Excerpt from webinar, Don’t Wait: Take Decisive Action to Reduce Occupancy Costs, on April 2, 2020
Retail’s Trajectory of Recovery
Devon Wolfe – Senior Vice President of Analytics, Tango
“We’re expecting a trajectory of recovery that is going to take a while. And what will be interesting is tracking it and understanding where we are in that curve. The area that’s circled in red is by far the most important part here. Which is the question of as intervals go along, when do we know that we’ve gotten to the new normal? Will it be a little bit below as this chart indicates in the green? Will it be way below? And where are we in each stage? That’s what Tango is focusing on going forward is trying to help retailers understand this. And certainly, those are the questions that retailers are going to be asking going forward.
“What we believe is that in a rapidly changing market dynamic where things are changing from month to month to month to month with different situations. Different areas of your store fleet have opened while other areas are closing. Still, other areas are in flux. What’s important is to be able to have a mechanism in place to be able to understand those shifts, interpret them, and then act on them rapidly.
“This is where we see that machine learning really helps. Because what we can do is feed in this data that we continuously recalibrate. As we get more new data, we can feed it in and understand where the shifts are happening in the market dynamics – and understand what is driving them. This has big effects. If what we find is driving these market dynamics are things that are marketable or involve ways that operations could change, like allowing people into the store or helping them feel comfortable coming into the store, those can help retailers get back on their feet quickly.
“If you go back to that other side, what we see are these intervals. If you look at that red diagram, you’ll see little dots in there that are intervals of change as we go along. What we’re recommending is that companies be very quick to recalibrate and reassess all the data inputs possible at many different stops along the way. Now, it might be the seven times that are on here. It might be three or four times before we get to that green area. Right now, we don’t know.
“But what we do know is that we do have a lot of data out there that can help us, a lot of data that is available to us, and a lot of techniques that help us act on that quickly. That’s what we believe a smart retailer should do.
“Finally, the last thing I would say about closing the gap is to remember that mobile data that you saw earlier on with that one example that was animated. One of the things that we will be looking at and tracking very carefully are the changes in mobile data activity that show where people are moving and how much that is benchmarked against what it was pre-COVID. We believe that data will be a very, very important predictor for retailers to look at as they evaluate their store networks and decide which stores are coming along and which are worse than others.”
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