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MAPPING SOCIAL MEDIA CONVERSATIONS
Tell us about the patent that you got
in 2005 and how effectively it is being
put to use today?
After my PhD, while I was working at
Honeywell Labs, I worked on algorithms
to help teams of autonomous agents
come up with good ways of jointly per-
forming tasks together. Most of the ex-
isting work in that area was too slow to
deploy in real life situations. My patent
related to exploiting the structure within
teams of agents to enable them to come
up with decent ways of working together
in a relatively short time. The expected
uses of this were in search and rescue op-
erations, military scenarios with autono-
mous bots, and in video games where you
compete against a swarm. At Germin8,
we are not using the work done under
this patent in any way.
We are in the era of Analytics 3.0.
Do you see visible signs of Big Data
analytics bringing transformation in
companies?
More and more, we are seeing companies
using Big Data analytics across their
entire organisation. The use cases vary
based on the department and industry but
they are becoming more and more com-
mon. For instance, Big Data analytics is
getting used by R&D teams to develop new
products, by Marketing teams to plan and
evaluate new products and campaigns, by
Public Relations teams to identify poten-
tial crisis and monitor their mitigation,
by Sales teams to identify leads, by Cus-
tomer Care to catch customer complaints
and queries early for faster resolution,
by Customer Loyalty to understand how
to retain customers and how to reward
valuable customers, by Risk teams to de-
tect fraud and non-compliance, etc. Every
week we at Germin8, see new innovative
uses of Big Data and the good news is that
the use cases are coming from customers
and not just from us.
What are the insights that a media
and entertainment company benefit
from Explic8?
A media company would use Explic8 to
listen to what their audience is saying
online in Social Media. These could be
conversations the audience is having
directly with the company on their own
social media channels or among them-
selves. The company could then leverage
these conversations to understand what
new content their audience wants, what

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THE
INFLUENCE
OF ANALYTICS,
GERMIN8 WAY
Pickle chats with Ranjit Nair, CEO, Germin8 on
the impact of Big Data analytics in the Media and
Entertainment Sector and how the industry can benefit
from engaging with Germin8
they think about the existing content,
campaigns and competitors. This leads
to actions like improving content and the
marketing of this content.
What is the difference between com-
panies who use data analytics and
who don’t?
Analytics is only useful if it is actionable
and the organisation is geared to react
to the analytics. By inference, an organi-
sation that uses analytics is one that is
open to what their consumers are saying
and nimble enough to react to it. So off
the bat, they are already at an advantage
over organisations that are closed to us-
ing analytics. The analytics itself gives
the companies using them a huge advan-
tage because their products and commu-
nications are more optimised and hence
would perform better.
You have some of the top advertis-
ing agencies as clients. How do they
make use of services provided from
your end?
Our advertising agency clients use Ex-
plic8 in many creative ways. Depend-
ing on what they are contracted to do it
could be to provide insights on how they
can improve their product or service of-
fering, insights on what would work on
campaigns, insights for communication
strategies, insights for rebranding strat-
egies, providing online reputation man-
agement services, identifying promoters,
detractors and key influencers, etc. That’s
just the tip of the ice berg. Agencies are
getting smarter on using Explic8 to de-
liver more and more actionable insights
to their customers that can transform the
company’s image and communication.
Tell specifically, how does a broad-
caster benefit from using analytics?
What are the various insights that a
broadcaster get about a programme
from the social media interactions?
For a broadcaster, it starts with what
content they should air. For a news show,
this could mean content based on under-
standing what people feel about different
current affairs topics like the upcoming
General Elections, for a reality show it
could mean more air time for contestants
who are most talked about in social me-
dia, and for a soap, it could mean getting
ideas relating to their plot and cast.
Next, such analytics can help the broad-
caster decide on how to promote their

