2nd CFP: AAAI-18 workshop: AI and Marketing Science
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Georgios Theocharous
Oct 12, 2017, 7:44:37 PM10/12/17
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Workshop URL: https://sites.google.com/site/aaai18aims/
Purpose and Scope
The revolution in the digital economy has rapidly changed the way companies manage, execute and measure the effectiveness of marketing strategies as well as the delivery of marketing products and services. The widespread growth in digital marketing tools and platforms have led to diverse sources of marketing, advertising and consumer behavioral data, often available in real time. This opens the door to the application of a wide range of AI techniques in areas traditionally considered as being part of Marketing Science (MS). This includes the use of machine learning, deep learning, sequential decision making, bandits and sequential testing, recommendation systems, game theory, knowledge representation, market design and optimization, and so on for the purpose of marketing resource optimization, managerial decision making, competitive behavior modeling, deconstruction of consumer behavior, and campaign automation and optimization.
Research in these areas have been largely carried out in separate communities until now. Within the AI and machine learning community, the focus has been on developing new and more efficient computational models and techniques, geared towards specific tasks. Within the MS community, the focus has been on exploiting machine learning methods and scalable data methods for addressing important business problems that marketers face. Consequently, researchers publish in separate journals and conferences. It is our conviction that these two separate communities have a lot to benefit from each other’s work, problems and insights.
This workshop seeks to bring together researchers and practitioners from AI and from MS communities to share ideas, challenges, opportunities and successes. It will aim to identify important research directions and to identify opportunities for synthesis and unification.
Topics
We are calling for research contributions in the following areas:
Optimizing marketing decisions under resource constraint
Automated decision making with feedback
Optimizing spend across channels
Automation and optimization of marketing campaign
Optimization in the delivery of marketing messages
Understanding consumer psychology
Personalization and optimization of marketing assets and contents
Bandits and sequential testing for marketing strategies
Customer journey modeling
Consumer life time value optimization and sequential decision making
Personalization and recommendation
Intent recognition and user modeling on the web and in marketplaces and e-commerce
Causal inference and measuring the effectiveness of marketing message
Automatic clustering and audience segmentation
Predictive analytics and forecasting of key performance metrics in commerce
Applications of deep learning in predictive analytics
Representation learning for marketing data
Knowledge representation for marketing science insights
Learning in games and mechanism design for marketing
Social games and marketing methods.
Due to the diversity of disciplines represented in this area, related contributions in other fields, are also welcome.
Format
We solicit research contributions that report new research results or ongoing research. The workshop’s proceedings can be considered non-archival, meaning contributors are free to publish their results later in archival journals or conferences. Research contributions should follow the same AAAI format guide line and template (see http://www.aaai.org/Publications/Templates/AuthorKit18.zip) and should be limited to a maximum of 6 pages in AAAI format excluding the references.
The submission deadline for the workshop is Oct 29, 2017. Submission should be sent to aims201...@gmail.com. Acceptance notification will be sent out on November 9, 2017.
Organizers
Hung Bui (Adobe Research)
Pradeep Chintagunta (University of Chicago)
S Muthukrishnan (Rutgers University)
Tuomas Sandholm (CMU)
Atanu Sinha (Adobe Research and Emeritus, University of Colorado)
Georgios Theocharous (Adobe Research)
Purpose and Scope
The revolution in the digital economy has rapidly changed the way companies manage, execute and measure the effectiveness of marketing strategies as well as the delivery of marketing products and services. The widespread growth in digital marketing tools and platforms have led to diverse sources of marketing, advertising and consumer behavioral data, often available in real time. This opens the door to the application of a wide range of AI techniques in areas traditionally considered as being part of Marketing Science (MS). This includes the use of machine learning, deep learning, sequential decision making, bandits and sequential testing, recommendation systems, game theory, knowledge representation, market design and optimization, and so on for the purpose of marketing resource optimization, managerial decision making, competitive behavior modeling, deconstruction of consumer behavior, and campaign automation and optimization.
Research in these areas have been largely carried out in separate communities until now. Within the AI and machine learning community, the focus has been on developing new and more efficient computational models and techniques, geared towards specific tasks. Within the MS community, the focus has been on exploiting machine learning methods and scalable data methods for addressing important business problems that marketers face. Consequently, researchers publish in separate journals and conferences. It is our conviction that these two separate communities have a lot to benefit from each other’s work, problems and insights.
This workshop seeks to bring together researchers and practitioners from AI and from MS communities to share ideas, challenges, opportunities and successes. It will aim to identify important research directions and to identify opportunities for synthesis and unification.
Topics
We are calling for research contributions in the following areas:
Optimizing marketing decisions under resource constraint
Automated decision making with feedback
Optimizing spend across channels
Automation and optimization of marketing campaign
Optimization in the delivery of marketing messages
Understanding consumer psychology
Personalization and optimization of marketing assets and contents
Bandits and sequential testing for marketing strategies
Customer journey modeling
Consumer life time value optimization and sequential decision making
Personalization and recommendation
Intent recognition and user modeling on the web and in marketplaces and e-commerce
Causal inference and measuring the effectiveness of marketing message
Automatic clustering and audience segmentation
Predictive analytics and forecasting of key performance metrics in commerce
Applications of deep learning in predictive analytics
Representation learning for marketing data
Knowledge representation for marketing science insights
Learning in games and mechanism design for marketing
Social games and marketing methods.
Due to the diversity of disciplines represented in this area, related contributions in other fields, are also welcome.
Format
We solicit research contributions that report new research results or ongoing research. The workshop’s proceedings can be considered non-archival, meaning contributors are free to publish their results later in archival journals or conferences. Research contributions should follow the same AAAI format guide line and template (see http://www.aaai.org/Publications/Templates/AuthorKit18.zip) and should be limited to a maximum of 6 pages in AAAI format excluding the references.
The submission deadline for the workshop is Oct 29, 2017. Submission should be sent to aims201...@gmail.com. Acceptance notification will be sent out on November 9, 2017.
Organizers
Hung Bui (Adobe Research)
Pradeep Chintagunta (University of Chicago)
S Muthukrishnan (Rutgers University)
Tuomas Sandholm (CMU)
Atanu Sinha (Adobe Research and Emeritus, University of Colorado)
Georgios Theocharous (Adobe Research)
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