A Series B SaaS company I advised had 47 metrics on their product dashboard. They reviewed them weekly in a 90-minute meeting. Nobody could explain what most of them meant. The ones people did understand were often contradictory. Engagement was up but retention was down, signups were growing but activation was flat.
I asked the CEO: "If you could only look at 7 numbers to understand product health, which would they be?" He could not answer. Neither could the VP of Product. Neither could the Head of Data.
That is the problem. When you track everything, you understand nothing. After working with dozens of B2B SaaS teams, I have landed on 7 metrics that reliably separate healthy products from ones that are slowly dying. They are not the only metrics that matter, but they are the ones you should look at first.
1. Net Revenue Retention (NRR)
What it measures: The revenue retained from existing customers after accounting for churn, downgrades, and expansion.
Why it matters: NRR is the single best indicator of product-market fit in B2B SaaS. An NRR above 100% means your existing customers are growing their spend faster than you are losing revenue to churn. This means your product is becoming more valuable over time, not less.
Benchmarks:
- Below 90%: Your product has a serious value delivery problem
- 90-100%: You are treading water. Churn and contraction offset expansion
- 100-120%: Healthy. You are growing without needing new logos
- Above 120%: Best-in-class. Companies like Snowflake and Datadog consistently hit 130%+
What to do with it: NRR is a lagging indicator. By the time it drops, the damage happened months ago. Track it monthly but investigate the leading indicators: usage trends, support ticket sentiment, and expansion pipeline.
The PM's role is to identify which product behaviors correlate with expansion and which correlate with churn, then build accordingly. If customers who use your API expand 3x faster, invest in API documentation and onboarding. If customers who skip onboarding churn at 2x the rate, invest in activation before building new features.
2. Activation Rate
What it measures: The percentage of new signups who complete the key actions that predict long-term retention.
Activation rate is the hinge metric of any SaaS product. A user who does not activate will not retain, will not expand, and will not refer. Everything upstream of activation (marketing, sales, onboarding) is wasted if the user does not cross this threshold.
How to define it: Your activation event should be the moment a user first experiences the core value of your product. For Slack, it was sending 2,000 team messages. For Dropbox, it was putting a file in the folder. For your product, it is whatever action most strongly predicts 90-day retention. Finding this action requires a correlation analysis between early behaviors and long-term retention. It is one of the most valuable analyses a product team can run.
Benchmarks:
- Below 20%: Your onboarding is broken or your product requires too much effort before value
- 20-40%: Average for B2B SaaS. There is significant room to improve
- 40-60%: Strong. Your time-to-value is working
- Above 60%: Excellent. Your product likely has strong self-serve capability
What to do with it: Run a correlation analysis between early user actions (within the first 7 days) and 90-day retention. Find the action with the strongest correlation. That is your activation event. Then rebuild your onboarding to drive users toward that action as fast as possible.
3. Feature Adoption Rate
What it measures: The percentage of active users who use a specific feature within a given time period.
Tracking overall product usage is not enough. You need to know which features are driving value and which are dead weight. Feature adoption rate tells you whether the things you built are actually being used.
How to track it: For each major feature, measure: (Users who used the feature in the past 30 days) / (Total active users in the past 30 days). Track this over time to see adoption curves. Is usage growing, flat, or declining?
What the numbers tell you:
- A feature with high adoption and high retention correlation is your core. Protect it.
- A feature with low adoption but high retention correlation has a discoverability problem. Fix the onboarding.
- A feature with high adoption but no retention correlation might be interesting but not valuable. Investigate why.
- A feature with low adoption and no retention correlation is a candidate for removal.
What to do with it: Build a feature adoption dashboard that your team reviews monthly. Use it to inform prioritization decisions. Do not build Feature B until you understand why Feature A is underperforming.
4. Time-to-Value (TTV)
What it measures: How long it takes a new user to experience the product's core value for the first time.
Time-to-value is the most underrated metric in SaaS. It directly drives activation rate, but it also shapes user perception. A product that delivers value in 5 minutes creates a fundamentally different impression than one that requires a 3-week implementation.
How to measure it: Define your "value moment" (the first time a user accomplishes the job they signed up for), then measure the median time from signup to that moment. Track this by segment. Enterprise users may have longer TTV by design, but self-serve users should not.
Benchmarks:
- Under 5 minutes: You have a strong self-serve product (think Loom, Calendly)
- 5-30 minutes: Acceptable for tools requiring some setup
- 30 minutes to 7 days: You need guided onboarding or customer success support
- Over 7 days: You are losing a significant portion of your signups before they see value
What to do with it: Map every step between signup and value moment. Identify which steps have the highest drop-off. Then either eliminate the step, simplify it, or provide default values. Every unnecessary step between a user and their first "aha moment" is a leak in your funnel.
5. NPS (or a Satisfaction Signal)
What it measures: Customer willingness to recommend your product, typically on a 0-10 scale.
