SYSTEM FOR AUTOMATIC SEGMENTATION AND RANKING OF LEADS AND REFERRALS
Various embodiments for providing a system for the automatic segmentation and ranking of leads and referrals are described herein. An embodiment operates by receiving historical data including information about prospective customers who purchased one or more products. A set of segments of the prospective customers are identified, the historical data is grouped into the set of segments, and a predictive model for a conversion is generated for each segment based on the grouped historical data. A processor generates two or more predictive scores a new prospective customer, wherein each predictive score is based on the generated predictive model for two or more of the segments to which the new prospective customer belongs. The predictive score for the at least one new prospective customer is ranked along with predictive scores of a plurality of other prospective customers for display for at least one of the two or more segments.
1 . A computer-implemented method, comprising:
receiving historical data including information about prospective customers who purchased one or more products;
identifying a set of segments of the prospective customers;
grouping the historical data into the set of segments;
generating a predictive model for a conversion for each segment of the set of segments based on the grouped historical data;
generating, by a processor, two or more predictive scores for at least one new prospective customer, wherein each predictive score is based on the generated predictive model for two or more of the segments to which the at least one new prospective customer belongs; and
providing the predictive score for the at least one new prospective customer ranked along with predictive scores of a plurality of other prospective customers for display for at least one of the two or more segments.
2 . The method of claim 1 , further comprising:
identifying a first portion of the history data to use as training data and a second portion of the historical data to use as validation data, wherein the grouping comprises grouping the training data into the set of segments.
3 . The method of claim 2 , wherein the generating the predictive model further comprises:
submitting the validation data to predictive model to generate a set of intermediate results; and
comparing the intermediate results to actual results from the validation model, wherein the actual results indicate whether a sale was converted.
4 . The method of claim 3 , further comprising:
determining, based on the comparison, that the predictive model exceeds a threshold; and
activating the predictive model to receive data for the at least one new prospective customer based on the threshold being exceeded.
5 . The method of claim 1 , wherein each segment corresponds to one of the one or more products.
6 . The method of claim 5 , wherein the set of segments include a global segment that includes data across all of the one or more products.
7 . The method of claim 6 , wherein the providing comprises:
determining that the at least one new prospective customer falls into two of the segments, and wherein the at least one new prospective customer is ranked differently for each of the two segments.
8 . A system comprising:
a memory; and
at least one processor coupled to the memory and configured to perform operations comprising:
receiving historical data including information about prospective customers who purchased one or more products;
identifying a set of segments of the prospective customers;
grouping the historical data into the set of segments;
generating a predictive model for a conversion for each segment of the set of segments based on the grouped historical data;
generating, by a processor, two or more predictive scores for at least one new prospective customer, wherein each predictive score is based on the generated predictive model for two or more of the segments to which the at least one new prospective customer belongs; and
providing the predictive score for the at least one new prospective customer ranked along with predictive scores of a plurality of other prospective customers for display for at least one of the two or more segments.
9 . The system of claim 8 , the operations further comprising:
identifying a first portion of the history data to use as training data and a second portion of the historical data to use as validation data, wherein the grouping comprises grouping the training data into the set of segments.
10 . The system of claim 9 , wherein the generating the predictive model further comprises:
submitting the validation data to predictive model to generate a set of intermediate results; and
comparing the intermediate results to actual results from the validation model, wherein the actual results indicate whether a sale was converted.
11 . The system of claim 10 , the operations further comprising:
determining, based on the comparison, that the predictive model exceeds a threshold; and
activating the predictive model to receive data for the at least one new prospective customer based on the threshold being exceeded.
12 . The system of claim 8 , wherein each segment corresponds to one of the one or more products.
13 . The system of claim 12 , wherein the set of segments include a global segment that includes data across all of the one or more products.
14 . The system of claim 13 , wherein the providing comprises:
determining that the at least one new prospective customer falls into two of the segments, and wherein the at least one new prospective customer is ranked differently for each of the two segments.
15 . A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
receiving historical data including information about prospective customers who purchased one or more products;
identifying a set of segments of the prospective customers;
grouping the historical data into the set of segments;
generating a predictive model for a conversion for each segment of the set of segments based on the grouped historical data;
generating, by a processor, two or more predictive scores for at least one new prospective customer, wherein each predictive score is based on the generated predictive model for two or more of the segments to which the at least one new prospective customer belongs; and
providing the predictive score for the at least one new prospective customer ranked along with predictive scores of a plurality of other prospective customers for display for at least one of the two or more segments.
16 . The non-transitory computer-readable storage medium of claim 15 , the operations further comprising:
identifying a first portion of the history data to use as training data and a second portion of the historical data to use as validation data, wherein the grouping comprises grouping the training data into the set of segments.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the generating the predictive model further comprises:
submitting the validation data to predictive model to generate a set of intermediate results; and
comparing the intermediate results to actual results from the validation model, wherein the actual results indicate whether a sale was converted.
18 . The non-transitory computer-readable storage medium of claim 17 , the operations further comprising:
determining, based on the comparison, that the predictive model exceeds a threshold; and
activating the predictive model to receive data for the at least one new prospective customer based on the threshold being exceeded.
19 . The non-transitory computer-readable storage medium of claim 15 , wherein each segment corresponds to one of the one or more products.
20 . The non-transitory computer-readable storage medium of claim 19 , wherein the set of segments include a global segment that includes data across all of the one or more products.