memory, and programmable. Represents any computer program product, apparatus, and / or device, such as logic devices (PLDs), including machine-readable media that receives machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. A machine-readable medium can store the machine instructions non-temporarily, such as a non-temporary solid-state memory, a magnetic hard disk, or an equivalent storage medium. Alternatively or additionally, machine-readable media may temporarily store the machine instructions, such as a processor cache or other random access memory associated with one or more physical processor cores. it can.

  In order to provide user interaction, one or more aspects or features of the inventive subject matter described herein may be used to display information to a user, such as a liquid crystal display (LCD) or a light emitting diode. It can be implemented in a computer, including a display device, such as an (LED) monitor, or a keyboard or pointing device, such as a mouse or trackball, that allows the user to provide input to the computer. Other types of devices can be used to provide user interaction as well. For example, the feedback provided to the user can be any form of sensory feedback, such as, for example, visual feedback, audio feedback, or haptic feedback, and input from the user can be in any form including acoustic, speech, or haptic input. Although it may be received, it is not limited to these. Other possible input devices include touch screens or other touch sensitive devices such as single or multipoint resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and related Including, but not limited to, interpretation software.

  The subject matter described herein can be embodied in a system, apparatus, method, and / or article that depends on a desired configuration. The implementations set forth above do not represent all implementations consistent with the subject matter of the invention described herein. Rather, they are only some examples that are consistent with the forms associated with the described subject matter. A few variations have been described in detail above, but other changes and additions are possible. In particular, further properties and / or variations can be further provided for those shown herein. For example, the implementations described above can be directed to various combinations or subcombinations of the disclosed features and / or to combinations or subcombinations of a plurality of additional features of the disclosure. Further, to achieve the desired results, the logic flows shown in the accompanying drawings and / or described herein do not necessarily require the particular order shown or sequential ordering. Other implementations are within the scope of the appended claims.

Claims (31)

