User retention metrics: 10 KPIs to track and improve in 2026

A sales team closes 100 new customers last quarter. Three months later, only 60 remain active. The other 40? Gone without a trace, taking their revenue and acquisition investment with them.

This scenario unfolds across revenue teams everywhere, but most organizations only discover the problem when it’s too late to fix. User retention metrics change that dynamic by revealing which customers stay engaged, which ones drift away, and which warning signs predict churn. These measurements track everything from basic retention rates to sophisticated predictive scores, giving revenue leaders the visibility needed to protect existing revenue while identifying expansion opportunities.

This guide covers the ten essential user retention metrics every revenue team should track in 2026, from foundational measurements like customer retention rate and churn rate to advanced indicators like predictive retention scores and net revenue retention. Each metric reveals different aspects of customer behavior, with practical calculation methods and real examples showing how centralized customer data platforms automate retention tracking across entire customer bases.

Key takeaways

• Track the metrics that predict revenue loss: start with customer retention rate and churn rate to see who’s leaving, then add customer lifetime value to understand the real financial impact of each lost relationship.
• Focus on expansion revenue, not just keeping customers: net revenue retention above 100% means your existing customers are spending more even when some leave; this drives sustainable growth faster than acquiring new customers.
• Spot at-risk customers before they disappear: use predictive retention scores and activation rates to identify warning signs like declining usage or feature abandonment, giving you time to intervene.
• Centralize retention tracking with a unified platform: unify all customer data, interactions, and retention metrics in one place with automated dashboards and AI-powered alerts provided by solutions like monday CRM that notify your team when customers show churn warning signs.
• Build your measurement program gradually: start with two to three core metrics you can act on immediately, establish baselines, then add sophisticated tracking as your team develops expertise and systems.

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What are user retention metrics?

User retention metrics tell you a simple truth: are your customers sticking around or walking away? They track how customers behave, how often they use your product, and how deeply they engage, ultimately showing whether your sales process builds relationships that last or just closes one-off deals.

Retention metrics function as a comprehensive health monitor for customer relationships. They reveal when customers disengage, reduce activity levels, or increase product adoption. These signals identify which accounts require immediate attention before churn occurs and revenue is lost.

For revenue teams, these metrics directly impact what keeps leadership up at night: predictable revenue, sustainable growth, and smart resource allocation. Knowing exactly who stays and who leaves means you can nail your forecasts, put resources where they matter most, and walk into leadership meetings with rock-solid confidence.

The real cost of losing customers

Losing customers costs far more than the immediate revenue loss. Customer acquisition cost (CAC) represents the sales, marketing, and onboarding investment required to win each customer. When they leave, that entire investment vanishes.

Consider this: a customer who costs $500 to acquire but pays $100 monthly and leaves after three months creates a $200 net loss — not just $300 in missed revenue. The compounding effect makes customer loss even more expensive.

Lost customers eliminate multiple revenue opportunities:

• Referral potential: missed opportunities for word-of-mouth recommendations to new prospects.
• Case study value: missing testimonials and success stories for marketing.
• Expansion revenue: lost upsell and cross-sell opportunities.
• Long-term growth: a customer who would have upgraded to a $300 monthly plan after one year represents $2,400 in lost annual revenue.

Retention metrics identify at-risk customers before they churn, creating opportunities for proactive intervention that saves both the relationship and the revenue.

User retention vs customer retention

User retention and customer retention measure different aspects of the customer relationship. Understanding both perspectives gives you the complete picture of relationship strength.

• User retention: focuses on product usage and engagement behaviors — how often someone logs in, which features they use, how many actions they complete, and whether their activity levels increase or decrease over time.
• Customer retention: focuses on the business relationship itself — whether customers continue paying, renew contracts, maintain their accounts, or expand their spending.

These metrics can diverge in revealing ways:

• Subscription without engagement: a customer might retain their subscription (positive customer retention) but rarely use the product (poor user retention), signaling future churn risk despite current revenue.
• Engagement without revenue: a user might engage heavily with a free product (strong user retention) without converting to paid status (no customer retention).

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10 essential retention metrics to track

The following metrics provide comprehensive visibility into customer behavior and business health. Each metric reveals different aspects of your retention performance, from basic customer counts to predictive risk scoring.

