MACHINE LEARNING MODEL FOR PREDICTING STATE OF AN OBJECT REPRESENTING A POTENTIAL TRANSACTION

An online system stores objects representing potential transactions of an enterprise. The online system uses machine learning techniques to predict likelihood of success for a potential transaction object. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data as training dataset for a predictor model. The online system extracts features describing potential transaction objects and provides these as input to the predictor model for predicting the likelihood of success of a given potential transaction. The online system may use predictions of likelihood of success of potential transactions to identify a set of potential transactions that should be acted upon to maximize the benefit the enterprise within a time interval, for example, by the end of the current month.

BACKGROUND

Field of Art

The disclosure relates to machine learning techniques for predicting states of objects representing potential transactions.

Description of the Related Art

Online systems used by enterprises store large amount of data describing entities associated with the enterprise such as user accounts, documents, transactions, and so on. Examples of such online systems include multi-tenant systems that are configured to store data of multiple enterprises. These online systems provide tools for users associated with enterprises configured to allow the users to store and manage information processed by the enterprises, for example, for managing user interactions or transactions of the enterprise. Conventional tools supported by such online systems are often inadequate in terms of providing valuable analysis of the user interactions or guidance in terms of subsequent actions that users need to perform so as to maximize their impact on the enterprise.

DETAILED DESCRIPTION

An online system stores data of one or more enterprises and provides tools that provide valuable information to users of the enterprise. These tools provide analysis of objects representing entities associated with an enterprise, for example, objects storing information associated with potential transactions of the enterprise. For example, a user associated with the enterprise may interact with a third party in anticipation that the third party would perform a transaction that benefits the enterprise. The online system uses machine learning techniques to predict likelihood of success of a potential transaction. For example, the online system uses predictions of likelihood of success of potential transactions to identify potential transactions that should be acted upon to maximize the benefit the enterprise within a time interval, for example, by the end of the current month. The online system provides recommendations of potential transactions for users of the enterprise to focus on, to maximize the value to the enterprise, for example, by increasing month end revenue numbers.

FIG. 1shows an overall system environment illustrating an online system for storing and analyzing objects associated with potential transactions, in accordance with an embodiment. The overall system environment includes an online system100, one or more client devices110, a third party system120, and a network170. Other embodiments may use more or less or different systems than those illustrated inFIG. 1. Functions of various modules and systems described herein can be implemented by other modules and/or systems than those described herein.

FIG. 1and the other figures use like reference numerals to identify like elements. A letter after a reference numeral, such as “135a,”indicates that the text refers specifically to the element having that particular reference numeral. A reference numeral in the text without a following letter, such as “135,” refers to any or all of the elements in the figures bearing that reference numeral (e.g. “135” in the text refers to reference numerals “135a”and/or “135b”in the figures).

A client device110is used by users to interact with the online system100. A user interacts with the online system100using client application115. An example of a client application115is a browser application. In an embodiment, the client application115interacts with the online system100using requests sent over network170. The client device110presents user interfaces configured by the online system100via the client application115.

A third party system120is associated with a third party that may be an enterprise. The third party may be a potential customer of an enterprise associated with an online system100. The third party system120includes a user account store135bthat stores information describing users of the third party system120. The third party system120may include other components not shown inFIG. 1.

An enterprise E1may store information describing activities of the enterprise E1on the online system100. In an example scenario, a user U1of enterprise E1identifies an enterprise E2(a third party) as a potential customer for a product or service offered by enterprise E1. Accordingly, the user U1of the enterprise E1identifies a potential transaction between enterprise E1and enterprise E2related to the product or service offered by enterprise E1. The potential transaction may be a sale of the product or service or an agreement that results in enterprise E2using the product or service of enterprise E1in exchange for certain remuneration, for example, a monetary payment. The potential transaction is also referred to herein as an opportunity.

The user U1interacts with users of enterprise E2to ensure that the potential transaction or the opportunity is converted to an actual transaction that is successfully executed. In this situation, the potential transaction or opportunity is considered successful or is closed as a success. Alternatively, the user U1may determine that the likelihood of having a successful transaction with enterprise E2is below a threshold and accordingly determine that the opportunity is closed as a failure.

