How the analysis of Social Big Data has radically changed the way in which we monitor social networks and deal with business intelligence

1. Big Data is what the market calls a large volume of data.
In the 1990s, large stores of data (Data Warehouses)
were already used by companies like Walmart to
identify consumer habits from their choices at the point
of sale. This was how it was discovered that parents
who bought beer were also buying disposable nappies,
allowing retailers to devise tied promotions.
In the last decade, the Mayor of New York, Michael
Bloomberg, also implemented a data warehouse for
the city. The aim was to improve decisions by the
public authorities. For example, which properties –
some 900,000 in the city – to inspect to prevent the
occurrence of large fires.
The first step was to identify the different data sources
that generate information about the city: on rented
properties, medical emergencies, complaints about
noise in the neighbourhood, crime reporting, even the
sighting of rats.
This data was unstructured and in different silos
(different databases according to the municipal
department dealing with the problem). It first went
through a process of structuring to make manipulation
possible. Then a statistical model was created to
handle the data. Once the model was implemented,
every new claim received by the city about a fire danger
was checked using the algorithm of the new model.
The result: the rate of reports dealt with that actually
01 | What Big Data is
02 | What has changed with the Social Media
03 | 4Ps of Insight: a framework to get started
04 | How to use the 4Ps of Insights right now
04 | Big Data applications
What Big Data is
represented an imminent danger jumped from 30% to
70%. Decisive forecasting enabled New York City to
make considerable resource savings, since it was able
to avoid sending out fire crews to false alarms.
A similar problem also related to New York’s exploding
manhole covers. The same problem has plagued other
cities in the world such as Rio de Janeiro, and in the
Big Apple it was also treated with Big Data. To find
out which manholes were on the point of blowing, a
large volume of different data such as charts of gas,
telephone, electricity ducts, as well as traffic in the
immediate vicinity was cross-referenced.
Just like NYC, the American retailer Target also
transformed its big data into insights, e.g. when it
predicted whether its female consumers were pregnant
a few months before giving birth. The process involved
the analysis of a large volume of data on products
consumed and buying habits, enabling Target to have a
more personalised communication with the customer.
There is even a story that one irate father called Target’s
Marketing Department complaining that his teenage
daughter had begun to receive offers of baby products
by email. Some time later he discovered that his
daughter actually was pregnant (and he didn’t know).
In both cases, New York City and Target, unstructured
data formed a large volume of data (big data) that,
after passing through statistical analyses, enabled
important insights to be identified into both current and
future consumer habits.
01
SOCIAL BIG DATA
How the analysis of Social Big Data has radically changed the
way in which we monitor social networks and deal with
business intelligence
SOCIAL BIG DATA
August 2013

2. 02
The social media present a new challenge: these
days, companies have more external data on their
consumers than internal data. There are more than
1 billion Facebook users worldwide. Twitter, with 200
million active users, generates over 400 million tweets
daily.
The volume of this information grows by the day. From
2008 to date, we at E.life have stored 600 million data
points, or units of data, posts, comments and shares
from Twitter and Facebook. More than half of this data
has been stored since 2012. Just to mention some
figures, there are more than 30 million Brazilian users
(and they’re growing exponentially).
The challenge is not only to have access to this data,
but also to analyse the information differently. The first
change is: the software is much more user-friendly and
allows us to see everything, i.e. N=all. When looking at a
large volume of data it is possible to use sophisticated
statistical techniques and identify patterns of behaviour
that could not be identified just by looking at last
month’s buzz.
One important change is that the market has undergone
datafication, i.e. data-fying a phenomenon is to put it in
a quantifiable format so that it can be tabulated and
analysed. Today the more technological shields we
use the more we ‘datafy’ our day-to-day lives. A simple
Twitter bio or a single day of check-ins on Facebook
provide more data today than mankind ever thought
possible in past decades.
Another change is the end of data silos. Just like
New York City had to put in a single data warehouse
information that belonged to different municipal
departments, companies also have the same challenge.
Withoutasingledatawarehousethatgathersconsumer
information scattered across all the software used by
the company, it will be impossible to gain insight into
their behaviour. At this point, social networks have a
great competitive advantage: consumer data about
various dimensions of their behaviour can already be
found in one place: what do they drink? where do they
go? what do they watch on TV? What do they buy? It’s
all there on the social networks.
What has changed with the
Social Media
What has changed?
Datafication = datafying a phenomenon is to put it in a quantifiable
format so that it is tabulated and analysed. Datafication is different
from Digitalisation.
Digitalisation
N=all. Today it is possible to analyse everything and massive
amounts of data.
Data sample
No more data silos. Data from different sources in one enormous
data store.
Data silos
Information can be re-used. The cost of storage in servers in the
cloud allows you to save a large volume of data and reuse it, even
if the data has been collected for some other purpose.
Data used only once and then
discarded
‘What’ is better than ‘Why’ for Big Data. Often we will not get the
explanations for everything but we will have more certainty about
what we are talking about thanks to the huge volume of data
analysed.
Why
The end of having just one version of the truth. The data tell
stories and the stories depend on the questions asked. In other
words, get used to having many versions of the truth according to
the questions you ask.
A single version of the truth
THEN NOW

