Quant Semiotics mines insights from massive amounts of web and social content that no human lifetime will be long enough (or dull enough) to read though.
When the word-count gets really big in massive web and social media datasets, words stop behaving like words and we have to begin treating them like numbers. This is when Quant Semiotics comes into its own, hence the name.
Quant Semiotics is a great tool for finding insights within huge amounts of web or social content that no human lifetime will be long enough (or dull enough) to read though. It uses Natural Language Processing and Network Analysis techniques to reveal cultural insights at scale that simply aren't accessible to conventional methods of qualitative interpretation or statistical data analysis.
In the spirit of eating our own dog food we thought it would be fun to use Quant Semiotics to study our own sector - the Media & Market Research industry. We analysed 29,000 news articles covering a 17 year period to map the evolution of new innovations and trends in this market.
Quant Semiotics helped a new health brand understand the hopes, fears and desires at play within the anti-ageing skincare market. This came to life through an activation framework based on 5 key market segments, each characterised by specific customer need states and aspirations.
Quant Semiotics uses three network topology metrics to identify repeating network patterns within large volumes of web and social data to forecast real-world innovation trends. The metrics are called Keyword Connectivity, Strategic Significance and Concept Centrality.
Quant Semiotics is a data analysis and segmentation methodology that combines Natural Language Processing and Complex Network Analysis to segment a wide range of quantitative and natural language datasets. It leans heavily on a branch of Information Theory called Complex Systems Science that analyses bodies of text as networks of interconnected elements.