Product Management (PM)¶
Product Management (PM) oversees product strategy, development, and lifecycle to deliver solutions that meet user needs and business goals.
Performance Management¶
Performance management isn’t just about tracking numbers—it’s about using those numbers to learn, iterate, and celebrate progress together. When you measure what matters, you give your team a roadmap to real impact. To guide Product Management in using metrics as tools for focus, learning, and growth—not just scorekeeping—so teams can improve both product and process.
Regularly review metrics in team retrospectives and monthly check-ins; tie trends to experiments and product changes; celebrate key improvements and dig into root causes for negative shifts; update targets and definitions as strategy evolves.
Focus Areas and Top KPIs¶
| Focus Area | Top KPIs |
|---|---|
| Activation & Onboarding | - Activation Rate - Onboarding Completion Rate - Drop-Off Rate During Onboarding - Percent Completing Key Activation Tasks - Time to First Key Action |
| User Engagement & Adoption | - Engagement Rate - Feature Adoption / Usage - Monthly Active Users - Session Frequency - Usage Depth |
| Retention & Customer Health | - Customer Retention Rate - Activation Cohort Retention Rate (Day 7/30) - Net Revenue Retention - Churn Risk Score - Customer Health Score |
| Product-Led Growth & Expansion | - Product Qualified Leads - Self-Serve Upgrade Rate (Post-Activation) - Expansion Revenue - Activation-to-Expansion Rate - Expansion Opportunity Score |
| Feedback & Advocacy | - Net Promoter Score - Customer Feedback Score - Customer Satisfaction Score - Referral Prompt Acceptance Rate - Referral Conversion Rate |
Frameworks for Metric Selection¶
Choosing the right metrics is about clarity and focus—not tracking everything, but spotlighting what moves the needle. Use proven frameworks to ensure your KPIs actually drive the right product conversations. To help Product Management select, prioritize, and communicate metrics that align with company goals and product strategy without drowning in data.
HEART Framework¶
A user-centric model to balance holistic product health with actionable signals. HEART stands for Happiness, Engagement, Adoption, Retention, and Task Success.
Key Stages / Examples¶
- Pick 1-2 metrics in each dimension (e.g., Engagement Rate, Activation Rate, Onboarding Completion Rate)
- Tie metrics to specific user journeys or product goals
- Review regularly to adapt as the product evolves
North Star Metric Approach¶
Focuses on a single, leading metric that best captures the core value your product delivers to customers, with supporting KPIs to drive it.
Key Stages / Examples¶
- Define your product's North Star (e.g., Monthly Active Users, Activation Rate)
- Identify supporting metrics (e.g., Feature Adoption Rate, Time to First Value)
- Align teams and experiments to directly impact the North Star
Reporting Cadence and Structure¶
Routine, transparent reporting keeps everyone rowing in the same direction. The right cadence ensures your team is learning, adapting, and celebrating progress—not just checking boxes. To ensure that data-informed insights are delivered frequently and clearly enough to drive action, learning, and alignment across Product and adjacent teams.
Cadence Overview¶
- Level: Product Team & Leadership
- Frequency: Weekly tactical reporting; Monthly review for trends; Quarterly deep dive and strategy recalibration
- Audience: Product Management, Engineering, UX, Marketing, Executive Stakeholders
Examples¶
- Weekly: Dashboard updates on Activation Rate, Engagement Rate, and Drop-Off Rate
- Monthly: Retention and Feature Adoption progress review
- Quarterly: Strategic review of Product Qualified Leads and Customer Retention Rate
Standard Report Structure¶
- Executive Summary
- Key Metrics (with trends and targets)
- Highlights and Lowlights
- Insights & Recommendations
- Action Items and Follow-Ups
Common Pitfalls and How to Avoid Them¶
Even the most data-driven teams can get tripped up if they aren’t careful. Knowing where others stumble helps you stay focused, efficient, and ahead of the curve. To help Product Management sidestep the traps that undermine a strong data culture, so you spend time on what drives value—not busywork or vanity metrics.
