Most businesses today collect customer feedback and monitor customer interactions across different channels. But collecting all this information doesn’t automatically tell you what needs to change. You might know that customer satisfaction has dropped or that your support team is receiving more calls than usual, but do you know why it’s happening and what you should do next?
That’s where customer experience analytics becomes useful. There are four main types of analysis, and each answers a different business question. If you’re new to the subject, you can first read our guide to customer experience analytics.
In this article, we’ll look at the four types, the metrics you should track, and the tools that can help your team use customer data better.
4 Types of Customer Experience Analytics
Each type of analytics looks at customer data from a different angle. Some help you understand past performance, while others help you find problems, prepare for what’s coming, and decide what action to take.
| Type | Question It Answers | Example Metric | Typical Tool |
| Descriptive | What happened? | CSAT, call volume | Dashboards, survey platforms |
| Diagnostic | Why did it happen? | Contact drivers, sentiment | Voice and conversation analytics |
| Predictive | What is likely to happen? | Churn risk, contact forecasts | Predictive analytics platforms |
| Prescriptive | What should we do? | Next-best action, routing accuracy | Agent assist, workflow automation |
Let’s understand how each type works and where it fits into your customer experience operations.
- Descriptive Analytics: What Happened?
Descriptive analytics is usually where businesses start because it gives them a clear picture of what has already happened. It helps your team look at customer interactions and support performance over a specific period, so you can understand whether things are improving or getting worse.
For example, you can track how many calls your contact centre receives every day, whether customer satisfaction scores have changed over the last quarter, or how long customers are waiting before getting a response.
Some of the key metrics to track include:
- Call and contact volume: How many customers are reaching out to your business, and through which channels?
- CSAT and NPS trends: Are customers happy with the support they receive, and are they more or less likely to recommend your business?
- Wait and response times: How long does it take your team to respond to customers?
A contact centre manager, for instance, may notice that call volumes increase between 11 AM and 2 PM, while average handle time also goes up during those hours. This information helps the team understand when support demand is highest and plan resources accordingly.
Typical tools: Reporting dashboards, business intelligence platforms, and customer survey tools.
However, knowing what happened is only the beginning. You still need to understand why those changes occurred.
- Diagnostic Analytics: Why Did It Happen?
Let’s say your customer satisfaction score drops suddenly. You can see the change in your dashboard, but that doesn’t explain what went wrong. Were customers unhappy with the response time? Were they being transferred between departments? Or were they repeatedly contacting your team because their problems weren’t getting resolved?
Diagnostic analytics helps you find these answers by looking deeper into customer conversations, feedback, and support activity.
Two areas are particularly useful here:
- Contact drivers: These help you identify the main reasons customers are contacting your support team and whether certain issues are becoming more common.
- Sentiment and emotional intensity: These help you understand how customers feel during conversations, including whether they sound satisfied, confused, frustrated, or increasingly upset.
Voice and conversation analytics tools are especially useful because they can analyse every recorded call instead of depending on a small sample that someone reviews manually. This gives CX teams a much better chance of spotting recurring issues and understanding what customers are actually experiencing.
In simple terms, diagnostic analytics helps you stop guessing and start working with the reasons behind your numbers.
- Predictive Analytics: What is Likely to Happen?
Once you understand what has happened and why, the next step is to look ahead. Predictive analytics uses historical customer data and existing patterns to estimate what may happen in the future.
For example, a business can identify customers who may be at risk of leaving, estimate which customers are likely to contact support again about the same issue, or forecast when call volumes may increase.
Some common applications include:
- Churn risk: Identifying customers who may stop using your product or service.
- Repeat-contact likelihood: Finding customers who may need to contact support again for the same unresolved issue.
- Contact volume forecasting: Estimating future call or chat volumes so teams can plan staffing and resources.
This is particularly useful for businesses that want to address customer problems before they become bigger issues. However, predictive analytics needs clean and reliable historical data. If your customer records are incomplete or inconsistent, the predictions may not be very useful. So, it’s better to get your basic reporting and data practices right before moving to this stage.