The interactions between user U1and users associated with the enterprise E2may include online interactions with the third party system120, for example, via email, messenger, video conference, and so on. Other interactions between user U1and users associated with the enterprise E2may be performed outside the third party system120and/or the online system100. For example, the user U1and users associated with the enterprise E2may interact via phone, mail, or in person. However, information describing these interactions is provided to the online system100and stored by the online system100in connection with the potential transaction associated with enterprise E2.

The online system100stores information associated with one or more enterprises. The information stored in connection with an enterprise in the online system100includes objects representing various entities associated with the enterprise, for example, user accounts representing users, objects representing potential transactions, and so on. The information stored in connection with an enterprise in the online system100includes historical data representing various interactions associated with enterprises, for example, user interactions associated with a potential transaction.

The online system100represents potential transactions or opportunities as potential transaction objects. A potential transaction object may also be referred to herein as an opportunity object. The online system100uses historical data describing state changes of potential transaction objects to predict whether a new potential transaction would close as a success or a failure. Accordingly, the online system100predicts whether the new potential transaction would result in a transaction or fail to result in a transaction. The online system100may generate statistical aggregate information based on the predicted information. For example, the online system100may generate aggregate sales predictions for a given time period, for example, an expected revenue based on sales that are predicted to close successfully by the month end. The online system100compares various combinations of potential transactions that can be acted upon within a time interval and makes recommendations as to which potential transactions should be actively pursued by the associated users of the online system100to maximize the expected revenue.

The online system100includes a user interaction manager125, an object manager140, an object analyzer130, a user account store135, a tenant metadata store150, an object store155, and an object historical data store160. Other embodiments may include more or fewer modules than those indicated herein. Functions indicated herein as being performed by a module may be performed by other modules than those indicated herein.

The user interaction manager125configures user interfaces for presenting to users via client devices110. The user interaction manager125receives user interactions from client devices110. In an embodiment, the user interaction manager125configures a user interface that allows users to provide information describing user interactions that are performed outside the online system100. For example, if a first user of the online system100interacts with a second user of the third party system120via phone, the first user may provide information describing the call via a user interface to the user interaction manager125.

The object store155stores objects representing entities associated with an enterprise. An enterprise may be an organization, a business, a company, a club, or a social group. An object may represent an account representing a business partner or potential business partner (e.g. a client, vendor, distributor, etc.) of a user, and may include attributes describing a company, subsidiaries, or contacts at the company. As another example, an object may represent a project that a user is working on with an existing partner, or a project that the user is trying to get. An object may represent an account representing a user or another entity associated with the enterprise. For example, an account may represent a customer of the first enterprise. An object may represent a user of the online system.

In an embodiment, the object store155stores an object as one or more records in a database. An object has data fields that are defined by the structure of the object (e.g. fields of certain data types and purposes). For example, an object representing an entity may store information describing the potential customer, a status of the opportunity indicating a stage of interaction with the customer, and so on.

The object store155may be implemented as a relational database storing one or more tables. Each table contains one or more data categories logically arranged as columns or fields. Each row or record of a table contains an instance of data for each category defined by the fields. For example, an object store155may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc.

An object may include links or references to other objects. For example an opportunity object may include links to contact objects and account objects, an account object may include links to contact objects and so on. An object may have outgoing links that allow the object to refer to other objects as well as incoming links that allow other objects to refer to the object.

An object may represent a potential transaction, also referred to herein as an opportunity. For example, a user associated with a first enterprise may identify a third party, for example, another enterprise as a potential customer of a product or service offered by the first enterprise. The online system100creates a potential transaction object representing the engagement with the third party. In an embodiment, the user of the first enterprise provides information describing the potential transaction to the online system. The online system100stores the information describing the potential transaction or the opportunity as a potential transaction object.

A potential transaction object is associated with a potential transaction type depending on the type of engagement anticipated between the enterprise and the third party. Examples of potential transaction types include, a new engagement (for example, the first engagement between the enterprise and a third party), an add-on to an existing engagement (for example, an engagement anticipating that a third party previously engaged in a transaction with the enterprise would add a new product or service offered by the enterprise to the existing engagement), a renewal of an existing engagement, and so on.

