This report provides a unique, bespoke analysis into your Facebook brand pages. Your data is placed into context with an existing database of billions of likes and comments and analyzed through a trained machine learning and mapping algorithm. These insights are not available through conventional social monitoring and can vastly improve a facebook ad buy or social media strategy.
The unique visual interface is “Google maps for data”, allowing zoom in and exploration of any feature in a huge data set, identifying clusters at many levels of scale and coloring patterns across the data landscape ready for presentation. This allows for the delivery of bespoke requests quickly and accurately - requires user to sign in online with their Facebook page API token; analysis completed typically in 2-3 days.
The data contained within Facebook likes is as accurate as top psychometric profiling techniques and, when a certain number of likes are gathered, is better than close friends and family in predicting behaviour (eg. see the recent Cambridge paper by Kosinski and Stillwell ). Thus Facebook customer segmentation is an integral part in understanding your customers.
In addition to likes, Facebook comments are analyzed by both page and keyword. These comments are segmented into recurring themes, user type and emotion.
The report provides:
- A segmentation of all those who like a facebook page into subgroups of users with similar interests
- Gender and location demographics of subgroups engaging with the page, plus the key features that define them
- Top likes of those that comment on a page and many other metrics
Comments on a page are analyzed into:
- Typical interest profiles of those making a certain type of comment (what they like)
Place your facebook page into context with:
- Other Competitors
- Top Keywords
Access can be granted to an analytics system which allows independent exploration and analysis of the facebook like and comment dataset. This includes capacity to enable real time classification of data via an API layer.
This report can be customized to answer many other Facebook related questions - please contact us for more information.
 Private traits and attributes are predictable from digital records of human behavior, Michal Kosinski, David Stillwell, Thore Graepel, Psychometrics Centre, University of Cambridge, PNAS 2013.