NPS, developed by Fred Reichheld at Bain, is imperfect. It is a lagging indicator, it does not tell you why someone is or is not a promoter, and the methodology has well-documented flaws. But it is still useful as a directional signal, especially when tracked over time and segmented by cohort.
Why it still matters: The absolute number is less important than the trend. NPS dropping quarter over quarter is a warning, even if the absolute score looks fine. NPS that varies wildly by segment (promoters in enterprise, detractors in SMB) tells you something specific about where your product-market fit is strong and where it is not.
How to use it well:
- Measure it quarterly, not just annually
- Segment by customer size, tenure, and use case
- Always follow up with detractors to understand the "why"
- Do not optimize for the score. Optimize for the underlying experience
The alternative: If NPS feels too blunt, consider Customer Effort Score (CES) or product-specific satisfaction surveys. The specific instrument matters less than having a consistent qualitative signal alongside your quantitative metrics.
Calculate your own NPS from your latest survey data:
6. DAU/MAU Ratio (Engagement Depth)
What it measures: The proportion of monthly users who engage on any given day. Also called the "stickiness ratio."
A DAU/MAU ratio of 50% means that on any given day, half of your monthly users are active. This tells you how embedded your product is in daily workflows. For B2B SaaS, this metric separates tools that are essential from tools that are used occasionally.
Benchmarks:
- Below 10%: Your product is used occasionally, maybe for monthly reports or quarterly planning
- 10-20%: Weekly usage pattern. Typical for project management and analytics tools
- 20-40%: Strong daily engagement. Your product is part of the workflow
- Above 40%: Exceptional. Your product is a daily essential (think Slack, Figma, or Linear)
Context matters: Not every product should target high DAU/MAU. A quarterly planning tool with 10% DAU/MAU is fine. A communication tool with 10% DAU/MAU has a problem. Compare your ratio to products with similar usage patterns, not to all SaaS.
What to do with it: If your DAU/MAU is lower than expected for your category, investigate what drives daily return. Are there use cases that should be daily but are not? Can you add notifications, digests, or triggers that bring users back? Can you embed your product more deeply into existing workflows?
7. Expansion Revenue as Percentage of New ARR
What it measures: How much of your new annual recurring revenue comes from existing customers versus new logos.
Why it matters: In mature B2B SaaS, expansion revenue should be a major growth engine. If 100% of your growth comes from new customers, you are on a treadmill. You have to keep acquiring just to maintain growth. If 30-50% comes from expansion, your growth compounds.
Benchmarks:
- Below 15%: Your product is not expanding within accounts. Pricing or packaging may be the issue.
- 15-30%: Average. You have some expansion but it is not systematic.
- 30-50%: Strong. Your land-and-expand motion is working.
- Above 50%: Exceptional. Your product has strong network effects or usage-based growth.
What to do with it: Map the expansion triggers. What causes a customer to upgrade, add seats, or buy more modules? For usage-based products, identify the thresholds where usage bumps into pricing tiers. For seat-based products, identify which features drive department-wide adoption beyond the initial buyer.
Want to practice tracking these metrics on a B2B SaaS product from day one? Explore our collection of B2B SaaS ideas with pre-defined target metrics, pricing models, and expansion strategies. Filter by Finance & Billing, HR & Operations, or Analytics & Data categories.
How These 7 Metrics Connect
These metrics are not independent. They form a causal chain:
TTV drives Activation Rate (faster value = more activation). Activation drives Feature Adoption (activated users explore more). Feature Adoption drives DAU/MAU (more features used = more reasons to return daily). DAU/MAU drives NPS (daily users are more likely to be promoters). NPS drives NRR (promoters retain and expand). NRR drives Expansion Revenue (retained customers grow).
If your NRR is dropping, do not start by trying to fix NRR. Trace the chain backward. Is it an activation problem? A TTV problem? A feature adoption problem? The root cause is usually 2-3 steps upstream of the symptom.
What I Would Cut from Your Dashboard
If you are tracking more than these 7 plus a handful of feature-specific metrics, consider cutting:
- Vanity metrics: Total signups, page views, total registered users. These only go up and tell you nothing about health.
- Duplicative metrics: If you track NRR, you do not also need gross churn, net churn, logo churn, revenue churn, and dollar-weighted churn as separate dashboard items. Pick one.
- Lagging-only metrics: Annual contract value is useful for finance but does not help a PM make product decisions this sprint.
Seven metrics. Reviewed weekly. With enough depth to understand the why behind each number. That is all you need to know if your product is healthy. The key distinction is being data-informed rather than data-driven. Using metrics to guide judgment, not replace it.
Start with these seven. Get them on a single dashboard. Review them every Monday morning for 15 minutes. Within a month, you will understand your product's health better than you did with 47 metrics and a 90-minute meeting. Simplicity is not a compromise. It is a feature.
The best dashboard I have ever seen fit on a single screen: seven numbers, their week-over-week trends, and a red/yellow/green indicator for each. Anything more is noise. Your job is not to track every number. It is to track the right numbers and understand them deeply.