The potential transaction object includes various attribute, for example, information identifying the third party, information identifying an item offered by the first enterprise that is a subject of the potential transaction, for example, a product or service offered by the first enterprise, an amount representing a value of the potential transaction, a date of creation of the potential transaction object or the date of initiation of the interaction between the first enterprise and the third party in connection with the potential transaction, an identifier of the user creating the potential transaction object, an identifier of the potential transaction object, an expected closing date for the potential transaction, and so on.

A potential transaction object is associated with a state that represents a stage of the potential transaction. A potential transaction object is associated with a current state that may change based on user interactions associated with the object. For example, a newly created potential transaction object is initialized to a “initial” state. If the third party decides to purchase the product or service offered by the first enterprise, the state of the potential transaction object is updated to a “closed won” state. Similarly if the third party confirms that the third party would not purchase the product or service offered by the first enterprise, the state of the potential transaction object is updated to a “closed lost” state or “omitted” state. A potential transaction object is also associated with a category such that each category maps to one or more stages that each potential transaction object can have. In an embodiment, the various stages and categories are defined by each enterprise based on the process used by the enterprise.

In an embodiment, the online system100receives the names of various stages from a user, for example, an administrator associated with an enterprise. Examples of stages associated with a potential transaction include “pipeline”, “closed”, “omitted”, “committed”, and so on. For example, a potential transaction object that is newly created is initialized to a “pipeline” stage, a potential transaction object is moved to a “best case” stage responsive to some promising interactions with the third party, the potential transaction object is in a “commit” stage if the potential transaction is undergoing negotiations of contract details, the potential transaction object is in a “closed won” stage if an actual transaction is executed responsive to a sale, the potential transaction object is in an “omitted” stage (or a “closed lost” stage) if the potential transaction fails and the sales opportunity is lost. The online system associates each stage of the potential transaction with a state of the potential transaction object. The online system100receives the specification of each stage describing the user interactions associated with a potential transaction object that cause the potential transaction object to have a particular stage and the user interactions that cause the potential transaction object to change from one stage to another stage. In an embodiment, potential objects of different type have different pipelines of stages.

The object historical data store160stores historical information associated with various objects. The historical information is stored as records, each record storing an object identifier for identifying the object associated with the activity, for example, an identifier for a potential transaction object. In an embodiment, the object manager140is configured to detect changes in attributes belonging to a set of attributes associated with objects. If the object manager140detects a change in value in any attribute from the set of attributes, the object manager stores a record describing the attributes of the object in the object historical data store160. For example, for a potential transaction object, the object manager140stores a record in the object historical data store160if there is a change in value of any attribute including the state of the potential transaction object, an amount of the object, a predicted likelihood of success of the potential transaction, and so on. In an embodiment, the online system100uses the object identifier to associate various attributes describing the object with the record of the object historical data store160.

Accordingly, the object historical data store160stores activities associated with an object comprising, creation of the object, any state changes associated with the object, any user interactions associated with the object, any change in an amount associated with a potential transaction object, a change in the probability of a potential transaction object reaching a “closed won” state or a “closed lost” (if the change in the probability is more than a threshold value), a change in a predicted state that a potential transaction object is expected to close, and so on.

The object analyzer130uses the records stored in the object historical data store160as training data set for a machine learning model used for predicting information about the object, for example, for determining a probability of the object reaching a “closed won” state. The object state analyzer predicts a close data for a potential transaction object. The close date corresponds to a date that the potential transaction object is expected to reach a closed state, for example, a “closed won” state. Accordingly, the close date represents the date when an actual transaction corresponding to the potential transaction is executed, for example, responsive to a sale performed by the enterprise to a third party. The object analyzer130is described in detail in connection withFIG. 2.

The object manager140manages the stages of various potential transaction objects. The object manager140may receive input via the client application115indicating that the potential transaction has reached a particular stage. The object manager140modifies the state of the potential transaction object based on the received input. In an embodiment, the object manager140monitors user interactions performed by the user associated with the potential transaction object and modifies the state based on the monitored interactions.

