Predictive analytics for leads generation and engagement recommendations

Abstract

An automated predictive analytics system disclosed herein provides for generating sales leads with lead engagement recommendations. In one implementation, the system determines similarities between fitness, engagement, and intent characteristics of a plurality of target clients and fitness, engagement, and intent characteristics of an entity's existing clients. Subsequently, the system generates recommendations for engagement with the plurality of target clients, wherein components of the recommendations for engagement are based on determined similarities between the fitness, engagement, and intent characteristics of the plurality of target clients and the fitness, engagement, and intent characteristics of the entity's existing clients. The system presents the plurality of leads with the recommendations of engagement to using a graphical user interface (GUI) at the application layer of the system.

1 - 20 . (canceled) 
     
     
         21 . A predictive lead engagement method, comprising:
 receiving a lead engagement recommendation for engagement with a target client, the lead engagement recommendation based on a determined similarity between one or more of:
 a fitness, engagement, or intent characteristic of the target client, and 
 a fitness, engagement, or intent characteristic of an existing client; 
   generating a feature matrix for the target client based on the lead engagement recommendation,   comparing a value for the feature matrix for the target client with a value of a feature matric for the existing client; and   generating a target lead recommendation based on the compared value of the feature matrix for the target client with the value of the feature matrix for the existing client.   
     
     
         22 . The method of  claim 21 , wherein the intent characteristic of the target client is based on a publicly available browsing behavior of one or more employees of the target client. 
     
     
         23 . The method of  claim 21 , wherein the value for the feature matrix for the target client and the feature matrix for the existing client is based on a likelihood of high propensity lead engagement. 
     
     
         24 . The method of  claim 21 , further comprising receiving the lead engagement recommendation for engagement with the target client at a first time, and further comprising:
 updating the similarity between the one or more of the fitness, engagement, or intent characteristic of the target client and the fitness, engagement, or intent characteristic of the existing client at a second time subsequent to the first time, the updated similarity based on an updated fitness, engagement, or intent characteristic of the target client or the existing client.   
     
     
         25 . The method of  claim 24 , wherein the updated similarity is based on the updated fitness, engagement, or intent characteristic of the target client and the existing client. 
     
     
         26 . The method of  claim 21 , wherein the lead engagement recommendation is based on a determined similarity between the fitness, engagement, and intent characteristic of the target client and the fitness, engagement, and intent characteristic of the existing client, respectively. 
     
     
         27 . The method of  claim 21 , wherein the value of the feature matrix is a first value, and further comprising comparing a second value for the feature matrix for the target client with a second value of a feature matric for the existing client. 
     
     
         28 . The method of  claim 27 , further comprising generating the target lead recommendation based on the compared first value and the compared second value of the feature matrix for the target client with the compared first value and the compared second value of the feature matrix for the existing client. 
     
     
         29 . The method of  claim 27 , further comprising assigning a score to the first value and the second value of the feature matrix for the target client. 
     
     
         30 . The method of  claim 29 , further comprising removing the second value of the feature matrix for the target client. 
     
     
         31 . The method of  claim 29 , further comprising assigning a penalty to the second value of the feature matrix for the target client. 
     
     
         32 . The method of  claim 21 , wherein comparing the value for the feature matrix for the target client with the value of the feature matrix for the existing client includes computing the comparison using distance metrics between the value of the feature matrix for the target client and the value of the feature matrix for the existing client. 
     
     
         33 . The method of  claim 21 , further comprising outputting the target lead recommendation. 
     
     
         34 . A predictive lead generation system, comprising:
 a processor programmed to:
 receive a lead engagement recommendation for engagement with a target client, the lead engagement recommendation based on a determined similarity between one or more of:
 a fitness, engagement, or intent characteristic of the target client, and 
 a fitness, engagement, or intent characteristic of an existing client; 
 
 generate a feature matrix for the target client based on the lead engagement recommendation; 
 compare a value for the feature matrix for the target client with a value of a feature matric for the existing client; and 
 generate a target lead recommendation based on the compared value of the feature matrix for the target client with the value of the feature matrix for the existing client; and 
   an output configured to output the target lead recommendation.   
     
     
         35 . The system of  claim 34 , wherein the value for the feature matrix for the target client and the feature matrix for the existing client is based on a likelihood of high propensity lead engagement. 
     
     
         36 . The system of  claim 34 , wherein the processor is further programmed to:
 receive the lead engagement recommendation for engagement with the target client at a first time, and   update the similarity between the one or more of the fitness, engagement, or intent characteristic of the target client and the fitness, engagement, or intent characteristic of the existing client at a second time subsequent to the first time, the updated similarity based on an updated fitness, engagement, or intent characteristic of the target client or the existing client.   
     
     
         37 . The system of  claim 36 , wherein the processor is further programmed to update the similarity based on the updated fitness, engagement, or intent characteristic of the target client and the existing client. 
     
     
         38 . The system of  claim 34 , wherein the value of the feature matrix is a first value, and wherein the processor is further programmed to:
 compare a second value for the feature matrix for the target client with a second value of a feature matric for the existing client, and   generate the target lead recommendation based on the compared first value and the compared second value of the feature matrix for the target client with the compared first value and the compared second value of the feature matrix for the existing client.   
     
     
         39 . The system of  claim 38 , wherein the processor is further programmed to assign a score to the first value and the second value of the feature matrix for the target client. 
     
     
         40 . The system of  claim 39 , wherein the processor is further programmed to remove the second value of the feature matrix for the target client or assign a penalty to the second value of the feature matrix for the target client.