Web analytics is the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. However, Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a website. Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. It helps one to estimate how traffic to a website changes after the launch of a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views. 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 information: This stage usually take counts and make 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 money, saving money, or increasing marketshare.
Another essential function developed by the analysts for the optimization of the websites are the experiments
- Experiments and testings: 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 changes to web pages that increase or maximize 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.