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Which Important SaaS Metrics Should You Track?
min read
Ben Hale
Are you tracking the right metrics to help your SaaS business grow?
The pathway to predictable growth is anything but clear. To achieve sustainable scale, you need to focus on the right key SaaS metrics. It all depends on the context of your business—its unique operations, industry, growth stage, and more.
This checklist will help you filter 100+ potential metrics down to the 8-12 that drive sustainable and predictable SaaS business growth.
The SaaS Business Metrics Checklist: 5 Criteria for Evaluation
Here are the five criteria we use to evaluate which metrics matter most:
- Strategic Alignment
- Actionability
- Predictive Power
- Stage Relevance
- Reliability
When you evaluate operational metrics with these key criteria, you ensure effectiveness and increase the likelihood of accelerated and sustained growth.
Download this free plug-and-play scorecard to evaluate your SaaS metrics.
1. Strategic Alignment
To start, put first things first. The metrics you prioritize must directly tie to core business objectives like revenue growth, retention, or efficiency. If you focus on metrics that don’t align with your strategy, you’ll use your resources in the wrong places and fail to achieve key objectives.
This criterion is mission-critical. If a metric you’re considering doesn’t pass this litmus test, stop here and find one that does.
Strategic Metric Examples
Here are some strategic metrics you should consider if they align with your core objectives:
- Annual Recurring Revenue (ARR) growth - Track this if you're focused on measuring how fast your business is scaling, optimizing for growth goals.
- Net Revenue Retention (NRR) - Use this to see how effectively you're upselling existing customers and preventing customer churn.
- Revenue multipliers - Monitor these when you need to balance both growth and efficiency for profitability.
Case Study: Slack’s North Star Engagement Metric
Slack uses "Number of Teams Actively Using Slack" as a north star metric. This measurement supports Slack's strategic goal of increasing engagement and adoption. It’s also reflective of the value teams get from Slack’s collaboration-enhancing features.
2. Actionability
Can you influence the metric? The ability to influence what you measure is essential. The more control you have over actions that affect the outcome, the more effective the metric.
Actionable Metric Examples
Here are actionable metrics you can directly influence through your team's actions:
- CAC Payback Period - Track this to monitor sustainable acquisition spend, which you can control through sales and marketing efforts adjustments.
- Burn Multiple - Use this to flag capital-efficient growth (e.g., $1M net new ARR per $1.5M burned), which you can influence through revenue activities.
- Feature Adoption Scores - Monitor these to identify underused product areas where your product and engineering teams can make UX improvements.
- Customer engagement score - Track this to identify which product features drive retention and usage patterns for your users.
Case Study: Zendesk’s Customer Satisfaction Metric
Zendesk employs Net Promoter Score (NPS) to gauge customer satisfaction. By monitoring the comments of both detractors and promoters, Zendesk can take immediate action to improve customer experience.
3. Predictive Power
Can you use the metric to predict outcomes? The metrics you prioritize should be leading indicators that forecast business performance. This will help you gauge what needs to be done to compound desired outcomes and avoid unwanted outcomes. It also helps to prioritize the initiatives most likely to affect these outcomes.
Example Prediction Metrics
Here are leading indicators you can use to forecast your business performance:
- CMGR (Customer Monthly Growth Rate) - Track this as it often correlates with faster ARR growth when you pair it with NRR.
- Daily Active Users (DAU) and Monthly Active Users (MAU) - Monitor these numbers as they often telegraph higher retention rates you'll see later.
- Monthly recurring revenue - Use this to assess new customers and growth trajectory.
- LTM Free Cash Flow Rate - Track this to assess your company's ability to generate cash for funding future operations.
Case Study: Docusign’s Predictive Engagement Metrics
DocuSign used analytics on engagement metrics to predict which premium features would drive upgrades for free users. This strategy resulted in a 5% uptick in freemium-to-paid conversion rates, a significant increase since they were seeing 130,000 new daily users.
4. Stage Relevance
Is the metric a good indicator for a growth-stage SaaS company? Context matters. Many metrics are best suited for a specific level of company maturity. Exercise caution with metrics adopted from larger or smaller-scale companies; they may not be relevant to your business at its current stage.
