Union Knopf (India)

TechUPDATE
In our interactions with many buyers
and merchandisers in the industry, both in
India and abroad, we have found a very
practical challenge in placing bets on the
fashion products.
Typically, a brand or retailer reviews
the past data to look at at a higher level
what worked and what did not. These
insights are in terms of fitting, price,
washes, style, etc and that goes as an input
to future buys. A very important thing to
note is that buying is finally initiated on a
product, which is a set of aesthetic values
which the consumer would perceive.
Here is one of the many cases we have
encountered. You can try on yourself or
you may ask the same question to people
in your organization. We asked one of the
members
What colours work? What colours do not work ?
What works in t-shirts?
The person responded “Dark colors like
black and blue”.
Note: Image from Stylumia Insights Platform
Great. We then asked,
what in dark colors
worked?
He said “Bold prints ”
We then asked what in
black and blue does not
work?
There was a bit of silence.
There is a genuine problem here all of
us face, we remember what works most
of the time at a fine level. When we
search for what does not work at that
same level, we do not necessarily
remember. You can try this with any
category and set of attribute
combinations.
When we studied the data of the
organization using our capabilities of
computer vision, machine learning, what
we found was for every set of products that
worked in an attribute combination almost
equal amount of products that did not
work.
If this reality is not alive in the people
who make buying decisions that too at real
time, we will continue to make the same
pattern of error. As you can see in the
picture above we can see same hues of
colours sitting in working and not
working. Unless insights come with this
contra view, we will keep drawing narrow
conclusions.
Another challenge in fashion is
interpretation of what we see. We describe
fashion products using attributes. Let us
take for an example one of the key
attributes i.e colour. If through normal
analytics one has to come up with what
works in colour, we would say dark blue
contributes to x% of business and does
well and light pink which is y% and does
not do well. This is an insight which is not
at all actionable. Colours at RGB level are
only actionable.
There is a short coming of traditional
analytics and also practical difficulty of a
human to remember at the aesthetic level
that too combinations of attributes what
works and what does not.
We see an incomplete knowledge
chain. Today’s advances in technology like
Computer Vision enables us to see the
data in fashion like human eye and
capture all of this in a form which is easy
to take decision which is relevant and also
real time.
Imagine you can see insights of your
brand by product category, with
multiple attribute combinations over
seasons, store, region and with aesthetic
intelligence all in a single page so
intuitive that from a store sales person to
CEO will have same ease of access and
knowledge becomes democratized for
decision making.
All this for what? Keep making
products your consumers love always!!
Embracing technology to enable
decisions which take us close to customers
will set us ahead of the pack in this ever
competitive marketplace.
The article is contributed by Ganesh
Subramanian Founder & CEO
Stylumia Intelligence Technology
Private Limited. He was also the CEO of
Myntra.com
APPAREL&Fashion
January 2017
85