Analytics vs metrics in Customer Success: which one matters more?

Customer Success Managers (CSMs) work with a lot of numbers – NPS, customer health scores, product adoption rates, renewal forecasts and more. These metrics help track performance, but they don’t always explain why things happen.

For example, an NPS drop might suggest customer dissatisfaction, but it doesn’t tell you what caused it or how to fix it. That’s where analytics come in. While metrics show trends, analytics help uncover patterns, predict risks, and guide proactive engagement.

Relying only on metrics can lead to either data overload or missed insights. But when combined with analytics, they provide a clearer picture of customer behavior.

In this article, we’ll break down the key differences between analytics and metrics, explain how they work together, and show how CSMs can use them to improve retention and expansion.

Analytics vs metrics: What’s the difference?

It’s easy to assume that tracking more data leads to better decisions, but not all data is equally useful. Metrics and analytics serve different purposes, and understanding the distinction helps Customer Success teams move beyond reporting numbers to driving real outcomes.

How metrics and analytics compare

Both metrics and analytics are valuable, but they serve different purposes. Metrics provide visibility, while analytics turn data into action. To make informed decisions, CSMs need to move beyond just tracking numbers and start analyzing patterns that impact retention and growth.

Next, we’ll look at how to use both metrics and analytics effectively to drive Customer Success.

Why CSMs need both analytics and metrics

Metrics help Customer Success teams measure performance, but they don’t always provide the full picture. A low NPS score, an increase in churn or a dip in product usage might tell you there’s an issue, but without context, it’s just a number. 

This is where analytics come in – they help uncover patterns, identify risks, and guide proactive solutions.

For example, imagine your NPS drops from 60 to 50. The metric tells you satisfaction has declined, but it doesn’t explain the cause. By analyzing customer feedback, you might find that customers with lower NPS scores are frustrated with onboarding delays. 

This insight allows your team to take action – improving onboarding with automated playbooks and proactive engagement.

Without analytics, CSMs risk making assumptions based on surface-level data. A drop in a customer’s health score could mean they’re disengaged, or it could mean they recently submitted multiple support tickets. 

Understanding the root cause helps CS teams take the right next steps instead of reacting blindly. 

In the next section, we’ll look at how to apply both metrics and analytics in a way that leads to better customer outcomes and stronger retention.

How to use analytics and metrics effectively in Customer Success

Tracking data is only useful if it leads to meaningful action. Many CS teams track too many metrics, making it hard to identify what actually matters. Others focus on numbers without analyzing the reasons behind them. 

The key is to track the right metrics and use analytics to understand customer behavior and drive action.

1. Tracking the right metrics (don’t track everything, track what matters)

Not all metrics are equally important. Instead of tracking everything, focus on those that align with your key Customer Success goals.

• For retention: Track churn rate, renewal rate and net revenue retention (NRR) to understand how well customers are staying and growing with your product.
• For adoption: Monitor feature usage, onboarding completion rate and time to first value (TTV) to ensure customers are getting the most out of the product.
• For satisfaction: Measure NPS, CSAT and CES (Customer Effort Score) to gauge customer sentiment and ease of use.

Manually tracking these metrics across different platforms can be time-consuming and fragmented. A CS tool like Velaris streamlines the process by centralizing key data, and automatically pulling in insights from sales, onboarding, support and product teams. 

This gives you a complete view of each customer’s health, making it easier to identify risks and opportunities.

2. Using analytics to uncover patterns and predict customer behavior

Metrics tell you what’s happening, but analytics help you understand why. Instead of just noting churn or low NPS, use analytics to find patterns and predict risk before it’s too late.

Ask questions like:

• Which customer behaviors correlate with higher renewal rates?
• What is the most common reason for churn in a specific segment?
• Which customers are most likely to expand based on product usage trends?

AI-driven sentiment analysis, like the one in Velaris, can flag emails or support tickets that indicate dissatisfaction, allowing CSMs to step in before churn happens. 

Predictive analytics can also highlight at-risk accounts based on engagement levels and feature adoption, helping teams address potential problems before they impact retention.

3. Moving from data collection to action

Tracking data alone isn’t enough – CS teams need to act on the insights they gather. For example, if a customer’s health score drops, analytics can reveal that the decline is linked to low engagement and lack of feature adoption. 

Once the cause is identified, the next step is taking action:

Step 1: Trigger a personalized outreach campaign using automated email sequences.

Step 2: Assign a playbook to the CSM, outlining steps to proactively re-engage the customer.

Step 3: Set up an automated workflow to deliver in-app messages that highlight underused features and encourage adoption.

By leveraging tools like Velaris, you can automate these processes, ensuring that at-risk customers receive timely, relevant engagement while freeing up your team to focus on higher-value interactions.

Using both analytics and metrics ensures that CS teams are not just tracking numbers but actively improving retention and expansion. Next, we’ll look at common mistakes teams make when working with data.

Common mistakes when dealing with analytics and metrics

Collecting data is easy, but using it effectively is where many Customer Success teams struggle. Some focus on the wrong numbers, others track too much data without a clear strategy and many fail to turn insights into action. 

Here are some common mistakes to avoid:

1. Focusing on vanity metrics

Numbers like a high NPS or increased login frequency can look good on reports, but without context, they don’t tell the full story. For example, an NPS of 70 might seem strong, but if detractors are churning at a high rate, that number alone won’t help retention. 

So, instead of just tracking scores, analyze how they correlate with renewals, expansion and long-term engagement.

2. Tracking too much data without knowing what to do with it

More data doesn’t always mean better insights. If your dashboard is cluttered with dozens of metrics, it becomes harder to identify what actually matters. 

Instead, focus on a core set of metrics tied to specific goals – like churn rate for retention, feature adoption for product engagement and customer health scores for risk assessment.

3. Ignoring predictive analytics and only reacting to problems

Many teams rely on historical data to make decisions, but reacting after issues arise can mean missing opportunities to prevent churn or drive adoption. 

Predictive analytics can help spot risks early, such as customers whose usage patterns indicate potential churn. AI-powered tools, like Velaris’ sentiment analysis, can also detect dissatisfaction in customer interactions before it escalates.

4. Failing to close the feedback loop

Collecting NPS, CSAT or feature request data is only useful if it leads to action. If customers provide negative feedback but never hear back, they may not see the value in staying. 

Instead, follow up with detractors, address concerns and use their feedback to drive meaningful improvements. Avoiding these mistakes helps Customer Success teams turn data into real impact. 

Conclusion

Metrics and analytics both play an important role in Customer Success, but using them effectively requires more than just tracking data. Metrics help measure performance, while analytics provide the context needed to take action. 

By focusing on the right data, using analytics to uncover patterns and proactively addressing risks, CSMs can make better decisions that lead to higher retention and expansion.

The challenge is managing this data efficiently without getting lost in numbers or missing critical insights. A Customer Success platform like Velaris can help by centralizing key metrics, providing predictive analytics and automating actions based on customer behavior – so you can spend less time sorting through data and more time engaging with customers.

If you’re looking for a way to make data-driven decisions more efficiently, book a demo today to see how Velaris can help.