Understanding Contact Center Data: What to Track and How to Track It

The acceleration of technology in recent years has dramatically reshaped consumer behavior and customer expectations. Rather than relying on a single phone line and basic call-center metrics, modern contact centers now engage customers across multiple channels—voice, email, chat, social media, and messaging apps—and must analyze far richer data sets to deliver an exceptional customer experience.

Keeping up with this rapid evolution requires organizations to collect and interpret a broad range of quantitative and qualitative data points. Understanding what to track, why it matters, and how to use these insights is critical for any brand that wants to stand out in today’s CX-driven marketplace.

Get Smarter With Contact Center Data: Quantitative vs. Qualitative

In today’s CX-driven world, tracking the right contact center data is essential to improving service, driving revenue, and staying competitive.

As Ashok Narayanan, strategic technology officer at Working Solutions, puts it:
“Technology enables you to delve into data, find the root causes, and make correlations with buying patterns—so you can present the most effective options to clients.”

With omnichannel communication now standard, the volume and variety of data can be overwhelming. The key is to focus on two types:

• Quantitative data — hard metrics like call resolution rate, handle time, or sales per agent. These are measurable, objective, and often already tracked.

• Qualitative data — soft inputs like sentiment, satisfaction, or tone. These reveal the “why” behind outcomes but require clear definitions and consistent interpretation.

Both are critical. Quantitative data tells you what happened. Qualitative insights help you understand why—and how to improve. Successful brands align the two to deliver smarter, more personalized customer experiences.

Quantitative metrics tell you what happened; qualitative insights explain why.

Six Essential Types of Contact Center Data

To get the full picture of performance and customer behavior, brands should track six key categories of contact center data:

• Text-Based: Emails, chats, SMS, and social DMs. Analyze sentiment, keywords, and issue types to uncover patterns.

• Voice-Based: Calls and transcripts. Use speech analytics to measure tone, agent performance, and compliance.

• Self-Service: Chatbot, IVR, and knowledge base usage. Identify where users succeed, stall, or abandon.

• Agent Desktop Data: Track time per task, app usage, and inputs to improve workflows and reduce errors.

• Cross-Channel Journeys: Map end-to-end experiences across touchpoints to identify friction and optimize handoffs.

• Post-Interaction & Back-Office: Analyze surveys (CSAT, NPS), billing interactions, and backend follow-ups for true service impact.

Collecting multiple data types gives a 360° view of customer interactions.

Turning Data Into Actionable Insights

Data is only valuable if it drives decisions. Combine AI-powered analytics with strong data governance to:

• Spot trends early

• Automate tasks where possible

• Reduce agent workload

• Improve bot and knowledge base performance

• Align actions with business KPIs

Work With Data Experts

A great data strategy turns complexity into clarity.

At Working Solutions, we help organizations harness both quantitative and qualitative insights from self-service, voice and omnichannel experiences.

Talk to an expert to turn your contact center data into strategic growth.