Social media analytics

Social Media Analytics as a part of social analytics is the process of gathering data from stakeholder conversations on digital media and processing into structured insights leading to more information-driven business decisions and increased customer centrality for brands and businesses.^[1]

"Social Media Analytics is the art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision making. It is a science, as it involves systematically identifying, extracting, and analyzing social media data (such as tweets, shares, likes, and hyperlinks) using sophisticated tools and techniques. It is also an art, interpreting and aligning the insights gained with business goals and objectives. To get value from analytics, one should master both its art and science."^[2]

Seven Layers of Social Media Analytics
Social media at a minimum has seven layers of data.^[3] Out of the seven layers, some are visible or easily identifiable (e.g., text and actions), and other are invisible (e.g., social media and hyperlink networks).

• Textual data (such as Tweets and comments)
• Network data (such as Facebook Friendship Network, and Twitter follow-following network)
• Actions (such as likes, shares, views)
• Hyperlinks (e.g., hyperlinks embedded within text)
• Mobile data (e.g., mobile application data
• Location data
• Search engines data

Social media analytics can also be referred as social media listening, social media monitoring or social media intelligence.

Digital media sources for social media analytics include social media channels, blogs, forums, image sharing sites, video sharing sites, aggregators, classifieds, complaints, Q&A, reviews, Wikipedia and others.

Social media analytics is an industry agnostic practice and is commonly used in different approaches on business decisions, marketing, customer service, reputation management, sales and others.^[4] There is an array of tools that offers the social media analysis, varying from the level of business requirement. Logic behind algorithms that are designed for these tools is selection, data pre-processing, transformation, mining and hidden pattern evaluation.

In order to make the complete process of social media analysis a success it is important that key performance indicators (KPIs) for objectively evaluating the data is defined.

Social media analytics is important when one needs to understand the patterns that are hidden in large amount of social data related to particular brands.^[5]

Homophily is used as a apart of analytics, it is a tendency that a contact between similar people occurs at a higher rate than among dissimilar people. According to research, two users who follow reciprocally share topical interests by mining their thousands of links. All these are used for taking major business decision in social media sectors.

The success of social media monitoring (SMM) tools may vary from one company to another. According to Soleman and Cohard (2016), beyond technical factors related to social media monitoring (SMM) (quality of sources, functionalities, quality of the tool), organizations must also take into account the need for new capabilities, human, managerial and organizational skills to take advantage of their SMM tools.^[6]

See also

• Natural language processing
• Sentiment analysis
• Data mining

References

1. IT Glossary, Gartner. "Social Analytics - Gartner IT Glossary". www.gartner.com. Retrieved 25 February 2015.
2. Khan G. F., 2015, seven layers of social media analytics: Mining business insights from social media text, actions, networks, hyperlinks, apps, search engine, and location data, CreateSpace Independent Publishing Platform.
3. Khan G. F., 2015, seven layers of social media analytics: Mining business insights from social media text, actions, networks, hyperlinks, apps, search engine, and location data, CreateSpace Independent Publishing Platform.
4. Search Business Analytics, Tech Target. "Social Media Analytics". techtarget.com. Retrieved 25 February 2015.
5. Tera, Data. "Capitalize On Social Media With Big Data Analytics". www.forbes.com. Retrieved 27 May 2015.
6. Soleman, Ramzi; Cohard, Philippe (3 - 4 of March 2016). "Success Factors of Social Media Monitoring". ICTO 2016: Information and Communication Technologies in Organizations and Society. Retrieved 17 April 2016. Check date values in: |date= (help)