Abstract
Premise: New technologies and data analytics represent more and more an evolution of the traditional understanding of sports marketing as they are becoming more accessible, reliable, and feasible. However, they still require training in data skills and technology integration with strategic and operational marketing to be used efficiently by sports marketers as superior tools for value creation and delivery. Sports marketers should capitalize on technology’s benefits by 1-harnessing the power of data; 2-thinking in an agile manner; 3-looking at the customer/fan experience end-to-end. Aims: This project starts from the above premise. Based on a database of 12,000 players, 100,000 observations, and 18,000 videos already available to the researchers, we aim to understand applications of new technologies in sports, both on data collection and generation (i.e., data analytics in sports) and in the experience/interface with the consumers. “Experience” refers to how the consumers perceive the features of teams or individual players and how they affect their willingness to participate, engage, and ultimately- their effectiveness (e.g., in terms of sponsorship activities connected to those players or teams). “Interface” refers to new technologies embedded into wearables, such as the internet of things. Ultimately, the project aims to understand how new technologies benefit sports marketers (scholars and practitioners). Research questions and why they matter: This project’s objective is articulated alongside three research questions: 1. How do new technologies affect fans’ sports consumption and experience? As tickets’ prices rise and the at-home experience improves, game attendance declines. Organizations must recognize that fans’ experiences are evolving. Older stadiums start falling behind in infrastructure and technology: the future stadium is a platform allowing fans to engage with teams/athletes in new ways through technology-mediated, personalized settings: the game is only a small part of the main attraction. 2. How can internet-of-things-driven predictive analytics help improve the safety of players and teams? Embedded devices such as smart insoles and built-in chips allow big data processing through feature extraction and machine Ministero dell'Università e della Ricerca MUR - BANDO 2022 learning algorithms to provide more reliable safety evaluation models for teams and individuals during game and training sessions (e.g. monitoring critical body-parameters). 3. How can Big data and analytics help data collection and generation in sports marketing? Unlike scholarly data resources such as Google Scholar, Mendeley, and Web of Science, there are very few shared sports data platforms for researchers. Answering this question contributes to unifying data islands currently independently built by various teams, creating a multidimensional shared data platform comprising athletes, coaches, teams, and countries.
Project details
Unibo Team Leader: Daniele Scarpi de Claricini
Unibo involved Department/s:
Dipartimento di Scienze Aziendali
Coordinator:
ALMA MATER STUDIORUM - Università di Bologna(Italy)
Total Eu Contribution: Euro (EUR) 200.481,00
Total Unibo Contribution: Euro (EUR) 81.227,00
Project Duration in months: 24
Start Date:
28/09/2023
End Date:
28/02/2026
