10 Metrics Every Product Manager Should Know – Effective product management in today’s data-driven world relies heavily on metrics and key performance indicators to guide strategic decisions and measure the success of products. Having a strong grasp of core product and customer metrics is an essential skill for any product manager looking to understand user behavior, optimize the product experience, and drive business growth.
10 Metrics Every Product Manager Should Know
This comprehensive guide will cover 10 of the most important metrics every product manager should be familiar with. Understanding these key metrics and how to leverage them will empower product managers at companies of all stages and across all industries to make smart product choices that create winning customer experiences.
Sprint Velocity
One of the most fundamental metrics a product manager at a scrum-based organization needs to be well-versed in is sprint velocity. Sprint velocity measures the amount of product backlog completed by a scrum development team within a single sprint. Essentially, it serves as an indicator of the team’s capacity to deliver working, production-ready software.
Sprint velocity is calculated by summing the total story points for each user story completed in a sprint and dividing that by the number of sprints. Story points are an abstract measure of the relative complexity and effort required to implement a user story. By tracking velocity over consecutive sprints, product managers gain greater visibility into the development team’s predictability and ability to deliver features.
Understanding sprint velocity is critical for several reasons:
- It enables more accurate sprint planning and forecasting. By establishing a velocity baseline over several sprints, product managers can better estimate what story points can realistically get completed in upcoming sprints.
- It provides insight into development team productivity. Velocity can expose workflow inefficiencies, capability and resourcing gaps, or technical issues impeding development.
- It accounts for fluctuations in team capacity. Velocity will vary due to changes in team composition, workflows, backlog complexity, and other events. Tracking this metric helps plan around fluctuations.
- It prevents overscoping sprints. With velocity as a gauge, product managers can avoid overburdening the development team by scoping too many story points for a sprint.
To leverage sprint velocity effectively, product managers should set a velocity baseline by calculating the metric over several sprints at least. Expect variances and do not strictly enforce velocity, but monitor trends over multiple sprints to spot patterns and account for continual fluctuations. Use velocity to thoughtfully adapt both the scope and availability of resources to meet delivery timelines for important initiatives.
Time to Market
Time to market refers to the duration it takes from product conception to when it first becomes available for customers to purchase and use. In other words, it measures the agility and efficiency of the entire product development process, from initial ideation to launch.
For products in industries with fierce competition, speedy time to market can mean gaining a strong competitive advantage. Releasing desired features ahead of rivals can help win over customers, especially early adopters. For startups, minimizing time to market is crucial when racing toward product-market fit.
Time to market is calculated by tracking the number of days elapsed between kickstarting development and launching the product. The clock starts ticking from the conception of the core idea and stops when the product can be purchased or accessed by customers. A shorter time to market is widely considered better.
This metric provides valuable insight into how streamlined and responsive product development processes are. Lengthy-time to market signals potential issues and delays within:
- Research and discovery
- Requirements gathering
- Design and prototyping
- Engineering and testing
- Regulatory approvals
- Marketing and sales alignment
For product managers, assessing time to market helps identify pain points and inefficiencies in the product development lifecycle. It enables setting more realistic launch timelines and can motivate further optimization of workflows between cross-functional teams. Leveraging agile processes, minimizing the complexity of initial feature sets, and frequent collaboration helps compress time-to-market cycles.
Product managers should trace time to market for every product launch, from MVPs to subsequent releases. Comparing the metrics across launches provides a benchmark to improve upon. Regularly optimizing for faster time to market translates directly into pleasing customers sooner and seizing the competitive edge.
DAUs/MAUs
DAUs (daily active users) and MAUs (monthly active users) are crucial metrics that quantify user engagement and adoption of digital products and services.
DAUs refer to the number of unique users who actively use an app or service daily. MAUs indicate users who use the product at least once within a 30-day month. These metrics only count each user once, even with multiple sessions per day or month.
Tracking both DAUs and MAUs over time provides a dynamic view of product usage and popularity. Growth in active users signals that the product is creating value for customers and gaining traction. Declines may indicate problems with retention or engagement.
Evaluating DAUs and MAUs is important for several reasons:
- It demonstrates sustainable adoption beyond initial curiosity. High ratios of DAUs/MAUs indicate retention of engaged users.
- It highlights usage frequency and loyalty. Frequent daily use implies users find ongoing value in the product.
- It enables cohort analysis. Analyzing trends by user acquisition period spotlights issues impacting specific user segments.
- It focuses on engagement initiatives. Low MAU or DAU points to dissatisfaction among inactive users that engagement campaigns can target.
For consumer apps, benchmarks can provide context around healthy DAU/MAU ratios, generally between 20-40%. However, the levels can vary widely depending on product type and age. So tracking the absolute and relative trends is most insightful.
Product managers should continuously monitor daily and monthly active users, digging into the usage habits of both highly engaged and passive users. User interviews and surveys can reveal why metrics are trending up or down. The findings guide product optimization and user targeting to maximize engagement across the user lifecycle.
