Product metrics

Product metrics tell you how well your product delivers value to users. For B2B SaaS startups, these metrics are crucial for understanding product-market fit and predicting revenue growth.

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Daily Active Users (DAU)

Daily Active Users measures the number of unique users who meaningfully engage with your product each day.

DAU is often considered the heartbeat of your product. Unlike vanity metrics like total registered users, DAU tells you who’s actually getting value from your product on a regular basis. For B2B SaaS companies, consistent daily usage often indicates that your product has become integral to users’ workflows, which directly correlates with retention and expansion opportunities.

The key insight DAU provides is immediacy—it shows you in real-time whether your product changes, marketing campaigns, or external factors are positively or negatively impacting user engagement. A sudden drop in DAU can signal everything from technical issues to competitive threats, while sustained growth in DAU often predicts revenue growth.

However, DAU isn’t just about the number—it’s about the quality of engagement. A user who logs in and immediately leaves provides a very different signal than one who spends meaningful time completing core workflows. This is why defining what constitutes “active” is crucial for this metric to be actionable.

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How to calculate

DAU = Count of unique users who performed meaningful actions in your product today

Define meaningful actions based on your product’s core value:

• For analytics tools: Viewing reports, creating dashboards, running queries
• For collaboration tools: Sending messages, sharing files, commenting
• For CRM tools: Adding contacts, updating deals, sending emails

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Data sources needed

Product analytics platforms track user actions and events, providing detailed insights into how users interact with your product. Tools like Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow capture user behavior data that’s essential for calculating DAU accurately.

Product databases store user activity directly in your application database, giving you the most comprehensive view of user actions. PostgreSQL, MySQL, Snowflake, BigQuery, and Azure SQL Database contain the raw user interaction data that forms the foundation of your DAU calculations.

Customer data platforms like Segment and Rudderstack provide unified user activity tracking across multiple touchpoints. These platforms aggregate data from various sources to give you a complete picture of user engagement.

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Basedash AI prompt example

Show me daily active users over the past 30 days from our Mixpanel events data, where users performed core actions like creating dashboards or running queries

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Benchmarks

• DAU growth rate: 5-10% week-over-week for healthy B2B SaaS products
• DAU/MAU ratio: 0.15-0.25 for most B2B tools (higher indicates stickier product)

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Weekly Active Users (WAU)

Weekly Active Users tracks unique users who engage meaningfully with your product over a seven-day period.

WAU often serves as the primary engagement metric for B2B SaaS products because it better reflects business software usage patterns. Unlike consumer apps that might see daily usage, many business tools are used several times per week but not necessarily every day. This makes WAU a more realistic indicator of product health and user engagement for B2B companies.

The power of WAU lies in its ability to smooth out daily variations while still providing timely feedback on product performance. It captures users who might skip a day or two but are still regularly engaged with your product. For products used in business contexts, this weekly view often correlates more strongly with customer satisfaction and long-term retention than daily metrics.

WAU also helps you understand usage patterns and seasonality. Business software often sees lower usage on weekends and holidays, which can create noise in daily metrics but becomes clear when viewed through a weekly lens.

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How to calculate

WAU = Count of unique users who performed meaningful actions in your product over the past 7 days

Track rolling 7-day periods to smooth out weekly seasonality effects.

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Data sources needed

Product analytics platforms provide comprehensive user engagement tracking, allowing you to monitor weekly usage patterns and identify trends in user behavior. Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow offer robust weekly active user tracking capabilities with advanced segmentation options.

Application databases contain user activity logs and session data that provide the raw foundation for WAU calculations. PostgreSQL, MySQL, Snowflake, BigQuery, and Azure SQL Database store detailed user interaction records that can be aggregated to calculate weekly engagement metrics.

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Basedash AI prompt example

Create a line chart showing weekly active users for the past 12 weeks from our Amplitude user data, grouped by week

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Benchmarks

• 10-20% week-over-week growth: Strong for early-stage B2B SaaS
• WAU/MAU ratio: 0.6-0.8 for well-engaged B2B products

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Monthly Active Users (MAU)

Monthly Active Users measures unique users who engage with your product over a 30-day period.

MAU provides the broadest view of your product’s reach and is particularly useful for understanding long-term engagement trends and overall product adoption. For B2B SaaS companies, MAU often serves as a leading indicator of revenue potential, especially in freemium or usage-based pricing models.

The monthly timeframe captures users with various engagement patterns—from daily power users to those who check in weekly or use your product for specific monthly tasks. This comprehensive view helps you understand your total addressable user base and identify opportunities for increasing engagement frequency among less active users.

MAU is also crucial for understanding the relationship between your user base and your revenue. In B2B contexts, tracking both total MAU and paying MAU helps you understand conversion rates and the revenue potential of your user base growth.

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How to calculate

MAU = Count of unique users who performed meaningful actions in your product over the past 30 days

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Data sources needed

Product analytics platforms capture monthly user engagement data and provide sophisticated user segmentation capabilities for MAU analysis. Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow excel at tracking long-term user engagement patterns and cohort behavior over monthly periods.

