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Understanding Social Media Analytics

• Focus: Creating Value With Social Media Analytics

• Chapter 3, Third Edition by Dr. Gohar F. Khan

Learning Outcomes

Upon concluding this chapter, readers will:

• Grasp foundational concepts, tools, historical development, and the social media analytics industry landscape.

• Attain familiarity with the nine-layer framework of social media analytics.

• Differentiate between social media analytics and business analytics to enhance business intelligence.

• Understand different types of analytics: descriptive, diagnostic, predictive, and prescriptive in the context of social media.

• Identify prevalent limitations and challenges associated with social media analytics.

Introduction to Social Media Analytics

• Definition: Social media analytics is the art and science of extracting valuable insights from vast amounts of semi-structured and unstructured social media data.

• Purpose: Facilitates informed decision-making.

Key Concepts of Social Media Analytics

• Social Media Analytics:

  • It combines the art of interpretation with the science of systematic data extraction and analysis.

  • Involves data mining from public databases with emphasis on legal and ethical considerations.

The Four Vs of Big Data

• Characteristics of data relevant to analytics:

  • Volume: Huge quantities of data generated (Projected 40 Zettabytes by 2020).

  • Variety: Different forms of data encountered (structured, unstructured).

  • Velocity: Speed at which data is generated and analyzed.

  • Veracity: Reliability and quality of data.

Objective of Social Media Analytics

• To make informed business decisions based on extracted data insights.

Interest in Social Media Analytics

• Understanding trends and public interest in social media analytics over time.

Comparison: Social Media Analytics vs. Business Analytics

• Data Type: Semi-structured/unstructured (Social Media) vs. Structured (Business)

• Data Accessibility: Public (Social Media) vs. Private (Business)

• Sharing Patterns: Wider dissemination in social media creates more value, while business data is tightly controlled.

Purpose of Social Media Analytics

• Key business purposes include:

  • Measuring brand loyalty

  • Tracking product/service impact

  • Business forecasting

  • Market research

  • Customer engagement

  • Analyzing feedback

  • Generating leads

Nine Layers of Social Media Analytics

• Framework for understanding social media data:

  • Location Analytics: Mapping user locations.

  • Text Analytics: Extracting insights from text content.

  • Actions Analytics: Tracking user engagements (likes, shares).

  • Search Engine Analytics: Historical data analysis of search patterns.

  • Hyperlink Analytics: Analyzing social media links.

  • Apps Analytics: User interactions with mobile applications.

  • Multimedia Analytics: Insights from images, videos, and audio.

  • Demographic Analytics: Understanding audience characteristics.

Types of Social Media Analytics

• Descriptive Analytics: What happened? Historical data insights.

• Diagnostic Analytics: Why did it happen? Understanding correlations and outcomes.

• Predictive Analytics: What will happen? Data forecasting and trend analysis.

• Prescriptive Analytics: What to do? Decision modeling and optimization of outcomes.

Challenges in Social Media Analytics

• Data Quality: Issues of accuracy and reliability in unstructured data.

• Integration: Difficulty in combining data from multiple sources.

• Privacy Concerns: Ethical issues when extracting data.

• Real-Time Analysis: Need to act quickly on emerging data trends.

• Skill Gaps: Shortage of skilled professionals in data analytics.

Overcoming Challenges

• Advanced Technology: Utilize sophisticated tools and methods for data extraction and analysis.

• Skilled Professionals: Investing in training and development.

• Data Governance: Establishing policies for robust data management.

• Ethical Considerations: Prioritizing user privacy and ethical data practices.