Title: Quantitative Methods in Data-Driven Digital Marketing Research
Firms take data-driven decisions to improve their operation. Large amounts of data are available regarding customer transactions, pricing, advertising. They are collected through field and lab experiments, surveys, and more and more via online platforms. Digitalization influences the marketing activity of firms, marked by a shift towards the online stores, social media, and mobile marketing. Thus, firms learn how to make use of the modern technologies, such as digitalization of information and the Internet of Things, to understand shopping and purchase behavior, market trends, identify focus groups, improve communication and promotion. To take advantage of such progress tailored statistical, predictive and computational methods, as well as structural models, are being developed and applied. The track is aimed at academic scientists and practitioners who conduct either theoretical or applied research related to digital and big-data in marketing. A non-exhaustive list of topics includes:
- Game theory
- Bayesian methods
- Machine learning
- Data science
- Audio-and video-processing
- Customer targeting
- Pricing strategies
- Assortment optimization
- Demand estimation
- Social media strategies
- Mobile marketing optimization
- Online store concept development
- Internet of Things development.
Fields econometrics, computer science, marketing, operation-research, statistics.