Aula 7 18042013

Sistemas de Informação para Indústria
© António Grilo 2013Departamento de Engenharia Mecânica e Industrial
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Class 7
Social Networks and Web Analytics
Sistemas de Informação
para a Indústria
António Grilo
Prof. Auxiliar FCT-UNL
Joaquim Fonseca
Ph.D. Student

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Social networks and Web analytics

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Social networks and Web analytics
1. Why WEB matters?
2. Social networks
3. WEB analytics
4. Web Competitive Intelligence

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Why WEB matters?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004 2005 2007 2008 2009 2010
Year
Internet usage in USA
% of total population
Source: http://www.internetworldstats.com/am/us.htm

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Why WEB matters
Source: http://www.pordata.pt

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Why WEB matters
 According to McKinsey:
 There are more than 1.500.000.000 social network
users…
 …which make up to 80% of total internet users.
 70% of companies use social networks
 90% recognize the benefit
 Workers spend 28 hours every week writing
emails, searching information, and collaborating
internally.
Source: The social economy: Unlocking value and productivity trough social
networks, McKinsey July 2012

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Why WEB matters
 The internet has approx. 50 billion webpages.
Source: http://www.worldwidewebsize.com/

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Why WEB matters
 Knowledge and insights

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Why WEB matters
Source: www.emarketer.com -
Ecommerce Sales Topped $1 Trillion for First
Time in 2012
In 2012 B2C e-commerce
had an estimated value of:
$ 1 trillion.
and growing…

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Why WEB matters
Source: The social economy: Unlocking value and productivity trough social
networks, McKinsey July 2012
 According to McKinsey:
 900 to 1.300 billion dollars potencial on social
networks:
• 1/3 of users preferences can be influenced.
• 2x communication effectiveness inside the
organization.
• 20-25% increase in knowledge worker
productivity.

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Why WEB matters
 Why WEB matters?
• Large audience.
• Vast source of data, information and knowledge.
• Value:
• Market.
• Process efficiency.
• Knowledge.

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Social networks

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Social networks
 Definitions
 Value
 Proposes
 Insights
 Efficiency

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Social networks
 Definitions:
- Social media is a group of platforms and tools that users
employ to share information, photos, videos, and other contents.
(Turban and Lai, 2011)
- Social technologies are “products and services that
enable social interactions in the digital realm and provide
distributed rights to communicate and add, modify, or consume
content. They include social media, Web 2.0, and enterprise
collaboration technologies.”
(Capturing business value with social technologies
McKinsey Quarterly, November 2012)

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Social networks
 Platforms used by companies using social
networks:
Source: Social marketing industry report: How maketers are using social to
grow their business, Stelzner MA, 2012

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Social networks
 Value:
• Increased financial performance and market share.
(Bughin J., byers A.H., and Chui M., 2011)
• Decreased marketing costs.
• Quick information spread and greater reach.
(Kirtis A.K. and Karaham F., 2011)
• Direct company-customer communication channel.
• Customer insights.

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Social networks
 Propose
 Marketing
 Customers relationship
 Direct communication channel
 Active response to events
 Insights

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Social networks
 Marketing:
 Adds
 Official pages
 Campaigns
 Promotions

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Social networks

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Social networks
Location
Age
Gender
Interests
Connections
Audience

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Social networks

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Social networks
 Direct communication channel
 Active response to events

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Social networks
 Insights

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Social networks
Publications User
engagment
 Efficiency – DEA
 How can organizations measure efficiency on social
networks?

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Social networks
 Using DEA (Data Envelopment Analysis) input to output
efficiency can be measure.
Source: Avaliação da eficiência das empresas na utilização das redes sociais digitais:
Data Envelopment Analysis aplicada ao Facebook, Mafalda Pinheiro, 2012
...

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Social networks
 Using DEA (Data Envelopment Analysis) input to output
efficiency can be measure.
...

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Social networks
 Also, target input can be defined and benchmarks identified.

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WEB analytics

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WEB analytics
 Objectives
 Sentiment analysis
 User behavior
 Knowledge search

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WEB analytics
 Objectives
 Collecting useful insights to support decision making.
 Insights can be:
 Customers preferences and interests.
 Strong and weak analysis.
 Competition comparison.
 Web navigation optimization.
 Better product and communication strategies,
 …

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WEB analytics
 Sentiment Analysis
 Analysing subjective and unstructure text to extract
sentiment towards a subject.
 Due to large amounts of data, human interpreation is not
feasible.
 More or less complex algorithms.
 Low efficiency.

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WEB analytics

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WEB analytics

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WEB analytics
 User behavior

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WEB analytics
 User behavior

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WEB analytics
 Knowledge and insights

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WEB competitive intelligence

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 Concept.
 Framework.
 Value:
 Competitive Intelligence.
 Functional.
 Case Study: public tenders in Diário da República.

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WEB

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ONTOLOGY BASED STORAGE

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 Competitive Intelligence Value
• Market characterization.
• Competitors characterization.
• Monitoring competitors activities.
• Organization’s performance evaluation.
• Core business vs secundary business.
• Market tendencies.
• Business volume previsions.
• Strategic shifts evaluation.
• Consumers’ sentiment analysis.

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 Functional Value
• Suppression of search activities.
• Automation of internal processes.
• Increases availability for added value tasks.
• Information flow efficiency.
• Data standardization.

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 Programed in Visual Basic.
 Developement process supported with Chrome and
Protégé.
 2010, 2011 e until May 2012 data collection.
 15,432 valid instances.
 20000 (aprox.) collected documents.
 Autonomous and continuos operation.

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 Análise de dados...

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Aula 7 18042013

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