Big Data Analytics Improves Customer Service for a Leading Multinational Company
All the queries that a customer asks a call center gets recorded for quality and training purposes. However, not much of an analysis has been done with this huge pile of unstructured data, which is a major reason driving the need for big data analytics in call center scenario as companies have realized that these […]
All the queries that a customer asks a call center gets recorded for quality and training purposes. However, not much of an analysis has been done with this huge pile of unstructured data, which is a major reason driving the need for big data analytics in call center scenario as companies have realized that these recordings can be used effectively. Natural language processing (NLP) directs customers’ calls to the respective department when a particular word or phrase are said in an order. With advancement in NLP systems and intervention of big data analytics, brands can improve customer service and operational efficiency for call center operations.
Big data analytics help call centers improve customer journey and offer customized solutions to the clients. A detailed analysis of the collated data helps understand customers’ needs and learn the patterns in their query behavior. Quantzig’s big data analytics designs queuing and regression algorithms to estimate arrival rate and service times for inbound calls to improve service levels during peak hours. We also use predictive analytics to identify and improve important services that were negatively affected due to longer waiting times.
The Business Challenge
A leading multinational company in the United States wanted to analyze the call center data to streamline its processes and improve overall operational efficiency and customer service. The client was finding it difficult to manage operations for hotel reservations, airlines, rentals as well as service centers. The client wanted Quantzig’s big data analytics team to analyze the inbound data to improve customer service and operational efficiency for call center operations across the business.
The research team assessed various operational data, marketing, and human resource data, which included information about timestamp, CSR information and location, CSR status, and transactions. With this information, the team developed a descriptive analysis of the variables to organize and summarize the data.
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Big Data Analytics Assessment Benefits
- Created an integrated data repository that was continuously updated by multiple data sources
- Enabled real-time data analysis across both operational and marketing data
- Developed highly accurate forecasts for call arrival rates at different times of the day
- Identified estimation and forecasting parameters from the data
Big Data Analytics Predictive Insights
- Enabled call center managers to balance center resources with future workload
- The patience of customers increased linearly as their years of association with the client increased
- Visually represented the data for operational performance
- The average abandonment rates of loyal customers were around 30% during weekends and 42% during weekdays
- Agent service times were mainly dependent on the type of issue and agent skill