Frequent Pitfalls and How to Avoid Them:¶
| Issue | Solution |
|---|---|
| Chasing too many metrics at once (data overload) | Prioritize a handful of KPIs that connect directly to your product’s goals and user journey. |
| Relying on vanity metrics instead of actionable KPIs | Focus on metrics that drive decisions and learning—for example, Activation Rate over raw Page Views. |
| Not tying metrics to hypotheses or experiments | Always connect your KPIs to a clear question or outcome you want to test, so every number has a purpose. |
| Lack of context or storytelling in reporting | Pair metrics with insights, user stories, or qualitative feedback to bring the numbers to life. |
| Siloed data access or analysis | Make dashboards and reports easily accessible across teams and encourage shared ownership of results. |
How to build a Data-Aware Culture¶
A data-aware culture isn’t built overnight—it’s a journey. Start with trust and curiosity, grow with discipline and transparency, and soon your team will be learning (and winning) together. To give Product Management a tactical blueprint for embedding data habits into daily work, so everyone is empowered to use insights as a springboard for progress.
Foundational Elements¶
- Leadership buy-in and visible use of data in decision-making
- Shared, accessible definitions of all key metrics
- Clear linkage between product goals and KPIs
- Frictionless access to dashboards and self-serve analytics
- Celebration of learning (not just winning) from data
Team Practices¶
- Kick off every project or sprint with a data-driven hypothesis and success metric
- Hold regular metric reviews and retros—what’s working, what’s not, what did we learn?
- Encourage questions and challenge assumptions with real user evidence
- Document insights, not just raw numbers, and share them widely
- Build fast feedback loops between user behavior, product changes, and outcomes
Maturity Stages¶
| Stage | Description |
|---|---|
| Foundational | Basic tracking of core product metrics, but data is siloed and used reactively, often just for reporting. |
| Emerging | Teams start using metrics for decision-making and experimentation, with regular reviews and more consistent definitions. |
| Established | Data is woven into daily routines and prioritization; teams proactively look for insights and act on them quickly. |
| Advanced | A culture of experimentation, continuous learning, and cross-team sharing drives innovation and outsized impact; data and qualitative feedback are seamlessly integrated. |
Why Data Aware Culture Matter¶
A data-aware culture empowers product teams to make smart, confident decisions grounded in evidence, not hunches. It’s about building habits that put facts before assumptions, so you can align teams, learn fast, and deliver what truly matters to users. To help Product Management unlock better outcomes by embedding data-driven thinking into everyday work, making insights accessible and actionable for everyone involved.
Relevant Topics:
- Enables faster, more confident decision-making by backing up intuition with evidence.
- Uncovers true user needs and product-market fit through measurable outcomes.
- Reduces risk and waste by surfacing what’s working—and what’s not—early in the process.
- Promotes alignment across teams, making priorities and progress transparent.
- Fosters a culture where learning from both wins and misses is celebrated, not feared.
Other Related KPIs¶
| Metric | Description |
|---|---|
| Action-to-Activation Time Lag | Action-to-Activation Time Lag measures the time it takes for a user to move from their first meaningful action (e.g. sign-up or click) to reaching activation. It helps assess onboarding speed and the friction between interest and value realization. |
| Activated-to-Follow-Up Engagement Rate | Activated-to-Follow-Up Engagement Rate measures the percentage of activated users who engage with the product again within a specific time window. It helps evaluate short-term retention and stickiness post-activation. |
| Activation Cohort Retention Rate (Day 7/30) | Activation Cohort Retention Rate (Day 7/30) measures the percentage of users who, after reaching activation, return to use the product 7 or 30 days later. It helps evaluate how well activation leads to ongoing engagement and early product adoption. |
| Activation Conversion Rate | Activation Conversion Rate measures the percentage of users who reach the activation milestone out of all users who entered the onboarding or trial flow. It helps evaluate onboarding effectiveness and product-led growth readiness. |
| Activation Progression Score | Activation Progression Score measures how far a user has progressed through a predefined series of activation milestones. It helps track onboarding momentum and identify where users drop off before reaching full activation. |
| Activation Rate | Activation Rate measures the percentage of users who reach a predefined milestone that signifies meaningful initial engagement or product adoption. This milestone, often referred to as "activation," represents the moment when users experience the core value of the product for the first time. |
| Activation-to-Expansion Rate | Activation-to-Expansion Rate measures the percentage of activated accounts that go on to expand—typically by adding users, upgrading plans, or increasing usage. It helps assess whether activation is leading to monetization and account growth. |
