MALO 001 Gary Angel, Analyst Before Analytics Was Cool

Gary Angel
From washing floors and cleaning toilets to selling a successful analytics agency to EY and founding another cutting-edge agency, Gary Angel is truly an analytics thought leader. He shares his fruitless foray into selling digital analytics software and his unique perspective on applying what he knows about digital analytics (everything there is to know) to real-world, in-store, customer journey analytics. Learn from Gary's experience with machine learning, the Internet of Things, and how COVID-19 changes the face of predictive analytics. Gary also gets seriously excited about a book you should read... on philosophy.

Gary Angel - truly an analytics thought leader.

Gary Angel, the author of the digital analytics breakthrough blog on Funcitonalism, founded Semphonic, one of the first digital analytics agencies, and sold it to EY after 16 years.

Gary started his path to analytics before “computer programmer” was a legitimate profession, doing segmentation and targeting. He even went so far as build his own web analytics tool before there was a digital analytics industry. But the consulting side proved much more profitable once companies like Web Side Story and Omniture (now Adobe) showed up.

Today, Gary is founder and CEO of started Digital Mortar applying his wealth of knowledge about online analytics to bricks-and-mortar stores. His first finding? Analytics maturity is no better offline than online. Yes, there are a fair amount of sophisticated analytics going into merchandising and supply chain management, the customer journey? Customer experience? Only in-person surveys. Gary is dedicated to improving that from simple occupancy (how many people are we allowed to have in the store at a time given COVID-19?), to path analysis and employee tracking.

The fun part? The need for – and challenges with machine learning. Gary reveals the solutions he’s come up with to deal with untrustworthy data, insufficient quantities of data, and building robust models. They collect wi-fi data which turns out to be subpar on a good day, so they replicate the flakiness of it in order to train their models.

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