DATABASE SYSTEMS AND METHODS FOR DYNAMIC QUOTE GUIDANCE
1. A method comprising:
- obtaining, by a server from a client device coupled to the server over a network, one or more values for one or more attributes of a quote;
obtaining, by the server from a database, a pricing model for the quote, the pricing model being determined based on a relationship between historical values for the one or more attributes and pricing information associated with a plurality of quotes maintained in the database;
determining, by the server, expected pricing information for the quote based on the one or more values for the one or more attributes using the pricing model; and
providing, by the server, a graphical indication of the expected pricing information on the client device.
Computing systems, database systems, and related methods are provided for guiding a user defining a quote for a product. One method involves a server obtaining one or more values for one or more attributes of a quote from a client device coupled to the server over a network, obtaining an expected pricing model for the quote from a database, determining expected pricing information for the quote based on the one or more values for the one or more attributes using the model, and providing a graphical indication of the expected pricing information on the client device. The expected pricing model is determined based on historical relationships between quote attributes and price for previously-closed quotes.
- 1. A method comprising:
obtaining, by a server from a client device coupled to the server over a network, one or more values for one or more attributes of a quote; obtaining, by the server from a database, a pricing model for the quote, the pricing model being determined based on a relationship between historical values for the one or more attributes and pricing information associated with a plurality of quotes maintained in the database; determining, by the server, expected pricing information for the quote based on the one or more values for the one or more attributes using the pricing model; and providing, by the server, a graphical indication of the expected pricing information on the client device.
- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
- 12. A method comprising:
determining, by a server, an expected pricing model for a product based on a relationship between historical values for one or more attributes of one or more closed quotes associated with the product and historical discount percentages associated with the one or more closed quotes, the historical values and historical discount percentages associated with the one or more closed quotes being maintained in a database coupled to the server; providing, by the server to a client device coupled to the server over a network, a quote graphical user interface (GUI) display; receiving, by the server, one or more values for one or more attributes of a quote for the product from the quote GUI display; calculating, by the server, an expected discount percentage for the quote based on the one or more values using the expected pricing model; and providing, by the server, a graphical indication of the expected discount percentage on the quote GUI display.
- View Dependent Claims (13, 14, 15)
- 16. A database system comprising a server coupled to a database and communicatively coupled to a client device over a network to present thereon a graphical user interface (GUI) display comprising a first graphical representation of an expected discount percentage associated with a quote for a product defined using the GUI display and a second graphical representation of an assessment associated with the quote, wherein:
the server calculates the expected discount percentage based at least in part on one or more values for one or more attributes of the quote provided by a user via the GUI display using an expected pricing model obtained from the database; the expected pricing model is determined based on a relationship between historical values for the one or more attributes of a plurality of closed quotes for the product that are maintained in the database and pricing information associated with the plurality of closed quotes maintained in the database; and the server determines the assessment based at least in part on a relationship between the expected discount percentage and an input discount percentage provided by the user via the GUI display.
- View Dependent Claims (17, 18, 19, 20)
Embodiments of the subject matter described herein relate generally to database systems, and more particularly, to methods and systems that support dynamic graphical user interfaces responsive to changes to attributes or parameters of a quote created using a database system.
Modern software development is evolving away from the client-server model toward network-based processing systems that provide access to data and services via the Internet or other networks. In contrast to traditional systems that host networked applications on dedicated server hardware, a “cloud” computing model allows applications to be provided over the network “as a service” or “on-demand” by an infrastructure provider. The infrastructure provider typically abstracts the underlying hardware and other resources used to deliver a customer-developed application so that the customer no longer needs to operate and support dedicated server hardware. The cloud computing model can often provide substantial cost savings to the customer over the life of the application because the customer no longer needs to provide dedicated network infrastructure, electrical and temperature controls, physical security and other logistics in support of dedicated server hardware.
Customer relationship management (CRM) systems have been developed to allow individuals to manage contacts, customers, sales, opportunities, and the like. Discounting is a common practice engaged in by salespersons to match net price with customer value and/or situation. However, discounts may often be priced based on anecdotal evidence (e.g., a salesperson'"'"'s personal knowledge of prior deals), a one-size fits all approach, or an otherwise incomplete set of factors that leads to suboptimal pricing, which may undesirably impact a salesperson'"'"'s performance and/or the company'"'"'s bottom line. From a customer perspective, mis-priced deal could set the wrong value expectations and later lead to customer dissatisfaction or churn. Accordingly, it is desirable to provide database systems and methods that leverage available data to improve guidance for deal pricing.
