|When:||Wednesday December 14, 2016 | 12:00 PM - 2:00 PM|
|Place:||The Penn Club (map)
30 West 44th Street
New York, NY
Call (212) 362-0302 or email
|Cost:||$60 for non-members. Member signup fee discounted from cost for first-time attendees.
$50 for chapter members.
|Topic:||Bridging the Gap: Connecting online behaviors to offline purchases|
Despite many advances, digital advertising has been struggling to address the most basic advertising question: can we measure and draw conclusions about the effectiveness of advertising on consumer behavior? Being fraught with challenges like questionable viewability, attribution and fraud in a pure online setting, consumer packaged Goods (CPG) companies have additional challenges gauging advertising effectiveness: Purchases do not happen online and linking third parties scanner data from grocery stores introduces further scale limitations and match uncertainty.
Here we present a case study showing that it is possible to seamlessly digitize, access and integrate real world physical behaviors into a large-scale digital experiment. We show that using using causal analytical methods combined with machine learning, sufficient statistical power can be achieved to measure significant and fine-grained effects. The resulting insights cannot only be used to assess the efficacy and economic viability of running display advertising campaigns, but also to optimize creatives for each prospective customer -- allowing marketers to not only to find the most promising prospects, but also to match them to the right creative.
Claudia leads the machine learning efforts that power Dstillery’s digital intelligence for marketers and media companies. With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world. Prior to joining Dstillery in 2010, Claudia worked at IBM’s Watson Research Center, focusing on data analytics and machine learning. She holds a PhD in Information Systems from New York University (where she continues to teach at the Stern School of Business), and an MA in Computer Science from the University of Colorado.Reka Daniel-Weiner
Reka a quantitative scientist specializing in human decision making. Currently, she is a Data Scientist at Dstillery, where she uses computational modeling, machine learning and empirical data to help clients understand how to optimize their campaigns. Prior to that, she was conducting research at the Princeton Neuroscience Institute exploring human decision making in multidimensional environments. She holds a PhD in cognitive psychology from the Otto-von-Guericke University in Magdeburg, Germany.