| Active Feature Usage Rate | Active Feature Usage Rate measures the percentage of active users who engage with a specific feature within a given time period. It helps determine the feature’s relevance, discoverability, and stickiness. |
| Average Order Value | Average Order Value (AOV) refers to the average amount of money spent each time a customer places an order. It’s a key metric used to track customer purchasing behavior and assess the effectiveness of sales and marketing efforts. |
| Average Resolution Time | Average Resolution Time (ART) measures the average amount of time it takes to fully resolve a customer issue or support ticket from the moment it is raised to when it is marked as resolved. |
| Average Revenue Per User | Average Revenue Per User (ARPU) is a metric that represents the average amount of revenue generated per user or customer over a specific time period, typically calculated on a monthly or yearly basis. |
| Breadth of Use | Breadth of Use measures the number of distinct features, modules, or product areas used by a single customer or account. It helps assess product adoption depth and customer stickiness. |
| Churn Risk Score | Churn Risk Score is a predictive metric that estimates the likelihood of a customer canceling or downgrading within a given period. It helps identify at-risk accounts for proactive retention efforts. |
| Complaints Received | Complaints Received refer to the number of formal or informal complaints submitted by customers or users about a product, service, or experience. These complaints highlight dissatisfaction and can cover a range of issues, from product defects to customer service challenges. |
| Cost of Poor Quality | Cost of poor Quality (COPQ) refers to the costs incurred by an organization due to defects, inefficiencies, and errors in product or service delivery. It includes the financial impact of delivering substandard quality, both in internal operations and external customer-facing activities. |
| Customer Downgrade Rate | Customer Downgrade Rate measures the percentage of existing customers who reduce their subscription value (e.g., lower tier, fewer seats, removed features) within a given period. It helps assess product fit, pricing friction, and account health risk. |
| Customer Effort Score | Customer Effort Score (CES) measures how easy it is for customers to accomplish a task, such as resolving an issue, making a purchase, or using a feature. Typically, customers are asked to rate their experience on a scale, with lower effort indicating a better experience. |
| Customer Engagement Score | Customer Engagement Score measures how actively and consistently a customer is interacting with your product, content, or brand. It helps assess product adoption, value realization, and retention potential. |
| Customer Feedback Retention Score | Customer Feedback Retention Score measures the retention rate of customers who have provided feedback (positive, negative, or neutral). It helps assess whether engaging customers in feedback loops improves loyalty and long-term value. |
| Customer Feedback Score | Customer Feedback Score measures customer sentiment and satisfaction based on responses to feedback requests, often collected through surveys, ratings, or qualitative input. This metric provides direct insight into customer opinions about your product, service, or overall experience. |
| Customer Feedback Score (Post-activation) | Customer Feedback Score (Post-activation) measures the average rating or sentiment provided by customers after reaching a defined product activation milestone. It helps assess product satisfaction and value delivery in early stages. |
| Customer Retention Rate | Customer Retention Rate (CRR) measures the percentage of customers a company retains over a given period. It reflects the ability to keep customers engaged, satisfied, and loyal to the brand, minimizing churn. |
| Customer Support Tickets | Customer Support Tickets is a classification metric that organizes customer support inquiries into predefined categories, such as technical issues, billing problems, product questions, or feature requests. This helps identify trends and prioritize areas for improvement. |
| Daily Active Users | Daily Active Users (DAU) measures the total number of unique users who engage with a product, app, or website on a given day. Engagement criteria may vary by product, such as logging in, completing a transaction, or performing a specific action. |
| DAU/WAU Ratio | DAU/WAU Ratio compares the number of Daily Active Users (DAU) to Weekly Active Users (WAU) over a specified time period. It represents the proportion of weekly users who engage with your product daily, offering insight into how often users return. |
| Drop-Off Rate | Drop-Off Rate measures the percentage of users who leave a process, page, or journey before completing a desired action. This metric identifies points of friction or disengagement, helping you optimize user flows for better retention and conversion. |
| Drop-Off Rate During Onboarding | Drop-Off Rate During Onboarding measures the percentage of users who start but do not complete the onboarding process. It helps identify friction points in user activation and early product engagement. |
| Engagement Depth (First 3 Sessions) | Engagement Depth (First 3 Sessions) measures how thoroughly new users or visitors interact with your product or content during their first three sessions. It helps assess early-stage user interest and value perception. |