Net Promoter Score (NPS)
Net Promoter Score, or NPS, is a customer loyalty metric that aims to gauge how likely users are to recommend a product or service to others. It’s captured through a simple survey question:
On a scale of 0-10, how likely are you to recommend [product] to a friend or colleague?
Based on their score, respondents are segmented into three groups:
- Promoters (9-10) – Loyal enthusiasts who will promote the product.
- Passives (7-8) – Satisfied but indifferent customers.
- Detractors (0-6) – Unhappy customers that may damage the brand.
By subtracting the percentage of detractors from the percentage of promoters, the NPS is calculated on a -100 to 100 scale. For example, 40% of promoters and less 20% of detractors give an NPS of 20.
NPS matters because research shows customer loyalty and organic promotion directly impact business growth. Products with higher NPS tend to experience faster growth through word-of-mouth referrals. High promoters also increase retention and can become brand advocates.
For product managers, monitoring NPS provides a pulse on user satisfaction and areas for improvement:
- Low NPS indicates lackluster customer experiences deserving investigation.
- Segmenting NPS can spotlight dissatisfaction among certain user demographics.
- Tracking NPS over time and milestones gauges the impact of product changes.
- Comparing NPS to competitors highlights relative performance.
Acting on NPS involves prioritizing promoter feedback, minimizing detractor pain points through targeted product enhancements, and activating promoters for referrals and reviews. Pairing NPS trends with related metrics like churn provides a fuller picture to guide strategy.
Customer Acquisition Cost (CAC)
Customer acquisition cost, or CAC, is a metric that quantifies the total expenses incurred to acquire new customers. Specifically, it measures the average cost of winning over each new customer by spending on sales, marketing, and other related activities.
The calculation is simple:
CAC = Total acquisition costs / Total new customers
For example, if a company spent $100,000 last month on sales and marketing initiatives and acquired 1,000 new customers, their CAC is $100 ($100,000 / 1,000).
Understanding CAC is critical for several reasons:
- It reveals the efficiency of sales and marketing efforts in attracting customers. Lower CAC means spending less to acquire each customer.
- It informs budgeting and resourcing of acquisition programs. Teams can calibrate budgets based on target CAC.
- It highlights the most and least efficient acquisition channels. Analyzing CAC by channel focuses on future investments.
- Comparing CAC to customer lifetime value shows acquisition profitability. Customers acquired below their lifetime value are profitable.
Product managers should monitor CAC closely across acquisition channels and campaigns. As products scale, aim to reduce CAC while expanding marketing reach. Set CAC targets and optimize spending and tactics to lower costs. But avoid slashing CAC at the expense of quality – acquiring the wrong customers can be even more expensive long term.
Customer Lifetime Value (CLTV)
While CAC focuses on customer acquisition costs, customer lifetime value (CLTV) evaluates the revenue generated from a customer throughout the entire relationship. It seeks to quantify long-term customer profitability.
CLTV is calculated by multiplying the average revenue earned per user by the average lifetime of a user in months or years. For example, if users generate $5 per month on average over an estimated 5-year lifespan, the lifetime value per user is $300 ($5 x 12 months x 5 years).
This metric is powerful for several reasons:
- It gauges customer loyalty beyond a single purchase. Higher CLTV indicates customers who continually engage and purchase.
- It highlights segments yielding the highest long-term value. Focusing retention and expansion efforts here has a major upside.
- Comparing CLTV to CAC shows acquisition profitability and ROI. Acquiring customers with CLTV exceeding CAC equals profitability.
For product managers, evaluating CLTV guides profitable growth through:
- Identifying high-value user segments to tailor experiences for.
- Prioritizing retention in features and campaigns, especially for valuable customers.
- Informing monetization and pricing strategies.
- Optimizing acquisition channels and budgets based on CLTV potential.
Monitoring CLTV enables product and growth teams to focus on the highest-impact opportunities to maximize lifetime customer value.
Monthly Recurring Revenue (MRR)
For subscription businesses like SaaS, Monthly Recurring Revenue (MRR) is one of the most important metrics to track. MRR refers to the predictable revenue that a company can expect to generate each month through ongoing subscription fees.
MRR is calculated by summing up the monthly fees paid by all current subscribers. For example, if a SaaS product has:
- 500 users paying $10/mo
- 200 users paying $20/mo
- 100 users paying $50/mo
Their MRR is 500 * $10 + 200 * $20 + 100 * $50 = $7,000
Monitoring MRR provides several key insights:
- It shows reliable revenue growth. MRR scales through new customers and expanded subscriptions from existing users.
- It highlights revenue fluctuations. Declining MRR may indicate issues with churn or retention.
- It enables accurate forecasting. MRR signals revenue run rate to better predict future income.
- It quantifies the impact of churn. Lost MRR shows revenue damage of churn that needs offset through expansion and acquisition.
For product managers at SaaS companies, keeping a pulse on MRR trends is crucial. Growth in MRR signifies acquiring new customers and expanding seat utilization among current users. Stagnant or declining MRR should prompt an investigation into underlying retention issues. MRR must expand over time for the business to thrive.