3. 03
4Ps of Insights: a whole new
framework to deal with
Social Media Big Data
For decades marketers have used the famous 4 Ps of
Marketing to develop campaigns and communication
strategies. No matter what kind of business, one can
start a marketing strategy asking basic questions
about the 4 Ps: Product, Price, Place and Promotion.
Market and Opinion researchers, though, never had an
equivalent theoretical Framework as simple as that;
until now.
At E.Life we are proud of outside-the-box thinking.
That means we are also willing to make mistakes and
learning a lot during the process until we can offer the
best solution for our clients. After almost 10 years
delivering Insightful reports exclusively based on social
media data, we thought it was about time we attempted
to summarise our work in the simplest way possible.
We came up with what we think could be a thorough
framework to start any Intelligence Project based on
social media and general digital data. We call it: The
four P´s of Insights.
Preferences
Bio
Brand Mentions
Intention/Action
Apps
RTs
Places
Location
Check-ins
People
Bio
Brand Mentions
Intention/Action
Personality Traits
Pricing
Price or cost Mentions
Brand Mentions
KEY
INSIGHT
INDICATIORS
Preferences Pricing
Ecommerce has become pervasive. Hordes of people
shop at Ebay, Amazon or Zara or they get loads of
information before going to a real store. Product prices
float accompanying new trends by the hour. Social Big
Data and traditional datawarehouses can set these
prices by the minute. Hence we chose our second
P, pricing instead of price to emphasize the current
dynamics nature of how much money a company can
charge for a product.
Possible questions that can be answered with
Social Big Data:
• What is the real-time perception of whisky drinkers
on the price of the drink in social networks? And how
has this price perception varied historically in the last
two years
• What is the perception of consumers about the
prices of airline tickets, by carrier/brand??
In a multi-choiced world people cannot be pinned down
by classic demographics any more. We are back to
being tribes of hunter-gatherers looking for food, fun,
beliefs, and 1000 other things. The Social Media allow
us for the first time to cluster people in Tribes, ranging
from a few hundred people who like Catena Zapata
wine to hundreds of millions that adore McDonalds
and everything in between. We picked Preferences as
our first P because it translates so well the new era of
Likes.
Possible questions that can be answered with
Social Big Data:
• What do consumers who define themselves as
parents on Twitter drink at weekends? Beer or Wine?
• Mothers who go to football matches usually shop
at which supermarkets and support which teams?
In a study of data on 59,000 parents on Twitter in 2013
in Brazil, we found that those who define themselves
as a father in their Bio usually drink Heineken beer and
most often support Santos or Flamengo.

