IDEAS & Data Application Lab co-host this live webinar

Live: Labeling Foot Traffic in Dense Locations

IDEAS & Data Application Lab co-host this live webinar:

Mobile sensor data is enabling us to better understand user behavior. It is now possible to accurately and persistently model foot traffic to brick-and-mortar retail stores and other points of interest in near real-time. However, this capability comes with increasing state-of-the-art data processing and machine learning challenges.
Locations of stores in dense areas, such as shopping malls, are often indistinguishable using lat/long or street address data because of their close geographical proximity. This creates a complex problem for accurate foot traffic estimation to these stores, especially in the absence of accurate, large-scale ground truth data. The problem is further aggravated by the diverse nature of user behavior and visit frequency to stores of different categories.
In this talk, I describe our approach at Sense360 to solve this. In particular, I will talk about framing this issue as a probabilistic learning problem, engineering features from point-of-interest data, and using regularization. The developed approach is currently being used at Sense360 to further boost the accuracy of our market research insights.

Om Patri is a Data Scientist at Sense360, a market research and insights firm in Culver City, CA, which enables some of the world’s largest restaurant and retail companies to continuously measure and optimize their business in real-time. He focuses on using AI and machine learning approaches to leverage sensor data for marketing, research and analytics. He has a Ph.D. in Computer Science from the University of Southern California, where he worked on time series modeling in energy applications.

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