Methods and systems for ranking leads based on given characteristics

• US 10,394,834 B1
• Filed: 10/22/2018
• Issued: 08/27/2019
• Est. Priority Date: 12/31/2013
• Status: Active Grant

• Current Assignee: Massachusetts Mutual Life Insurance Company
• Original Assignee: Massachusetts Mutual Life Insurance Company
• Inventors: Ross, Gareth, Walker, Tricia
• Primary Examiner(s): Singh, Gurkanwaljit
• Application Number:
  US16/167,212
• Time in Patent Office: 309 Days
• Field of Search: None
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 7/00 Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Systems and methods for ranking and appraising leads according to their quality are disclosed. The disclosed method operates within a systems'"'"' architecture configured to rank and auction leads. One or more client computing devices allow an agent to request for the ranking and appraisal of a set of leads. Following the request, a ranking module implements one or more software modules for assessing the quality of each lead and ranks the set of leads according to a quality criterion. Next, a price modeling module defines a floor price for the set of leads. The solutions derived from the software modules are stored in an internal database where they are available to other software modules operating within the system architecture for ranking and auction leads. In some embodiments, the ranking results are used for tracking results and developing insight about the value of leads and the effectiveness of the ranking method.

Claims

1. A computer-implemented method comprising:

periodically scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents to extract one or more characteristic values associated with a set of leads;

upon receiving from a computer of an agent, a selection of a lead attribute from a set of lead attributes, filtering, by the first processor, lead information from the one or more social networking web documents to obtain a set of filtered lead information comprising only a first subset of leads containing the lead attribute;

executing, by the first processor, a machine-learning model to calculate a score for each lead within the first subset of leads where the machine-learning model is configured to calculate the score for each lead based on a learning dataset;

while the first processor is executing the machine-learning model, iteratively updating, by a second processor of the computer, the learning dataset of the machine-learning model based on modified data associated with each lead, wherein the second processor is configured to periodically query one or more databases to receive inputs on the modified data associated with each lead and, in an event that the second processor determines that data associated with the lead attributes is changed, the second processor adjusts the learning dataset;

generating, by the first processor, a second subset of leads that imply a highest potential value of each lead based on the score; and

updating, by the first processor, a graphical user interface of the computer of the agent with information comprising the second subset of leads.

2. The computer-implemented method according to claim 1, wherein the lead information comprises at least one of:

identity, age, ethnicity, place of residence, number of dependent persons, identity of dependent persons, expenditure, savings, approximate market value of assets and their composition, education, professional situation, contact information, shopping preferences, travel preferences, hobbies, social activities, active lifestyle, online behavioral patterns, medical and health records.

3. The computer-implemented method according to claim 1, wherein the lead information comprises one or more categories selected from a group consisting at least one of:

geography, demographics, asset value, and credit score.

4. The computer-implemented method according to claim 1, wherein the set of lead attributes associated with the lead comprises at least one of:

economic stability, credit card usage, liquid assets, education level, discretionary spending, and retirement assets.

5. The computer-implemented method of claim 1, further comprising ranking, by the first processor, each of the first subset of leads based on their corresponding score.

6. The computer-implemented method of claim 5, further comprising triaging, by the first processor, each ranked lead that imply the highest potential value of each lead based on the score.

7. The computer-implemented method of claim 1, wherein the machine-learning model utilizes a naive bayes and a logistic regression technique.

8. The computer-implemented method of claim 1, wherein the lead is a potential new customer.

9. The computer-implemented method of claim 1, wherein the lead is a group of potential new customers.

10. The computer-implemented method of claim 1, wherein the score is calculated based on a support vector machine and a random forest technique.

19. The system of claim 1, wherein the first processor is configured to rank each of the first subset of leads based on their corresponding score.

20. The system of claim 19, wherein the first processor is configured to triage each ranked lead that imply the highest potential value of each lead based on the score.

11. A system for ranking leads, the system comprising:

a computer having a first and a second processor;

a memory containing a program that, when executed by the first and the second processor, is configured to perform an operation comprising;

periodically scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents to extract one or more characteristic values associated with a set of leads;

upon receiving from a computer of an agent, a selection of a lead attribute from a set of lead attributes, filtering, by the first processor, lead information from the one or more social networking web documents to obtain a set of filtered lead information comprising only a first subset of leads containing the lead attribute;

executing, by the first processor, a machine-learning model to calculate a score for each lead within the first subset of leads where the machine-learning model is configured to calculate the score for each lead based on a learning dataset;

while the first processor is executing the machine-learning model, iteratively updating, by a second processor of the computer, the learning dataset of the machine-learning model based on modified data associated with each lead, wherein the second processor is configured to periodically query one or more databases to receive inputs on the modified data associated with each lead and, in an event that the second processor determines that data associated with the lead attributes is changed, the second processor adjusts the learning dataset;

generating, by the first processor, a second subset of leads that imply a highest potential value of each lead based on the score; and

updating, by the first processor, a graphical user interface of the computer of the agent with information comprising the second subset of leads.

12. The system according to claim 11, wherein the lead information comprises at least one of:

identity, age, ethnicity, place of residence, number of dependent persons, identity of dependent persons, expenditure, savings, approximate market value of assets and their composition, education, professional situation, contact information, shopping preferences, travel preferences, hobbies, social activities, active lifestyle, online behavioral patterns, medical and health records.

13. The system according to claim 11, wherein the lead information comprises one or more categories selected from a group consisting at least one of:

geography, demographics, asset value, and credit score.

14. The system according to claim 11, wherein the set of lead attributes associated with the lead comprises at least one of:

economic stability, credit card usage, liquid assets, education level, discretionary spending, and retirement assets.

15. The system of claim 11, wherein the lead is a potential new customer.

16. The system of claim 11, wherein the lead is a group of potential new customers.

17. The system of claim 11, wherein the score is calculated for each lead based on a support vector machine and a random forest technique.

18. The system of claim 11, wherein the machine-learning model utilizes a naive bayes and a logistic regression technique.