In some embodiments, an online system100is a multi-tenant system. The online system100stores metadata describing the tenants in tenant metadata store150. Each tenant may be an enterprise as described herein. As an example, one tenant might be a company that employs a sales force where each salesperson uses a client device110to manage their sales process. Thus, a user might maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process.

In one embodiment, online system100implements a web-based customer relationship management (CRM) system. For example, in one embodiment, the online system100includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, webpages and other information to and from client devices110and to store to, and retrieve from, a database system related data, objects, and webpage content.

With a multi-tenant system, data for multiple tenants may be stored in the same physical database, however, tenant data typically is arranged so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. The tenant metadata store150stores information that allows identification of data for different tenants, for example, using identifiers that uniquely identify each tenant. The tenant metadata store150stores various stages of potential transaction objects defined by an enterprise.

In certain embodiments, the online system100implements applications other than, or in addition to, a CRM application. For example, the online system100may provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. According to one embodiment, the online system100is configured to provide webpages, forms, applications, data and media content to client devices110to support the access by client devices110as tenants of online system100. As such, online system100provides security mechanisms to keep each tenant's data separate unless the data is shared.

A multi-tenant system may implement security protocols that keep data, applications, and application use separate for different tenants. In addition to user-specific data and tenant-specific data, the online system100may maintain system level data usable by multiple tenants or other data. Such system level data may include industry reports, news, postings, and the like that are sharable among tenants.

It is transparent to customers that their data may be stored in a table that is shared with data of other customers. A database table may store rows for a plurality of customers. Accordingly, in a multi-tenant system various elements of hardware and software of the system may be shared by one or more customers. For example, the online system100may execute an application server that simultaneously processes requests for a number of customers.

The online system100and client devices110shown inFIG. 1can be executed using computing devices. A computing device can be a conventional computer system executing, for example, a Microsoft Windows™-compatible operating system (OS), Apple™ OS X, and/or a Linux distribution. A computing device can also be a client device having computer functionality, such as a personal digital assistant (PDA), mobile telephone, video game system, etc. The online system100stores the software modules storing instructions for embodiments, for example object analyzer130.

The interactions between the client devices110and the online system100are typically performed via a network170, for example, via the Internet. In one embodiment, the network uses standard communications technologies and/or protocols. In another embodiment, the entities can use custom and/or dedicated data communications technologies instead of, or in addition to, the ones described above. The techniques disclosed herein can be used with any type of communication technology, so long as the communication technology supports receiving by the online system100of web requests from a sender, for example, a client device110and transmitting of results obtained by processing the web request to the sender.

System Architecture

FIG. 2shows the system architecture of an object analyzer130for analyzing potential transaction objects, in accordance with an embodiment. The object analyzer130comprises a machine learning model220, a feature extraction module240, an object ranking module230, a recommendation engine250, a predictor model220, and a statistical analysis module260. Other embodiments may include more or fewer modules. Functionality indicated herein as being performed by a particular module may be performed by other modules.

The machine learning module210trains the predictor models220by extracting features describing potential transaction objects that were previously processed and creating a feature vector. The machine learning module210stores the predictor models220. In an embodiment, the machine learning module210uses dimensionality reduction (e.g., via linear discriminant analysis, principle component analysis, etc.) to reduce the amount of data in the feature vector to a smaller, more representative core set of features.

The training set for the predictor models includes positive and negative examples comprising potential transaction objects that resulted in actual transactions and those that failed to result in actual transactions. Machine learning algorithms used include support vector machines (SVMs), boosting for other algorithms (e.g., AdaBoost), neural net, logistic regression, naïve Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, boosted stumps, etc.

In an embodiment, the machine learning module210uses random forest classification based on predictions from a set of decision trees. Each decision tree splits the source set into subsets based on an attribute value test. This process is repeated in a recursive fashion. A decision tree represents a flow chart, where each internal node represents a test on an attribute. For example, if the value of an attribute is less than or equal to a threshold value, the control flow transfers to a first branch and if the value of the attribute is greater than the threshold value, the control flow transfers to a second branch. Each branch represents the outcome of a test. Each leaf node represents a class label, i.e., a result of a classification.

