Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize web usage. Common tools include Google Analytics, Adobe Analytics, and Matomo. Core concepts are sessions (groups of user interactions within a time frame), users (individual visitors identified by cookies or user IDs), and pageviews (single views of a page). Conversions are desired actions like purchases, sign-ups, or form submissions, and conversion rate measures the share of visitors or sessions that convert—calculated as \(\text{Conversion Rate} = \frac{\text{Conversions}}{\text{Total Visitors or Sessions}} \times 100\). Bounce rate indicates the percentage of sessions in which users leave after viewing only one page. Within a funnel, a macro conversion is the main desired action (purchase, lead form, demo booking), while a micro conversion is a smaller but meaningful step toward it (adding to cart, starting checkout, scrolling a pricing page).
KPIs (key performance indicators) are measurable values that show how effectively goals are being achieved. Common digital marketing KPIs include traffic, conversions, conversion rate, CTR, CPC, revenue, ROAS, email opens, and engagement. Marketers distinguish outcome metrics—tied directly to business results like revenue, leads, or sign-ups—from vanity metrics (such as raw follower counts) that look impressive but do not strongly correlate with business outcomes. A north star metric is a single key metric that best captures the core value a product delivers to customers. Leading indicators (such as trial starts or demo requests) predict future performance, while lagging indicators (such as revenue or total sign-ups) reflect results after they have happened.
Source tracking relies on UTM parameters—tagged query strings added to URLs—with the core parameters being utm_source, utm_medium, utm_campaign, utm_term, and utm_content. A consistent UTM naming convention keeps data clean and analyzable across teams. GA4 (Google Analytics 4) is the latest version of Google Analytics, focused on events and user-centric measurement, replacing Universal Analytics, which focused on sessions and pageviews; in GA4, an event is any user interaction or occurrence tracked on a site or app, such as a click, scroll, or purchase. Tracking channel attribution, analyzing dark social (traffic and sharing in private channels like messaging apps or email that is hard to track directly), running cohort analyses on groups sharing a start date or characteristic, and running multi-channel campaigns that use multiple channels in coordinated ways all support a fuller picture of performance.
Attribution is the process of assigning credit for conversions across the touchpoints of a customer journey, governed by an attribution model that determines how credit is distributed. Common models include last-click (all credit to the final touchpoint before conversion), first-click (all credit to the first touchpoint), linear (even distribution across touchpoints), time decay (closer touchpoints weighted more heavily), position-based (first and last emphasized), and data-driven (machine learning assigns credit based on observed impact). Multi-touch attribution distributes credit across multiple touchpoints; view-through attribution gives some credit to ads seen but not clicked. A/B testing compares two versions of a page or element (the control versus the variation), and A/B/n testing extends that to three or more variations. Statistical significance indicates that observed differences are unlikely due to chance; statistical power is the probability a test detects a real effect when one exists. A holdout group—a portion of the audience that does not receive a campaign—lets marketers measure true incremental lift, and incrementality quantifies the additional results caused by a campaign compared to what would have happened without it. A hypothesis backlog is a prioritized list of test ideas, each with a testable hypothesis, expected impact, and plan, fueling a steady cadence of growth experiments.