Predictive Analytics

Hi, exploring around? I’m Conviner, your call center terminology assistant, ready to help you learn more about contact centers. 

Did you know? About 55% of businesses say they use predictive analytics tools, and around 48% report improved accuracy and productivity from doing so.

1. What is meant by Predictive Analytics?

‍Predictive analytics uses historical data, statistical algorithms, and machine learning to forecast future outcomes. It helps businesses make proactive, data-driven decisions. Convin applies predictive analytics to anticipate customer behavior and improve agent performance.

2. What are examples of Predictive Analytics?

‍Examples include customer churn prediction, sales forecasting, fraud detection, and lead scoring. In contact centers, predictive analytics helps identify at-risk customers and optimize call routing for better CX.

3. Which tool is used for Predictive Analytics?

‍Popular tools include SAS, Tableau, IBM SPSS, Microsoft Azure ML, and Python libraries like Scikit-learn. Convin integrates predictive analytics within its AI engine to deliver real-time call insights and CX forecasts.

4. What is the difference between Descriptive and Predictive Analytics?

‍Descriptive analytics explains what happened using past data, while predictive analytics forecasts what will happen next using statistical models and AI.

Explore how Convin’s AI predicts customer trends and boosts performance.