Components of Data Analytics Framework for improved Customer Service

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Customer service is a key aspect for businesses to excel. So, for an organisation to drive operational excellence, be competitive and to meet changing customer needs integrating data analytics into customer service is inevitable. Dataplatr's contact centre analytics competency in Descriptive, Diagnostic, Predictive & Prescriptive analytics helps businesses to unlock this potential.
Dataplatr's contact centre data analysis focuses on descriptive analytics, which helps businesses to analyze historical call center data using our BI and data visualisation tools to understand what has happened through various key performance metrics like Service Level (SL), Average Handle Time (AHT), First Call Resolution (FCR), and Customer Satisfaction (CSAT) for identifying the evolving customer needs.
Contact centre analytics capability of Dataplatr in data mining & diagnostic analytics allows businesses to identify the inefficiencies in customer service and its root cause using metrics like RCA(root cause analysis), CES(customer efforts score), sentiment analysis and NPS (net promoters score) to gain deeper insights into customer interactions and understand customer pain points for enhancing the customer service.
Dataplatr uses its machine learning algorithm and call centre metrics analytics and reporting skills to predict customer churn rate, customer lifetime value and identify the next best action. Also our predictive analytics and modeling capability helps in forecasting call volumes, staffing and training needs which allows businesses to be proactive and future ready.
Dataplatr's expertise in prescriptive analytics utilizes real time data analytics and decision trees in providing call centre analytics solution which enables businesses to make data driven decisions and help them in gaining competitive advantage by improving operational efficiency, increasing customer satisfaction and enhancing customer experience."