A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures.
Embodiments of the subject matter described herein generally relate to database systems and methods for dynamically updating graphical user interface (GUI) displays based on user-defined attributes for a quote. As described in greater detail below, artificial intelligence is utilized to identify price-correlative factors based on historical quotes or deals and generate a corresponding pricing model, which, in turn may be applied to the attribute values defined for a current quote of interest to determine expected pricing information for the current quote (e.g., an expected discount price, an expected discount amount or percentage, a qualitative assessment of the quote, and the like). A salesperson may utilize the expected pricing information to reliably identify or determine which attributes of the quote may be adjusted to achieve a desired outcome, with the expected pricing information being dynamically updated in response to user-initiated changes to the values for different attributes of the quote. To this end, in some embodiments, the GUI display may identify or otherwise indicate which attributes of the quote correspond to the variables or factors in the pricing model that are most influential or most strongly correlated to the expected pricing outcome.
Additionally, reactive guidance may be provided to the user to help the user identify his or her qualitative pricing performance with respect to the current quote. For example, the quote may be qualitatively assessed or scored with respect to historical quotes or deals maintained in the database system, with corresponding indicia provided to the user to identify the qualitative performance associated with the current quote. As one example, historical data for similar quotes may be utilized to derive a normal discount similar quotes (e.g., the 50th percentile) and a target discount (e.g., the 85th percentile). If the user proposes a discount percentage that is less than the targeted discount percentage, reactive guidance may be provided using a visually distinguishable characteristic (e.g., a green color) or other symbology that indicates an above target qualitative state for the quote, while a proposed discount percentage between the target and normal discount would result in reactive guidance indicating an average qualitative state, and a proposed discount percentage greater than the normal discount would result in reactive guidance indicating a below average qualitative state. Thus, a user may identify how overpriced or underpriced the proposed quote may be. Various different approval, notification, or other workflow rules or rule-based logic may also be performed on the current quote based on the qualitative scoring of the quote. In one or more exemplary embodiments, dashboard GUI displays depicting the qualitative or quantitative quote performance multiple quotes are also provided to facilitate retrospective analysis of the historical pricing performance for individual users, groups, deal teams, etc.
The server 102 generally represents a computing device, computing system or another combination of processing logic, circuitry, hardware, and/or other components configured to support the conversational interaction processes, tasks, operations, and/or functions described herein. In this regard, the server 102 includes a processing system 120, which may be implemented using any suitable processing system and/or device, such as, for example, one or more processors, central processing units (CPUs), controllers, microprocessors, microcontrollers, processing cores and/or other hardware computing resources configured to support the operation of the processing system 120 described herein. The processing system 120 may include or otherwise access a data storage element 122 (or memory) capable of storing programming instructions for execution by the processing system 120, that, when read and executed, cause processing system 120 to support the processes described herein. Depending on the embodiment, the memory 122 may be realized as a random-access memory (RAM), read only memory (ROM), flash memory, magnetic or optical mass storage, or any other suitable non-transitory short or long-term data storage or other computer-readable media, and/or any suitable combination thereof. In one or more embodiments, the programming instructions cause the processing system 120 to create, generate, or otherwise facilitate the application platform 124 that generates or otherwise provides instances of a virtual application at run-time (or “on-demand”) based at least in part upon code and other data that is stored or otherwise maintained by the database 104. Accordingly, for purposes of explanation but without limitation, the server 102 may alternatively be referred to herein as an application server 102.
In exemplary embodiments, the programming instructions also cause the processing system 120 to create, generate, or otherwise facilitate an application 126 that supports that allows users to create or otherwise define quotes and modify attributes of quotes in response to automated guidance provided by a price guidance application 128, as described in greater detail below. Depending on the embodiment, the quoting application 126 and/or the price guidance application 128 can be integrated with or otherwise incorporated as part of a virtual application, or be realized as a separate or standalone process, application programming interface (API), software agent, or the like that is capable of interacting with the client device 106 independent of the virtual application to perform actions with respect to the database 104.