Retention Rate
Retention rate is the percentage of users that continue engaging with a product over a defined time period. This metric quantifies user loyalty and provides critical insight into customer churn and loss.
Cohort analysis is often used to measure retention. User cohorts are defined based on a common trait, such as sign-up date or first purchase month. The cohort retention rate is calculated as:
Retention Rate = # of users still active after [time period] / # of users in the original cohort
For example, if an app has 200 users sign up in January, and 150 of them are still active in March, the Month 2 retention rate is 75% (150/200).
Tracking retention over time highlights trends in user behavior and loyalty:
- High initial retention shows products deliver on early user expectations. This indicates short-term stickiness.
- Retention rates declining rapidly over time signal the inability to continually engage and delight users.
- Long-term retention must remain high. This minimizes churn and fuels growth through referrals and expansions.
For product managers, monitoring retention by cohort provides insights into improving user experiences:
- Early drop-offs may indicate onboarding friction that needs smoothing.
- Feature usage data can reveal causes of lagging retention needing innovation.
- Surveying defecting users identifies pain points to address.
- Optimizing retention boosts growth and lowers acquisition costs.
Retention is a product’s lifeblood. Assessing trends arms product managers with data to maximize it.
Feature Usage
Analyzing feature usage and adoption is critical for optimizing a product’s user experience and driving engagement. This metric refers to the percentage of users that utilize specific features or areas of a product.
Feature usage can be tracked by monitoring analytics events and actions associated with features, such as:
- Signing into accounts
- Clicking menu or page items
- Activating in-product tutorials
- Viewing pages or content
The data reveals insights such as:
- The most and least used features or pages
- Adoption rates for newly launched capabilities
- Feature usage segmented by user demographic
- Changes in usage over time based on enhancements
Understanding feature usage matters because it enables data-driven product optimization:
- Doubling down investment on popular features that provide core value
- Reprioritizing or removing rarely used features that provide minimal value
- Highlighting feature gaps that present opportunities for enhancement
- Optimizing information architecture and menus by usage levels
For product managers, tracking feature usage helps align roadmaps with real user behavior. Low usage signals an ineffective or confusing feature needing rework or removal. High usage demonstrates value and engagement that can be expanded upon. Monitoring usage over time provides context around whether product changes move the needle. This ultimately optimizes the user experience to be intuitive and focused on customer needs.
Time to Feature Adoption
The speed at which users adopt and start actively engaging with new features after launch offers actionable insights into user behavior. Monitoring time-to-feature adoption involves tracking the number of days until a certain percentage of users have used a recently introduced capability.
For example, a messaging app may track how many days it takes until 15% of MAUs send a message using a new multimedia format after it is launched. Faster adoption signals an intuitive feature that seamlessly blends into user workflows.
Measuring time to adoption evaluates several key factors:
- Feature relevance addressing real user needs. Quick adoption indicates value.
- Ease of discovery and access to the feature within the user interface.
- Effectiveness of in-product cues and notifications pointing users to new capabilities.
- Overall usability and intuitiveness of the feature.
For product teams, accelerating time to adoption demonstrates direct user benefits. It shows the feature is well-designed, introduced properly, and integrated smoothly into natural usage flows. Slow adoption may signal convoluted implementations requiring simplification or better onboarding to promote usage.
Monitoring this metric ultimately helps product managers make roadmap prioritization decisions. Quickly adopted features can warrant further investment to broaden appeal. Lagging adoption flags areas needing refinement or better communication to highlight benefits.
Conclusion
Effective product management in today’s data-rich landscape requires a strong command of key metrics to guide strategic decision-making. While many metrics may be tracked, the 10 covered in this guide represent some of the most enlightening that all product managers should continuously monitor.
A recap of the key metrics:
- Sprint Velocity – Development capacity
- Time to Market – Release agility
- DAUs/MAUs – Engagement
- Net Promoter Score – User Loyalty
- Customer Acquisition Cost – Acquisition efficiency
- Customer Lifetime Value – Long-term profitability
- Monthly Recurring Revenue – Growth potential
- Retention Rate – User stickiness
- Feature Usage – Product optimization
- Time to Feature Adoption – Feature appeal
Leveraging these metrics transforms product management from guesswork to a data-driven practice. The metrics provide crucial insights into all aspects of the user lifecycle – from product appeal and adoption to retention and growth. They enable benchmarking performance, identifying high-impact areas for attention, and focusing product enhancement and business efforts to maximize results.
Product managers should analyze trends in the metrics, gain insights into what is driving changes, and take targeted action informed by the data. Metrics reveal what is working well to double down on and what areas need attention. No single metric tells the full story, so examine them in combination to make smart product and strategy decisions.
Adopting a metrics-driven approach is essential for product managers to deliver experiences that attract, engage, and measurably retain customers. With the foundation provided by these 10 key metrics, product managers at companies of any size or industry can confidently use data to build successful products that exceed customer expectations.