1. Customer retention rate

Customer retention rate measures the percentage of customers who continue their relationship with your business over a specific time period. This foundational metric provides a direct view of whether you successfully maintain your customer base.

The retention rate formula:

((Customers at End of Period – New Customers During Period) / Customers at Start of Period) × 100.

• Example calculation: a business starting January with 1,000 customers, acquiring 200 new customers, and ending with 1,100 total customers calculates: ((1,100 – 200) / 1,000) × 100 = 90% retention rate.

2. Churn rate

Churn rate measures the percentage of customers who stop using your product during a specific time period — essentially the inverse of retention rate. Many businesses prefer tracking churn because it highlights the problem directly, making losses more visible and urgent.

The churn rate formula:

(Customers Lost During Period / Customers at Start of Period) × 100.

Two types of churn require different solutions:

• Voluntary churn: customers actively cancel due to dissatisfaction, lack of value perception, or competitive alternatives.
• Involuntary churn: customers leave through failed payments, expired credit cards, or administrative oversights.

Revenue vs user churn impact:

A business losing 10 small customers at $50 monthly experiences 10% user churn but only $500 in revenue churn. Losing one large customer at $5,000 monthly represents just 1% user churn but $5,000 in revenue churn — ten times the financial impact.

3. Customer lifetime value (CLV)

Customer lifetime value represents the total revenue you expect to earn from a customer throughout their entire relationship. This forward-looking metric transforms retention from a defensive activity into a strategic investment.

The CLV formula:

(Average Purchase Value × Purchase Frequency × Customer Lifespan)

• Example calculation: a customer spending $100 per purchase, making 2 purchases monthly, and remaining active for 24 months generates: $100 × 2 × 24 = $4,800 in total lifetime value.

CLV drives critical business decisions across multiple functions:

• Marketing teams: use CLV to determine acceptable customer acquisition costs.
• Customer success teams: prioritize high-CLV customers for white-glove service.
• Product teams: weight feature requests from high-CLV segments more heavily.

The 3:1 benchmark:

The 3:1 CLV to CAC ratio provides a benchmark for sustainable growth. A business with $3,000 CLV can afford up to $1,000 in customer acquisition costs while maintaining healthy unit economics. Companies with sophisticated pricing and packaging practices achieve roughly 16 percentage points higher NRR(https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-net-revenue-retention-advantage-driving-success-in-b2b-tech) than peers with only basic practices, demonstrating how strategic pricing decisions directly impact customer lifetime value.

4. Net revenue retention (NRR)

Net revenue retention measures the percentage of recurring revenue retained from existing customers, including expansion revenue from upsells, cross-sells, and usage increases. NRR above 100% indicates that expansion revenue exceeds revenue lost to churn and downgrades. In fact, according to a McKinsey analysis of over 100 B2B SaaS companies, top-quartile valued firms post a median NRR of 113% versus 98% for bottom-quartile peers.

Let’s look at a tale of two companies with dramatically different retention stories:

• Company A: retains 90% of customers at $100 monthly each. Starting with 1,000 customers and $100,000 MRR, they end the year with 900 customers and $90,000 MRR.
• Company B: retains only 80% of customers, but remaining customers expand spending by 50%. Starting with 1,000 customers and $100,000 MRR, they end with 800 customers but $120,000 MRR.

Company B’s superior NRR (120% vs 90%) indicates healthier growth despite losing more customers. Expansion revenue comes through three primary mechanisms:

• Upsell revenue: customers upgrade to higher-tier plans.
• Cross-sell revenue: customers purchase additional products.
• Usage expansion: revenue increases from consumption-based pricing.

5. Monthly recurring revenue retention

MRR retention measures the percentage of monthly recurring revenue retained from one month to the next, excluding new customer revenue. This granular metric provides faster feedback loops than annual measurements.

The net MRR retention formula:

((MRR at Start – MRR Churn + Expansion MRR) / MRR at Start) × 100

• Example calculation: starting MRR of $100,000, losing $7,000 to churn and downgrades, but gaining $5,000 in expansion revenue: (($100,000 – $7,000 + $5,000) / $100,000) × 100 = 98% net MRR retention.

