Data & Analytics

Data & Analytics collect, process, and analyze data to help organizations make informed decisions, improve strategies, and solve problems.

Performance Management

Performance management is about learning and course-correcting, not just hitting numbers. Transparency and context make KPIs truly useful. To drive accountability and improvement by linking team and individual contributions to business outcomes—while celebrating progress and learning from setbacks.

Hold monthly performance reviews using structured scorecards, discuss what moved the needle (and what didn’t), and share learnings cross-functionally. Set quarterly targets and revisit KPIs as the business evolves.

Focus Areas and Top KPIs

Focus Area	Top KPIs
Product Adoption & Engagement	- Activation Rate - Monthly Active Users - Customer Engagement Score - Stickiness Ratio - Feature Adoption / Usage
Customer Retention & Growth	- Customer Retention Rate - Net Revenue Retention - Expansion Revenue Growth Rate - Churn Risk Score - Expansion Activation Rate
Acquisition & Funnel Performance	- Trial Sign-Up Rate - Conversion Rate - Onboarding Completion Rate - Visitor-to-Sign-Up Conversion Rate - Lead-to-SQL Conversion Rate
Operational Efficiency	- Cost Per Ticket - Average Resolution Time - First Contact Resolution - Onboarding Drop-off Rate - Session Length
Revenue & Expansion	- Expansion Revenue Growth Rate - Net Revenue Retention - Expansion Revenue - Expansion Opportunity Score - Self-Serve Upsell Revenue

Frameworks for Metric Selection

Choosing the right metrics is about clarity, focus, and action—think beyond vanity, and anchor every KPI to a real business outcome. To ensure teams measure what matters, drive alignment, and avoid wasted effort tracking irrelevant or misleading metrics.

North Star Metric Alignment

Identify a single, guiding metric that best reflects long-term customer value and connects to growth. Supporting metrics should ladder up to this North Star.

Key Stages / Examples

• Define the North Star (e.g., Monthly Active Users in PLG SaaS).
• Select supporting metrics (e.g., Activation Rate, Stickiness Ratio, Customer Engagement Score).
• Review quarterly to ensure relevance as the business evolves.

Input/Output Metric Mapping

Distinguish between leading (input) and lagging (output) indicators. Track both to spot early signals and measure impact.

Key Stages / Examples

• Map Activation Rate (leading) to Customer Retention Rate (lagging).
• Track Engagement Rate (leading) and Expansion Revenue Growth Rate (lagging).
• Diagnose gaps where inputs are strong, but outputs lag—or vice versa.

Reporting Cadence and Structure

Consistent, well-structured reporting keeps everyone aligned and focused on progress—not just busywork. To ensure that insights are delivered at the right time, to the right people, with just enough context for informed action.

Cadence Overview

• Level: Team, Department, Executive
• Frequency: Weekly (operational), Monthly (strategic), Quarterly (deep dive)
• Audience: Data & Analytics team, business owners, cross-functional stakeholders, executive leadership

Examples

• Weekly: Activation Rate and Onboarding Completion Rate for product teams.
• Monthly: Customer Engagement Score and Net Revenue Retention for leadership.
• Quarterly: Cohort Retention Analysis and Expansion Revenue Growth Rate for board-level review.

Standard Report Structure

• Executive Summary
• Key Metrics & Trends
• Insights & Analysis
• Action Items & Owners
• Risks or Roadblocks
• Appendix (detailed data, methodology)

Common Pitfalls and How to Avoid Them

Avoid these classic traps to keep your data efforts sharp, relevant, and credible. To help teams sidestep waste, confusion, and frustration—so data becomes a trusted partner in decision-making, not a source of stress.

Frequent Pitfalls and How to Avoid Them:

Issue	Solution
Tracking too many metrics (analysis paralysis)	Focus on a core set of actionable KPIs that align with business goals and review regularly.
Using vanity metrics that don’t drive action	Prioritize leading and lagging indicators that tie directly to outcomes you control.
Lack of clear ownership for metrics	Assign an owner to every key metric and make responsibilities public.
Inconsistent data definitions and sources	Standardize metric definitions and centralize documentation to avoid confusion.
Reporting without recommended actions	Always pair data with concrete insights and next steps—turn reports into roadmaps.