Each decision tree uses a subset of the total predictor variables to vote for the most likely class for each observation. The final random forest score is based on the fraction of models voting for each class. A model may perform a class prediction by comparing the random forest score with a threshold value. In some embodiments, the random forest output is calibrated to reflect the probability associated with each class.

In an embodiment, a different machine learning model is trained for each type of the potential transaction (e.g., new business, an add-on business, a renewal and so on). This is so because the stages of each potential transaction type may be defined differently by the enterprise. Furthermore, the patterns of changes and user interactions for different potential transaction types may be different. Accordingly, the weights of features for predicting success of potential transactions of different potential transaction types may be different. Accordingly, a different machine learning model is trained and stored for each type of the potential transaction.

The feature extraction module240extract features of potential transaction objects for use by machine learning module210or the predictor model220. The feature extraction module240extract features of potential transaction objects for use in training models by the machine learning module210. The feature extraction module240also extract features of potential transaction objects for predicting information describing potential transaction objects, for example, for predicting a likelihood of a potential transaction object resulting in a successful transaction. In an embodiment, the feature extraction module240represents a feature using a name and a value.

Examples of features extracted by the feature extraction module240include the following: a rate of user interactions associated with the potential transaction object within a past time interval; a rate of updates to the potential transaction object; total number of updates to the potential transaction object since the potential transaction object was created; the age of the potential transaction object, i.e., the number of days since the potential transaction object was created; the number of days since last update to the potential transaction object; number of category changes since creation of the potential transaction object; a current category of the potential transaction object; number of days since the potential transaction object entered the current category; number of changes to the category since the potential transaction object was created; number of times the potential transaction object was in each category previously; total number of days spent in each category; the amount associated with the potential transaction object; the total number of updates made to the amount in a given time interval or since the potential transaction object was created.

In an embodiment, the predictor model220predicts the likelihood of success of a potential transaction object as a weighted aggregate value based on various features associated with the potential transaction object. The predictor model220determines that the likelihood of success of a potential transaction object is higher for a potential transaction object having a high rate of user interactions associated with the potential transaction object within a past time interval. The predictor model220determines that the likelihood of success of a potential transaction object is higher for a potential transaction object having a high rate of updates to the potential transaction object. The predictor model220determines that the likelihood of success of a potential transaction object is higher for a potential transaction object having a high total number of updates to the potential transaction object since the potential transaction object was created; The predictor model220determines that the likelihood of success of a potential transaction object is inversely proportionate with the age of the potential transaction object if the age of the potential transaction object exceeds a threshold value. The predictor model220determines that the likelihood of success of a potential transaction object is inversely proportionate to the number of days since last update to the potential transaction object. The predictor model220determines that the likelihood of success of a potential transaction object is higher for a potential transaction object having a high number of category changes since creation of the potential transaction object. The predictor model220determines that the likelihood of success of a potential transaction object is inversely proportionate to a number of days since the potential transaction object entered the current category. The predictor model220determines that the likelihood of success of a potential transaction object is directly proportionate to a number of changes to the category since the potential transaction object was created. The predictor model220determines that the likelihood of success of a potential transaction object is directly proportionate to the total number of updates made to the amount in a given time interval or since the potential transaction object was created.

The recommendation engine250recommends certain potential transactions to the user via the client application115. The recommended potential transactions represent potential transactions for which the users are advised to perform certain actions associated with the potential transaction. The action associated with a potential transaction may depend on the current stage of the potential transaction. For example, an associated with a potential transaction may require a user of the enterprise to contact a corresponding user of the third party associated with the potential transaction. As another example, an action associated with a potential transaction may represent adjustment of an amount associated with the potential transaction. In an embodiment, the recommendation engine250identifies potential transaction that are likely to have the highest impact on the revenue of the enterprise at the end of a given time interval, for example, at the end of the month.

The object ranking module230ranks the recommended potential transaction objects for presenting to the user. In an embodiment, object ranking module230ranks the recommended potential transaction objects in order of their impact on the revenue of the enterprise at the end of a given time interval. In an embodiment, the object ranking module230ranks the recommended potential transactions based on a weighted aggregate value that is proportionate to a predicted likelihood that a potential transaction would result in a successful transaction and an amount associated with the potential transaction.