Effective MRR tracking requires multiple analytical views:

• Monthly trends reveal seasonal patterns and concerning declines
• Cohort tracking shows how each customer group’s retention evolves
• Segment breakdown compares retention across plan types and company sizes

6. Daily, weekly, and monthly active users

Active user metrics measure unique user engagement within specific time periods — daily (DAU), weekly (WAU), or monthly (MAU). The ratios between these metrics reveal engagement depth and product stickiness.

DAU/MAU ratio shows what percentage of monthly users engage daily. Different ratio ranges indicate varying engagement levels:

• 20%+ DAU/MAU: High engagement — users find daily value
• 10-20% DAU/MAU: Moderate engagement — regular but not essential usage
• Less than 10% DAU/MAU: Low engagement — occasional usage indicating retention risk

Choosing the right metric: The appropriate activity metric depends on your product’s natural usage patterns. CRM systems benefit from daily tracking because users should engage every workday. Project management platforms fit weekly cycles. Financial software often has monthly patterns tied to business cycles.

7. Repeat purchase rate

Repeat purchase rate measures the percentage of customers who make more than one purchase within a specific time period. This metric particularly matters for e-commerce and transactional businesses where customers make discrete purchase decisions.

The formula: (Customers Who Made Multiple Purchases / Total Customers) × 100

Example calculation: An online retailer tracking 10,000 customers over 90 days, with 3,000 making two or more purchases: (3,000 / 10,000) × 100 = 30% repeat purchase rate.

Strategies to increase repeat purchase rates focus on creating compelling reasons to return:

• Timing optimization: Use purchase history to trigger outreach at optimal windows
• Personalized recommendations: Suggest relevant products based on past purchases
• Post-purchase engagement: Maintain relationships between purchases with valuable content

8. Activation rate

Activation rate measures the percentage of new users who complete a key action indicating they’ve experienced your product’s core value. Users who activate are significantly more likely to become long-term customers.

Activation vs acquisition: Activation differs from simple sign-up. Creating an account represents acquisition, not activation. True activation requires users to experience meaningful value — the moment they understand why your product matters. Companies that run sophisticated value-realization and adoption journeys see about a seven-percentage-point higher NRR than peers with basic approaches, highlighting the retention impact of effective activation programs.

Finding your activation moment requires analyzing behavior patterns:

• Correlation analysis: Compare behaviors of retained users versus churned users
• User interviews: Ask successful customers what convinced them of the product’s value
• Time to value tracking: Measure how quickly users reach their activation moment

Industry activation examples:

• Social media platforms see activation when users add 10 connections
• Project management platforms achieve activation when teams create their first collaborative project
• CRM systems reach activation when users add 20 contacts and log their first deal

9. Feature adoption rate

Feature adoption rate measures the percentage of users actively using specific product features. Users who adopt more features typically show higher retention rates because they’ve integrated the product more deeply into their workflows.

Analyzing feature adoption reveals which capabilities drive long-term success:

• Feature correlation: Compare retention rates between feature adopters and non-adopters
• Adoption patterns: Group users by feature usage and compare retention rates
• Stickiness factors: Identify features that create switching barriers

How features create stickiness:

• Data accumulation: Valuable data sets that are difficult to migrate
• Workflow integration: Embedded daily processes
• Network effects: Connections with other users

10. Predictive retention score

Predictive retention score calculates the probability that a customer will remain active based on behavior patterns and usage data. Scores typically range from 0-100, with higher scores indicating higher retention likelihood.

AI and machine learning analyze multiple data points simultaneously to predict churn risk:

• Behavioral patterns: Changes in login frequency or feature usage
• Usage trends: Whether activity increases, remains stable, or declines
• Support interactions: Frequency and sentiment of customer service contacts

How monday CRM transforms retention tracking

Revenue teams using monday CRM centralize all customer data and interactions in one platform, eliminating the need to switch between multiple platforms or manually combine data from different sources. The platform adapts to different business models and retention tracking needs, whether managing simple customer lists or complex enterprise relationships.

The unified data model connects sales activities, customer communications, support interactions, and product usage in a single view. Sales teams see support ticket history, customer success teams access sales notes, and executives view complete customer timelines without requesting reports from multiple departments.

