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Tech partner of
Hearing doesn't scale.
If it did, people would love employee engagement surveys, CFO’s would base the forecast on the social listening report, and the word “unforeseen" would go away.
Lots of charts, little actionable learning: Why?
Most quant research, NLP, and text analytics take the views people express, then chop them into bits that have little relevance to how people think. The result is the illusion of empirical rigor, and 'insight' that seldom correlates with real-world outcomes. If you notice any of the approaches below, you have a hearing problem
Sentiment Trap
Keyword Trap
Sentiment is sometimes helpful, but by itself, sentiment is hard to action without the 'why'.
People don't think in keywords, yet most tools run based on keywords or word vectors - which miss experiential drivers.
Topic model trap
Topic models capture one narrow aspect of what people say. They can't measure the wider context.
Complexity Trap
You don't have time for ‘paralysis by
analysis’ decks or for a new data
science initiaitve.
One-size-fits-all trap
Force-fitting the views people express
into standard categories or models?
Expect blindspots!
Mismatch Trap
Tracking studies seldom correlate with performance, and research findings are often hard to action.
By leveraging the power of large language models, narrative analytics captures the practical and experiential drivers that other approaches miss
Phrasia is easy to use - even for non-technical people. It works in up to 60 languages in a single analysis - making multi-market analysis dramatically clearer and more actionable.
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