User databases maintain comprehensive records of user activity that form the backbone of monthly active user calculations. PostgreSQL, MySQL, Snowflake, BigQuery, and Azure SQL Database store user interaction data that can be aggregated to understand monthly engagement trends across different user segments.

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Basedash AI prompt example

Show monthly active users trend over the past 6 months from our PostgreSQL users table, including breakdown by free vs paid users

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Key variations

• Total MAU: All active users (free and paid)
• Paying MAU: Only users from paying accounts
• MAU by feature: Users of specific product capabilities

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Benchmarks

• Monthly MAU growth: 15-25% for early-stage B2B SaaS
• Paying MAU percentage: Varies by business model (freemium: 2-5%, trial-based: 15-25%)

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Session duration and frequency

Session metrics reveal how users spend time in your product and how often they return.

Understanding session patterns helps you gauge product stickiness and identify opportunities for improving user experience. Long sessions might indicate either high engagement or difficulty completing tasks, while very short sessions could signal quick value delivery or immediate frustration. The context matters enormously.

Session frequency tells you how integral your product has become to users’ workflows. Products that users return to multiple times per day or week are typically more valuable and defensible than those used occasionally. This metric also helps you understand the natural usage patterns for your product category and whether you’re meeting user expectations.

Together, session duration and frequency paint a picture of how users interact with your product and can guide product development priorities. If users have long sessions but low frequency, you might focus on creating more regular use cases. If they have frequent but very short sessions, you might work on deepening engagement.

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How to calculate

Average session duration = Total time spent in product ÷ Number of sessions

Session frequency = Number of sessions per user per time period

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Data sources needed

Product analytics platforms excel at session tracking and user behavior analysis, providing detailed insights into how users interact with your product over time. Mixpanel, Amplitude, Google Analytics 4, Snowplow, and Segment offer comprehensive session tracking capabilities with advanced user flow analysis.

Application logs contain server-side session data that provides the most accurate view of user engagement duration and frequency. PostgreSQL, MySQL, BigQuery, and other application databases with session tables store detailed timing information that’s essential for precise session metrics.

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Basedash AI prompt example

Create a table showing average session duration and session frequency by user segment over the past month from our Mixpanel session data

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What good looks like

Session duration benchmarks:

• Productivity tools: 15-45 minutes average
• Analytics/reporting tools: 5-20 minutes
• Communication tools: Multiple short sessions throughout the day

Session frequency benchmarks:

• Daily use tools: 4-8 sessions per week per user
• Weekly use tools: 1-3 sessions per week per user

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Feature adoption rate

Feature adoption measures what percentage of your users engage with specific product capabilities.

Feature adoption is critical for understanding which parts of your product deliver the most value and which might be candidates for improvement or removal. High adoption rates for key features often correlate with higher retention and expansion revenue, while low adoption rates might indicate features that are hard to discover, difficult to use, or don’t solve important problems.

This metric helps guide product development priorities by showing you which features drive engagement and which are ignored. It’s particularly valuable for identifying successful features that could be enhanced or expanded, as well as unsuccessful ones that might be consuming development resources without delivering user value.

Feature adoption also helps with customer success and onboarding. Understanding which features lead to higher retention allows you to guide new users toward those capabilities more quickly, potentially improving activation rates and time to value.

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How to calculate

Feature adoption rate = (Users who used feature ÷ Total eligible users) × 100

Calculate for different time periods (weekly, monthly, quarterly) and user segments.

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Data sources needed

Product analytics platforms provide detailed feature usage tracking that’s essential for understanding adoption patterns across your user base. Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow capture granular feature interaction data that enables comprehensive adoption analysis.

Product databases store feature interaction logs that provide the raw data foundation for feature adoption calculations. PostgreSQL, MySQL, Snowflake, and other application databases with feature usage events contain detailed records of when and how users engage with specific product capabilities.

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Basedash AI prompt example

Show feature adoption rates for our top 10 features over the past 3 months from our Amplitude events, broken down by user plan type

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Key insights

• Adoption by user segment: How different customer types use features
• Time to adoption: How long it takes users to discover and use features
• Adoption correlation: Which features correlate with retention and expansion

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Benchmarks

• Core features: 60-80% adoption among active users
• Advanced features: 20-40% adoption
• New features: 10-30% adoption in first 90 days

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User activation metrics

Activation measures how quickly and effectively new users reach their “aha moment” and experience your product’s core value.

Activation is arguably the most important product metric for growth because it bridges the gap between acquisition and retention. Users who never activate are unlikely to become long-term customers, regardless of how they found your product. A strong activation rate indicates that your onboarding process effectively guides users to value and that your product delivers on its initial promise.

The challenge with activation is defining it correctly. The best activation metrics focus on behaviors that strongly correlate with long-term retention and expansion. These might include completing key workflows, achieving specific outcomes, or reaching usage thresholds that indicate the product has become valuable to the user.

