Speaker Bio & Abstract
BiographyWith over 25 years of experience in marketing and analytics, Rupen understands business needs and how data and analytics can help solve them. A Chartered Marketer, he has worked with organizations of all sizes to unlock the value of information in decision-making and business strategy. He specializes in helping organizations understand population trends, segmentation and performance benchmarking and appears regularly on CTVs Canadian and U.S. federal election coverage as a commentator on voter demographics in key races. Rupen held positions with Nielsen, Compusearch, Campbell Soup and CIBC prior to joining Environics Analytics and earned a masters degree in urban planning from the University of Toronto.AbstractThe volume of mobile-device generated location data has increased dramatically over the past few years. This mobile location data presents new opportunities to better understand population movements and behaviours. Public sector and private sector applications can include identifying populations visiting a retail location or municipal park, overnight travellers to tourism regions or the pandemic-induced mobility changes to name just a few. These raw mobile location datasets are massive; however, big data alone does not produce insights. To harness this spatial big data generated by I.o.T. devices, it is necessary to contextualize the observed devices using machine learning models to be reflective of the population as a whole. The analysis must also be executed in a strict regulatory environment whereby the data is aggregated, deidentified and modelled. Geodemographic techniques can be used to both normalize this big data stream as well as generate insights into these populations-on-the-move for both private and public sector commercial research applications while respecting the privacy of individuals and adhering to the compliance environment.