| Engagement Metrics | Engagement Metrics are data points that measure how users interact with your product, content, or campaigns. They assess the depth, frequency, and quality of user interactions, providing insights into customer interest, satisfaction, and loyalty. |
| Engagement Rate | Engagement Rate measures the level of interaction users have with your content, product, or campaigns relative to the size of your audience. It provides insight into how effectively your efforts capture attention and encourage meaningful user actions. |
| Error Rate | Error Rate measures the percentage of errors or failures occurring during a specific process, interaction, or system operation. It reflects the quality and reliability of a product, service, or workflow. |
| Escalation Rate | Escalation Rate measures the percentage of customer support cases or issues that are escalated to a higher level of support, such as specialized teams, managers, or senior agents. It reflects the complexity of issues and the ability of frontline support to resolve them effectively. |
| Exit Rate | Exit Rate is the percentage of visits to a specific webpage or app screen that end with the user leaving the site or app entirely. It shows how often a particular page or screen is the last one visited during a session. |
| Exit Reason Frequency (Segmented) | Exit Reason Frequency (Segmented) measures how often specific reasons for churn or cancellation occur across different customer segments. It helps identify patterns in churn behavior and root causes by cohort. |
| Expansion Activation Rate | Expansion Activation Rate measures the percentage of existing accounts that adopt a new product, feature, or service that can lead to upsell or cross-sell. It helps track momentum in expansion readiness and usage. |
| Expansion Feature Usage Frequency | Expansion Feature Usage Frequency measures how often a specific upsell-eligible feature is used by existing accounts. It helps assess product stickiness, value realization, and readiness for expansion. |
| Expansion Revenue | Expansion Revenue refers to the additional revenue generated from existing customers through upselling, cross-selling, add-ons, or increased usage over time. It’s a key component of revenue growth strategies, particularly in subscription-based or SaaS businesses. |
| Expansion Revenue Growth Rate | Expansion Revenue Growth Rate measures the rate at which revenue from existing customers grows over a given period due to upselling, cross-selling, or increased usage. It reflects the success of efforts to expand the value of current customer relationships. |
| Expansion Revenue Potential (Forecasted) | Expansion Revenue Potential (Forecasted) estimates the total revenue that could be unlocked from your existing customer base via upsell, cross-sell, or usage-based growth. It helps quantify upside within the base. |
| Feature Adoption / Usage | Feature Adoption measures the percentage of users who actively engage with a specific product feature over a given period. It indicates how successfully a feature resonates with your audience and integrates into their workflow or usage patterns. |
| Feature Adoption Rate (Early) | Feature Adoption Rate (Early) measures the percentage of new users who use a key feature within their first few sessions or days. It helps evaluate onboarding effectiveness and early value realization. |
| Feature Adoption Rate (Ongoing) | Feature Adoption Rate (Ongoing) measures the percentage of active users who regularly use a key product feature over a longer period. It helps track sustained value delivery and product adoption health. |
| Feature Adoption Velocity (Top 3 Features) | Feature Adoption Velocity (Top 3 Features) measures the average time it takes for new users to adopt your top 3 product features after onboarding. It helps assess onboarding effectiveness and early value alignment. |
| Feature-Based ARPU | Feature-Based ARPU measures the average revenue generated per user who actively uses a specific feature. It helps quantify feature value and its impact on monetization. |
| First Critical Feature Reuse Rate | First Critical Feature Reuse Rate measures the percentage of users who return to use a key feature for a second time within a set period. It helps assess whether the feature delivered enough value to encourage repeat behavior. |
| First Feature Usage Rate | First Feature Usage Rate measures the percentage of new users who use at least one core feature during their initial sessions. It helps assess early product interaction and onboarding effectiveness. |
| First Session Completion Rate | First Session Completion Rate measures the percentage of new users who complete a defined onboarding or usage flow during their first session. It helps track early-stage friction and product clarity. |
| First-time User Conversion Rate | First-Time User Conversion Rate measures the percentage of new users or visitors who complete a desired action, such as making a purchase, or subscribing during their first interaction with your product or service. |
| Free-to-Paid Conversion Rate (Self-Serve) | Free-to-Paid Conversion Rate (Self-Serve) measures the percentage of users who upgrade from a free plan or trial to a paid plan without direct sales intervention. It helps track product-led growth effectiveness. |
