Contact Center Analytics are the analysis of standard contact center metrics to identify trends, impacts, causes, and results. Contact centers regularly need to record, measure, and analyze different types of data.
The four most common contact center analytics consist of the following:
- Contact Center Analytics
- Customer Analytics
- Speech Analytics
- Predictive Analytics
Contact center team analytics measure the performance of agents and quality assurance personnel. Internal contact center teams focus on different metrics that are relevant to the industry of their product or service.
For contact centers, common performance metrics for internal teams include:
- ASA = Average Speed of Answering
- FCR = First-Call Resolution
- AHT = Average Handle Time
- CSAT = Customer Satisfaction Score
- NPS = Net Promoter ScoreSM
- CES = Customer Effort Score
Customer analytics is the analysis of multiple customer-related data sources to identify customer trends, interaction opportunities, and to serve as a source for modeling.
Customer analytics can be historical or predictive. Data sources include the voice of the customer, behavior data, demographics, and purchase data.
Speech analytics are the analysis of transcribed voice and ingested text engagements, along with metadata including CRM (customer relationship management) and notes to identify trends, the voice of the customer insights, performance drivers, and other insights.
Advanced speech analytics are derived from both NLP (natural language processing) and NLU (natural language understanding) technologies.
Predictive analytics is the use of big data, artificial intelligence, advanced algorithms, and intuitive machine learning techniques to create predictions about future results based on historical data.
In addition, predictive analytics allows you to calculate expected (CLTV) customer lifetime value, anticipate future consumer behavior, and identify customers likely to churn