METHOD FOR IDENTIFYING PROSPECTS BASED ON A PROSPECT MODEL
Systems and methods are provided for identifying prospects based on a prospect model. A set of primary features are extracted from historical data for an opportunity between an organization and an entity. A data container is generated to represent the set of primary features and a set of secondary features associated with the entity. Neighboring data containers, within a set of data containers that includes the data container, are grouped into data container groups. A data container group is selected to represent a combination of features of the entity predicted to yield the opportunity for the organization. The combination of features are used to generate and transmit content to the entity.
1 . A method, comprising:
extracting a set of primary features from historical data for an opportunity between an organization and an entity;
generating a data container to represent the set of primary features and a set of secondary features associated with the entity;
grouping neighboring data containers, within a set of data containers that includes the data container, into data container groups;
selecting a data container group representing a combination of features of the entity predicted to yield with opportunity for the organization; and
utilizing the combination of features to generate and transmit content to the entity.
2 . The method of claim 1 , comprising:
generating the data container as a vector that represents the set of primary features and the second of secondary features in a multi-dimensional feature space.
3 . The method of claim 1 , comprising:
grouping the neighboring data containers in a multi-dimensional feature space into the set of data container groups.
4 . The method of claim 1 , comprising:
generating a notification for a prospect identified from traffic data associated with a set of page views of a website, wherein the prospect is selected based upon a fit score corresponding to a similarity between the data container group and the data container associated with the prospect.
5 . The method of claim 1 , comprising:
generating a model characterizing attributes of a target prospect for the organization, wherein a fit score is used to select the entity as corresponding to the target prospect.
6 . The method of claim 1 , comprising:
generating, utilizing a prospect model, a target prospect profile specifying a list of attributes and entities that exhibit attributes associated with the opportunity to occur, wherein the target prospect profile is utilized to create the content.
7 . The method of claim 1 , comprising:
characterizing and recommending, utilizing a prospect model, attributes and entities that exhibit the attributes.
8 . The method of claim 1 , comprising:
utilizing fit scores assigned to entities to generate and provide notifications identifying prospects that exhibit an interest in the organization.
9 . The method of claim 1 , comprising:
utilizing fit scores assigned to entities to generate a first type of notification for a first subset of prospect and a second type of notification for a second subset of prospect.
10 . A computing device comprising:
a memory comprising machine executable code; and
a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to:
extract a set of primary features from historical data for an opportunity between an organization and an entity;
generate a data container to represent the set of primary features and a set of secondary features associated with the entity;
group neighboring data containers, within a set of data containers that includes the data container, into data container groups;
select a data container group representing a combination of features of the entity predicted to yield with opportunity for the organization; and
utilize the combination of features to generate and transmit content to the entity.
11 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
utilize fit scores assigned to entities to generate a first type of notification for a first subset of prospect and a second type of notification for a second subset of prospect.
12 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
generate the data container as a vector that represents the set of primary features and the second of secondary features in a multi-dimensional feature space.
13 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
group the neighboring data containers in a multi-dimensional feature space into the set of data container groups.
14 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
generate a notification for a prospect identified from traffic data associated with a set of page views of a website, wherein the prospect is selected based upon a fit score corresponding to a similarity between the data container group and the data container associated with the prospect.
15 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
generate a model characterizing attributes of a target prospect for the organization, wherein a fit score is used to select the entity as corresponding to the target prospect.
16 . The computing device of claim 10 , wherein the machine executable code causes the processor to:
generate, utilizing a prospect model, a target prospect profile specifying a list of attributes and entities that exhibit attributes associated with the opportunity to occur, wherein the target prospect profile is utilized to create the content.
17 . A non-transitory machine readable medium comprising instructions for performing a method, which when executed by a machine, causes the machine to:
extract a set of primary features from historical data for an opportunity between an organization and an entity;
generate a data container to represent the set of primary features and a set of secondary features associated with the entity;
group neighboring data containers, within a set of data containers that includes the data container, into data container groups;
select a data container group representing a combination of features of the entity predicted to yield with opportunity for the organization; and
utilize the combination of features to generate and transmit content to the entity.
18 . The non-transitory machine readable medium of claim 17 , wherein the instructions cause the machine to:
generate, utilizing a prospect model, a target prospect profile specifying a list of attributes and entities that exhibit attributes associated with the opportunity to occur, wherein the target prospect profile is utilized to create the content.
19 . The non-transitory machine readable medium of claim 17 , wherein the instructions cause the machine to:
characterize and recommend, utilizing a prospect model, attributes and entities that exhibit the attributes.
20 . The non-transitory machine readable medium of claim 17 , wherein the instructions cause the machine to:
utilize fit scores assigned to entities to generate and provide notifications identifying prospects that exhibit an interest in the organization.