Promoting the use of data science in marketing.
Every year, Adobe funds a university faculty research program to promote the understanding and use of data science in the area of marketing. Our goal is to encourage both theoretical and empirical development of solutions to problems in marketing.
Adobe will provide funding support of up to $50,000 to a North American academic institution, college, or university for each selected research proposal. Awards will be in the form of an unrestricted gift to the academic institution under the names of the researches who submitted the proposal.
Topics for research include, but are not limited to, the following areas:
Analytics Cloud
Transforming insights from the trillions of customer touch points that we track on behalf of brands into advanced analytical algorithms to make high-value predictions about consumer experiences.
Marketing Cloud
Analyzing data to better deliver personalized digital experiences to customers across websites, applications, emails and notifications.
Advertising Cloud
Using data to help brands design and deliver effective advertising campaigns across search, display, social, video and mobile channels to reach and acquire customers efficiently.
Grant Submission Guidelines & FAQ
Who is eligible to receive the grant?
Full-time faculty members from North American universities
How long should the proposal be?
Preferably two pages
Submission requirements:
Title, proposer name and contact information, research goals, description of project(s), use of funds
Award Range:
Up to $50,000 (US)
*Adobe does not pay for overhead on unrestricted gifts. This unrestricted gift should not be used for indirect costs, administrative costs, and overhead charges.
Deadlines:
August 17, 2018 and February 15, 2019
Send proposals and questions to dma-research-awards@adobe.com
February 2018 Award Winners
Stanford University's Ashish Goel & Duke University's Kamesh Munagala
Cohort Selection in Advertising
University of Michigan's Danai Koutra
User Stitching: A Representation Learning and Hashing Perspective
University of Minnesota's Gordon Burtch and Soumya Sen
Designing Effective Ad-Video Delivery Strategies
University of Arizona's Junming Yin and University of Washington's Yong Tan
Efficient Marketing Campaign Across Millions of Users and Products
University of North Carolina's Mohit Bansal
Multimodal Dialogue and Reasoning
University of Massachusetts' Philip Thomas
Off-Policy Policy Evaluation for Marketing
UC Santa Barbara's William Wang
Deep Inverse Reinforcement Learning for Multimodal Data Storytelling
Carnegie Mellon University's Zachary Chase Lipton
Learning Multi-resolution Representations of Customer Behavior from Clickstream Data
New Jersey Institute of Technology's Zhi Wei
KPI-Driven Content Understanding and Generation With Applications to Digital Marketing
Visit Adobe Research for more information.
Research never stops.