The client device 106 generally represents an electronic device coupled to the network 108 that may be utilized by a user to access the application platform 124 on the application server 102 to retrieve data from the database 104 via the network 108. In practice, the client device 106 can be realized as any sort of personal computer, mobile telephone, tablet or other network-enabled electronic device. In exemplary embodiments, the client device 106 includes a display device, such as a monitor, screen, or another conventional electronic display, capable of graphically presenting data and/or information provided by the application platform 124 along with a user input device, such as a touchscreen, a touch panel, a mouse, a joystick, a directional pad, a motion sensor, or the like, capable of receiving input from the user of the client device 106. The illustrated client device 106 executes or otherwise supports a client application 107 that communicates with the application platform 124 on the server 102 using a networking protocol. In some embodiments, the client application 107 is realized as a web browser or similar local client application executed by the client device 106 that contacts the application server 102 and/or application platform 124 using a networking protocol, such as the hypertext transport protocol (HTTP) or the like, to access or otherwise initiate an instance of a virtual application presented on the client device 106.
In exemplary embodiments, the database 104 stores or otherwise maintains data for integration with or invocation by a virtual application in objects organized in object tables 110. In this regard, the database 104 includes a plurality of different object tables 110 configured to store or otherwise maintain alphanumeric values, metadata, or other descriptive information that define a particular instance of a respective type of object associated with a respective object table 110. For example, the virtual application may support a number of different types of objects that may be incorporated into or otherwise depicted or manipulated by the virtual application, with each different type of object having a corresponding object table 110 that includes columns or fields corresponding to the different parameters or criteria that define a particular instance of that object.
In exemplary embodiments described herein, the database 104 stores or otherwise maintains application objects (e.g., an application object type) where the application object table 110 includes columns or fields corresponding to the different parameters or criteria that define a particular application capable of being generated or otherwise provided by the application platform 124 on a client device 106. In this regard, the database 104 may also store or maintain graphical user interface (GUI) objects that may be associated with or referenced by a particular application object and include columns or fields that define the layout, sequencing, and other characteristics of GUI displays to be presented by the application platform 124 on a client device 106 in conjunction with that application. Additionally, the database 104 stores or otherwise maintains additional database objects for association and/or integration with the application, which may include custom objects and/or standard objects, such as, for example, opportunity objects, quote objects, product objects, and the like, as described in greater detail below.
In exemplary embodiments, the database 104 also includes or otherwise maintains one or more tables 112 that include one or more rules or criteria associated with respective types of database object types that may be applied to entries in the various database object tables 110. For example, a validation rule provides validation criteria for one or more fields (or columns) of a particular database object type, such as, minimum and/or maximum values for a particular field, a range of allowable values for the particular field, a set of allowable values for a particular field, or the like. Additionally, the validation rule may provide a default value to be assigned to a field (or column) of a particular database object table 110 when the value for that field of a particular record or entry in that database object table 110 does not satisfy the validation criteria for that field. In some embodiments, the validation rules associated with a particular database object type may identify or otherwise indicate required fields for that particular object. Exemplary embodiments described herein also utilize notification rules, approval rules, or other rules that provide criteria or logic for performing different automated actions based on the value(s) for particular field(s) or column(s) of a given object or related objects.
Additionally, the database 104 stores or otherwise maintains metadata 114, which may be utilized to perform data manipulation and/or formatting. For example, the metadata 114 may include or define describe any number of workflows, process flows, formulas, business logic, structure and other database components or constructs that may be associated with a particular application database object. In this regard, in some embodiments, the metadata 114 may associated with a particular type of application or other database component may identify or otherwise indicate other database objects may be required for supporting the particular workflows, process flows, formulas, business logic, or other aspects of the logical structure of that application.