4. Places
In the 20th century researchers used the zip code to
classify men and women. We picked Places in the plural
to differ from this approach: people are on the move
doing facebook checkins, taking photographs and
tagging them in Instagram, tweeting their location and
so on. The new demographic analysis must consider
the digital geography of a consumer.
Possible questions that can be answered with
Social Big Data:
• Where will the digital data go from Rio de Janeiro
and São Paulo from check-ins on social networks? This
has already been answered in an E.life study in 2012.
• What is the difference in men’s and women’s
behaviour in Mexico when they visit points of sale?
People
By stating their preferences online, consumers are
leaving an enormous behaviour footprint. Again
demographics from the 20th century do not suffice for
characterizing the new citizen. Personality traits can
be inferred from preferences and the other way around,
helping companies to understand the fundamentals
of the ever-changing consumer. We picked People as
our fourth and last P, to make it clear that companies
are dealing with very well-informed, complex and
sophisticated human beings, who do not fit into old
fashioned social-demographics anymore.
Possible questions that can be answered with
Social Big Data:
• What food and drink consumption habits do vegan
mothers have?
• What is the difference between men and women
in relation to media consumption on TV? And what is
the difference between doctors and lawyers in media
consumption on TV?
How can you use the
4Ps of Insights right now?
Start your research by asking questions in your
domain for each P. Say you are a Shopping Mall. For
the Preferences dimension, how about: which brands,
hobbies, activities, TV shows are consumers that come
to your Mall sharing online? For the Pricing dimension:
Are people associating expensive/cheap and other
price-related adjectives to any stores in your mall? Are
there any trends you should be aware of to fine-tune
your pricing? For the Place dimension: who is checking-
in on your mall? Where else are they checking-in? And
finally, for the People dimension: what personality-traits
map into what preferences?
Learn more about the 4Ps of Insights at
www.4PsOfInsights.com
Big Data applications
SOCIAL TARGETING: TAKE ONE-TO-ONE
MARKETING TO THE NEXT LEVEL
Twitter has 250 million users worldwide, Facebook has
4 times more. Most consumers also carry their mobile
phones and post photos on Instagram or inform their
location via Foursquare check-ins, and most of this
data ends up again on those two platforms. What if
an insurance company knew that a given person who
once visited their web site has just bought a car? Or a
pet food company just learned that you tweeted a lot
about your cat? Definitely a great way to generate new
sales or branding, right? With Social Targeting this is
now possible.
Social targeting is a way to find out more about your
site visitors by inviting them to use their Twitter or
Facebook user name to log on to your web site. Both
platforms allow developers to ask permission to see
likes, bios and other data that can then be aggregated
to find the best fit for each visitor. Say you are a
Pharmaceutical company and the person who just
logged in is a doctor (when we last looked we found
15,000 people who identify themselves as doctors on
one of our monitoring projects). Or an ecommerce that
learned you are a big scuba diving fan. In both cases
you can use e-mail or messaging services from one
of the two platforms to create a unique offer to your
visitor and hopefully increase your conversion rate.
04

5. YOU ARE WHAT YOU MENTION
Companies are also going beyond these basic
matching procedures and are using sophisticated
statistical processing to infer what a given consumer
with a “mention story” might be interested in. One
popular technique is clustering, where it is possible
to group people according to their similarities. You
may have the hipsters cluster mentioning Mad Men
and Daft Punk or the game fanatic cluster mentioning
top on-line games. A new visitor can automatically be
classified under one of these clusters and successful
past offers to individuals belonging to it may also work
for this new person.
Another technique is called association rules, where
the algorithm infers things like: if a consumer is male
and has mentioned an airline and a car brand, there is
a high probability that he will respond to a credit card
add. Precision increases with the amount of data a
web site can gather. Amazon is the best example in the
ecommerce realm and has been doing this for decades
now.
SOCIAL FOR REAL
Socialtargetingcanalsobetakenintothefieldviasmart
phones. At E.life we have developed a Shopping Mall
check-in app with a twist: every time a user checks-in
on a store he collects points to get discounts or freebies
from that venue. Before he does it though, the app asks
permission to get his likes or his Twitter info. Now the
marketing folks are looking at the clusters generated
by each store to improve their offers. The next logical
step will be to create on-the-fly promotions as soon as
a user does a check-in even including combined stores
in the Mall.
There is no doubt that we are watching a revival of the
one-to-one marketing projects preached in pre-internet
times. But now, with the ubiquitous mobile web and
social platforms the outlook looks far brighter. Does
your company web site have a social login? Maybe it is
a good idea to have it. It is time to get to know each of
your visitors more personally.
More at: www.4psofinsights.com
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