A computer program product comprising a machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform an operation, the operation comprising:
Accessing a repository storing aggregate information regarding service revenue updates, the aggregate information including historical data generated by one or more commercial entities, wherein the historical data is one or more commercial Defined according to one or more metrics associated with an update of one or more service assets by an entity;
Generating a prediction model based on the set information accessed from the repository;
Defining a set of parameters representative of at least one proposal for renewal of service assets within a sales period, wherein the at least one proposal is managed by a first commercial entity using a recurring revenue management system Steps associated with at least one recurring asset
Calculating a predicted outcome for the at least one proposal using the set of parameters of the at least one proposal as an input to the prediction model, wherein the predicted outcome is within the sales period. Representing the potential outcome of the at least one proposal according to a selected one of the one or more metrics associated with the update of the service asset of
Including computer program products. The operation further includes generating a visual representation of the predicted outcome for display in an electronic dashboard.
The computer program product of claim 1. The operation is
Receiving data representing the at least one proposal according to the one or more metrics;
Comparing the data representing the at least one proposal to a predicted outcome calculated for the at least one proposal;
The computer program product of claim 1. The recurring revenue management system is provided via a software as a service (SaaS) framework,
The repository is accessible by the SaaS framework;
The computer program product of claim 1. The operation further includes generating a visual representation of the data representing the predicted outcome and the at least one suggestion for display in an electronic dashboard.
The computer program product of claim 1. The metric includes a time period before the due date of the recurring revenue asset represented by the information of the set;
The computer program product of claim 1. The operation is receiving at least one user-provided constraint on the set of parameters, wherein the user-provided constraint defines the context of the predicted outcome for the prediction model. Further comprising steps
The computer program product of claim 1. The one or more metrics represent a proposal's accuracy limit that represents the number of repetitions of the at least one proposal required between a first proposal and a final establishment of sales associated with the at least one proposal Including children,
The computer program product of claim 1. The input to the predictive model further includes service level management (SLM) data of assets associated with the service asset, wherein the SLM data includes information representing usage of the asset and one or more people associated with the asset. Including one or more of information representing a satisfaction level by a plurality of persons, information representing a person's opinion regarding the asset, and information representing a person's opinion regarding a service asset related to the asset ,
The computer program product of claim 1. The SLM data is input to the prediction model from an electronic source of service asset usage data or social media data.
The computer program product according to claim 9. The operation is
Deriving a benchmark based on the predictive model applied to historical data or collected current data;
Providing the benchmark simultaneously with the predicted metric for a particular proposal;
The computer program product of claim 1. At least one programmable processor;
A machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform an operation;
The operation is
Accessing a repository storing aggregate information regarding service revenue updates, wherein the aggregate information includes historical data generated by one or more commercial entities, wherein the historical data includes the one or more Defined according to one or more metrics associated with renewal of one or more service assets by a commercial entity;
Generating a prediction model based on the set information accessed from the repository;
Defining a set of parameters representative of at least one proposal for renewal of service assets within a sales period, wherein the at least one proposal is managed by a first commercial entity using a recurring revenue management system Steps associated with at least one recurring asset
Calculating a predicted outcome for the at least one proposal using the set of parameters of the at least one proposal as an input to the prediction model, wherein the predicted outcome is within the sales period. Representing a possible outcome of the at least one proposal according to a selected one of the one or more metrics associated with an update of the service asset of
Ordinary revenue management system. The operation further includes generating a visual representation of the predicted outcome for display in an electronic dashboard.
The ordinary profit management system according to claim 12. The operation is
Receiving data representing the at least one proposal according to the one or more metrics;
Comparing the data representing the at least one proposal to the predicted outcome calculated for the at least one proposal.
The ordinary profit management system according to claim 12. The recurring revenue management system is provided via a software as a service (SaaS) framework,
The repository is accessible by the SaaS framework;
The ordinary profit management system according to claim 12. The operation further includes generating a visual representation of the data representing the predicted outcome and the at least one suggestion for display in an electronic dashboard.
The ordinary profit management system according to claim 12. The metric includes a time period before the due date of the recurring revenue asset represented by the information of the set;
The ordinary profit management system according to claim 12. The operation is receiving at least one user-provided constraint on the set of parameters, wherein the user-provided constraint defines a context of the predicted outcome for the prediction model. Further comprising the steps of:
The ordinary profit management system according to claim 12. The one or more metrics represent a proposal's accuracy limit that represents the number of repetitions of the at least one proposal required between a first proposal and a final establishment of sales associated with the at least one proposal Including children,
The ordinary profit management system according to claim 12. The input to the predictive model further includes service level management (SLM) data of assets associated with the service asset, wherein the SLM data includes information representing usage of the asset and one or more people associated with the asset. Including one or more of information representing a satisfaction level by a plurality of persons, information representing a person's opinion regarding the asset, and information representing a person's opinion regarding a service asset related to the asset ,
The ordinary profit management system according to claim 12. The SLM data is input to the prediction model from an electronic source of service asset usage data or social media data.
The ordinary profit management system according to claim 20. Accessing, by one or more processors, a repository storing collection information relating to service revenue updates, wherein the collection information includes historical data generated by one or more commercial entities; Data is defined according to one or more metrics associated with an update of one or more service assets by the one or more commercial entities;
Generating a prediction model based on the set information accessed from the repository by the one or more processors;
Defining, by the one or more processors, a set of parameters representative of at least one proposal for renewal of service assets within a sales period, wherein the at least one proposal comprises a recurring revenue management system; Using and relating to at least one recurring revenue asset managed by the first commercial entity;
Calculating, by the one or more processors, a predicted outcome for the at least one proposal using the set of parameters of the at least one proposal as an input to the prediction model, the prediction The outcome to be represented represents the likelihood of the outcome of the at least one proposal according to a selected one of the one or more metrics associated with the renewal of service assets within the sales period; and
Including methods. Generating, by the one or more processors, a visual representation of the predicted outcome for display in an electronic dashboard;
The method of claim 22. Receiving, by the one or more processors, data representing the at least one proposal according to the one or more metrics;
Comparing, by the one or more processors, data representing the at least one proposal to the predicted outcome calculated for the at least one proposal;
The method of claim 22. The recurring revenue management system is provided via a software as a service (SaaS) framework, and the repository is accessible by the SaaS framework;
The method of claim 22. Further comprising the step of generating, by the one or more processors, a visual representation of the data representing the predicted outcome and the at least one suggestion for display in an electronic dashboard.
The method of claim 22. The metric includes a time period before the due date of the recurring revenue asset represented by the information of the set;
The method of claim 22. Receiving, by the one or more processors, at least one user-provided constraint on the set of parameters, wherein the user-provided constraint is the predicted for the prediction model; Further comprising the step of defining the context of the outcome;
The method of claim 22. The one or more metrics represent a proposal's accuracy limit that represents the number of repetitions of the at least one proposal required between a first proposal and a final establishment of sales associated with the at least one proposal Including children,
The method of claim 22. The input to the predictive model further includes service level management (SLM) data of assets associated with the service asset, wherein the SLM data includes information representing usage of the asset and one or more people associated with the asset. Including one or more of information representing a satisfaction level by a plurality of persons, information representing a person's opinion regarding the asset, and information representing a person's opinion regarding a service asset associated with the asset,
The method of claim 22. The SLM data is input to the prediction model from an electronic source of service asset usage data or social media data.
The method of claim 30.

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