A Novel Digital Coupon Use Prediction Model Based on XGBoost

It was found that traditional predicting models of coupons could only process small-scale data, thus were unable to precisely predict whether consumers would use new digital coupons in the future. By introducing the feature engineering and the machine learning algorithm of Extreme Gradient Boosting (XGBoost), a novel Digital Coupon Use Prediction Model (DCUPM) was established to deal with vast amount of historical data and to provide high-precision prediction of the future digital coupons use of consumers. In this paper, an example of this DCUPM was then presented and analyzed based on the data provided by Alibaba Cloud. The result shows the Area under the Curve (AUC) is calculated to be 0.896, which demonstrates the reliability of the proposed DCUPM. And features with higher scores can be used to achieve targeted delivery of digital coupons for businesses. Introduction In recent years, the use of digital coupons has exploded. A survey conducted by e-Marketer, a leading global market research institute, showed that in 2015, a total of 121.3 million people redeemed e-coupons in the U.S. grocery retail industry, an increase of 7.6% over the same period last year [1]. Since digital coupons are recordable, a large amount of use data will generate in real time when consumers purchase. And the data contains valuable information about consumer behavior which can be used to identify consumers who are more likely to be attracted to discounts. If companies directly distribute digital coupons to this group of consumers, they can not only effectively reduce the delivery cost, but also avoid the damage of brand reputation, resulting from excessive delivery. Therefore, it is important to predict whether consumers will use digital coupons in the future. In reality, Didi, a mobile app for taxi, and Alipay are typical cases. However, it is not easy to handle large-scale data and extract features of consumers, companies, digital coupons and so forth. Large amount of data and complexity of behavior analysis make traditional methods in Econometrics ineffective. Fortunately, the rising and development of data science in recent years provides a solution to this problem. Machine learning is a new way to analyze data and performs well in prediction [2]. In this paper, the XGBoost algorithm in machine learning is used to establish a novel Digital Coupon Use Prediction Model. Since the primary challenges in digital coupon use prediction is the lack of information between current moment and predictive moment, we solve this problem by selecting part of historical data as test data set based on the time sequence, which is not used at the beginning. Then, the prediction results will be compared with the test set data to verify the model. As training set, the remaining historical data is used for feature extraction. The raw data can be transformed into consumer feature, company feature, digital coupon feature, and their interactive feature as the input to train the XGBoost model. The trained model is utilized to predict whether consumers appearing in the test set will use digital coupons in the next month. Meanwhile, feature scores will be given by model. 775 Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 4th Annual International Conference on Management, Economics and Social Development (ICMESD 2018) Advances in Economics, Business and Management Research (AEBMR), volume 60