Overview Social media monitoring has become a critical tool in the aviation and airline industry, helping companies manage their reputation, manage daily operational customer-centric issues,
There is a reason why we continue to rely on automated tools (i.e softwares) to monitor social media – it saves us time. With few
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.
In this article, I will share a bit more how we have adopted social data with other data sources to predict an election outcome in a small constituency in Malaysia with 97% accuracy.
Before we answer that, let us share some facts on the internet and mobile landscape in Malaysia. We are a proud nation of 120% mobile users with more than 17 million smart phone users.
Overview Social media monitoring has become a critical tool in the aviation and airline industry, helping companies manage their reputation, manage daily operational customer-centric issues,
There is a reason why we continue to rely on automated tools (i.e softwares) to monitor social media – it saves us time. With few
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.
In this article, I will share a bit more how we have adopted social data with other data sources to predict an election outcome in a small constituency in Malaysia with 97% accuracy.
Before we answer that, let us share some facts on the internet and mobile landscape in Malaysia. We are a proud nation of 120% mobile users with more than 17 million smart phone users.