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Step 1: Build automated retention dashboards

Real-time metric widgets display current retention rates, churn metrics, customer lifetime value calculations, and feature adoption rates across customer segments. Each widget updates automatically as new data flows into the system, providing always-current visibility into retention performance.

Dashboard customization by role:

• Executives see high-level retention trends and revenue impact
• Sales managers view team-specific retention rates and at-risk customer lists
• Customer success teams access detailed customer health scores and engagement metrics

Automated data refresh eliminates manual report generation. Dashboards pull data directly from customer records, updating in real-time as activities occur. A customer logging their first deal immediately affects activation rate calculations. A customer reducing their subscription immediately impacts MRR retention metrics.

Step 2: Set up smart retention alerts

Automated alert systems notify teams when retention metrics cross critical thresholds, ensuring intervention opportunities before problems become irreversible.

Churn warning triggers identify customers showing early warning signs:

• A customer whose usage declines 40% in two weeks generates an automatic alert
• A customer who hasn’t logged in for 14 days triggers a re-engagement notification

Metric threshold alerts notify stakeholders when retention rates change significantly:

• Monthly retention dropping below 90% triggers an alert to leadership
• MRR retention declining 5 percentage points month-over-month notifies the CFO

Automated workflow assignment ensures alerts translate to action. At-risk customers automatically assign to customer success representatives based on account ownership or territory. High-value at-risk customers escalate to senior team members.

Step 3: Leverage AI for retention insights

AI capabilities on monday CRM detect patterns that human analysis might miss. The platform’s AI analyzes customer communications to extract sentiment, summarize interactions, and identify retention risks automatically.

Timeline summary creates clear summaries of all communication events, including emails, calls, meetings, and notes. Sales and support teams can instantly understand customer history, saving valuable time.

Sentiment detection flags negative or at-risk language in customer communication. When AI spots concerning patterns in emails, it alerts your team before customers decide to leave.

Automated email composition helps teams respond quickly to at-risk customers. AI drafts personalized re-engagement emails based on customer history and interaction patterns.

Step 4: Unify sales and success data

The integrated timeline displays all customer interactions chronologically. Sales calls, support tickets, product usage milestones, billing events, and email communications appear in one unified view.

Cross-team visibility benefits:

• Customer success managers see the sales promises that set expectations
• Sales reps see support issues that might affect renewal conversations
• Teams can @mention colleagues to clarify contract terms, flag retention risks, or share feature updates

Contextual collaboration keeps all customer-related communication in one place rather than through separate communication channels. End-to-end retention reporting spans the entire customer lifecycle, revealing how acquisition channel affects retention, how onboarding quality impacts activation rates, and how feature adoption correlates with expansion revenue.

Build your retention measurement strategy

Trying to track all 10 metrics at once can lead to complexity overload. A better approach is to start small and build gradually. Start with the basics that align with your immediate goals and what you can actually measure today. Then build up your tracking muscles as your team gets sharper and your systems more sophisticated.

For new businesses: Begin with basic retention rate and churn rate to establish baseline measurements. These metrics require minimal setup — simply tracking how many customers remain active month over month.

For growing businesses: Add customer lifetime value and activation rate to optimize acquisition and onboarding investments. CLV determines appropriate customer acquisition spending, while activation rate reveals whether new customers experience value quickly enough.

For mature businesses: Implement predictive retention scores and advanced segmentation for proactive management. Historical data enables sophisticated prediction models that identify at-risk customers before they churn.

Build your retention measurement program incrementally:

1. Choose 2-3 core metrics aligned with immediate goals
2. Establish baseline measurements using existing data
3. Set achievable improvement targets to create momentum
4. Review metrics regularly on consistent schedules
5. Add sophisticated metrics gradually as capabilities mature

The key is starting with metrics you can act on immediately, then building sophistication over time. Teams that try to implement everything at once often abandon their measurement programs due to complexity overload.

The content in this article is provided for informational purposes only and, to the best of monday.com’s knowledge, the information provided in this article is accurate and up-to-date at the time of publication. That said, monday.com encourages readers to verify all information directly.