This document contains information and data that AAUM considers confidential. Any disclosure of Confidential Information to, or use of it by any other

This document contains information and data that AAUM considers confidential. Any disclosure of Confidential Information to, or use of it by any other party, will be damaging to AAUM. Ownership of all Confidential Information, no matter in what media it resides, remains with AAUM.

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Ordo Ab Chaoleveraging social media sentiments

- 2 -

Problem DescriptionCurrent Process

Market Segments

Size of Addressable Market

Only about half of the businesses using social media networks like Facebook, Twitter, or LinkedIn have any idea as to a return on investment.

Businesses doesn’t know how to leverage the insights effectively for their operations.

Sound understanding of business objectives will be helpful to devise appropriate methodologies to extract/analyze information from web.

Many players existing to categorize sentiments based on text mining techniques but no solution existing on predicting social media based on advanced analytics real time to benefit the business goals.

Articulating the current scenario in social media

Th

e P

rob

lem Most of the businesses have presence in web, get

feedback to gather from comments and derive insights manually or by leveraging software (just classification) available in the market.

All business segments – No differentiation

Assuming 40% of the companies have presence in the web. And 50 % of them have adopted social media or using social media techniques. This is a very captive market looking at the impact made by social media in the recent years.

- 3 -

Solution DescriptionProposed Process

Market Segments

Size of Addressable Market

Aaum have developed a working concept to predict/categorize social media comments by various business parameters.

Customized advanced analytical algorithms, dictionary have been developed to accomplish this.

This product has potential to change the business paradigm.

With continuous data mining and right infrastructure, it is now possible to know people’s choice specific to the business.

Just the tip of the iceberg and Sky is the limit!

Please refer to the proof of concept in the next slide.

Aaum’s solution envisioned

Ou

r Solu

tion

Automatic sentiment mining from social media.

Predict social media opinions real time based on advanced analytics to benefit the business objectives.

All business segments – No differentiation

Assuming 40% of the companies have presence in the web. And 50 % of them have adopted social media or using social media techniques. This is a very captive market looking at the impact made by social media in the recent years.

Business objectives

• Generate more word of mouth

• Increase customer loyalty

• Bring outside ideas into organization

• Increase product/brand awareness

• Improve new product success ratios

• Improve public relations effectiveness

• Reduce customer acquisition costs

• Reduce customer support costs

• Reduce market research costs

• Reduce product development costs

Prioritized business

• Generate more word of mouth <2

• Increase customer loyalty <4

• Bring outside ideas into organization

<3

• Increase product/brand awareness <5

• Improve new product success ratios

• Improve public relations effectiveness

<1

• Reduce customer acquisition costs

• Reduce customer support costs

• Reduce market research costs

• Reduce product development costs

- 4 -

Problem DescriptionCompetitive comparison

Competitive positioning by key factors

Social media is sunrise industry.

Many software companies are making tools to come up with solutions to categorize the comments based on basic text mining techniques

None of these tools have good predictive solutions for the business to derive insights for their operations.

Aaum’s solution is a blue ocean product to leverage the benefits of analytics in social media.

Competitive landscape

Com

petitio

n A few commercially available packages

SAS, SPSS– Performs text mining and categorizes by key words

– Adopts a generic approach

Customized solutions (Many vendors)

Based on dictionary classification and partially on advanced analytics

Visibli, Semioboard, viralheat, etc

– More of web analytics

A comprehensive solution with continuous monitoring is lacking in this space!

Differentiators Aaum’s solution

Competition

Text mining and automatic comments extraction

Partial

Prediction by business parameters

Partial

Prediction by advanced analytical techniques and therefore better prediction/accuracy

Continuous mining of data and hence not limited to one time “api” search

Case Study: Comparative Sentiment Analysis on Pongal release movie

“What people expressed before the release & post the release”

Case S

tud

y

- 6 -

Data collected from various sources based on the business rules formed....

