Method and system for obtaining leads based on data derived from a variety of sources

• US 10,565,602 B1
• Filed: 02/21/2018
• Issued: 02/18/2020
• 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): Gills, Kurtis
• Assistant Examiner(s): Kyereme-Tuah, Akosua
• Application Number:
  US15/901,047
• Time in Patent Office: 727 Days
• Field of Search: None
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method and system for obtaining leads based on data derived from a variety of sources is disclosed. The method is executed by a system that includes a data processing module within a leads management system, among others system components. The data processing module scans one or more external data sources; collects lead data from those sources; analyzes the data collected; identifies attributes of interest about one or more potential quality leads; identifies one or more high quality leads; creates a profile for those high quality leads; stores those profiles in an internal database; and generates a list of one or more high quality leads.

Claims

1. A method comprising:

receiving, by a computer, a request to identify one or more leads from a graphical user interface displayed on a user computer, wherein the request comprises a category of lead information, a set of attributes of the category, and a threshold of an attribute of the category;

querying, by the computer, an internal database to identify a first set of leads corresponding to the received category of lead information and the set of attributes that satisfy the threshold;

continually web-crawling, by the computer, one or more social networking databases to obtain lead information related to a second set of leads based on the category and the set of attributes;

extracting, by the computer, one or more characteristic values from the second set of leads;

continually executing, by the computer, a computer-generated artificial intelligence model to generate one or more inferences associated with the second set of leads, wherein the computer applies the one or more characteristic values of the second set of leads to the computer-generated artificial intelligence model that utilizes a neural network pattern matching algorithm to generate the one or more inferences;

determining, by the computer, a subset of the second set of leads corresponding to at least one attribute from the set of attributes or at least one inference from the one or more inferences satisfies the threshold;

executing, by the server, a machine learning model utilizing a logistic regression algorithm to calculate a score for each lead within the first set of leads and the subset of the second set of leads, wherein the score corresponds to a likelihood of each lead purchasing a product; and

automatically displaying, by the computer on an interactive graphical user interface of a plurality of agent devices, the first set of leads and the subset of the second set of leads ranked in accordance to each lead'"'"'s score, the integrative graphical user interface comprising;

an indicator corresponding to a minimum price associated with each lead within the first set of leads and the subset of the second set of leads,

an input component configured to receive a price input associated with each lead within the first set of leads and the subset of the second set of leads,

wherein the interactive graphical user interface is configured to receive price inputs within a predetermined time,

wherein the server determines an effectiveness value of the minimum price for at least one lead within a third set of leads that did not receive the price input, the server determining the effectiveness value based on at least one attribute of the at least one lead.

2. The method of 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 method of claim 1, wherein the categories of lead information comprises at least one of:

floor price, geography, demographics, purchase behavior, and purchase history.

4. The method of claim 1, wherein the category is one or more categories of lead information.

5. The method of claim 1, wherein the lead is a potential new customer.

6. The method of claim 1, wherein the lead is a group of potential new customers.

7. The method of claim 6, wherein the group of potential new customers is associated with an attribute of a category.

8. The method of claim 1, wherein the one or more social networking databases comprise databases from at least one of:

websites, banner ads, online shopping, catalog purchases, and referrals.

9. The method of claim 1, further comprising:

assigning, by the computer, a value associated with each attribute of the set of attributes and the one or more inferences, wherein each attribute is associated with a weight indicating an importance level of the attribute based on criteria and standards received from the graphical user interface, and wherein the value is determined based on the weight.

10. The method of claim 9, further comprising:

determining, by the computer, a score based on averaging the values associated with each attribute;

upon the score satisfying a minimum threshold;

generating, by the computer, in the internal database, a profile for the lead having a score that satisfies the minimum threshold; and

automatically storing, by the computer, in the internal database, the lead having the score that satisfies the minimum threshold.

11. A system comprising:

a computer processor;

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

receiving, by the computer processor, a request to identify one or more leads from a graphical user interface of a user computer, wherein the request comprises a category of lead information, a set of attributes of the category, and a threshold of an attribute of the category;

querying, by the computer processor, an internal database to identify a first set of leads corresponding to the received category of lead information and the set of attributes that satisfy the threshold;

continually web-crawling, by the computer, one or more social networking databases to obtain lead information related to a second set of leads based on the category and the set of attributes;

extracting, by the computer processor, one or more characteristic values from the second set of leads;

continually executing, by the computer, a computer-generated artificial intelligence model to generate one or more inferences associated with the second set of leads, wherein the computer applies the one or more characteristic values of the second set of leads to the computer-generated artificial intelligence model that utilizes a neural network pattern matching algorithm;

determining, by the computer processor, a subset of the second set of leads corresponding to at least one attribute from the set of attributes or at least one inference from the one or more inferences satisfies the threshold;

executing, by the server, a machine learning model utilizing a logistic regression algorithm to calculate a score for each lead within the first set of leads and the subset of the second set of leads, wherein the score corresponds to a likelihood of each lead purchasing a product; and

automatically displaying, by the computer processor on an interactive graphical user interface of a plurality of agent devices, the first set of leads and the subset of the second set of leads ranked in accordance to each lead'"'"'s score, the integrative graphical user interface comprising;

an indicator corresponding to a minimum price associated with each lead within the first set of leads and the subset of the second set of leads,

an input component configured to receive a price input associated with each lead within the first set of leads and the subset of the second set of leads,

wherein the interactive graphical user interface is configured to receive price inputs within a predetermined time,

wherein the server determines an effectiveness value of the minimum price for at least one lead within a third set of leads that did not receive the price input, the server determining the effectiveness value based on at least one attribute of the at least one lead.

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

floor price, geography, demographics, purchase behavior, and purchase history.

14. The system of claim 11, wherein the category is one or more categories of lead information.

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 16, wherein the group of potential new customers is associated with an attribute of a category.

18. The system of claim 11, wherein the one or more social networking databases comprise databases from at least one of:

websites, social media and networks, banner ads, online shopping, catalog purchases, and referrals.

19. The system of claim 11, wherein the computer processor is further configured to perform the operation comprising:

assigning, by the computer processor, a value associated with each attribute of the set of attributes and the one or more inferences, wherein each attribute is associated with a weight indicating an importance level of the attribute based on criteria and standards received from the graphical user interface, and wherein the value is determined based on the weight.

20. The system of claim 19, wherein the computer processor is further configured to perform the operation comprising:

determining, by the computer processor, a score based on averaging the values associated with each attribute;

upon the score satisfying a minimum threshold;

generating, by the computer processor, in the internal database, a profile for the lead having a score that satisfies the minimum threshold; and

automatically storing, by the computer processor, in the internal database, the lead having the score that satisfies the minimum threshold.