This course will explore emerging methods and applications for understanding online user behavior on popular social media platforms. This course will expose students to a variety of real-world business cases, a collection of data analytics tools, best practices and hands-on exercises. Students will learn how to 1) identify analytics problems, 2) use data analytics tools and identify types of analysis to be performed, and 3) close the loop (the process of taking the analysis results and interpreting it contextually).
This course aims to provide students with a broad coverage and examples of social analytics techniques and trends underlying the current and future development. Upon completion of the course, students will be able to:
- Extract social media data via social APIs and custom scripts.
- Extract social networks from non-network data such as transactional/operation data as well as textual conversations.
- Computationally identify and quantify social influencers.
- Computationally extract and identify trending topics.
- Visualize social networks and text analysis results.
- Deploy custom scripts in Amazon Web Services.
Students proposed ways to find out the most ideal means to engage the target audience by leverage on existing trends and behavioural patterns online, and suggested a digital media approach whereby they can increase awareness using their website and social media platforms to drive traffice to encourage business.
Students analysed the organisation's environment and suggest insights to upcoming product trends, general customer sentiments and strategies to counter competitors.
Students proposed strategies on how to help the company minimise marketing costs as well as increase their probability of successful customer acquisitions for their renting/leasing services through idenfying prospective customers who are likely to be interested in their service from social media data, and improve their current offerings that are aligned to current market trends.
Students analysed on the competitors' advertising strategies, online reviews and customer insights, including doing a sentiment analysis on their products vis-a-vis their competitors. The students also provided an influencer analysis based on social blade data on the proportion of followers, the engagement rate of influencers and who the influencers are (and their effectiveness).
Students analysed the factors that affect the experiences of tourists at hawker centres and attractions, including providing recommendations to tackle problems areas and promote positive experiences.
