José P. Chan is vice president for business
development with Celect, a cloud-based, predictive
analytics SaaS platform that helps retailers optimize
inventory portfolios in stores and across supply
chains. Previously, he worked internationally in
retail for more than two decades. Chan has extensive
experience in buying, marketing, merchandising,
and planning, and has grown and turned around
retail store networks. He holds a master’s degree
from MIT, an MBA from the University of Rochester,
and a bachelor’s degree from Cornell University
and AAS Fashion Institute of Technology.
How would a team begin to whittle
away at this problem and come
up with a viable strategy that
maximizes revenue? Until now,
merchants and planners did not
have the tools that allowed them to
do their jobs with great precision.
Now blind spots can be easily
considered and specific business
questions like these can be quickly
answered: Is the retailer investing
in the right styles? Is it overbuying
or underbuying? Did it allocate the
right product in the right stores?
As much as technology has changed
how consumers shop, it has also
benefited retailers by providing a better
way to model future outcomes that are
not constrained by old mental models.
When these data are aggregated and
subjected to machine learning and
analytics computations, the resulting
customer choice model can accurately
predict future customer intent.
With current tools, any modeling
has to be done manually with Excel
spreadsheets. For example, creating
an optimal store assortment for a
single store may take days using
spreadsheets, whereas creating
an optimal store assortment with
machine learning takes only a
few seconds. Moreover, since
each department—e.g., men’s,
women’s, accessories, home,
jewelry, etc.—perform planning
calculations separately, crucial
subtleties are lost in the process.
For example, machine learning can
automatically factor in the effect
of complementary or substitute
products and come up with optimal
future outcomes. This is simply not
feasible with the tools in use today.
Other retail applications include
instantaneous vendor comparisons
that visually demonstrate business
potential from the high level—
store, region, department—to the
most granular level, such as fabric,
color, and style. It is now even
possible to create distinct and
precise clusters based on shopping
patterns, rather than the A,B,C
revenue/size and region models.
Not a Panacea
Although predictive analytics can
help in many areas of retail, there
must first be a clear strategic road
map from the C-suite before it can
be applied effectively. Predictive
analytics cannot just be thrown into
the mix. It must be incorporated into
a well-crafted, thoughtful strategy
and used in clearly defined areas that
address some of the most pressing
problems, such as inventory. Any new
initiative or solution must answer
a specific business question.
Additionally, it is important to
remember that technology, on its own,
cannot solve all business problems.
Moreover, to achieve long-term
success, the overall strategy needs
to look beyond quarterly results.
Amazon has taught The Street to
not expect immediate profitability.
This has given the company
flexibility to innovate and dominate
many of the markets it enters.
Finally, it is important to remember
that when applying predictive
analytics to problems, there must
be an element of human intuition
factored in, as this creates a more
balanced approach. Predictive
analytics complements human
intuition; it is not a replacement
for it. The current retail phase of
disruption will create winners
and losers. Retailers should have
the right predictive analytics
tools to improve their chances for
being on the winning side. J
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