Skip to main content

Applications for Machine Learning in Shopper Measurement and Tracking

Description

In this session, we’ll introduce the technology and analytics involved in tracking the customer journeys in the physical world. We’ll briefly survey the data collection technologies used, the type of data that gets generated and look at some basic visualizations to understand how the data lays out. Then we’ll deep dive into three areas where machine learning can be fruitfully applied. First, we’ll look at problems in data quality and the application of machine learning to identify the behavioral patterns of Associates vs. Shoppers. This is critical for extracting associate data from the core shopper data stream in retail analytics. Second, we’ll look at issues in “zone-stitching” – following the customer across camera zones. Zone-stitching is essential for accurate journey measurement using video technologies. Finally, we’ll look at store path optimization – a classic optimization model designed to identify the “best” path through the store for a given type of shopper.

Contributors

  • Gary Angel

    Considered one of the leading customer analytics and digital measurement experts in the world, Gary is the CEO and founder of Digital Mortar. Digital Mortar provides comprehensive collection and measurement of the customer journey in retail stores. Previously, Gary led Ernst & Young’s Digital Analytics Practice. EY acquired Gary’s last venture – Semphonic - in 2013. President and founder, Gary grew Semphonic to be the leading digital analytics practices in the United States. Voted the most Influential Industry Contributor by the Digital Analytics Association, Gary blogs at measuringthedigitalworld.com. His book, Measuring the Digital World, was published in 2016 by the Financial Times Press.