Dr. Jim Koehler is Principal Statistician at Google. His work focuses on statistical
models for Google's ads business. He received a double A.B. in Statistics and Applied
Mathematics from U.C. Berkeley and a PhD in Statistics from Stanford University in
1990. Prior to Google he was a tenured Associate Professor of Statistics at the
University of Colorado and has over 15 years of industry experience in Marketing,
Environmental Engineering, Telecommunications, Statistical Genetics, and
Chemometrics.
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A Hierarchical Bayesian Approach to Improve Media
Mix Models Using Category Data
Yueqing Wang, Yuxue
Jin, Yunting Sun, David Chan, Jim
Koehler
Google Inc. (2017)
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Bayesian Methods for Media Mix Modeling with
Carryover and Shape Effects
Yuxue Jin, Yueqing
Wang, Yunting Sun, David Chan, Jim
Koehler
research.google.com, Google Inc., 76 Ninth Avenue Google New York NY 10011 (2017)
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Geo-level Bayesian Hierarchical Media Mix
Modeling
Yunting Sun, Yueqing Wang, Yuxue Jin,
David Chan, Jim Koehler
Google Inc (2017)
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TV Impact on Online Searches
Ying Liu, Yoni Schwarzkopf, Jim Koehler
Google Inc. (2017)
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Cross Panel Imputation
Yunting Sun, Jim Koehler, Nicolas
Remy, Wiesner Vos
Google, Inc. (2016), pp. 1-18 (to appear)
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Measuring Cross-Device Online Audiences
Jim Koehler, Evgeny Skvortsov, Sheng Ma, Song Liu
Google, Inc. (2016), pp. 1-33 (to appear)
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Advertising on YouTube and TV: A Meta-analysis of
Optimal Media-mix Planning
Georg M. Goerg, Christoph Best, Sheethal Shobowale, Jim Koehler, Nicolas
Remy
Journal of Advertising Research (JAR), vol. 57 (2015), pp. 283-304 (to appear)
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How Many Millennials Visit YouTube? Estimating
Unobserved Events From Incomplete Panel Data Conditioned on Demographic
Covariates
Georg M. Goerg, Yuxue Jin, Nicolas
Remy, Jim Koehler
TBD, Google, Inc. (2015), pp. 1-27 (to appear)
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How Many People Visit YouTube? Imputing Missing
Events in Panels With Excess Zeros
Georg M. Goerg, Yuxue Jin, Nicolas
Remy, Jim Koehler
; SAGE Publications - edited by Herwig Friedl and Helga Wagner, Linz, Austria
(2015), pp. 1-6
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Inferring causal impact using Bayesian structural
time-series models
Kay H. Brodersen, Fabian Gallusser,
Jim Koehler, Nicolas Remy, Steven L. Scott
Annals of Applied Statistics, vol. 9 (2015), pp. 247-274
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Data enrichment for incremental reach
estimation
Aiyou Chen, Jim Koehler, Art Owen, Nicolas Remy, Minghui Shi
Google Inc. (2014), pp. 1-21 (to appear)
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A Method for Measuring Online Audiences
Jim Koehler, Evgeny Skvortsov, Wiesner Vos
Google Inc (2013), pp. 1-24 (to appear)
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The Optimal Mix of TV and Online Ads to Maximize
Reach
Yuxue Jin, Jim
Koehler, Georg M. Goerg, Nicolas Remy
research.google.com, 76 Ninth Avenue (2013), pp. 1-16
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Impact Of Ranking Of Organic Search Results On The
Incrementality Of Search Ads
David Chan, Deepak Kumar, Sheng Ma, Jim Koehler
Google Inc. (2012)
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Periodic Measurement of Advertising Effectiveness
Using Multiple-Test-Period Geo Experiments
Jon Vaver, Jim
Koehler
Google Inc. (2012)
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The Incremental Reach and Cost Efficiency of Online
Video Ads over TV Ads
Yuxue Jin, Sheethal Shobowale, Jim Koehler, Harry Case
Google Inc (2012), pp. 1-17
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Incremental Clicks Impact Of Search Advertising
David Chan, Yuan
Yuan, Jim Koehler, Deepak Kumar
Google, Inc. (2011)
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Incremental Clicks: The Impact of Search
Advertising
David X. Chan, Yuan Yuan, Jim
Koehler, Deepak Kumar
Journal of Advertising Research, vol. 51, no. 4 (2011), pp. 643-647
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Measuring Ad Effectiveness Using Geo
Experiments
Jon Vaver, Jim
Koehler
Google Inc. (2011)
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Prediction of Advertiser Churn for Google
AdWords
Sangho Yoon, Jim Koehler, Adam Ghobarah
JSM Proceedings, American Statistical Association (2010) (to appear)
