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Retail Renaissance: How Predictive Analytics is Transforming Site Selection
5
Rapidly adjust models to
changing circumstances
Since March of 2020, just about everything has been in
flux. Stores have closed left and right, and businesses are
constantly modifying the in-person shopping experience to
accommodate new guidelines and expectations. Consumer
behavior has changed, too. Traditionally, historical data has
played an essential role in site selection. But now, retailers are
being forced to make long-term decisions based on rapidly
changing data.
In the past, sales forecasts and site models have taken
months to develop. They've been built on years or even
decades of data, and producing a new iteration could take
just as long. That doesn't cut it in an environment where
last year's data has such little bearing on the present. The
variables are changing too quickly for most retailers to keep
up. If you want to survive COVID and thrive in a post-COVID
economy, you need the ability to pivot quickly and recalibrate
your models based on current data.
How do shifting health guidelines and consumer behavior
impact ecommerce and brick-and-mortar sales? As
businesses reopen and employees return to campus, how
does that change traffic patterns and where your target
demographic spends most of their time? What if everyone
keeps working f rom home forever?
With Tango, you can quickly and easily reconfigure your
model and adapt your forecast to fit what you're currently
seeing in the trade area, not just historical patterns. In such
a fast-paced environment, this is especially critical as you
consider upcoming lease renewals, store closures, and
relocation projects.