How to build a Data-Aware Culture

Building a data-aware culture is a journey: start simple, keep it practical, and celebrate every win along the way. To turn data into a daily habit across teams—so insight, not intuition, drives your next move.

Foundational Elements

• Clear, accessible metric definitions and dashboards.
• Consistent training on data tools and interpretation.
• Open forums for sharing learnings and best practices.
• Visible leadership support and participation.
• Recognition for data-driven wins—big and small.

Team Practices

• Run regular 'show and tell' sessions to share data stories.
• Encourage questions and debate around metric trends.
• Document assumptions, limitations, and context for every KPI.
• Foster peer review of data analyses to catch blind spots.
• Link data to customer outcomes and business impact, not just numbers.

Maturity Stages

Stage	Description
Foundational	Data is collected and basic dashboards are available. Metric definitions are documented, but usage is limited to the analytics team.
Emerging	Teams refer to data in regular meetings. Some business decisions are backed by metrics, and data quality is actively improved.
Established	Data is central to most decisions. KPIs are owned by business units, and cross-functional teams collaborate on metric-driven projects.
Advanced	Data literacy is high across the org. Predictive analytics and experimentation are routine, and insights drive continuous innovation.

Why Data Aware Culture Matter

A data-aware culture is the backbone of smart, scalable decision-making. When teams treat data as a shared language, they spot opportunities faster, avoid costly missteps, and turn insight into action. To empower everyone in the organization—from analysts to execs—to use data confidently and responsibly, so business growth is fueled by facts, not gut feel.

Relevant Topics:

• Breaks down silos by making data accessible and actionable across teams.
• Drives alignment on goals and priorities using clear, trusted metrics.
• Enables faster course correction by surfacing issues and wins in real time.
• Boosts accountability—everyone can see what’s working, what’s not, and why.
• Builds buy-in for experimentation, learning, and continuous improvement.

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.
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 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.
Average Customer Lifespan	Average Customer Lifespan (ACL) refers to the total duration a customer remains actively engaged with a company’s product or service. It’s an estimated timeframe from the point of customer acquisition to churn, during which the customer is actively using, purchasing, or subscribing to the product.
Average Purchase Frequency	Average Purchase Frequency (APF) is a metric that measures how often customers make a purchase within a specified time period. It provides insight into customer behavior and the consistency of their interactions with a brand.
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.
Cohort Retention Analysis	Cohort retention analysis involves tracking a group of users (a cohort) over time to measure how many of them continue using a product or service, providing insights into retention and churn patterns.
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 Lifetime Value	Customer Lifetime Value (CLV) represents the total revenue a business expects to earn from a customer over the entire duration of their relationship. It is a predictive metric that combines customer spending, loyalty, and retention rates to quantify the value of each customer.
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.
Downgrade to Churn Conversion Rate	Downgrade to Churn Conversion Rate measures the percentage of customers who downgrade their plan or usage and later churn. It helps identify whether downgrades are leading indicators of customer loss.
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.
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 Intent Signal Rate	Expansion Intent Signal Rate measures the percentage of accounts showing behavioral or engagement signals that indicate interest in upgrading, expanding, or purchasing add-ons. It helps identify and prioritize expansion-ready accounts.
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-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 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.
Intent Signal Volume (3rd-party)	Intent Signal Volume (3rd-party) measures the number of buying intent signals collected from external sources (e.g., Bombora, G2, media partners) over a defined time period. It helps quantify market interest beyond owned channels.
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.
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.
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.
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.
Referral Funnel Drop-Off Rate	Referral Funnel Drop-Off Rate measures the percentage of users who begin but do not complete the referral process—like opening the referral flow but not sending an invite. It helps identify friction points within the referral journey.
Referral Readiness Score	Referral Readiness Score is a predictive metric that assesses how likely a user or account is to make a referral based on behavioral, usage, and sentiment signals. It helps identify high-potential advocates before they take action.
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).
Signup Source Quality Rate	Signup Source Quality Rate measures the percentage of signups from a specific traffic source that meet defined quality criteria (e.g., ICP fit, activation, conversion). It helps evaluate the effectiveness and downstream potential of various acquisition channels.
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 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 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.
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.