Methods and systems for ranking leads based on given characteristics
First Claim
1. A computer-implemented method comprising:
- scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents associated with one or more leads within one or more databases and extracting lead information from the one or more social networking web documents, the lead information comprising one or more characteristic values;
classifying, by the first processor of the computer, the lead information into categories of lead information based on the one or more characteristic values of the lead information;
upon receiving from a computer of an agent, a selection of a first category of lead information and an attribute of the first category, filtering, by the first processor of the computer, the lead information to obtain a set of filtered lead information comprising only a subset of leads containing the attribute;
assigning, by the first processor of the computer, a score to each attribute associated with each lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria;
executing, by the first processor of the computer, a machine-learning algorithm technique to calculate a quality score for each lead, wherein the machine-learning algorithm technique is configured to calculate the quality score of each lead by computing a mean score for each lead based on a learning dataset comprising the each scored attribute;
while the first processor of the computer is executing the machine-learning algorithm, iteratively updating, by a second processor of the computer, the learning dataset based on modified data associated with each lead having a score for each attribute greater than the predetermined set of criteria;
periodically querying, by the second processor of the computer, the one or more databases to receive inputs on modified data associated with each lead and, in an event that the computer determines that the score of the attributes associated with each lead is changed, adjusting, by the second processor of the computer, the learning dataset;
ranking, by the first processor of the computer, each lead based on their corresponding quality score, wherein the computer ranks each lead in order of their corresponding implied quality score and a propensity to close a transaction, wherein the computer determines the propensity to close the transaction for each lead based on the one or more characteristic values associated to each lead;
andupdating, by the first processor of the computer, a graphical user interface of the computer of the agent with auction information comprising the ranked leads and the quality score.
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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.
61 Citations
20 Claims
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1. A computer-implemented method comprising:
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scanning, by a first processor of a computer comprising at least two processors, one or more social networking web documents associated with one or more leads within one or more databases and extracting lead information from the one or more social networking web documents, the lead information comprising one or more characteristic values; classifying, by the first processor of the computer, the lead information into categories of lead information based on the one or more characteristic values of the lead information; upon receiving from a computer of an agent, a selection of a first category of lead information and an attribute of the first category, filtering, by the first processor of the computer, the lead information to obtain a set of filtered lead information comprising only a subset of leads containing the attribute; assigning, by the first processor of the computer, a score to each attribute associated with each lead from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria; executing, by the first processor of the computer, a machine-learning algorithm technique to calculate a quality score for each lead, wherein the machine-learning algorithm technique is configured to calculate the quality score of each lead by computing a mean score for each lead based on a learning dataset comprising the each scored attribute; while the first processor of the computer is executing the machine-learning algorithm, iteratively updating, by a second processor of the computer, the learning dataset based on modified data associated with each lead having a score for each attribute greater than the predetermined set of criteria; periodically querying, by the second processor of the computer, the one or more databases to receive inputs on modified data associated with each lead and, in an event that the computer determines that the score of the attributes associated with each lead is changed, adjusting, by the second processor of the computer, the learning dataset; ranking, by the first processor of the computer, each lead based on their corresponding quality score, wherein the computer ranks each lead in order of their corresponding implied quality score and a propensity to close a transaction, wherein the computer determines the propensity to close the transaction for each lead based on the one or more characteristic values associated to each lead; and updating, by the first processor of the computer, a graphical user interface of the computer of the agent with auction information comprising the ranked leads and the quality score. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for ranking leads, the system comprising:
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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; scanning, by the first processor, one or more social networking web documents associated with one or more leads within one or more databases and extracting lead information from the one or more social networking web documents, the lead information comprising one or more characteristic values; classifying, by the first processor, the lead information into categories of lead information based on the one or more characteristic values of the lead information; upon receiving from a computer of an agent, a selection of a first category of lead information and an attribute of the first category, filtering, by the first processor, the lead information to obtain a set of filtered lead information comprising only a subset of leads containing the attribute; assigning, by the first processor, a score to each attribute associated with the leads from the filtered lead information based on a measure of how each attribute satisfies a predetermined set of criteria; calculating, by the first processor executing a machine-learning algorithm technique, a quality score for each lead, wherein the machine-learning algorithm technique is configured to calculate the quality score of each lead by computing a mean score for each lead based on a learning dataset comprising the each scored attribute; iteratively updating, by the second processor, the learning dataset based on modified data associated with each lead having the score for each attribute greater than the predetermined set of criteria; periodically querying, by the second processor, the one or more databases to receive inputs on modified data associated with each lead and, in an event that the computer processor determines that the score of the attributes associated with each lead is changed, adjusting, by the computer processor, the learning dataset; ranking, by the first processor, each lead based on their corresponding quality score, wherein the computer processor ranks each lead in order of their corresponding implied quality score and a propensity to close a transaction and determines the propensity to close the transaction for each lead based on the one or more characteristic values associated to each lead; and updating, by the first processor, a graphical user interface of the computer of the agent with auction information comprising the ranked leads and the quality score. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification