| US 11,798,090 B1 | ||
| Systems and methods for segmenting customer targets and predicting conversion | ||
| Mubbashir Nazir, Kolkata (IN); Atanu Maity, Kolkata (IN); Chun Wang, Austin, TX (US); Patrick John Thielke, Houston, TX (US); Wensu Wang, Katy, TX (US); and Ligang Bai, Houston, TX (US) | ||
| Assigned to Data Info Com USA, Inc., Austin, TX (US) | ||
| Filed by DataInfo Com USA, Inc., Austin, TX (US) | ||
| Filed on Dec. 31, 2019, as Appl. No. 16/732,258. | ||
| Application 16/732,258 is a continuation in part of application No. 16/146,590, filed on Sep. 28, 2018, granted, now 10,866,962, issued on Dec. 15, 2020. | ||
| Claims priority of provisional application 62/564,468, filed on Sep. 28, 2017. | ||
| Int. Cl. G06Q 40/08 (2012.01); G06V 10/75 (2022.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06N 3/084 (2023.01); G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06N 5/01 (2023.01) | ||
| CPC G06Q 40/08 (2013.01) [G06F 18/211 (2023.01); G06F 18/213 (2023.01); G06N 3/084 (2013.01); G06N 5/01 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06V 10/7553 (2022.01)] | 20 Claims |
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1. A method for creating a set of look-a-like prospects, the method comprising:
receiving historical data regarding prospects, wherein the historical data comprises one or more input variables and a target, wherein the target comprises a conversion status of each prospect; selecting features from the historical data to create a feature set, wherein the feature selection comprises:
performing correlation shape analysis on each input variable with respect to the target to generate a set of highly correlated input variables;
training at least one machine learning model based on the feature set; identifying segments in a predefined range of conversion percentage based on the trained at least one machine learning model; determining a most suitable model from the at least one machine learning model; and generating a set of look-a-like prospects based on the determined model. |