The statistical analysis module260performs statistical analysis of potential transaction objects to present various types of statistical information describing the potential transaction object. In an embodiment, the statistical analysis module260determines and presents estimates of revenue at the end of a time interval, for example, expected revenues at the end of a month.

Overall Process

The processes associated with object analysis performed by online system100are described herein. The steps described herein for each process can be performed in an order different from those described herein. Furthermore, the steps may be performed by different modules than those described herein.

FIG. 3shows the process of analyzing potential transaction objects, in accordance with an embodiment. The object historical data store160receives and stores310historical data describing activities associated with potential transaction objects. An example of historical data stored in object historical data store160is illustrated inFIG. 4.

The machine learning module210of the object analyzer130uses the records stored in the object historical data store160as training data set for training320a machine learning model used for predicting information about the object, for example, the predictor model220. In an embodiment, the predictor model220determines a score indicating a probability of a potential transaction object reaching a “closed won” state.

The predictor model220receives various potential transaction objects and determines330a score value representing probability of each potential transaction object reaching a “closed won” state. The statistical analysis module260performs various types of analysis of the scores of the potential transaction objects for presentation to the user. For example, the statistical analysis module260predicts a month end revenue based on a particular selection of potential transaction objects.

The recommendation engine250recommends a set of potential transaction objects that maximizes the month end revenue. Accordingly, if the users associated with the recommended set of potential transaction objects performed actions associated with the recommended set of potential transaction objects, the states of these potential transaction objects are expected to change such that the predicted revenue numbers are realized at the end of a month.

FIG. 4shows an example of historical data associated with a potential transaction object stored in the online system, in accordance with an embodiment. A record stored in the object historical data store160includes attributes describing an activity associated with the record. The attributes comprise, an identifier410afor the potential transaction or opportunity corresponding to the activity represented by the record, the creation date410bof the record indicating the date of the activity, a category410cfor the potential transaction at the time of creation of the record, the state410dof the potential transaction object representing the current stage of the potential transaction, an amount410eassociated with the potential transaction (e.g., an amount representing a value of a product or service that is a subject of the potential transaction), an expected close date410frepresenting a date when the potential transaction is expected to close, the creation date410gof the potential transaction object identified by the identifier410a,a type410hof the potential transaction (for example, a type describing the engagement associated with the potential transaction, such as, a purchase, an upgrade, an upsell, and so on), a flag410iindicating whether the potential transaction is closed, a flag410jindicating whether the transaction is won or lost if the transaction is closed. Other embodiments may include more or fewer attributes in each record.

FIG. 5shows the process of modifying the state of a potential transaction object, in accordance with an embodiment. The object manager140creates510a potential transaction object describing a potential transaction of an enterprise associated with the online system, for example, an enterprise representing a tenant of a multi-tenant system. In an embodiment, the object manager140creates510the new object in response to a request from a user associated with an online system, for example, a user responsible for performing user interactions associated with the potential transaction.

The object manager140initializes520the state of the potential transaction object created. The online system100may determine and store default value for the initial state of a potential transaction. For example, the online system may receive from a user of the online system, a default value of “initialized” as the initial state of the potential transaction object.

The online system100receives530information describing user interactions associated with the online system. In an embodiment, the user interaction manager125receives from the client application115, an interaction associated with the potential transaction object and stores information describing the interaction in the object historical data store160. For example, the client application115may allow a user of the online system100to interact with a user of a third party system120associated with the potential transaction via email or another online communication.

The object manager140determines540whether the interaction caused a change in state of the potential transaction object. The object manager140changes550the state of the potential transaction object based on the determination540. For example, if the object manager140determines that no user interactions associated with the potential transactions were performed for more than a threshold amount of time, the object manager140changes the state of the potential transaction object to an “inactive” state. Similarly, if the object state manager140determines that an interaction associated with the potential transaction object having an “inactive” state was performed, the object manager140modifies the state of the potential transaction object to an “active” state. The object manager140stores information describing the interaction as a record in the object historical data store160.

