Sentiment analytics is the assessment of customer input to determine opinions, emotions, and attitudes about products, brands, marketing campaigns, etc. This technology relies heavily on natural language processing (NLP), computational linguistics, and machine learning to mine data sources. Sources of sentiment analytics data include blogs, social media, product reviews, etc.
Contact centers use sentiment analytics to assess the nature of a customer's comment in a phone call, e-mail, text message, or chat session. The analysis combines the acoustic characteristics, customer’s voice, and the conversation context and then gives a single score. The score can be positive, negative, or neutral, and determines the relative sentiment or emotion.
Factors Considered During Sentiment Analytics Scoring
Some considerations made during sentiment analytics include:
- The rate of speech
- Changes in stress
- The amount of physical stress in the voice
The Benefits of Sentiment Analytics
Sentiment analytics gives insights into growing customer service issues. For example, organizations can identify frequently used phrases, terms, and concepts whenever customers call. Conducting sentiment analytics allows management to identify problems and address them before they establish themselves.
Other benefits of sentiment analytics include:
Getting insight into the effectiveness of call center agents and customer support representatives
- Gauging the overall sentiment about the products and services of a business
- Identifying common problems and pain points in the delivery of customer support
- Monitoring opinions and attitudes about services, products, and customer support
- Giving organizations a unified view of the full customer journey
SuccessKPI.com provides organizations with sentiment analytics that enable contact center management to get a deeper understanding of the entire customer journey through all channels.