Here’s the bottom line for any B2B SaaS founder:
- Most of us are tracking the wrong metrics. We’re obsessed with user-level numbers like Monthly Active Users (MAU), a holdover from the B2C world that hides serious problems in our account base.
- The future of B2B analytics lies in account-centric metrics: Monthly Active Accounts (MAA), Account Stickiness (DAA/MAA), and Feature Penetration Rate.
- This shift, powered by AI, moves your team from guessing to knowing, providing a true picture of business health and enabling proactive growth with a lean team.
We've all been there. You're prepping for a board meeting or an investor update, and the first question is always, "What's your MAU?" Monthly Active Users has become the default health metric for software companies. It’s simple, it's a big number, and it looks great on a slide. But for a B2B SaaS business, it’s a vanity metric. Worse, it’s a dangerous one.
In the B2B world, you don't sell to users; you sell to companies. Your revenue comes from accounts, and your retention depends on becoming embedded in an organization's workflow. An account with 100 users who log in once a month is often far less healthy and valuable than an account with five power users who live in your product every single day. Yet, MAU might tell you the first account is healthier.
This reliance on user-level metrics creates a fundamental disconnect. Your team celebrates a rising MAU, while underneath the surface, account-level engagement is shallow, key features go unadopted, and churn risk is quietly building. For lean, growth-stage teams, this noisy signal is a luxury you can't afford. It's time to measure what actually matters.
The Illusion of Health: Why MAU Fails B2B
The core problem with MAU is that it treats all user activity as equal. It averages out engagement across your entire user base, creating an illusion of health that can mask critical issues.
Imagine you have two paying customers:
- Account A: A 100-person company. 50 of their employees logged in once this month to view a report. Your MAU for this account is 50.
- Account B: A 10-person company. 8 of their employees logged in 20 out of 22 workdays. Your MAU for this account is 8.
On paper, Account A looks more engaged. But which account is more likely to churn? Account A's usage is wide but incredibly shallow. They aren't dependent on your tool. Account B, on the other hand, has made your product a daily habit. They are deeply engaged and far less likely to leave. MAU not only fails to capture this nuance; it actively points you in the wrong direction.
This leads to a reactive cycle. You're constantly fighting churn you didn't see coming because your primary health metric was telling you everything was fine. You’re left digging through dashboards after the fact, trying to figure out what went wrong.
The New Guard: Account-Centric Metrics for the AI Era
To get a true picture of your business, you need to shift your focus from the individual user to the collective account. This is the foundation of a modern, proactive growth strategy. Here are the metrics that should be on your daily digest.
1. Monthly Active Accounts (MAA)
This is the absolute baseline. It answers the most fundamental question: Are the companies that pay us actually using the product? It's a simple count of the number of distinct accounts (companies) with at least one active user in a given month. If your MAA isn't growing, your business isn't growing, regardless of what your user count says.
2. Account Stickiness (DAA/MAA)
This is the true hero metric for B2B SaaS. Account Stickiness measures the ratio of Daily Active Accounts (DAA) to Monthly Active Accounts (MAA). It tells you what percentage of your active customer base is using your product every single day.
$Account Stickiness = (Daily Active Accounts / Monthly Active Accounts) * 100%$
Why is this so powerful? It measures habit formation at the company level. A high stickiness ratio means your product isn't just a "nice-to-have" tool that teams check once a month; it's a core part of their daily operations. For a product designed for daily use, a stickiness ratio of 30-50% is a sign of a very healthy, embedded product. This single number is a far better predictor of long-term retention than MAU will ever be.
3. Feature Penetration Rate
It's not enough for an account to be "active." They need to be using the features that deliver the most value—the ones that make your product sticky. The Feature Penetration Rate measures, for a given account, what percentage of their active users have adopted a key feature.
For example, if you have a critical collaboration feature and only 10% of users at your largest account have ever touched it, that account is at high risk. They are not experiencing the full value of your product. Tracking this metric helps you identify accounts that need a nudge toward your stickiest features, turning a potential churn risk into a success story.
4. The Predictive Health Score
These historical metrics are powerful, but the real evolution comes from looking forward. A Predictive Health Score moves beyond tracking what happened yesterday to forecasting what is likely to happen next. This is where AI becomes a game-changer.
Instead of just looking at one or two metrics, a predictive model can analyze hundreds of behavioral signals in your product data: declining account stickiness, low feature penetration, a drop in the number of active users within an account, and more. It synthesizes these signals into a single, forward-looking score that flags an account's risk of churning in the next 30 or 60 days. This allows you to automate your churn prediction and gives your team a prioritized, data-driven watchlist.
From Guessing to Knowing: The Proactive Transformation
When you adopt these account-centric metrics, the entire dynamic of your GTM and CS teams changes.
- Clarity over Confusion: Conversations shift from vague goals like "increase engagement" to specific, actionable objectives like "increase Account B's stickiness from 20% to 30% by driving adoption of the reporting suite."
- Foresight over Firefighting: Your CS team stops being surprised by cancellation emails. They see the leading indicators—a slow dip in account stickiness or a failure to adopt a new feature—weeks in advance and can intervene proactively.
- Efficiency over Effort: Your lean team can finally focus their limited time where it will have the most impact. Instead of treating all accounts equally, they can prioritize outreach to those with declining predictive health scores or identify expansion opportunities in accounts with high feature penetration.
This is the essence of PLG 2.0: using deep, account-level intelligence to guide every interaction, making your growth engine more efficient and effective.
Of course, tracking these metrics can be a challenge. Most traditional product analytics tools were built for a B2C, user-centric world. Calculating account-level stickiness or feature penetration often requires complex SQL queries or custom dashboard configurations that are a pain to maintain.
That's why we built GrowthCues. Our platform is designed from the ground up for B2B SaaS. We automatically calculate these crucial account-level metrics and use AI to generate predictive insights, delivering them in plain English directly to your team. We handle the analysis so you can focus on what you do best: building relationships and growing your business.
The AI era isn't just about building smarter products for your customers; it's about building a smarter company. That starts with measuring what truly matters.