Web analytics

Web analytics measures how people discover, use and respond to digital properties so an organisation can improve experience, engagement and commercial outcomes. It combines market context with behavioural data from the organisation’s own sites and experiments.

When to use it

Review off-site market and competitor signals periodically—annually in a stable market and more often when conditions move quickly. Monitor on-site performance continuously enough to detect technical failures, campaign effects and changing journeys.

Use the channel to test messages, offers, pricing and landing experiences before committing to more expensive media, while recognising that behaviour may differ between online tests and other contexts.

Questions include:

• How many eligible people visit, and how is that changing?
• Which audiences and acquisition sources create useful engagement or value?
• What searches, pages and journeys lead to success or abandonment?
• Which content is unused, confusing or technically weak?
• How long and how deeply do people engage, using metrics appropriate to the experience?
• What proportion completes the intended outcome?
• Which market and competitor trends should influence the digital strategy?

Origins

Web analytics began with server-log analysis in the early commercial web and expanded through page tagging, cookies, campaign attribution and digital experimentation. As sites became applications used across devices, the field moved from counting page views toward events, users, journeys and business outcomes. Contemporary practice must also account for consent, browser restrictions, data minimisation and the limits of cross-device identity.

What it is

Off-site analytics examines the wider digital market: potential audience, search demand, competitors, referral ecosystems and trends. On-site analytics measures behaviour within properties you control, including acquisition, navigation, events, conversion and retention.

Why it matters

A website’s existence does not prove that it helps users or the business. Analytics makes performance observable and allows teams to diagnose weak journeys, prioritise research and test changes quickly.

Digital experiments can compare landing pages, messages or offers before broader rollout. Speed and low marginal distribution cost are advantages, but valid testing still requires a clear hypothesis, appropriate randomisation, enough observations, guardrail metrics and protection against repeated peeking.