| Immediate Time to Value | Immediate Time to Value (ITTV) refers to the time it takes for a customer to experience the initial, meaningful value of a product or service after their first interaction. It focuses on the speed at which customers realize a quick win or tangible benefit. |
| Key Feature Exploration Rate | Key Feature Exploration Rate measures the percentage of users who engage with a high-value feature for the first time—regardless of whether they complete or repeat use. It helps evaluate feature discoverability and user curiosity. |
| Meaningful Session Frequency | Meaningful Session Frequency measures how often users return and complete a set of high-value actions within a session. It helps quantify behavior quality, not just raw usage. |
| Monthly Active Users | Monthly Active Users (MAU) is the total number of unique users who engage with a product, service, or platform within a given month. Engagement can include logging in, performing key actions, or interacting with specific features, depending on the product’s goals. |
| Multi-Session Activation Completion Rate | Multi-Session Activation Completion Rate measures the percentage of users who complete the full activation flow across more than one session. It helps track long-path engagement and sustained activation behavior. |
| Number of Monthly Sign-ups | Number of Monthly Sign-Ups is the total count of new users, customers, or accounts that sign up for a product, service, or platform within a given month. |
| Onboarding Completion Rate | Onboarding Completion Rate measures the percentage of users who successfully complete the onboarding process, transitioning from new sign-ups to fully onboarded users. It reflects how effectively your onboarding flow prepares users to engage with your product or service. |
| Onboarding Drop-off Rate | Onboarding Drop-Off Rate measures the percentage of users who begin the onboarding process but fail to complete it. It highlights where users lose interest or encounter obstacles during onboarding. |
| Page Views | Page Views refers to the total number of times a specific webpage is loaded or viewed by users. It counts every instance of a page being loaded, regardless of whether it’s the same user viewing the page multiple times. |
| Paywall Hit Rate | Paywall Hit Rate measures the percentage of users who encounter a paywall or upgrade prompt during their session. It helps quantify how often users reach the limits of free access. |
| Percent Completing Key Activation Tasks | Percent Completing Key Activation Tasks measures the share of users or accounts who complete one or more predefined activation actions within a given timeframe. It helps assess early engagement quality and product onboarding effectiveness. |
| Percent of Accounts Completing All Key Trial Actions | Percent of Accounts Completing All Key Trial Actions measures the share of trial accounts that complete all pre-identified actions during the trial. It helps evaluate readiness to convert and alignment with the product’s core value during the trial window. |
| Percent of Accounts Completing Key Activation Milestones | Percent of Accounts Completing Key Activation Milestones measures the proportion of accounts that reach predefined, high-value activation checkpoints. It helps determine whether users are progressing toward long-term adoption. |
| Percent of Accounts Reaching Product-Qualified Lead (PQL) Status | Percent of Accounts Reaching Product-Qualified Lead (PQL) measures the proportion of trial or freemium accounts that meet your product usage thresholds to be flagged as sales-ready. It helps quantify the efficiency of product-led qualification. |
| Percent of Accounts with 3+ Activated Users | Percent of Accounts with 3+ Activated Users measures the share of accounts where at least three individual users have completed activation steps. It helps identify depth of adoption and signals potential virality or team-based expansion. |
| Percent of Accounts with Multi-Role Engagement | Percent of Accounts with Multi-Role Engagement measures the share of accounts where users from two or more distinct roles are actively using the product. It helps identify cross-functional adoption and account maturity. |
| Percent of Retained Feature Users | Percent of Retained Feature Users measures the proportion of users who continue to use a specific feature over a defined retention window. It helps assess feature stickiness and long-term value. |
| Percent of Users Engaging with Top Activation Features | Percent of Users Engaging with Top Activation Features measures how many new users interact with the highest-impact features tied to activation. It helps assess onboarding effectiveness and early value delivery. |
| Proactive Support Engagement Rate | Proactive Support Engagement Rate measures the percentage of users who respond to or engage with support initiatives before submitting an issue or ticket. It helps track the effectiveness of preemptive support and self-service education. |
| Product Adoption Rate | Product Adoption Rate measures the percentage of users or customers who adopt a product or feature within a specific time period after its introduction. It reflects how well the product resonates with its target audience and fulfills their needs. |
