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 and the topics below are just examples of some of the topics of interest to Adobe. We are open to releasing anonymized data based on proposed research needs and if the research can benefit from the data.
Adobe will be providing funding support of up to USD50,000 to North American academic institutions, colleges and universities (under the name of the researchers who submitted the proposal) for each selected research proposal. Awards will be in the form of unrestricted gifts to academic institutions, under the names of the researchers who submitted the proposal. Topics of interest include, but are not limited to, the following areas:
• How can we algorithmically identify users across devices and across channels based on tracked data?
• How can we leverage GPU in analytics?
• How do we use distributed algorithms that work on large amounts of data to find the most important features that affect a certain outcome or describe a set of users?
• What are good distributed techniques for anomaly detection and correlation detection?
• What are good scalable methods for marketing mix attribution and contribution of marketing channels to observed outcomes?
• What are good techniques for detecting seasonal or trending patterns and leading indicators?
• How can we predict and evaluate the overall lifetime value of a customer?
• How do we predict or learn to match the right message or offer (next-best offer) to the right audience, at the right time, on the right channel?
• How can we automatically create segments and look-alike scores for users?
• Contextual analytics – How can we bridge visualization with creative assets and quantified/predictive impact?
• How can we utilize pattern recognition and machine learning to detect patterns in data derived from physical devices (Internet of Things)
• Data storytelling – How can automatic analysis (AI) be blended with NLG to produce data stories, or narratives?
• How can we automatically create great layouts for websites based on the content (text, video, images, navigation, cart, etc.) you want on the site?
• How can we evolve or optimize website layout and content based on how users engage with the site? For example, adding a link to content that is often searched for or moving highly watched video to the top, etc.
• How do we automatically convert paper or digital forms into responsive forms for mobile devices? How can we also include recognition of data entry fields and display tags, automatic grouping into correct sets for data collection panels, and final layout.
• How can we be more intelligent (beyond responsive) about automatically optimizing content, layout and site structure for different screen sizes (from smartphone to big screens)?
• How can we help the author of an experience by suggesting content (text, images, video) that compliment the content that has already been added to the experience? For example, by identifying mood, topic, etc. from text and images and matching new content with existing content.
• How can we automatically generate ad copy from data? For example, ad copy for hotel descriptions or product articles?
• How can we make automatic suggestions to text for improved Google rankings?
• How can we construct and leverage ontologies (i.e., taking advantage of knowledge regarding concept hierarchies) in tagging of assets for tag enrichment and visualization?
• How can we suggest topics for new stories based on available content and external signals (like social media) and suggest available content or assets that can be (re-)used for this?
• How can we do budget allocation, coordination and timing of advertisements in a multi-channel setting?
• How do we measure ad creative effectiveness in different channels and across devices?
• What are the best methods for attributing revenue to multiple marketing touch points and frameworks to evaluate these methods?
• What are good marketing models that combine offline and online consumer behavior?
• How do we build scalable, predictive models and optimization algorithms for decisioning in a real-time bidding marketplace?
• What are good ad formats and market structures for mobile ads?
• How do we predict consumers buying propensity using cross device data aggregation and analysis?
• What is the effect of price and inventory information on online ads based on consumer buying decisions?
• What are good techniques for revenue and cost estimation for long-tail biddable entities in online advertisements?
• What is the most impression-efficient design of experiments?
• How can we recommend good content or creatives for testing based solely on characteristics of the content or creative, and based on the users who will see the creative?
• How can we automatically suggest and assemble good creatives from a repository of assets?
• How do we recommend products to maximize conversion or engagement given product catalogs, video catalogs, web sites, click streams, and conversion data?
• What is the best way to conduct automatic A/B testing of digital experiences i.e., automatically generating variations of layout or content, while adhering to aesthetics and brand requirements?
• How do we best analyze multiple concurrent A/B tests (not necessarily of the same duration) for the same user experience or journey in order to avoid confounding effects?
• How do we incorporate into product search algorithms multiple requirements of a) relevance to the end user need, b) the marketer's need to optimize for key revenue and profitability metrics, c) trends based on seasonality, weather and buzz about specific product categories/brands/products, d) the device on which the search is being performed and the location/immediacy that it connotes?
• How do you drive engagement to video content? How do you measure engagement with video content and what are the factors that affect engagement?
• What are good techniques for discovering or recommending video content?
• What is a good bidding language and design for a marketplace for selling video ads?
• What is the right number and duration of ads for video content?
• How do you combine television advertising with online video advertising, and what are good techniques to measure the effectiveness of online video advertising?
• For each given topic, how do you identify the people who are the thought leaders and those who can influence the audience that you want to reach?
• How do you measure the conversation around your brand and determine what else is everyone talking about in your space so that you can drive those conversations to your brand?
• How do you filter noise from social conversations to only hear things that are important to you?
• What is the real impact of social media? What should you really measure? Is it really just about likes and shares? How can you measure the impact of social media on your brand (both positive and negative)?
• How do we determine topics for conversation with your brand followers based on what is going on in the news, what brands want to communicate, and what your fans are talking about?
• When should you start paying for promoting conversations? When does it make sense to publish information versus pay for placement?
• How do we conduct probabilistic stitching of cross device identities e.g. identify that a person is the same across desktop and mobile device?
• How to automatically segment users based on cross channel behavioral data?
• How can we estimate the life time value of a user?
• Audience intelligence – How can we predict the behavior of a user or an audience segment?
• Audience planning – How can we recommend the right audience for a specific offer or a product promotion?
Research grant submission guidelines
• Full-time faculty members from North American universities are eligible for the grant
• The proposal should preferably be two pages long and include the following:
− Proposer name and contact information
− Research goals
− Description of project(s)
− Use of funds
• Award range: Up to USD50,000
• Two deadlines: August 21, 2015 and February 19, 2016
• Please send proposal/s and questions directly to email@example.com