15 Topics
10 Days
Programme Overview
Training Description
Who should attend
This course is ideal for:
- Social Media Managers and Specialists
- Digital Marketing Analysts
- Market Researchers and Consumer Insights Teams
- Brand Strategists and Public Relations Professionals
- Data Scientists working with unstructured text data
- Product Managers gathering customer feedback
Session Objectives
- Master advanced sentiment analysis modeling techniques.
- Integrate social data with business intelligence platforms.
- Develop predictive social models for trend forecasting.
- Conduct thorough social listening for crisis detection.
- Measure social media ROI using advanced attribution.
About the Course
This course elevates proficiency from basic monitoring to advanced, actionable social intelligence, focusing on the rigorous measurement and interpretation of unstructured social data. Participants will learn how to leverage sophisticated analytical tools and machine learning principles to move beyond simple vanity metrics like likes and shares. The curriculum deep-dives into advanced sentiment analysis models, predictive social modeling, and how to integrate social data with broader business intelligence platforms for holistic decision-making. By mastering data preparation, cleansing, and visualization, you'll be equipped to uncover crucial consumer insights, track campaign ROI accurately, and proactively manage brand reputation in real-time, turning raw conversations into strategic business foresight.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Overview of social media analytics and its significance
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Subtopic 1.2:
Key metrics and KPIs for tracking social media performance
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Subtopic 1.3:
Understanding social media platforms' data structures
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Subtopic 2.1:
Introduction to popular social media analytics tools (e.g., Google Analytics, Hootsuite, Brandwatch)
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Subtopic 2.2:
Exploring AI-powered tools for in-depth analysis
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Subtopic 2.3:
Setting up and integrating platforms for tracking social media data
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Subtopic 3.1:
Tracking engagement metrics: likes, shares, comments, retweets, and clicks
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Subtopic 3.2:
Measuring reach, impressions, and conversions
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Subtopic 3.3:
Analyzing audience demographics and behaviors
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Subtopic 4.1:
Understanding sentiment analysis: its role in marketing and brand management
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Subtopic 4.2:
Types of sentiment: positive, negative, and neutral
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Subtopic 4.3:
Introduction to Natural Language Processing (NLP)
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Subtopic 5.1:
Sentiment classification and keyword extraction
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Subtopic 5.2:
Understanding polarity and subjectivity in social media content
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Subtopic 5.3:
Advanced sentiment analysis with machine learning algorithms
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Subtopic 6.1:
Techniques for collecting data from various social media platforms
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Subtopic 6.2:
Using APIs and web scraping tools for data extraction
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Subtopic 6.3:
Ethical considerations and data privacy concerns
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Subtopic 7.1:
Social listening tools and strategies
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Subtopic 7.2:
Tracking brand mentions and key trends across platforms
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Subtopic 7.3:
Setting up real-time social media monitoring
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Subtopic 8.1:
Introduction to predictive analytics for forecasting trends
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Subtopic 8.2:
Identifying emerging trends through data
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Subtopic 8.3:
Using historical data to predict future social behavior
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Subtopic 9.1:
Segmenting audiences based on behavior, preferences, and demographics
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Subtopic 9.2:
Building audience personas from social media data
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Subtopic 9.3:
Personalizing content for different audience segments
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Subtopic 10.1:
Creating interactive dashboards for social media insights
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Subtopic 10.2:
Best practices for presenting social media analytics data
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Subtopic 10.3:
Tools for generating reports with actionable insights
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Subtopic 11.1:
Measuring and managing brand sentiment online
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Subtopic 11.2:
Using sentiment analysis for brand reputation management
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Subtopic 11.3:
Strategies for handling crises based on sentiment trends
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Subtopic 12.1:
Understanding influencer sentiment analysis
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Subtopic 12.2:
Measuring the impact of influencer campaigns
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Subtopic 12.3:
Tracking social advocacy and its role in brand perception
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Subtopic 13.1:
How AI and machine learning enhance sentiment analysis
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Subtopic 13.2:
Using deep learning models for more accurate sentiment detection
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Subtopic 13.3:
Integrating AI-based solutions for continuous optimization
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Subtopic 14.1:
Tailoring content strategy based on social media insights
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Subtopic 14.2:
Adjusting social media campaigns for better engagement
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Subtopic 14.3:
Aligning business objectives with social media metrics
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Subtopic 15.1:
Navigating ethical issues in social media data collection
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Subtopic 15.2:
Protecting user privacy while using social media analytics
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Subtopic 15.3:
Legal considerations for sentiment analysis and data usage
