Best customer service analytics

Service analytics tools help you understand how well your company supports customers and encourages purchases.

These insights can help you raise performance, strengthen client relationships or improve sales outcomes.

Common types and use cases include:

• Operational performance analytics. Track team efficiency, response times and workflow bottlenecks.

• Service-to-sales analytics. Understand how service interactions influence pipeline health, deal outcomes and revenue growth.

• Customer support analytics. Measure ticket volume, resolution rates and agent performance.

• Customer experience (CX) analytics. Monitor satisfaction trends and sentiment across interactions.

• Self-service analytics. Analyze Knowledge Base and chatbot usage to improve automated support.

Unlike tracking performance through Microsoft Excel spreadsheets, these tools offer real-time, actionable insights into service delivery and business impact.

Customer service data analytics help you understand how customers request and receive help from your team.

For example, customer service analytics software (such as Qualtrics or Chattermill) automatically reviews tickets, conversations and feedback to create reports.

These providers help you reveal trends, recurring issues and performance gaps that you can use to refine your service.

Service analytics platforms and data integrations show what customers need, how your workflow performs and where your team can create smoother experiences.

These types of business intelligence tools (or BI tools) offer data models that allow you to make decisions that strengthen service, client and sales management.

Typical functions help you:

• Shorten response and resolution times

• Increase customer satisfaction and loyalty

• Reveal product or process problems early so you can fix them

• Use team capacity more efficiently

The metrics your service-led business tracks depend on your industry and goals.

Choose those that help you assess operations, support and sales.

For example, data teams often value key metrics like:

• Service uptime or reliability. Measure system or product availability for clients.

• Feature usage or adoption rates. See which parts of your service clients rely on most.

• Response or turnaround time. Track efficiency in delivering your service.

• Client satisfaction and retention. Gauge how well the service meets expectations.

• Revenue or upsell generated. Link service performance to business impact.

• Service-related escalations or issues. Identify problem areas that could affect client success.

AI-driven tools help business users speed up data analysis, uncover hidden patterns and direct service teams more efficiently.

For example, Pipedrive’s AI report generator makes data exploration far easier.

Simply ask questions in natural language (e.g., “How is my sales team performing?”) and get custom CRM insights in seconds.

You can also use the API to embed your favorite third-party AI apps and further enrich customer data with insights.