Call center data is any type of data stored in a contact center. Call center data can include information such as customer accounts, agent performance records, and company finances. This type of data can be sensitive, especially when customer accounts contain payment information. These days, most call center data is stored in cloud-based servers. Some may feel as if this isn’t safe and prefer to have their data kept in on-site servers. However, technology has advanced significantly, and cloud-based servers rely on advanced network and system security controls to ensure that hackers cannot access a company’s sensitive information. Most call centers opt for cloud-based storage since on-site servers can be extremely costly to install and maintain. Troubleshooting is much easier with cloud-based servers, and usually involves only a phone call to tech support.

In order to keep information safe, call centers employ strict cyber security protocols to prevent hackers from gaining access into their servers. In addition, call centers aim to keep up with updated data protection laws. Most are PCI DSS and SOC 2 compliant. Depending on the industry a call center serves, leadership may also implement HIPAA, FISMA, or FedRAMP compliance standards. It’s critical for call centers to protect the data they store. Implementing strict standards prevents others from stealing information. For example, if an agent decides to write down a customer’s credit card number to ensure they aren’t missing any numbers, it can be very easy for that information to fall in the wrong hands. Call centers can be held liable if an agent is found to have been stealing credit card information.

Here’s how to analyze data in your call center: 

First, define your key performance indicators (KPIs): Identify the metrics that matter most to your business, such as average handle time (AHT), first call resolution (FCR), customer satisfaction (CSAT), and abandonment rate. 

Then identify relevant data sources: Collect data from various call center sources, such as call logs, customer surveys, quality assurance reports, and agent performance reports. Make sure you clean and organize your data by preparing data sets from different data sources and combining them into one system to avoid errors. Ensure your data is clean by identifying and removing duplicates, filling in missing fields, and ensuring accurate labeling of data points. Now you can move onto visualizing your data. Create visual representations such as charts, graphs, and dashboards to make it easier for key stakeholders to understand data sets and metrics. Finally, the fun part: analysis! Analyze your data by examining patterns, trends and anomalies to understand the story behind the data. Leverage key data analysis tools like pivot tables, heat maps, and regression analysis to extract insights.

Once you understand trends and patterns, you can develop actionable recommendations based on that analysis. This includes identifying areas of improvement within your call center, and creating an action plan for improvement that could encompass developing customer experience programs, agent training plans, or process optimization.

You’ll want to monitor progress and refine your processes on an ongoing basis. Continue monitoring metrics and operations to validate and refine assumptions, recommendations, and implementation efforts.

Operational: Operational analytics focus on the performance of call center operations, including metrics such as call volume, average handle time, first call resolution rate, and service level agreements (SLAs).

Customer Experience: Customer experience analytics measure customer satisfaction levels, such as customer surveys and feedback, net promoter score (NPS), customer effort score (CES), and customer sentiment analysis.

Quality Assurance (QA): Quality assurance analytics assess the performance of individual agents, measuring factors such as compliance with scripts, call scripts, and quality control protocols.

Speech: Speech analytics analyze customer conversations and agent call handling techniques, identifying speech patterns for trending insights related to customer satisfaction, call duration, call quality, and customer needs.

Predictive: Predictive analytics use machine learning and algorithms to predict future trends and behaviors based on historical data. This type of analytics can help call centers understand future call volume predictions, customer preferences, and trends to make more informed decisions.

Text: Text analytics evaluate customer interactions with non-voice customer channels like email, chat, and messaging platforms. Text analytics focuses on data mining techniques like categorization and sentiment analysis to dig out customer preferences and topics of discussion, customer interactions, and common problems.