When a news story breaks, marketing departments need to know how customers will react. In the case of JCPenney’s kettle, traditional sentiment analysis predicted a very negative reaction. Emowatch’s full emotion analysis predicted laughter. In fact, the kettle went viral, sold out in minutes and their stock price remained stable. Try our brand analysis use case yourself.
Our second use case uses Emowatch emotion analysis assist ad and headline writers. Readers are more likely to share things which make them laugh. They comment on things which surprise them or make them angry. Furthermore Improving the click through rate of an ad campaign by a few points can have a significant effect on profitability. Put those two things together to write better ad headlines.
Financial service companies handle millions of customer support enquiries. They use text analysis to prioritise requests, and to produce regular performance reports. Traditional sentiment analysis differentiates positive from negative. Our 3Sent analysis also distinguishes sadness 😢 and anger 😡. Try our financial services use case yourself.