This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 248
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #307886
Title: Prediction of Advertiser Churn for Google AdWords
Author(s): Sangho Yoon*+ and Jim Koehler and Adam Ghobarah
Companies: Google and Google and Google
Address: 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
Keywords: classification ; churn ; data mining ; machine learning ; seasonality ; online advertisement
Abstract:

Google AdWords has thousands of advertisers participating in auctions to show their advertisements. Google has a very unique customer relationship with advertisers in that they participate in competition to show their advertisements. In this paper, we focus on retaining more AdWords advertisers by identifying and helping advertisers that are not successful in using Google AdWords. Due to advertisers' non-contractual relationship with Google and advertisement seasonality, it is not easy to identify which advertisers have churned. Our main contributions are first to propose a new definition of churn for AdWords, second to carefully select a homogeneous group of advertisers for better understanding and prediction of advertiser churn, and third to build a churn prediction model using state-of-the-art machine learning algorithms.


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