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Cognitive Computation
Erschienen in: Cognitive Computation 2/2019

13.11.2018

Large-scale Ensemble Model for Customer Churn Prediction in Search Ads

verfasst von: Qiu-Feng Wang, Mirror Xu, Amir Hussain

Erschienen in: Cognitive Computation | Ausgabe 2/2019

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Abstract

Customer churn prediction is one of the most important issues in search ads business management, which is a multi-billion market. The aim of churn prediction is to detect customers with a high propensity to leave the ads platform, then to do analysis and increase efforts for retaining them ahead of time. Ensemble model combines multiple weak models to obtain better predictive performance, which is inspired by human cognitive system and is widely used in various applications of machine learning. In this paper, we investigate how the ensemble model of gradient boosting decision tree (GBDT) to predict whether a customer will be a churner in the foreseeable future based on its activities in the search ads. We extract two types of features for the GBDT: dynamic features and static features. For dynamic features, we consider a sequence of customers’ activities (e.g., impressions, clicks) during a long period. For static features, we consider the information of customers setting (e.g., creation time, customer type). We evaluated the prediction performance in a large-scale customer data set from Bing Ads platform, and the results show that the static and dynamic features are complementary, and get the AUC (area under the curve of ROC) value 0.8410 on the test set by combining all features. The proposed model is useful to predict those customers who will be churner in the near future on the ads platform, and it has been successfully daily run on the Bing Ads platform.
Vorheriger Artikel A Framework for Privacy Quantification: Measuring the Impact of Privacy Techniques Through Mutual Information, Distance Mapping, and Machine Learning
Nächster Artikel Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution

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Fußnoten
5
Because CPC strategy is widely used in the search ads, the metric of click is usually used to show the performance of the advertisers.
 
6
Each advertiser creates the accounts in search ads with tree structures, including accounts, campaigns, ad group, and order items [1].
 
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Metadaten
Titel
Large-scale Ensemble Model for Customer Churn Prediction in Search Ads
verfasst von
Qiu-Feng Wang
Mirror Xu
Amir Hussain
Publikationsdatum
13.11.2018
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 2/2019
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-018-9608-3

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