- Prescriptive Analytics: What Should We Do About It?
Prescriptive analytics takes things one step further. It doesn’t just tell you what happened or what might happen. It helps your team decide what action to take based on the information available.
For example, if a customer sounds frustrated during a call, the system may suggest transferring the interaction to a senior agent. Similarly, if an agent is handling a complex query, real-time prompts can help them find relevant information or decide the next step.
Some common applications include:
- Next-best action: Suggesting the most suitable action based on the customer’s issue, history, and current situation.
- Intent-based routing: Sending customers to the right team based on what they need, instead of making them go through unnecessary transfers.
- Real-time agent prompts: Providing agents with relevant information and suggestions while the conversation is still happening.
Tools such as AI agent assist and workflow automation help teams put these recommendations into action. This way, customer data becomes more than something you review in a monthly report. It starts helping your team make better decisions during everyday customer interactions.
Operational CX Metrics Most Teams Skip
Customer satisfaction and loyalty metrics are important, but they don’t tell you everything about how your support team is performing. You also need to look at the operational side because delays, repeat calls, and unresolved issues can directly affect the customer experience.
Here are four metrics worth tracking:
- First Response Time: The time your team takes to respond to a customer after receiving their query.
- Average Resolution Time: The total time it takes to resolve a customer issue.
- First Contact Resolution (FCR): The percentage of customer issues resolved during the first interaction without another call or follow-up.
- Referral Rate: The percentage of interactions referred or transferred to another team or department.
Along with these, keep tracking NPS, CSAT, Customer Effort Score (CES), Customer Lifetime Value (CLV), and churn. These metrics help you understand customer loyalty, satisfaction, effort, and long-term business value. You can explore our related guides to understand each metric in more detail.
Choosing CX Metrics and Tools Based on Your Business Maturity
You don’t need to track every possible metric or invest in an advanced analytics platform right away. What matters is choosing the right metrics for your current business needs and building from there.
If you’re just starting out then focus on CSAT and NPS to understand how customers feel about your business. Customer surveys are a good place to begin because they help you collect feedback without making things too complicated.
If your CX operations are growing, start tracking response time, resolution time, FCR, and CES. At this stage, contact centre analytics can help you understand customer interactions and identify operational gaps.
If your operations are more mature, add sentiment analysis and repeat-contact patterns to your reporting. Predictive analytics and AI-powered conversation analytics can help you uncover issues that basic reports may not show.
As your requirements grow, your tools can grow with them. You can begin with surveys and eventually introduce predictive analytics platforms when you have enough reliable data to support them.
The idea isn’t to use the most advanced tool available. It’s to use the one that helps your team answer the right questions.
Data Practices That Keep Customer Experience Analytics Trustworthy
Even the best analytics platform cannot give you reliable insights if the data going into it is incomplete or poorly managed. So, before making decisions based on customer data, make sure you follow a few basic practices.
- Collect data with a clear purpose: Track information that connects to a business goal or a specific customer experience problem instead of collecting everything without knowing how you’ll use it.
- Store customer information securely: Use proper access controls and make sure sensitive customer data is available only to authorised people.
- Clean and organise your data: Remove duplicate records, correct missing information, and keep data consistent across your systems.
- Don’t confuse correlation with causation: Just because two things happen around the same time doesn’t mean one caused the other. Always look at the context before deciding what needs to change.
These practices may seem basic, but they make a big difference to the quality of your analysis and the decisions you make from it.
Conclusion
Understanding customer experience is one thing. Knowing what to do with that information is what makes the difference. With InsightX by CXFirst, your team can analyse customer conversations and uncover useful insights at scale.
Book a Demo to see how InsightX can support your CX operations.
FAQs
No. You can start with descriptive analytics and gradually add the other types as your data, technology, and business requirements grow.
Start with descriptive analytics. First understand your current performance through metrics such as CSAT, NPS, and response times. Then move to deeper analysis.
CX metrics are individual measurements, such as CSAT or response time. CX analytics looks at these measurements and other customer data to identify patterns, understand problems, and guide business decisions.
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