human intuition with predictive
analytics that leverages machine
learning can yield better outcomes.
In essence, predictive analytics
can help guide human intuition to
help improve financial results.
There are many mathematical
techniques that can be used in
predictive analytics to find answers
using retail data. One of the most
common is the classic statistical
method based on cause and effect,
linear regression. For example, linear
regression can help answer the
question: If the advertising budget
is increased by 5 percent, how much
will sales increase next season? This
logic assumes that advertising drives
sales. More variables can be added
to see what else, besides advertising,
might drive sales, but the outcome
will always be based on a linear
model. However, there is another
mathematical technique that is better
suited for the nonlinear complexities
of retail— machine learning.
With machine learning a future
question can be answered by taking
into account millions of complex
possibilities in a nonlinear manner.
Machine learning uses algorithms—
very simply, a set of rules—to process
data, learn from that data, and make
future predictions. This allows for a
more robust predictive model, which
can continually improve over time.
In this context, questions like, “What
should a future fall/winter buy look
like?” can be answered. When coupled
with machine learning, predictive
analytics is immensely valuable in