Improving activation often provides the highest ROI of any product improvement because it impacts every new user. Small improvements in activation rates compound over time, affecting customer acquisition cost, lifetime value, and overall growth efficiency.

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How to calculate

Activation rate = (Users who completed key actions ÷ New signups) × 100

Define activation based on actions that correlate with retention:

• Connected data source or completed setup
• Created first meaningful output (report, dashboard, project)
• Invited team members or collaborated
• Used core feature multiple times

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Data sources needed

Product analytics platforms capture user onboarding and activation events that are crucial for understanding how effectively you convert new signups into active users. Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow provide detailed activation funnel tracking and user journey analysis.

User databases maintain comprehensive records of signup and activation milestone tracking, providing the foundation for activation rate calculations. PostgreSQL, MySQL, Snowflake, and CRM systems like HubSpot and Salesforce store user progression data through key activation milestones.

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Basedash AI prompt example

Create a funnel chart showing signup to activation rates for the past month from our Mixpanel data, including time to activation by signup source

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Time to activation

Median time to activation = Time it takes 50% of users to complete activation milestones

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Benchmarks

• Overall activation rate: 40-60% for B2B SaaS products
• Time to activation: Under 24 hours for simple products, under 1 week for complex ones
• Day 1 activation: 15-25% of new signups should activate on day 1

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Engagement scoring

Engagement scoring creates a composite measure of how actively and meaningfully users interact with your product.

Unlike single metrics that might not capture the full picture, engagement scores combine multiple behaviors to create a holistic view of user health. This approach is particularly valuable for B2B products where different users might engage in different ways—some might be heavy feature users, others might focus on collaboration, and still others might primarily consume information.

Engagement scores help you segment users more effectively than simple usage metrics alone. They can identify champions who might become advocates, users at risk of churning, and prospects ready for upselling. This segmentation enables more targeted customer success efforts and product development priorities.

The key to effective engagement scoring is weighting different actions based on their correlation with business outcomes like retention and expansion. Not all user actions are created equal—some indicate deep product value while others might be superficial interactions.

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How to calculate

Simple engagement score approach:

• Basic actions (login, page views): 1 point
• Core actions (creating content, using key features): 3-5 points
• Advanced actions (collaboration, integrations): 7-10 points
• High-value actions (sharing, teaching others): 10+ points

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Data sources needed

Product analytics platforms provide complete user activity tracking that’s essential for building comprehensive engagement scores across multiple user behaviors. Mixpanel, Amplitude, Segment, Google Analytics 4, and Snowplow capture the diverse range of user interactions needed to create meaningful engagement scores.

Application databases store detailed user interaction logs and activity history that form the foundation of engagement scoring systems. PostgreSQL, MySQL, Snowflake, and customer success platforms like Vitally and Totango maintain comprehensive user behavior records that can be weighted and scored.

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Basedash AI prompt example

Show user engagement score distribution and trends over the past quarter from our PostgreSQL user activity data, segmented by account tier

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Engagement segments

• Champions (80-100 points): Your most engaged power users
• Regulars (50-79 points): Solid, consistent users
• Casual users (20-49 points): Light usage, expansion opportunity
• At-risk (0-19 points): Low engagement, churn risk

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Benchmarks

• Champion percentage: 15-25% of active users should be champions
• At-risk percentage: Keep below 30% of active users
• Score trends: Look for improving scores over user lifetime

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Cohort analysis for product metrics

Cohort analysis tracks how different groups of users behave over time, providing insights into product improvements and user lifecycle patterns.

Product cohort analysis is essential for understanding whether your product changes are actually improving user engagement over time. By grouping users based on when they first used your product and tracking their behavior, you can see if newer users are more engaged than older ones, indicating successful product improvements.

This analysis helps separate the signal from the noise in your product metrics. Overall engagement might be flat while newer cohorts are actually performing much better than older ones, suggesting that your recent changes are working but haven’t yet impacted the overall numbers.

Cohort analysis also reveals the natural lifecycle of user engagement with your product. Most products see initial enthusiasm followed by some decline as users settle into regular patterns. Understanding these patterns helps you set realistic expectations and identify opportunities to re-engage users at specific lifecycle stages.

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Types of product cohorts

Signup cohorts: Group users by when they first signed up Activation cohorts: Group users by when they first activated Feature cohorts: Group users by when they first used specific features

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Key insights

• Improving engagement: Are newer user cohorts more engaged?
• Lifecycle patterns: How does engagement change over the user lifetime?
• Product-market fit: Do cohorts show increasing engagement over time?
• Seasonal effects: How do external factors affect different user groups?

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Implementation

Track cohorts weekly and monthly to understand both short-term and long-term patterns. Focus on cohorts large enough to provide statistically significant insights, typically requiring at least 100-200 users per cohort.

Product metrics should guide your decisions about feature development, user experience improvements, and customer success initiatives. The key is focusing on metrics that correlate with business outcomes and user satisfaction, not just vanity metrics that make you feel good but don’t drive decisions.