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Web analytics

Web analytics is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Web analytics rather than just read about it. In short: Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize web usage. Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research and assess and improve website effectiveness.

Web analytics — main illustration
Web analytics — illustration

Key takeaways

  • Web analytics belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Web analytics to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Web analytics from memory before moving on to harder problems.

Reference excerpt

Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize web usage. Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research and assess and improve website effectiveness. Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. It can be used to estimate how traffic to a website changes after launching a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views, or creates user behaviour profiles. It helps gauge traffic and popularity trends, which is useful for market research.

Basic steps of the web analytics process

Most web analytics processes come down to four essential stages or steps, which are:

Collection of data: This stage is the collection of the basic, elementary data. Usually, these data are counts of things. The objective of this stage is to gather the data. Processing of data into metrics: This stage usually takes counts and makes them ratios, although there still may be some counts. The objective of this stage is to take the data and conform it into information, specifically metrics. Developing KPI: This stage focuses on using the ratios (and counts) and infusing them with business strategies, referred to as key performance indicators (KPI). Many times, KPIs deal with conversion aspects, but not always. It depends on the organization. Formulating online strategy: This stage is concerned with the online goals, objectives, and standards for the organization or business. These strategies are usually related to making a profit, saving money, or increasing market share. Another essential function developed by the analysts for the optimization of the websites is the experiments:

Experiments and testing: A/B testing is a controlled experiment with two variants, in online settings, such as web development. The goal of A/B testing is to identify and suggest changes to web pages that increase or maximize the effect of a statistically tested result of interest. Each stage impacts or can impact (i.e., drives) the stage preceding or following it. So, sometimes the data that is available for collection impacts the online strategy. Other times, the online strategy affects the data collected.

Web analytics categories There are at least two categories of web analytics, off-site and on-site web analytics.

Off-site web analytics refers to web measurement and analysis regardless of whether a person owns or maintains a website. It includes the measurement of a website's potential audience (opportunity), share of voice (visibility), and buzz (comments) that is happening on the Internet as a whole. On-site web analytics, the more common of the two, measures a visitor's behaviour once on a specific website. This includes its drivers and conversions; for example, the degree to which different landing pages are associated with online purchases. On-site web analytics measures the performance of a specific website in a commercial context. This data is typically compared against key performance indicators for performance and is used to improve a website or marketing campaign's audience response. Google Analytics and Adobe Analytics are the most widely used on-site web analytics service; although new tools are emerging that provide additional layers of information, including heat maps and session replay. In the past, web analytics has been used to refer to on-site visitor measurement. However, this meaning has become blurred, mainly because vendors are producing tools that span both categories. Many different vendors provide on-site web analytics software and services. There are two main technical ways of collecting the data. The first and traditional method, server log file analysis, reads the logfiles in which the web server records file requests by browsers. The second method, page tagging, uses JavaScript embedded in the webpage to make image requests to a third-party analytics-dedicated server, whenever a webpage is rendered by a web browser or, if desired, when a mouse click occurs. Both collect data that can be processed to produce web traffic reports.

On-site web analytics There are no globally agreed definitions within web analytics as the industry bodies have been trying to agree on definitions that are useful and definitive for some time, that is to say, metrics in tools and products from different companies may have different ways of measuring and counting. As a result, the same metric name may represent the different meaning of data. The main bodies who have had input in this area have been the IAB (Interactive Advertising Bureau), JICWEBS (The Joint Industry Committee for Web Standards in the UK and Ireland), and The DAA (Digital Analytics Association), formally known as the WAA (Web Analytics Association, US). However, many terms are used in consistent ways from one major analytics tool to another, so the following list, based on those conventions, can be a useful starting point:

… excerpt ends here. Continue reading the full article.

Illustrations

Web analytics: Clickpath Analysis with referring pages on the left and arrows and rectangles differing in thickness and expanse to symbolize movement quantity
Clickpath Analysis with referring pages on the left and arrows and rectangles differing in thickness and expanse to symbolize movement quantity

Worked examples

Example 1 — a first encounter with Web analytics

Start with the simplest possible case. Write down what Web analytics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Web analytics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Web analytics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Web analytics

In research
Web analytics appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Web analytics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Web analytics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Audience measurement, Digital marketing, Market research, so understanding it makes those chapters shorter.
In everyday life
Look for Web analytics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Web analytics in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Web analytics means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Web analytics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Web analytics in simple terms?

Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize web usage. Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research and assess and improve website effectiveness.

Why does Web analytics matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Web analytics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Web analytics.

Tags

  • Audience measurement
  • Digital marketing
  • Market research
  • Web analytics

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