To improve overall customer experience, optimize call center operations, and ensure business growth, contact center analytics is the stepping stone. While converting data into relevant analytics is challenging, this blog will help you understand call center analytics and best practices. With this, you can deduce actionable insights that impact positive business outcomes.

Contact center analytics gives you a real-time 360-degree view of historical activities and customizable and pre-made reporting templates. In addition, call center analytics also helps you determine the potential areas that demand improvement to deliver excellent customer-supporting outsourcing services.

What Is Call Center Analytics?

Simply put, call center analytics is a process that involves call center data collection and analysis to enhance customer experience, customer service, agent performance, as well as operational efficiency. However, contact center analytics is not limited to call times. It also factors in certain human elements that influence how the calls are carried out, which includes customer experience. It is a key component for internal reporting optimization that provides actionable insights leading to data-driven decisions, better business processes, and a deeper understanding of customers.

Contact center analytics streamlines the process of tracking and reporting, to allow businesses to monitor and evaluate the metrics derived from call centers accurately and easily. With real-time notifications and alerts, supervisors can promptly respond to unforeseen issues and negate the risk of an escalation. In addition, cutting-edge technology, and AI equip analytics software with custom dashboards, behavioral predictions, and real-time updates.

Contact center analytics uses data from various communication channels and sources including voice and video recordings, voice call transcripts, chat history, CRM software, event logs, customer surveys, and IVR.

What Are The Different Types of Contact Center Analytics?

There are five key types of contact center analytics to consider:

1. Speech Analytics
   Speech analytics is one of the primary contact center analytics that gives insight into the interaction of customers with the call center by analyzing audio streams, voicemail, and IVR responses. This contact center analytics combines real-time transcript with Machine Learning (ML) and Artificial Intelligence (AI) to differentiate between speaks, pick up specific trigger words and keywords, remember speech patterns, and determine tone changes during the conversation. Contact centers analyze call recordings and transcripts to create summaries with deeper insights into customer sentiment and intent, agent performance, customer information and history, FAQs, recurring complaints and issues, and product-related information
2. Interaction Analytics
   Interaction analytics is another important contact center analytics that emphasizes interactions between customers and contact center agents across multiple channels. The primary goal of this contact center analytics is to enhance agent training, employee engagement, and customer satisfaction while revealing real business communications and customer relationships. Contact center analytics collates data from multiple sources including web chat, social media, emails, and voice calls. Furthermore, data is analyzed to allow experts to evaluate communication channels and pinpoint areas that need improvement.
3. Predictive Analytics
   Predictive analytics is a vital contact center analytics that uses historical data of contact centers to predict customer behavior. It is primarily driven by AI, ML, and automation, involving multiple sources of historical data. With a larger data pool and relevant data, this tool allows you to discover the factors influencing conversion. As a result, it ensures accurate prediction of successful follow-up calls. Accumulation of relevant historical data ensures the accuracy of prediction. With this contact center analytics, call centers can forecast conversions and detect fraud.
4. Text Analytics
   Text analytics is a critical contact center analytics that uses Natural Language Processing (NLP). It derives insights from text data that does not depend on speech transcription. It highlights crucial customer data like customer information and sentiments, recurring issues, etc. Text analytics is not limited to voice messages or communication. Data can be analyzed from multiple sources including customer survey feedback, social media comments and DMs, SMS conversations, and Chat Bot/Live Chat.
5. Desktop & Mobile Analytics
   Desktop analytics is an important contact center analytics that monitors, measures, and reports desktop computer performance. It helps businesses identify issues with security vulnerabilities, computer bandwidth, and allied problems. It can also monitor agent activities on organization desktops, providing insights into agents’ productivity.Mobile analytics, on the other hand, is a contact center analytics, that monitors and reports on various mobile devices and their service quality. It is used by companies with the mobile app for businesses or customers using the mobile version. It provides insights into KPIs like mobile app customer interactions, usage rate, issues with user interface, conversion rate, and CTRs.

Contact Center Analytics Best Practices

Successful call centers and customer service outsourcing companies rely on advanced software to track and review agent performance. The idea is to make data more easily accessible. Right communication channels and proper integrations are the need of the hour to derive vital contact center analytics. All businesses, regardless of size and volume, should follow the right approach:  

Real-Time Tracking for Supervisors and Agents  

Contact center analytics depends on real-time tracking. Using this, managers can get an overview of the data collected from the advanced analytics software with updates. Supervisors can leverage this data to detect and fix issues promptly.  This ensures customers are happy, reduces pressure on agents, and lowers attrition rates.

Data Integrations  

Data integration enables companies to synchronize data from multiple sources, including third parties. Hence, data from third-party CRM software such as Zapier, HubSpot, or Salesforce has been integrated automatically with the software to be included in CX analytics, reporting, and operations.  

It is imperative for contact centers to be integrated with CRM, email, and team chat. Besides collecting customer satisfaction and agent performance data, it also gathers insights for the team. Cloud-based contact centers also use secure APIs for exchanging data with services, which helps delve deeper into contact center analytics.   

Prompt Access To Call Data  

Call center data is available in multiple forms. It is often overwhelming to organize things if the correct strategy is not applied. Instead of storing data in hidden repositories, analytics software comes with pre-built reports connecting to vital business metrics.   

Using these reports, you can quickly convert data into actionable insights instead of struggling to build custom dashboards.  This makes the process of deriving contact center analytics easier for customer support outsourcing companies.     

Performance Metrics Tracker with Actionable Insights  

Considering that the one-size-fits-all formula is not applicable to all businesses, it is very important to come up with unique ways to track performance metrics. Reports definitely help you leverage call data, but they are usually not enough to cover all vital KPIs. Your business needs a robust analytics platform to track crucial performance metrics that matter to your unique business goals and add to the contact center analytics.   

Some of the common metrics like FRT (First Response Time), FCR (First Contact Resolution), ASA (Average Speed of Answer), AHT (Average Handle Time), Average Waiting Time, and Call Volume Trends. Monitoring these metrics throughout the customer journey enables you to create an excellent customer success process. You can only achieve deeper engagement with customers if you think beyond calls.   

Omnichannel Outlook Towards Customer Satisfaction  

It is pertinent to understand that contact center analytics goes hand in hand with other aspects of the business. Only a good analytics system unites cross-channel analytics and leverages a prudent omnichannel approach.   

Find a solution that is easy to integrate with the data of your contact center, such as live chat and SMS for text analytics, interactive AI response, and marketing data through emails, website visits, and campaigns.