The document discusses how businesses are leveraging big data and using data analytics to gain a competitive advantage. It provides examples of how Walmart acquired Kosmix to build social commerce applications using its Social Genome platform to aggregate in-store, online, and social data. Walmart implemented this through an application called Shopycat on Facebook that helped users identify gift ideas for friends based on their interests. Shopycat analyzed a vast product catalog and showed a 42% conversion rate during testing, with about half of users sharing it.

1. Kuliza
Never in the history of commerce has such
a deluge of data been vaunted before
an information-hungry, and social-savvy
audience. Only a decade ago, CERN, a
European research organization, set up one
of the world’s largest databases with over
11.5 billion web pages. Today, the average
supermarket has access to shopping data:
stores that are at least twice as big, if not
bigger in size.
When consumers use their credit cards at
restaurants, clothing stores, or other retail
businesses, those purchase choices are
recorded and processed. Within the hour,
businesses have the ability to unearth
underlying consumption patterns that can
be produced in real-time. In a matter of few
hours, not only does the user behaviour trend
become more evident, but also the correlation
between people, events, locations, and
preferences emerge from silhouettes to
reveal a fairly clear picture of how marketing
campaigns are performing. The availability
of such large amounts of actionable data is
transforming the communications landscape
and is also having a sibylline effect on the
fabric of social commerce.
What is Big Data?
Wikipedia defines Big Data as “Datasets
whose size is beyond the ability of typical
database software tools to capture, store,
manage, and analyze.” 2.5 quintillion bytes
is the amount of data created every day.
Although, this proliferation of data is an
evidence of an increasingly prying world, it
is possible for Big Data to positively impact
social commerce. While most research into
Big Data so far has focussed on addressing
questions related to its volume, this article
posits the case of the impact of Big Data
on businesses with a special emphasis
on social commerce. The article also
examines the potential value that Big Data
can create for organizations, and illustrate
and quantify that value.
Commerce
Thinking Big
Data
Businesses are leveraging big data and analyzing it to
gain a stronger competitive position.This article looks
at the significance of data and how it is used to conduct
experiments to develop the next generation of products
and services.
by Siddharth Balaravi

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In the old days most data
problems could be solved
as computing speed caught
up. Now, there’s this deluge
of new kinds of data which is
growing faster than Moore’s
law. We’ve basically broken
what Moore’s law can cope
with,and so we need a bunch
ofnewtechnologiestogeton
the right side of that again…

3. Kuliza
Walmart Labs, Shopycat, and
Big Commerce
In a bid to strengthen its commercial offerings,
Walmart acquired Kosmix and its Social
Genome in early 2011. The Social Genome
organizes the Internet into topic pages allowing
users to explore the Web by topic. This platform
then works as a Big Data application that is
capable of aggregating in-store, online, and
social data and analyzing them to power a
plethora of social commerce applications.
Walmart implemented this with Shopycat -
a Facebook application that was designed
to help shoppers identify better gifts for
friends and family. Shopycat takes a person’s
interests and Likes from Facebook and
combines this information with information
against a vast product catalogue to identify
interesting gift options. For instance, if one
has a friend that is known to quote Barnabus
“Barney” Stinson, a fictional character from
the CBS television series How I Met Your
Mother, it is quite likely that Shopycat would
suggest one pick costumes from the Star
Wars films as an ideal gift for such a friend.
During the month long marketing campaign
that Shopycat was tested, it performed
an astonishing 42% goal conversion rate.
About half of the users who used the app
shared the promotion with their friends, and
the virality garnered an incremental 25% lift
in conversions. Moreover, the cost of user
acquisition was $2.67, far less than the
allocated campaign targets. Such results
mark the success of such a program. Clearly,
in the world of Big Data, success lies in
connecting the dots in fundamentally new
ways that resonate with people, brands, and
social commerce.
Big data is here to stay as it offers a competitive
advantage with a projected 60% increase
in retailers’ operating margins. It provides
statistics and insights into user and purchase
behaviours which are key factors in influencing
shopping behaviours. These datasets allow
companies to test, experiment, analyze, and
thereby help them implement appropriate social
technologies and social shopping platforms.
References
“Big Data.” Wikipedia: The Free
Encyclopedia.Wikipedia Inc,02 Sep 2012.
“Case Study: Walmart.” ifeelgoods.
ifeelgoods,n.d.
Kirsner, Scott. “Richard Dale splits from
Sigma to raise money for new VC firm, Big
Data Boston.” Boston.com. Boston Globe, 09
Aug 2012.
“What is Big Data.” IBM.IBM,n.d.
“World Wide Web.” Wikipedia: The Free
Encyclopedia.Wikipedia Inc,07 Sep 2012.
The Value of Experimentation
The hype around Big Data stems from the
fact that it eschews a fundamentally different
type of decision-making: one that requires
a fundamentally different mindset to the
analyses of the data itself. Think of it as data-
driven decision-making on steroids. However,
far from the hype, foundational customization,
constant experimentation, and breakthrough
business models will be the new telltale signs
of competition as companies capture and
analyze vast volumes of data.
Using carefully crafted controlled experiments,
marketers have the ability to test theories,
hypotheses, and analyze results of business
decisions in near real time. These have a
striking resemblance to decisions made
in hindsight as well as when experiencing
one of those “I Wish I Knew” moments.
Thus, experimentation can help marketers
distinguish causation from correlation. This
reduces the variability of outcomes while
improving the overall probability that the
performance of the control variable increases–
sales, sign-ups, or any other goal.
Adaptive experimentation can take many
forms. Leading online and consumer goods
companies are test continuously. In some
cases, they divide a small, but statistically
significant portion of their web page views
to conduct experiments that reveal factors
that drive higher user engagement or greater
conversions. In the world of web analytics,
and digital media, this sort of experimentation
is commonly known as A/B testing or Split
testing. Similarly, companies selling physical
goods also depend on experimentation
to aid decisions, but Big Data can push
this approach to a new level. For instance,
McDonald’s has installed electronic devices
that gather operational data in few of its retail
outlets. These devices track and store details
such as customer interactions, traffic in
stores, ordering patterns, billing information,
time of the day, etc. Statisticians can then use
this data to model the correlation between
variations in menus, restaurant designs, and
training, among other things on the overall
productivity and sales.