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Retail Renaissance: How Predictive Analytics is Transforming Site Selection
7
Turn consumer behavior into
actionable data
In retail, we often recognize patterns in consumer behavior
that we can't measure or analyze in tangible, meaningful
ways.
The farther away your store is f rom a population, the less
people will be willing to buy f rom that location. If people
have to fight bumper-to-bumper traffic, cross over three
lanes of a busy street, or navigate congestion in the parking
lot in order to shop at a location, they'll avoid you at all costs.
You already know your store needs to be convenient and
accessible. There's only so much f riction people are willing to
put up with, even if they're loyal to your brand.
When machine learning has enough datasets to work with,
you can turn obvious consumer behavior into actionable
data. You can see why one site is near demand but not in a
position to take advantage of it. Or find out how being on an
endcap of a shopping center with poor visibility translates
into low traffic. And see exactly how much a store's capacity
depends on parking availability.
Tango makes it easy to translate "the obvious" into data your
models can actually use, so you never feel like you're making
costly decisions based on assumptions.