Example Growth Stage-Relevant Metrics
Here are metrics that matter most for your growth-stage SaaS company:
- Gross Revenue Retention - Track this to understand your baseline retention without expansion revenue, giving you a clear view of churn impact.
- NRR - Focus on this as it signifies the sustainability and scalability you need as a growth-stage company.
- Gross Margin - Track this as profitability becomes more essential while you scale your business.
Case Study: Buffer’s Stage-Relevant Metrics
Buffer, a growth-stage SaaS company, prioritized Average Revenue Per User (ARPU), customer churn rate, and Customer Lifetime Value (CLTV). These metrics helped inform pricing and customer success strategies for sustainable growth. Since using these metrics, Buffer has doubled ARR to more than $20M.
5. Reliability
Is the metric reliable? Accurate data collection and transmission is critical for a metric's success. You can assess metric reliability with consistency, accuracy, granularity, and verifiability.
This is especially important when preparing your SaaS business data for AI operations, where poor data quality undermines analytical ROI. The more you can trust a metric, the better it will meet all of the other criteria in this list.
Example Reliability Metrics
- Customer Lifetime Value (LTV) - Requires accurate historical data across multiple systems (billing, usage, support) and a consistent calculation methodology to be reliable.
- Customer Satisfaction Score - Only reliable when you maintain consistent survey methodology, timing, and sample representation. Inconsistent question phrasing, varying survey frequencies, or biased sample groups (like only surveying happy customers who respond to emails) can make this metric misleadingly positive or negative.
Case Study: eVisit's Demo Metric Evolution
One time at eVisit, we initially tracked "total demos" but found this metric unreliable for predicting revenue. Switching to "qualified demos" provided consistent, actionable data that accurately forecasted pipeline progression.
Making the SaaS Metric Cut
Now that you know the five criteria, here's how to apply them: Prioritize metrics that score at least 3 out of 5 on our evaluation
Take Net Revenue Retention as an example. It scores high because it...
- Aligns with your strategic retention goal
- Helps you predict future growth trends
- Remains critical whether you're at $1M or $50M ARR
That's 3 out of 5, making it worth tracking.
Compare that to vanity metrics like "total registered users." These typically fail on actionability (you can't directly influence registrations to drive total revenue) and predictive power (registrations don't reliably forecast actual business outcomes), so they score poorly and shouldn't make your priority list.
Analyzing SaaS Data with Machine Learning
Even with the right metrics in place, markets change rapidly and constantly. Your SaaS business needs to keep up. Machine learning models are evolving to automate data analysis.
You can use this technology to adjust metric priorities based on dynamic variables like market conditions. It can also identify additional strategies for moving the needle on key metrics, just like an analyst would.
You can use this technology to adjust metric priorities based on dynamic variables like market conditions. It can also identify additional strategies for moving the needle on key metrics, just like an analyst would.
For growth-stage SaaS companies, implementing AI in business operations can transform how you analyze and respond to metric changes.
Metric-to-Action Mapping Framework: What to Do When the Needle Moves
Tracking the right SaaS metrics is only half the game. The other half? Knowing exactly how to respond when one of them blinks red or surges green. Below is a high-level framework to help you turn metric shifts into decisive action.
Metric-to-Action Playbook
Beyond selecting the right metrics, implementing robust KPI tracking methods ensures consistent measurement and actionable insights for revenue churn prevention and customer retention optimization. This is especially critical for subscription-based companies where understanding the average customer lifecycle helps predict which paying customers will expand their usage versus those at risk of churning. The success of your entire business model depends on these insights.
Real-Time Responses Based on Your Ops Data with Chief
For growing SaaS companies, the margin between insight and action is thin. Chief doesn’t just surface your key metrics, it helps diagnose root causes and recommend specific, proven responses based on your ops data. You’ll know not only what’s changing, but what to do about it before it impacts growth.
👉 Want us to analyze your top SaaS metrics and surface 3 actionable insights for free?
Schedule a consultation to get started.