FIG. 6shows the process of training a machine learning model for predicting success of a potential transaction, in accordance with an embodiment. As shown inFIG. 6, the feature extraction module240retrieves records representing historical data describing various activities associated with potential transaction objects from the object historical data store160. The feature extraction module240extracts various features from the records retrieved from the object historical data store160. Examples of features610illustrated inFIG. 7include a feature610arepresenting a rate of user interactions associated with a potential transaction object, a feature610brepresenting a rate of updates to a potential transaction object, a feature610crepresenting an age of a potential transaction object, a feature610drepresenting a number of category changes to a potential transaction object, a feature610erepresenting an amount associated with a potential transaction object, and so on. The machine learning module210uses the extracted features to train the predictor model220.

FIG. 7shows the process of analyzing potential transaction objects using machine learning models, in accordance with an embodiment. The predictor model220receives710a set of potential transaction objects for performing analysis. The set of potential transaction objects may represent all potential transaction objects associated with an enterprise. The set of potential transaction objects may represent all potential transaction objects of a particular transaction type associated with an enterprise. Alternatively, the set of potential transaction objects may be determined in any other way.

The feature extraction module240extracts720features of each of the potential transaction objects. The predictor model220determines730a score for each potential transaction object of the set. In an embodiment, the score represents a likelihood that the potential transaction object would result in a successful transaction for the enterprise associated with the potential transaction object. The statistical analysis module260determines740statistical information describing the set of potential transaction objects. For example, the statistical information may represent an estimate of total revenue at the end of a time period, for example, a month. The object analyzer130presents the statistical information to users via the client application115.

FIG. 8illustrates how the predicted score for a potential transaction varies with time as a result of user interactions associated with the potential transaction, in accordance with an embodiment. The score represents the likelihood of success of a potential transaction represented by a probability of the potential transaction resulting in a “closed won” state. The chart illustrated inFIG. 8shows the variation of score along the y-axis with respect to time shown along the x-axis. In an embodiment, the predicted score for the potential transaction stays constant for a time period until certain event occurs that causes the score value to change, for example, increase.

FIG. 9illustrates a ranked list of potential transactions recommended by the online system, in accordance with an embodiment. The object ranking module230ranks the various potential transaction objects based on their significance. In an embodiment, the object ranking module230ranks the various potential transaction objects based on a weighted aggregate of the likelihood of the potential transaction closing successfully and an amount of the potential transaction. The object analyzer130sends the ranked list of potential transaction objects via the user interaction manager125to the client application115for presentation via a user interface. The user interface shows various attributes of the potential transaction objects including an identifier910aof the potential transaction object, an owner910bof the potential transaction object, an amount of the potential transaction associated with the potential transaction object, a stage of the potential transaction associated with the potential transaction object, a category, a close date, and a probability of success of the potential transaction object.

FIG. 10illustrates an example of statistical analysis for predicting an aggregate amount expected from potential transactions at the end of a time period, in accordance with an embodiment. The statistical analysis shows an aggregate value of an estimated revenue at the end of a time period, for example, at the end of the month. In the chart shown inFIG. 10, a worst case estimate, a best case estimate, and a projection representing the most likely scenario. The various scenarios represent the estimate of the revenue from potential transactions exceeding a threshold likelihood of success.

Alternative Embodiments

In an embodiment, the online system is a multi-tenant system and the feature object analyzer130determines a set of feature weights that optimizes the aggregate accessed results ranks for each tenant separately. Accordingly, a first set of feature weights is determined that predicts potential transaction object state information for a first tenant, a second set of feature weights is determined that potential transaction object state information for a second tenant, and so on. For example, the first set of feature weights may be for a tenant representing a first customer of the multi-tenant system, and the second set of feature weights may be for tenant representing a second customer of the multi-tenant system.

It is to be understood that the Figures and descriptions have been simplified to illustrate elements that are relevant for a clear understanding of the present invention, while eliminating, for the purpose of clarity, many other elements found in a typical online system. Those of ordinary skill in the art may recognize that other elements and/or steps are desirable and/or required in implementing the embodiments. However, because such elements and steps are well known in the art, and because they do not facilitate a better understanding of the embodiments, a discussion of such elements and steps is not provided herein. The disclosure herein is directed to all such variations and modifications to such elements and methods known to those skilled in the art.