Machine Learning and Optimal Store Path
My previous post covered the first half of my presentation on Machine Learning (ML) and store analytics at the Toronto Symposium. Here, I’m going to work through the case study on using ML to deriv...
My previous post covered the first half of my presentation on Machine Learning (ML) and store analytics at the Toronto Symposium. Here, I’m going to work through the case study on using ML to deriv...
Last week I spoke in Toronto at a Symposium focused on Machine Learning to describe what we’ve done and are trying to do with Machine Learning (ML) in our DM1 platform and with store analytics in g...
The most daunting part of doing shopper measurement isn’t the analytics, it’s the data collection piece. Nobody likes to put new technology in the store; it’s expensive and it’s a hassle. And...
One of the most important questions in analytics today is the role for bespoke measurement and analytics versus BI and data visualization tools. Bespoke measurement tools provide end-to-end measureme...
We just did our first non-incremental release of the DM1 store analytics platform since we brought it to market. It brings new analytics views to the Workbench, a host of UI and analytic tweaks, new ...
When we released the first full production version of DM1 in May, it was a transformational leap in customer location analytics. Now, six months later, we’re releasing the first major upgrade to DM...
It’s been a little more than a year now for me in store analytics and with the time right after Christmas and the chance to see the industry’s latest at NRF 2018, it seems like a good time to ref...
The perfect store tracking data collection would be costless, lossless, highly-accurate, would require no effort to deploy, would track every customer journey with high-precision, would differentiat...
Data collection technology is at the heart of in-store customer location analytics. In my past two posts, I’ve described some of the cool analytics and measurement that our second release of DM1 br...