In the illustrated embodiment, the database 104 stores or maintains pricing models 116 that may be utilized to determine expected pricing information, as described in greater detail below. For example, a price guidance application 128 on a server 102 may utilize artificial intelligence or machine learning techniques to determine which combination of attributes or variables of historical quotes for closed deals pertaining to a particular product (or combination of products) associated with a particular user, organization, or tenant are correlated to or predictive of the resulting price associated with those deals, and then determine a corresponding equation, function, or model for calculating the expected price for that particular product (or combination of products) based on that set of input variables. Thus, the pricing model is capable of characterizing or mapping a particular combination of attributes of a quote for a product (or combination of products) to an expected pricing that is consistent with historical quotes with similar attributes for previously closed deals for that product (or combination of products). It should be noted that the subset of input variables that are predictive of or correlative for a particular product or product combination may vary across different users, organizations or tenants, and/or the relative weightings applied to the respective attributes or variables of a respective predictive subset may also vary across different users, organizations or tenants based on differing correlations between a particular quote attribute and the historical quote data for that particular user, organization or tenant. It should be noted that any number of different artificial intelligence or machine learning techniques may be utilized to determine what input quote attributes are predictive of pricing, and the subject matter described herein is not limited to any particular modeling technique.
In the illustrated embodiment, the database 104 also stores or maintains historical deal data 118 that includes statistics or other metrics characterizing previously closed quotes or deals, such as, for example, average price per unit, median price per unit, average quantity, median quantity, average term, median term, and/or the like. As described in greater detail below, the historical deal data 118 may be utilized by the price guidance application 128 to qualitatively analyze quotes or provide other qualitative guidance based on the relationship between a current quote and the historical deal data 118. Similar to the pricing models 116, the historical deal data 118 may be calculated or otherwise determined by a price guidance application 128 on a server 102 analyzing the historical quotes for closed deals pertaining to a particular product (or combination of products) associated with a particular user, organization, or tenant.
In exemplary embodiments, the database 104 also includes a dashboard table 130 that maintains one or more dashboard GUI displays and one or more report visualizations associated therewith for graphically depicting the qualitative or quantitative performance associated with closed quotes or deals, or individual users, organizations, or tenants. For example, one or more bar charts, pie charts, gauge charts, tabular charts, and/or the like may be provided on a dashboard GUI display to depict the qualitative distribution of the closed quotes associated with a particular user or set of users, along with other graphical indicia or depictions of quantitative metrics associated with those closed quotes. In this regard, a sales manager may utilize the dashboard GUI display(s) to review the pricing performance of individual salespersons or a group of salespersons, as described in greater detail below. A deal review team (or deal desk) may use a dashboard to compare a given quote with previously approved deals with similar attribute values, while a pricing strategy team may use a dashboard with similar data to estimate model impact and guidance and/or pricing improvement opportunities.
After obtaining initial quote attribute values, the quote guidance process 200 automatically calculates or otherwise determines expected pricing information for the quote based on the initial quote attribute values using a model derived based on relationships between quote attribute values and pricing from historical data associated with previously closed or completed quotes (task 204). In this regard, in exemplary embodiments, the quoting application 126 provides the quote attribute values to the price guidance application 128 which accesses the database 104 to obtain the appropriate pricing model 116 associated with the particular product(s) associated with the quote. The quote attribute values are then input or otherwise provided to the equation or formula defined by the pricing model 116 to calculate expected pricing information for the current quote attribute values. For example, in one or more embodiments, the pricing model 116 automatically calculates an expected discount percentage of the default or standard pricing for the product(s) based on the current quote attribute values, resulting in an expected discount percentage that reflects historical correlations or relationships between those quote attributes and historical pricing for the relevant product(s). That said, in other embodiments, the pricing model 116 may be configured to calculate a per unit price, or another suitable pricing metric or statistic.
As described above, in exemplary embodiments, the expected pricing model calculates an expected discount percentage based on the subset of quote attributes that are most correlative to or predictive of price based on historical deal data for the product of interest, with different weightings assigned to different quote attributes. For example, machine learning or other artificial intelligence may be applied to historical deal data for the user'"'"'s organization or tenant to derive a formula or equation for an expected discount percentage as a function of the quote quantity and the customer tier based on a statistically significant relationship for those attributes with respect to the historical discount percentage. Using the respective weightings assigned to those attributes by the expected pricing model, the price guidance application 128 may calculate an expected discount percentage for the current quote based on the user-defined quantity associated with the quote and the tier associated with the current customer or client. In some embodiments, the price guidance application 128 may attempt to determine an expected pricing model in the absence of an existing pricing model 116 associated with the product(s) for the current quote.
