Glossary of Terms
A comprehensive glossary of key terms and concepts from the Metrics Mastery for Product Managers course.
A
A/B Testing : A method of experimentation where two versions of a webpage or app (A and B) are compared against each other to determine which one performs better. It is a primary tool for validating hypotheses with empirical evidence.
AARRR Framework ("Pirate Metrics") : A model for understanding the customer lifecycle, broken down into five stages: Acquisition, Activation, Retention, Referral, and Revenue. It's used as a diagnostic tool to identify leaks in the customer funnel.
Activation : The stage in the user journey where a user experiences the product's core value for the first time (the "Aha!" moment). It is a stronger signal of engagement than a simple sign-up.
Acquisition : The stage in the user journey covering all channels through which potential customers discover and come to a product.
C
Counter-Metric (Guardrail Metric) : A metric paired with a primary success metric to monitor for unintended negative consequences. It acts as a safety check to ensure that growth in one area doesn't harm another.
Customer Acquisition Cost (CAC) : The total cost to acquire a new customer, often calculated per channel to measure marketing efficiency.
Customer Lifetime Value (CLV / LTV) : The total revenue a business can expect from a single customer account over the entire duration of their relationship with the product.
D
Data-Driven : A philosophy where quantitative data and metrics are the primary, and often exclusive, arbiters of decision-making.
Data-Informed : A philosophy where quantitative data is treated as one crucial input among many, including qualitative research, domain expertise, and customer feedback.
H
HEART Framework : A user-centric framework developed by Google for measuring the quality of the user experience. It is an acronym for Happiness, Engagement, Adoption, Retention, and Task Success.
Hypothesis : A clear, testable statement that connects a specific change to an expected outcome. A strong hypothesis is the foundation of any rigorous experiment.
I
Input Metric : A specific user behavior or product lever that a team can directly influence, which is a leading indicator of an output metric. Teams focus on changing input metrics to affect the North Star Metric.
M
Metric Tree : A hierarchical decomposition of a North Star Metric into its contributing input metrics. It visually connects the high-level strategic output to the granular inputs that product teams can directly influence.
N
Net Promoter Score (NPS) : A survey-based metric that measures a user's willingness to recommend a product, calculated on a scale from -100 to 100. It is a common measure of customer loyalty and happiness.
North Star Framework : A model for managing products by focusing the entire company on a single, critical metric—the North Star Metric (NSM)—that represents the core value delivered to customers.
North Star Metric (NSM) : The one measurement that best captures the intersection of customer value and business impact. It is a leading indicator of sustainable business success.
O
Output Metric : A result or scoreboard metric (like the NSM) that tells you if you are winning. Output metrics cannot be directly changed; they are influenced by input metrics.
R
Retention : The stage in the user journey that measures the ability of a product to create lasting value that encourages repeat usage.
Referral : The stage in the user journey that measures the extent to which satisfied customers become advocates for the product, driving word-of-mouth growth.
Revenue : The stage in the user journey where user value is converted into business value.
S
Statistical Significance : A measure used in experimentation to determine whether the observed result is likely due to the change that was made, rather than random chance.
V
Vanity Metric : A metric that looks impressive on the surface (e.g., total registered users) but fails to correlate with real business impact or meaningful engagement.