Adobe is launching 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 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 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?
• 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 outcomes observed?
• 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?
• How can we translate visualizations into a text-based story or summary about the data?
• What is the most impression-efficient design of experiments for testing (multi-variate testing methodologies) that can discern as much information as possible about the impact of the creative element on a key performance metric?
• 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? Conversely, given a creative, can we suggest audiences and improvements to the content based on the target audience?
• How can we automatically suggest and assemble good creatives—images, headline copy, call to action elements, creative experiences, layouts, etc.—from a repository of assets such as product catalog, offers, web site, and marketing collateral elements that have been historically generated?
• How do we recommend products to maximize conversion or engagement given product catalogs, video catalogs, web sites, click streams, conversion data that are all encoded with customer IDs and their associated rich profiles?
• 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?
• 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 everyone is talking about in your space so we 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 our 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 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?
Research grant submission guidelines
• Full-time faculty members from 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 22, 2014 and February 20, 2015
• Please send proposal/s and questions directly to email@example.com