Visualizing Social Media Big Data.

1. Visualizing Social
Media Big Data
A big mountain of Social Buzz
hiding invaluable insights
March 2014
Every global Company now has software
to track and analyze the conversations
around their brand and competitors. Typical
reports vary little and may include sentiment
analysis, volume of buzz, top terms & topics,
most used social platforms and influentials
count. At E.life we have been doing this for
the last 10 years and eventually we realized
we could do a lot more simply because of
one single fact: the available social data
has just exploded in the last 36 months.
We started discussing alternatives to 1)
handle this big data (a whole new challenge
for brands since we used to talk about
thousands and now we are talking about
millions of posts) 2) create effective ways
to discover valuable insights from those
millions of posts we are gathering every day.
To handle the big
data explosion we
expanded our cloud
infrastructure using
Amazon services and
went from structured
databases to text
based and unstructured data processing
systems. This gave us the possibility of
querying and cross referencing millions of
social media items in seconds. That was the
easy part and involved a few brilliant E.life
engineers. One of the first applications of
our new infrastructure was done for the
biggest Brazilian TV Network, “Rede Globo”.
We now process the buzz of all their TV
Shows, including the famous soap operas
(novelas) and the Brazilian franchise of the
music show “The Voice” handling circa 10
million items each month.
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2. Managing the process of insights discovery
was the next challenge we began tapping
into. It soon proved to be an ongoing job,
since the range of our clients varies from
Oil and Energy to manufactured goods,
retail and airlines among others. The most
important task we posed ourselves was to
ask a kind of meta-question for each client,
namely: Which Questions should we ask the
Data*?
We came up with a
simple framework
we call the 4
Ps of insights,
which guides the
search for these
questions. We
also turned our
focus from only
performing brand monitoring to including
the whole Consumer Universe of a specific
brand. We defined the 4 Ps as follows: the
general Preferences of a consumer (she likes
yoga, watches old American comedy movies
and loves Family Guy), the Pricing sensitivity
(he likes fast food because it is cheap, he
plays golf and does not care if it involves
over expensive gear), the Places she goes
to (Gym, Starbucks and the local Mall) and
what kind of People these consumers are
(he is a father, she is a runner, he works at a
Bank, she is a Journalist).
Amazing as it seems we were able to find a
great deal of instances of those Ps by looking
at the social big data. We also designed
a lego-style dashboard which allows us
to quickly visualize the Ps extracted from
millions of items on a time interval. Below
(figure 1) we show an example of 3 Legopieces (or widgets in the geek jargon) of one
such dashboard sitting on top of hundreds of
thousands of mentions and digital check-ins
at Shopping Malls in Sao Paulo.
*An ongoing joke probably from the heroic beginnings
of data mining theory in the 70´s was: if you press the
data enough it will eventually confess.
Figure 1 – three widgets from our Check-in at Malls dashboard
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3. The First widget shows the total volume
of tweets made in Shopping Malls in Sao
Paulo in one day and more importantly how
many people had done these check-ins. The
second widget shows the ranking of Malls
according to volume of check-ins (which
we think is a rather useful metric for Mall
marketing directors) and finally the last
widget extracts from the Bio description how
consumers define themselves: Journalists,
Ad people and Designers form the top 3.
But things get a lot more interesting! When
we cross-referenced this data with other
categories we were able to figure out even
more about the consumers checking in at
Malls. Below we show two other widgets we
built on the Shopping Malls dashboard.
Figure 2 – Related Stores and TV Shows widgets
The related stores widget shows the most
cited retail chains mentioned by our sample
of consumers. We can also filter the data for
a specific Mall, giving marketing planners a
chance to figure out which chains will appeal
more to their customers. Finally the widget
“top related shows” tells us a bit about
the TV shows consumers most mention
on their favorite social platform. Again
this information can guide media buyers
to optimize their TV, Youtube and Netflix
campaigns.
gives access to a world of information that
traditional market research cannot begin
to dream of. A complete dashboard might
also include attributes such as gender,
musical preferences, fast food or fashion
chains, movies, hobbies, sport fans etc. The
technology and the data are there, your
imagination is the limit.
The “Check-ins at Malls” dashboard is just
one of infinite possibilities. Social big data
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4. Interested in a Social Big Data
dashboard for your company?
Send us an email at
contact@elifemonitor.com
and we’ll be very excited to help
you to discover incredible insights
for your business.
Jairson Vitorino, PhD
CTO of E.life
@jvitorino
www.elifemonitor.com/us
www.elife.com.br/home_uk