In one or more embodiments, the quote guidance process 200 applies one or more pricing rules to the current quote (task 205). In this regard, the pricing rules provide rule-based logic that may be utilized to influence or override the model-predicted expected pricing, for example, by imposing limits or other constraints on the model output. Pricing rules may also be applied to the attribute values for the current quote to facilitate providing guidance by determining expected pricing information when the historical data for the current product(s) of interest is otherwise insufficient to create an expected pricing model with a desired level of accuracy or reliability. For example, different pricing rules associated with one or more products may be defined by a user (or a user'"'"'s organization or tenant) and stored in the database 104 in association with the applicable product(s). In the absence of an expected pricing model for the current product(s) of interest, the price guidance application 128 may query the database 104 for any pricing rules associated with the current product(s) of interest and then apply the retrieved pricing rules to the current attribute values.
In one or more exemplary embodiments, the pricing rules may define different discounts to be applied based on different threshold values for different attributes of the quote, such as, for example, minimum or maximum discount percentages for a given quantity of product, amounts to increase or decrease the discount by when the quantity exceeds or fails to exceed a particular threshold, minimum or maximum discount percentages for a given term associated with the quote, amounts to increase or decrease the discount by when the term exceeds or fails to exceed a particular threshold, and the like. Percentiles or other statistical measures may also be identified based on historical data and incorporated into the pricing rules to guide the user towards a price consistent with historical pricing with a desired amount of uplift. Additionally, the pricing rules may incorporate other factors, such as, the identity of the customer or client, the tier associated with the customer or client, the historical data associated with that particular customer or client, and the like.
It should be noted that in some embodiments, the price guidance application 128 may obtain pricing rules from the database 104 and apply the pricing rules concurrently or in concert with the expected pricing model to override or otherwise constrain the expected pricing information output by the pricing model to a rule-based maximum or minimum pricing. For example, the pricing rules may set a maximum discount percentage not to be exceeded, such as, a maximum discount percentage for the current quantity of product independent of the deal term or other factors, a maximum discount percentage for the current deal term independent of quantity or other factors, a maximum discount percentage for the current customer tier or geographic region independent of other factors, etc.
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In exemplary embodiments, the quote guidance process 200 also calculates or otherwise determines a qualitative assessment of the current quote based on the historical deal data and provides graphical indicia of the qualitative assessment of the current quote (tasks 210, 212). In this regard, the current quote may be scored or otherwise compared to the historical deal data 118 and the various historical deal metrics to assign a qualitative state to the current quote that may be utilized to provide reactive guidance as the user modified aspects of the quote. For example, the current quote may be classified into one of multiple different qualitative categories based on one or more current quote attributes.
As described in greater detail below, in one or more exemplary embodiments, based on the current discount percentage associated with the current quote, the current quote is qualitative classified as being above average, average, or below average based on the relationship between the current discount percentage and historical discount percentages associated with the user or the user'"'"'s associated organization, group, or tenant. For example, the current quote may be classified as above average if the current discount percentage less than both the average and median discount percentage for the current product(s), as average if the current discount percentage less than only one of the average and median discount percentage for the current product(s), or as below average if the current discount percentage greater than both the average and median discount percentage for the current product(s). As another example, current quote may be classified based on different percentiles relative to the historical deal distribution. In this regard, it should be noted that any number of different criteria may be utilized to assign a qualitative state or score to a quote, and the subject matter is not limited to any particular qualitative assessment scheme. In this regard, in practical embodiments, a user may define the qualitative states or criteria to be applied to quotes associated with a particular product in a manner that is specific to a particular user, group, organization, and/or tenant.
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In exemplary embodiments, the loop defined by tasks 202, 204, 206, 208, 210, 212, 214 and 216 repeats to dynamically update the guidance provided to the user in response to changes to values for different attributes of the quotes. In this regard, based on the expected pricing information and discount-influencing factors presented by the quoting application 126, the user may modify the value(s) for one or more attributes of the quote to achieve a desired pricing that is more consistent with the expected pricing based on historical deals. For example, a salesperson negotiating with a customer or client seeking a particular unit price or discounting relative to the list price may identify potential ways to adjust or tailor the quote to satisfy the client'"'"'s objectives and match price with the client'"'"'s value or situation.
