Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize online behavior. Common tools include Google Analytics, Adobe Analytics, and Matomo. The latest version, Google Analytics 4 (GA4), is focused on events and user-centric measurement, in contrast to Universal Analytics, which focused on sessions and pageviews. Core building blocks include a session (a group of user interactions within a given time frame), a pageview (a single view of a page), a user (an individual visitor often identified by cookies or user IDs), and an event, a user interaction or occurrence tracked in GA4 such as clicks, scrolls, or purchases.
Key performance indicators (KPIs) 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. Conversions are desired actions completed by a user, such as a purchase, sign-up, or form submission, and conversion rate is the percentage of visitors who complete one, calculated as Conversions divided by Total Visitors or Sessions multiplied by 100. Bounce rate is the percentage of sessions in which users leave after viewing only one page. Vanity metrics, like raw follower counts, look impressive but don't strongly correlate with business outcomes, while outcome metrics, such as revenue, leads, or sign-ups, are tied directly to results. Leading indicators, like trial starts or demo requests, predict future performance, whereas lagging indicators like revenue reflect results after they happen.
Attribution is the process of assigning credit for conversions to different touchpoints in the customer journey, and an attribution model is a rule or set of rules that determines how that credit is distributed. Common models include last-click attribution (all credit to the final touchpoint), first-click attribution (all credit to the first touchpoint), linear attribution (equal credit across touchpoints), time decay, position-based, and data-driven attribution, which uses machine learning to assign credit based on observed impact. Multi-touch attribution distributes credit across multiple touchpoints, and view-through attribution gives some credit to ads that were seen but not clicked. Marketers also use UTM parameters, tagged query-string parameters added to URLs to track campaign sources, with the core parameters being utm_source, utm_medium, utm_campaign, utm_term, and utm_content. A UTM naming convention is a set of standard rules for how these parameters are named to keep tracking consistent.
Beyond standard analytics, marketers seek to measure true incremental impact. Incrementality is the additional results caused by a campaign compared to what would have happened without it, often measured using a holdout group, a portion of the audience that does not receive the campaign. A brand lift study measures changes in brand awareness, consideration, or intent after exposure to ads. Cohort analysis examines the behavior of groups of users who share a common characteristic or start date over time. North Star metrics identify a single key metric that best captures the core value a product delivers to customers, and marketing OKRs (Objectives and Key Results) are used to set and measure specific marketing goals. Qualitative feedback methods collect non-numerical insights through interviews, open-ended surveys, or user tests, while quantitative methods collect numerical data through analytics, structured surveys, or experiments. Tools like heatmaps visualize where users click, scroll, or move their cursor, and session recording captures anonymized replays of user sessions for UX research.