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Sensory Tools

Sensory or sentiment analysis tools are used primarily in automated emotion recognition, a technology that examines text data—and, increasingly, audio and images—to detect a customer’s emotional state.

Some sentiment analysis tools rely on tasks as basic as skimming a customer’s social media posts, while others are sophisticated enough to mimic human intelligence and understand the nuances of emotion. At the advanced end, this technology is known as affective computing or emotional artificial intelligence—the study and development of systems and devices that can recognize, interpret, process and simulate human emotions.

How companies use sensory tools

  • Customer relationship monitoring. Companies use sensory tools to monitor general sentiment for their products or services through indirect sources, such as social media and web scraping.
  • Competitive positioning. Increasingly, businesses are scanning social media with sensory tools to assess their relative brand position and share of voice, complementing other benchmarking methods.
  • Employee sentiment monitoring. Businesses are using sentiment detection to identify and support underperforming teams or projects, boost morale and preempt employee attrition.
  • Recommendation engine improvement. Emotional cues from customers can help improve search engine functionality and the relevance of results.
  • E-learning applications. Some e-learning tools can use biometric signals to adapt to a user’s emotional state, enhancing learning.
  • Product launches or campaigns. Companies apply sentiment analysis to social media and their own communication channels to understand the impact of new product releases or campaigns, so they can quickly adjust their approach.

Key considerations with sensory tools

Companies should keep a few things in mind when implementing sentiment analysis tools:

  • Include sentiment analysis as part of a broader approach. Sentiment analysis tools should not be the only way that companies listen to customers. Instead, they should be part of an overarching approach to customer experience management and closed-loop feedback, enabling decisions and action.
  • Embed quality control measures. While most sentiment detection algorithms use artificial intelligence and deep learning to continuously improve, they still are not perfect. Companies need to design checks and balances to ensure that insights are meaningful for decision making.
  • Protect privacy and data. Analyzing common data sources, such as information scraped from websites, social posts or employee emails, can raise significant concerns and may even be illegal in certain contexts. Leading companies develop a robust compliance and privacy management process.
  • Sentiment analysis
  • Affective computing
  • Emotional artificial intelligence

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