Dynamic Throttling of In-App Promotions to Reduce Marketing Spend Based on Machine-Learning

Publication Year:
2017
Usage 187
Downloads 136
Abstract Views 51
Repository URL:
https://www.tdcommons.org/dpubs_series/407
Author(s):
Fu, Bo; Bansod, Sourabh; Jain, Kunal; Price, Thomas; Chandrasekaran, Deepak Ramamurthi Sivaramapuram; Gupta, Prachi; Singh, Sarvjeet; Chew, Sue Yi; Xie, Jierui
article description
This document describes a technique of dynamically throttling a promotion or content item placement to reduce marketing spending using machine-learning. A data processing system can determine a click score or an auction score based on various factors. The data processing system can further determine a threshold, for example, by predicting an annoyance effect of showing the promotion or the content item to a user. If the click score or the auction score is below the threshold, the data processing system can throttle the promotion or the content item placement such that the promotion or the content item is not shown to the user.