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show and to understand whether their
campaigns are effective or not. For in-
stance, if the audience seems to like par-
ticular aspects of the show, these can be
highlighted in future promos.
Analytics will also help the broadcaster
understand whether the viewing audi-
ence likes the content that is being aired.
Analytics from social media can help
broadcasters and advertisers understand
how shows are faring, including qualita-
tive aspects like what about the show the
audience segments liked and disliked.
It is said that Big Data can also be used to
understand whether a movie or a TV se-
ries will be a hit before shooting it. Your
comments.
Analytics based on the online reactions
to promotions are a very good indicator
of how well the movie or TV series is like-
ly to perform. This is much like having a
test audience for a movie. You get a sense
of the expectations and impressions that
have been built up, and can take various
decisions like revising the promotions,
changing the marketing communication,
editing the show or movie, changing the
distributions.
You can also understand whether the
online advertising is reaching enough
people and whether they have been en-
gaged. This again allows the studio or
broadcaster to take corrective actions if
needed.
So, analytics is not just a predictive tool
to determine success or failure of a re-
lease. Way before the show or movie is
released, analytics can help provide in-
sights on how to make it a success.
Netflix bought “House of Cards” based
on thorough data analysis of their
33 million users. Will this become a
practice over a period of time?
Netflix has always been among the van-
guard when it comes to analytics for
content recommendations. It is not a sur-
prise that they would extend this to the
selection of new shows. I think this will
certainly become more and more com-
mon but a lot will depend on how much
companies are willing to spend on data
scientists and technology for such ana-
lytics.
How can the film industry benefit
from this?
A film producer or theatre chain could
use predictions on box office response to
decide on what the rights are worth and
what the distribution plan should be. For
instance, should it be a limited release
or a wide release, should I buy rights for
some states or all, etc.?
What according to you is the biggest
influence of the medium to the media
and entertainment sector?
The biggest influence of social media to
the media and entertainment is definite-
ly the reach that it affords to get the word
out about the show or movie, or in the
case of content that are available online
to actually bring viewers to where the
content is available. Analytics will play a
role in evaluating whether the marketing
is working, whether the content is work-
ing and what new content could poten-
tially work.
Can this help in decision making?
Very often we have to rely on our gut to
make a decision because of the absence
of data or the amount of time and mon-
ey conducting a survey would take. Use
of Big Data analytics will eliminate the
need of just going with your gut because
now you will have access to analytics
without slowing down your decision
making process significantly. This won’t
however replace human intuition and ex-
perience, which will still be necessary to
interpret the analysis and decide on the
best course of action based on the inter-
pretation of the analysis.
How credible is the data crunched?
The data can be highly credible if done
right because the views are largely un-
solicited and the volume of data can be
huge leading to high statistical signifi-
cance. The biggest threat to the credibil-
ity of data is whether the audience being
measured matches the audience for the
show. This threat stems from three con-
cerns,
1. There is a bias towards certain de-
mographic segments in the audience
measured online,
2. There are fake users in social media
Explic8 is Germin8’s product for
actionable insights for product, marketing
and sales teams across sectors based on
what consumers write in social media

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indulging in fake conversations,
3. The things people say in social media
have a strong negative bias.
All 3 are valid concerns that can be ad-
dressed through proper scientific ap-
proaches like stratified sampling, bias
sampling and pruning of fake profiles.
One must remember that traditional sur-
veys also sometimes suffer from similar
data credibility concerns like incorrect
sample selection, interviewer bias and
fake respondents.
Does data crunching and “influenc-
ing” help in monetization?
It helps both directly and indirectly. Di-
rectly because it can lead to ways of op-
timising the distribution of content and
indirectly because it can result in im-
proved content and improved marketing
leading to higher viewership.
Where are we heading?
In the coming days, we will see increased
internet penetration and an increasing
willingness for people across segments
and geographies to share their opinions.
This will result in a huge increase in the
volume of content for analysis and in the
number of regional languages that these
conversations will be in.
This will mean even greater applicabil-
ity of the data analytics but it will also
mean that tools need to be able to support
regional languages and handle this in-
crease in conversations.
With this, social media listening and ana-
lytics will then be highly applicable to re-
gional content and GEC channels.
❑ We set up an Explic8 tracker to crawl and
analyse conversations around the show
Satyamev Jayate Season 2 starting from
Mar 2, the launch date from across social
media sites.
❑ Conversations around Satyamev Jayate
have largely been on 2 platforms –
❑ Twitter – 69.13%
❑ Social networks – FB, G+ - 28.4%
❑ Men contributed to 66% of online conver-
sations related to Satyamev Jayate, which
is close to the average we see across sec-
tors. However, given the topic of the first
episode that aired on March 2nd, one
would have expected a higher participa-
tion from the female audience.
❑ Social Media – Twitter and FB are fast be-
coming the second screen for channels,
64.4% mentions came between 11am -2
pm, i.e. during the show time of 11 am to
1 pm.
Social media buzz distribution on broadcast day
❑ Broadcast day saw 61.4% of all conversa-
tions during the week. The key takeaway
from this graph for a channel can be how
to ensure that they keep the audience en-
gaged even after the show. Also, this can
be used to analyse the pre-show buzz
about the episode
Weekly buzz distribution
❑ Sentiments – The sentiments about the
show are mostly neutral and the conver-
sations seemed to agree with the show.
A few negative comments about Aamir
Khan charging too much and faking his
emotions were a minor proportion of the
overall conversations.
Indicative sentiment distribution based on sample data
❑ Top topics – rape, fighting rape, police
apathy, justice, change etc.
Social Media Conversations
of Satyamev Jayate Season 2