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...
We spend a lot of our time at Digital Mortar explaining to folks why door-counting isn’t remotely sufficient to drive retail analytics. Implicit in that assumption is that people are doing door-cou...
The hardest part about doing enterprise shopper journey measurement and analytics is data collection. Putting new hardware in the store is no joke – and yet it’s often necessary to get the measur...
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...
I had a sales conversation a little while back where the client told me they were looking to be able to “tell which direction someone went when they came through the door.” And I remember thinkin...
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...
Over the last year, we’ve released a string of videos showing DM1 in action. These are marketing videos, meant to show off the capabilities of the platform and give people sense of how it can be us...
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...