The quote GUI display 300 also includes a quote guidance region 304 that includes a listing 320 of the attributes or factors that are most influential to the expected discount (e.g., pre-existing licenses by the customer, the customer tier, the industry sector, etc.) (e.g., task 208). Additionally, in the illustrated embodiment, the quote guidance region 304 provides a listing 330 of potential quote modifications if a higher discount is required to win the deal. In this regard, as described above, the price guidance application 128 may automatically simulate various alternative scenarios by modifying one or more attributes of the quote, calculating or otherwise determining different expected discounts using the pricing model, and providing the quote modifications and resulting discount expectations to the quoting application 126, which, in turn generates the listing 330 of suggested quote modifications within the quote guidance region 304.
Referring now to
To generate the gauge chart 802, the quoting application 126 queries the database 104 to identify the relevant quote database objects 110 (e.g., all quotes associated with salespersons working under the supervisory user viewing the dashboard GUI display 800) completed within a specified timeframe, and then analyzes the fields associated with the retrieved quotes to determine the number of well-priced deals, the number of underpriced deals, and generate the corresponding portions 804, 806 of the gauge chart 802. The illustrated dashboard GUI display 800 also includes a trendline graph 808 that reflects the relative distribution of the pricing of completed deals with respect to time. The dashboard GUI display 800 also includes a region 810 that allows for review of in-progress quotes that are yet to be closed or completed, broken down by whether their currently-proposed pricing is above or below the expected pricing.
For example, the expected total for the quote depicted in
The dashboard GUI display 900 also includes a deal breakdown region 904 includes a bar chart having bars 910, 912 depicting the cumulative revenue differentials associated with the respective subsets of quotes categorized as well-priced or underpriced with respect to bars 914, 916 depicting the actual revenue and the projected revenue. In this regard, the length or amount associated with the projected revenue bar 914 represents the cumulative revenue associated with the existing quotes that would be expected based on the historical pricing model, as applied to each respective quote, and the length or amount associated with the actual revenue bar 916 represents the cumulative revenue associated with the existing quotes. The gained revenue bar 910 associated with well-priced deals (e.g., quotes with actual discount percentages less than their respective expected discount percentages or positive net revenue differentials) is adjacent to the projected revenue bar 914 and extends upwards from the top of projected revenue bar 914 by a length or amount corresponding to the cumulative revenue differential calculated based on the subset of well-priced deals. The missed revenue bar 912 associated with underpriced deals (e.g., quotes with actual discount percentages greater than their respective expected discount percentages or negative net revenue differentials) is between the gained revenue bar 910 and the actual revenue bar 916 and extends downwards from the top of gained revenue bar 910 by a length or amount corresponding to the cumulative revenue differential calculated based on the subset of underpriced deals. Thus, the bars 910, 912, 914, 916 in the deal breakdown region 904 allows the supervisory user to assess how the distribution of pricing across deals is impacting the bottom line revenue.
Thus, by virtue of the subject matter described herein, salespersons can price quotes with greater confidence and a better understanding of the proposed deal with respect to client situation to provide value-matched pricing. Additionally, managers or other supervisors may gain improved insights on the pricing performance of different salespersons and derive a better understanding of how different individuals'"'"' pricing which may be leveraged to help improve pricing performance by those individuals. Different approval rules, qualitative and/or quantitative deal assessment threshold, and the like may be employed to further guide quoting and drive pricing behavior, which, in turn, may improve revenue. Additionally, by virtue of the historically-derived pricing models accounting for a wide range of variables, the resulting pricing guidance provided to salesperson is consistent with expectations for a given order size, term length, industry sector, geographic region, customer tier, and/or the like, thereby reducing the likelihood that sales will be lost due to overpricing, while also ensuring pricing matches the client'"'"'s expected value from the products. Better matching price with customer value creates pricing that is tailored to increase revenue, improve deal win rate, and reduce sales cycles. Leveraging artificial intelligence or machine learning techniques to derive expected pricing models based on historical data also reduces the costs and lead time that could otherwise be incurred by outsourcing pricing guidance to third-party professional services.
