Petaling Jaya – More often than not, we rely on automated tools (i.e softwares) to monitor social media. There are more than 250 companies globally that offer social media monitoring tools which automate our daily tasks. As the internet and social media users continue to grow exponentially, measuring a brand or consumer sentiment becomes an[…]
Measuring consumer behaviour through social analytics is gaining popularity amongst marketers and social media analysts in the recent years.
This is not an assertion or a hypothesis. There have been research studies around the world, including a recent one by Indiana University Bloomington using Twitter data to predict the stock market. Behavioural finance researchers can now apply computational methods to large-scale social media data to better understand and predict markets.
In 2013, I spoke about the theory of “social media bubbles”. This concept was unheard of three to five years ago. However, the internet and social media such as Google, Facebook and Twitter have evolved and are now powered by sophisticated machine learning algorithms that work endlessly to predict user behaviour based on a set of online activities, mouse clicks and keywords. Some of the common machine learning algorithms used are Market-Basket Analysis, Naive Bayes Classifier and many other derivatives.
In the last decade or so, most of the decisions by communication strategists, agencies and PR practitioners are based on “folk theories”. It means theories that have been developed, tested and refined over time by practitioners about what works and what doesn’t, without much empirical evidence to validate the effectiveness of the campaign outcome and the rationale behind the campaign design.