Social Listening: Introduction to Social Media Data Analytics

With the overload of consumer data posted on social media every second, social listening tools and tactics have become a critical component in developing successful marketing strategy.

At the core, social listening is a branch of social media analytics that focus on learning about the sentiment of your audience towards your brand. This is done through monitoring of your social media channels to look for any customer's opinion, feedback, discussion, and direct or indirect mention of your brand.

By mastering social listening you can spot industry trends, generate data-driven marketing ideas and strategy, and improve customer experience through better interaction.

KEY TAKEAWAYS This session will give you an introduction to social media analytics particularly social listening. We will cover:

• Real life case studies of social listening and how it can impact your companies and product branding
• How to identify KOL and micro-influencer and incorporate them into your marketing strategy
• How to extract meaningful insights from sentiment analysis and how to use it to build and protect your brand reputation

After this sneak peek into how social media listening can work to boost your brand, Ken will reveal many more insights and information you can leverage from social media analytics if you explore its full potential.

Module 1: Social Media Data Collection: Getting Started Module 2: Social Media Data Analysis Module 3: Social Sentiment, Introduction to NLP Module 4: Introduction to Big Data

Ken Liu has over 10 years’ experience in financial industry and with the recent focus on applying AI.

He also co-founded Datatact, a startup focus on Machine Learning and Big Data analytics. The main project includes applying Natural Language Processing in digital marketing and social listening. Prior to that, he worked at Citi, Goldman Sachs and Credit Suisse as Algo-Trading developer.

Outside of the full-time work, Ken is also a part-time lecturer in HKUSpace. He is currently teaching Big Data, A.I. and Investing, Applied Financial Engineering and Investment Management and Financial Intelligence.