As used herein, a “tenant” or an “organization” should be understood as referring to a group of one or more users that shares access to common subset of the data within the multi-tenant database 1330. In this regard, each tenant includes one or more users associated with, assigned to, or otherwise belonging to that respective tenant. To put it another way, each respective user within the multi-tenant system 1300 is associated with, assigned to, or otherwise belongs to a particular tenant of the plurality of tenants supported by the multi-tenant system 1300. Tenants may represent customers, customer departments, business or legal organizations, and/or any other entities that maintain data for particular sets of users within the multi-tenant system 1300 (i.e., in the multi-tenant database 1330). For example, the application server 1302 may be associated with one or more tenants supported by the multi-tenant system 1300. Although multiple tenants may share access to the server 1302 and the database 1330, the particular data and services provided from the server 1302 to each tenant can be securely isolated from those provided to other tenants (e.g., by restricting other tenants from accessing a particular tenant'"'"'s data using that tenant'"'"'s unique organization identifier as a filtering criterion). The multi-tenant architecture therefore allows different sets of users to share functionality and hardware resources without necessarily sharing any of the data 1332 belonging to or otherwise associated with other tenants.
The multi-tenant database 1330 is any sort of repository or other data storage system capable of storing and managing the data 1332 associated with any number of tenants. The database 1330 may be implemented using any type of conventional database server hardware. In various embodiments, the database 1330 shares processing hardware 1304 with the server 1302. In other embodiments, the database 1330 is implemented using separate physical and/or virtual database server hardware that communicates with the server 1302 to perform the various functions described herein. In an exemplary embodiment, the database 1330 includes a database management system or other equivalent software capable of determining an optimal query plan for retrieving and providing a particular subset of the data 1332 to an instance of virtual application 1328 in response to a query initiated or otherwise provided by a virtual application 1328. The multi-tenant database 1330 may alternatively be referred to herein as an on-demand database, in that the multi-tenant database 1330 provides (or is available to provide) data at run-time to on-demand virtual applications 1328 generated by the application platform 1310.
In practice, the data 1332 may be organized and formatted in any manner to support the application platform 1310. In various embodiments, the data 1332 is suitably organized into a relatively small number of large data tables to maintain a semi-amorphous “heap”-type format. The data 1332 can then be organized as needed for a particular virtual application 1328. In various embodiments, conventional data relationships are established using any number of pivot tables 1334 that establish indexing, uniqueness, relationships between entities, and/or other aspects of conventional database organization as desired. Further data manipulation and report formatting is generally performed at run-time using a variety of metadata constructs. Metadata within a universal data directory (UDD) 1336, for example, can be used to describe any number of forms, reports, workflows, user access privileges, business logic and other constructs that are common to multiple tenants. Tenant-specific formatting, functions and other constructs may be maintained as tenant-specific metadata 1338 for each tenant, as desired. Rather than forcing the data 1332 into an inflexible global structure that is common to all tenants and applications, the database 1330 is organized to be relatively amorphous, with the pivot tables 1334 and the metadata 1338 providing additional structure on an as-needed basis. To that end, the application platform 1310 suitably uses the pivot tables 1334 and/or the metadata 1338 to generate “virtual” components of the virtual applications 1328 to logically obtain, process, and present the relatively amorphous data 1332 from the database 1330.
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The application platform 1310 is any sort of software application or other data processing engine that generates the virtual applications 1328 that provide data and/or services to the client devices 1340. In a typical embodiment, the application platform 1310 gains access to processing resources, communications interfaces and other features of the processing hardware 1304 using any sort of conventional or proprietary operating system 1308. The virtual applications 1328 are typically generated at run-time in response to input received from the client devices 1340. For the illustrated embodiment, the application platform 1310 includes a bulk data processing engine 1312, a query generator 1314, a search engine 1316 that provides text indexing and other search functionality, and a runtime application generator 1320. Each of these features may be implemented as a separate process or other module, and many equivalent embodiments could include different and/or additional features, components or other modules as desired.
The runtime application generator 1320 dynamically builds and executes the virtual applications 1328 in response to specific requests received from the client devices 1340. The virtual applications 1328 are typically constructed in accordance with the tenant-specific metadata 1338, which describes the particular tables, reports, interfaces and/or other features of the particular application 1328. In various embodiments, each virtual application 1328 generates dynamic web content that can be served to a browser or other client program 1342 associated with its client device 1340, as appropriate.