- 7 -

Removing the bias factor is a very important process in our approach in addition to other data cleansing activities and transformation techniques

UsernameNo. of tweets

AllAboutVijay 209rahulsundar_007 197AllAbtVijay 150TheVijay360 143VijayAnban 141Actor_Vijay 127techboss2011 126j_shanujan 119DineshSelvakuma 110abhi_vfrndz89 106VijayExpress 104tamizhanlogesh 97MelanieWF 96Vijay_theactor 95cinemausher 76rohith_vijayfan 74VIJAY_FansClub 73may_war 71cineandhra 61IIAYATHALABATHY 59

UsernameNo. of tweets

Dhananjayang 93Star_falilah 84techboss2011 51yuvanfansclub 50meyyappanram 45prakash_12345 44TamilStudios 42crajapriyan 40ActorMadhavan 40amala_amsfc 39Thus_S 30Magenraja 29Actor_Amalapaul 2997thstreetswgrx 29vigneshwar_m 27LatchmiGirl 27j_shanujan 27sruthii4u 26TweetSav 24rajg_kumar 24

‘Nanban’ ‘Vettai’

- 8 -

Key insights

Did the movie meet the expectations of the crowd

– Pre-release vs Post release

How did the movie perform?

– 7 days before vs 7 days after

Key Enabling factors

Key Deteriorating factors

Scope for improvement

– Preventive and corrective actions

See all by yourself in our Movie-meter dashboard!!!

– Prediction based on our training algorithms

- 9 -

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Nanban Vettai

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Our classification techniques are based on advanced analytical algorithms

- 10 -

Insights from our analysis

Pre release analysis shows a very good support for both the movies.

A very few negative criticisms

Heavy promotions witnessed in case of Vettai

Removal of bias factors provides an altogether different perspective!

Post release analysis also shows a very good support for both the movies.

Nanban as clearly emerged a ‘victor’

More positive comments about the movie, direction and the hero

And nobody cares for songs from any of the movies!!!

- 11 -

Possible applications

“Ordo Ab Chao” - our analytical product, a powerful listening tool to mine social media sentiments

– Local movies chosen to initiate buzz and to create viral marketing

The product demonstrated finds wide application areas especially

– FMCG companies – To understand the customer behavior and usage patterns.• Effectiveness of campaign launch, advertisements

– Company, Competition and Customer

• e.g. New brand launch

– Social media companies/Communities – to cater their services effectively to their customers.• e.g. Twitter?

– Entertainment industry - To understand the customer sentiments.• Academy awards, News channels, etc

- 12 -

Want to see trends, changing audience perspectives on the movie attributes?

vs

Take a dive into the next slides

- 13 -

Nanban –Insights for movie and hero

Hero

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Hero

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Movie-Nanban

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Pre & Post release insightsPre & Post release insights

- 14 -

Director -Nanban

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Heroine -Nanban

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Director-Nanban

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12/22/2011 12/25/2011 12/28/2011 12/31/2011 1/3/2012 1/6/2012 1/9/2012 1/12/2012 1/15/2012 1/18/2012 1/21/2012


Nanban –Insights for director and heroine

Pre & Post release insightsPre & Post release insights

- 15 -

Questions/Feedback?

Contact us

01 N, 1st floor IIT Madras Research Park, Kanagam road, Chennai – 600113

Tel :` +91 44 66469877, Fax:+91 44 66469877

Email: [email protected], Skype:b.rajeshkumar

Twitter: AaumAnalytics, Web: www.aaumanalytics.com

Facebook: http://www.facebook.com/AaumAnalytics

LinkedIn: http://www.linkedin.com/company/aaum-research-and-analytics-iit-madras

About Aaum

Aaum Research and Analytics founded by IIT Madras alumnus brings in extensive global business experience working with Fortune 100 companies in North America and Asia Pacific. Incubated at IIT Madras Incubator ecosystem with a focus on researching and devising the sophisticated analytical techniques to solve the pressing business needs of corporations ranging from Health Care, Entertainment, FMCGs, finance, insurance, retail, Telecom.

Aaum’s office at IIT Madras Research Park

Documents

This document contains information and data that AAUM considers confidential. Any disclosure of Confidential Information to, or use of it by any other