| Product Sharing Rate | Product Sharing Rate measures the percentage of users who share a part of the product experience with others—such as inviting teammates, generating shareable links, or embedding product outputs. It helps quantify virality and product-led acquisition. |
| Product-Engaged Leads (PELs) | Product-Engaged Leads (PELs) are users or accounts that demonstrate meaningful in-product behavior indicating buying intent or readiness for sales outreach. It helps connect product usage signals with sales qualification criteria. |
| Rate of Escalation to Higher Support Tiers | Rate of Escalation to Higher Support Tiers measures the percentage of customer support issues that require escalation from lower-tier support (e.g., frontline or basic support) to higher-tier support (e.g., advanced technical teams or specialized departments). |
| Resolution Time | Resolution Time measures the amount of time it takes to resolve a customer issue or ticket from the moment it is raised to when it is marked as resolved. It tracks the speed and efficiency of support teams in addressing customer concerns. |
| Returning Visitors | Returning Visitors are users who visit your website or app more than once during a specified time period. This metric highlights how well your content, product, or experience retains and re-engages users. |
| Revenue per Trial User | Revenue per Trial User measures the average revenue generated per user who enters a product trial—regardless of whether they convert or not. It helps quantify the trial program’s financial efficiency. |
| Scroll Depth | Scroll Depth measures how far users scroll down a webpage or piece of digital content. It provides insight into how much of the content users engage with and whether they reach critical sections, such as calls to action (CTAs) or key information. |
| Self-Serve Checkout Rate | Self-Serve Checkout Rate measures the percentage of users who successfully complete a purchase or upgrade through a self-serve flow without human intervention. It helps evaluate the effectiveness of your product-led conversion path. |
| Self-Serve Expansion Revenu | Self-Serve Expansion Revenue measures the total revenue generated from existing customers who independently upgrade or expand their usage without sales involvement. It helps track the scalability of your product-led growth engine. |
| Self-Serve Upgrade Rate (Post-Activation) | Self-Serve Upgrade Rate (Post-Activation) measures the percentage of activated users who upgrade to a paid plan through a self-serve flow, without sales or CS intervention. It helps evaluate the product’s ability to convert engaged users into paying customers. |
| Self-Serve Upsell Revenue | Self-Serve Upsell Revenue measures the revenue generated when existing users purchase additional features, services, or higher-tier plans independently through the product—without sales or CS involvement. It helps quantify scalable growth from within your product. |
| Session Frequency | Session Frequency measures how often users return to a website, app, or platform within a specific period. It tracks the average number of sessions per user, providing insights into user engagement and loyalty. |
| Session Length | Session Length measures the total time a user spends actively engaging with a website, app, or platform during a single session. It begins when a user starts interacting and ends when they leave or become inactive for a predetermined duration (e.g., 30 minutes of inactivity). |
| Short Time to Value | Short Time to Value (STTV) measures the time it takes for a customer to experience their first significant value or benefit from your product or service. This metric emphasizes achieving quick wins that demonstrate value early in the customer journey. |
| Signup Abandonment Rate | Signup Abandonment Rate measures the percentage of users who begin but do not complete the signup or account creation process. It helps identify friction points in your conversion funnel and reduce lost opportunities at the top of the funnel. |
| Signup Completion Rate | Signup Completion Rate measures the percentage of users who finish the full signup or account creation process after initiating it. It helps assess the efficiency and effectiveness of your conversion funnel entry point. |
| Signup Funnel Completion Rate | Signup Funnel Completion Rate measures the percentage of users who successfully complete all steps in a multi-step signup process. It helps identify friction points and optimize conversion flow across each stage. |
| Stickiness Ratio | Stickiness Ratio measures how often users return to your product by comparing daily active users (DAU) to monthly active users (MAU). It helps evaluate how “sticky” or habit-forming your product is. |
| Task Success Rate | Task Success Rate measures the percentage of users who successfully complete a specific task or goal on a website, app, or product interface. It indicates how effectively the design and functionality support user needs. |
| Ticket Volume | Ticket Volume is the total number of customer support tickets created within a specific timeframe. It represents the demand for support services and provides insight into user needs, product issues, or service performance. |
| Time Between Logins (Post-Activation) | Time Between Logins (Post-Activation) measures the average time elapsed between logins for users who have already completed activation. It helps track engagement frequency and detect signs of drop-off or stickiness in the user experience. |