The runtime application generator 1320 suitably interacts with the query generator 1314 to efficiently obtain multi-tenant data 1332 from the database 1330 as needed in response to input queries initiated or otherwise provided by users of the client devices 1340. In a typical embodiment, the query generator 1314 considers the identity of the user requesting a particular function (along with the user'"'"'s associated tenant), and then builds and executes queries to the database 1330 using system-wide metadata 1336, tenant specific metadata 1338, pivot tables 1334, and/or any other available resources. The query generator 1314 in this example therefore maintains security of the common database 1330 by ensuring that queries are consistent with access privileges granted to the user and/or tenant that initiated the request. In this manner, the query generator 1314 suitably obtains requested subsets of data 1332 accessible to a user and/or tenant from the database 1330 as needed to populate the tables, reports or other features of the particular virtual application 1328 for that user and/or tenant.
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In exemplary embodiments, the application platform 1310 is utilized to create and/or generate data-driven virtual applications 1328 for the tenants that they support. Such virtual applications 1328 may make use of interface features such as custom (or tenant-specific) screens 1324, standard (or universal) screens 1322 or the like. Any number of custom and/or standard objects 1326 may also be available for integration into tenant-developed virtual applications 1328. As used herein, “custom” should be understood as meaning that a respective object or application is tenant-specific (e.g., only available to users associated with a particular tenant in the multi-tenant system) or user-specific (e.g., only available to a particular subset of users within the multi-tenant system), whereas “standard” or “universal” applications or objects are available across multiple tenants in the multi-tenant system. For example, a virtual CRM application may utilize standard objects 1326 such as “account” objects, “opportunity” objects, “contact” objects, or the like. The data 1332 associated with each virtual application 1328 is provided to the database 1330, as appropriate, and stored until it is requested or is otherwise needed, along with the metadata 1338 that describes the particular features (e.g., reports, tables, functions, objects, fields, formulas, code, etc.) of that particular virtual application 1328. For example, a virtual application 1328 may include a number of objects 1326 accessible to a tenant, wherein for each object 1326 accessible to the tenant, information pertaining to its object type along with values for various fields associated with that respective object type are maintained as metadata 1338 in the database 1330. In this regard, the object type defines the structure (e.g., the formatting, functions and other constructs) of each respective object 1326 and the various fields associated therewith.
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The foregoing description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the technical field, background, or the detailed description. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations, and the exemplary embodiments described herein are not intended to limit the scope or applicability of the subject matter in any way.
For the sake of brevity, conventional techniques related to querying and other database functions, multi-tenancy, cloud computing, on-demand applications, artificial intelligence, machine learning, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. In addition, those skilled in the art will appreciate that embodiments may be practiced in conjunction with any number of system and/or network architectures, data transmission protocols, and device configurations, and that the system described herein is merely one suitable example. Furthermore, certain terminology may be used herein for the purpose of reference only, and thus is not intended to be limiting. For example, the terms “first,” “second” and other such numerical terms do not imply a sequence or order unless clearly indicated by the context.
Embodiments of the subject matter may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. In practice, one or more processing systems or devices can carry out the described operations, tasks, and functions by manipulating electrical signals representing data bits at accessible memory locations, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. When implemented in software or firmware, various elements of the systems described herein are essentially the code segments or instructions that perform the various tasks. The program or code segments can be stored in a processor-readable medium or transmitted by a computer data signal embodied in a carrier wave over a transmission medium or communication path. The “processor-readable medium” or “machine-readable medium” may include any non-transitory medium that can store or transfer information. Examples of the processor-readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an erasable ROM (EROM), a floppy diskette, a CD-ROM, an optical disk, a hard disk, a fiber optic medium, a radio frequency (RF) link, or the like. The computer data signal may include any signal that can propagate over a transmission medium such as electronic network channels, optical fibers, air, electromagnetic paths, or RF links. The code segments may be downloaded via computer networks such as the Internet, an intranet, a LAN, or the like. In this regard, the subject matter described herein can be implemented in the context of any computer-implemented system and/or in connection with two or more separate and distinct computer-implemented systems that cooperate and communicate with one another. In one or more exemplary embodiments, the subject matter described herein is implemented in conjunction with a virtual customer relationship management (CRM) application in a multi-tenant environment.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application. Accordingly, details of the exemplary embodiments or other limitations described above should not be read into the claims absent a clear intention to the contrary.