| Time in App | Time in App measures the total amount of time users spend actively engaging with a mobile or web application over a specific period. It reflects how much value users derive from the app and its ability to capture their attention. |
| Time on Page | Time on Page measures the average amount of time users spend on a single webpage. It reflects how engaging or relevant the content on that page is to visitors. |
| Time on Task | Time on Task measures the amount of time users take to complete a specific task or goal within a system, interface, or application. It reflects the efficiency and ease of use of your product or service. |
| Time to Basic Value | Time to Basic Value (TTBV) measures the time it takes for a new user or customer to achieve their first significant milestone or experience the basic value of a product or service. It represents how quickly the product delivers on its core promise to users. |
| Time to Exceed Value | Time to Exceed Value (TTEV) measures the time it takes for users to perceive that a product or service has exceeded their expectations or delivered greater-than-expected benefits. It’s a customer success metric that highlights when a user transitions from simply meeting their needs to experiencing delight or exceeding their goals. |
| Time to Expansion Signal | Time to Expansion Signal measures the average time it takes for an account or user to exhibit clear behavior that indicates readiness or potential for upsell, cross-sell, or plan expansion. It helps identify product maturity timing and sales opportunity windows. |
| Time to First Habitual Action | Time to First Habitual Action measures the average time it takes a user to perform a recurring, value-driving action for the second or third time — indicating the start of habit formation. It helps assess how quickly users are becoming engaged and sticky. |
| Time to First Key Action | Time to First Key Action measures the average time it takes for a new user to complete a product’s primary activation event — often referred to as the “aha moment.” It helps track how quickly users begin experiencing real value. |
| Time to First Repeat Action | Time to First Repeat Action measures the average time it takes for a user to repeat a key behavior (e.g., log in, run a report, send a message) after their first instance. It helps track habit-formation velocity and early product stickiness. |
| Time to First Value | Time to First Value (TTFV) measures the time it takes for a new user or customer to achieve their first meaningful experience with your product or service. It represents the point at which a user realizes initial value, validating their decision to engage with your solution. |
| Time to PQL Qualification | Time to PQL Qualification measures the average time it takes for a user or account to reach Product-Qualified Lead (PQL) criteria after signing up or starting a trial. It helps track how quickly users demonstrate high intent or sales-readiness via product usage. |
| Time to Resolution | Time to Resolution (TTR) measures the average time it takes for a support team to fully resolve a customer inquiry, issue, or ticket. It starts when the issue is reported and ends when it is marked as resolved. |
| Time to Self-Serve Sign-Up | Time to Self-Serve Sign-Up measures the average amount of time it takes for a prospect to sign up for a product or trial after their first meaningful touchpoint (e.g., site visit, ad click, content download). It helps track lead urgency and top-of-funnel conversion velocity. |
| Time to Value | Time to Value (TTV) measures the time it takes for a new customer to realize the promised value of a product or service after adoption. It tracks the duration from when a customer begins using the product to when they achieve their first meaningful benefit or milestone. |
| Time to Value (Expansion Features) | Time to Value (Expansion Features) measures the average time it takes for users or accounts to adopt and gain value from premium or advanced features after their initial onboarding or activation. It helps assess expansion readiness and product maturity velocity. |
| Trial Engagement Rate | Trial Engagement Rate measures the percentage of users who actively engage with your product during their trial period—using defined engagement behaviors like logins, feature usage, or team invites. It helps assess trial quality and onboarding effectiveness. |
| Upgrade Intent Signal Rate | Upgrade Intent Signal Rate measures the percentage of users or accounts that exhibit behaviors indicating a likely upgrade to a paid or higher-tier plan. It helps identify product-qualified upgrade opportunities early in the user journey. |
| Usage Depth | Usage Depth measures the extent to which users engage with the features, functionalities, or content of your product. It reflects how comprehensively users utilize available features, providing insight into their engagement and the product’s perceived value. |
| WAU/MAU Ratio | The WAU/MAU Ratio compares the number of Weekly Active Users (WAU) to Monthly Active Users (MAU). It represents the percentage of users who engage with your product weekly out of those who are active within a month. |
| Weekly Active Users | Weekly Active Users (WAU) measures the total number of unique users who engage with your product, service, or platform at least once during a specific week. It reflects the breadth of active engagement within a weekly timeframe. |