While there are many web analytics tools out there, the website statistics (metrics) that they reveal are pretty standard across all the tools. But just what do all these hundreds of web metrics and terms exactly mean and what should you do with them? They can be pretty confusing! So this post is for those of you who want a quick primer on these website metrics, and to help you understand which ones are most useful for analyzing and improving your blogs or websites. Anyway… I think I hear the class bell… lets get started!
If you are looking for information about how to improve your site with stats, please see our article Making Stats Work For You.
There are various terms used to describe the science of recording and interpreting website statistics. Web metrics, web analytics, web stats and site stats are examples. ‘E-metrics’ refers to analysis of electronic businesses.
The ‘metrics’ of web metrics refers to measurement, the science of measuring websites. Specifically, measuring website events, and extracting trends. For Opentracker, those events are human clicks.
Web Analytics is the act of distinguishing categories within recorded stats, and analyzing for patterns. The process of analytics means, literally, taking apart the whole of something in order to study its component parts.
Statistics are a scientific application. The goal is to form actions, for example website content management, based on the data which are recorded.
Apply statistics in order to reduce guesswork. Simple questions can be answered, for example, something very basic; are there more or less people coming to your site this week than last week? Is your site doing better or worse this week?
What should your stats tell you? They will inform you about numerous aspects of your traffic; the number of (returning) visitors, and how visitors surf through your pages. This information tells you about the content of your site and how visitors use it. Your traffic statistics are an indicator of website performance. Thus applied, stats can be effectively used to make updates.
When comparing different types of measurement, the classic scenario of "the difference between apples and oranges" often arises. In the same way, different website statistics programmes have unique ways of measuring important variables such as pageviews, unique visitors, and visits.
Therefore it is not always easy to compare the results generated by two statistics programmes to track one site. The process itself can be very useful, in terms of thinking through the differences in results and determining what is actually being measured. We encourage the use of numerous programmes, for example, combining a tracking service with log analysis.
If the method of measurement stays the same through time, then the results will be perfect for purposes of comparison. Therefore, choosing the method of measurement is important. Scientifically speaking, changing the method of measurement during an experiment invalidates the process.
If you compare results from two types of measurement you will find differences in numbers. For example, measuring pageviews vs. unique visitors, or the whole site vs. specific pages. If you compare the same statistics over time, you are not changing the method of measurement. This is the most accurate way of recording statistics. This will allow you to find patterns and definitive answers, for instance if traffic is growing or diminishing. Is your "Generate new leads" campaign working, are visitors returning over time? Do your efforts to bring targeted traffic through a PPC campaign lead to conversions? Do returning visitors generate more revenue than the first-time visitors?
In any statistical endeavour, the first step is to define what is being measured. In website cookie tracking, the common denominator is human events, clicks on a website, which are defined as pageviews.
Specifically, the statistics discussed here are a translation from raw data, clicks, and server-browser dialogues, into a user interface from which patterns can be discerned. The goal of web metrics is to extract patterns which tell you what is happening. The next step is to create actions, i.e. what to do about your traffic patterns.
Web metrics and analytics is an exciting field at this moment, because there are not many patterns being sought. An example might be comparing ‘bounce rate for first time visitors’ with ‘bounce rate for returning visitors’, which has not become a standard of analysis (aggregate bounce rate stats tell you how far into your site visitors are clicking).
For a practical guide, please see our article Making Stats Work For You.
Note: nothing can be measured with 100% accuracy. The skill lies in trying to keep measurements useful, despite the inability to reach 100% accuracy. An acceptable margin of inaccuracy within the scientific discipline of statistics is 5%. That does not make the world an uncertain place - it means that you have to be specific in knowing what is important. For example, trends, are trends rising or declining over time?
The process of determining what to measure involves the creation of definitions. There are always elements being under- or over-measured. That is why the system requires constant calibration, in terms of what people really want to know, which in turn determines what should be measured. An example would be the question "what constitutes a search engine?" Should the Yellow Pages and White Pages be included? There are new search engines & portals appearing every day. What criteria should be used to classify search engines? Our list of officially recognised search engine list, located on our forum, requires constant calibration.
Marketing strategy: it is important to focus on the the most important variables for you, and locate an application that provides these measurements in a clear format. For example, measuring the performance of specific keywords that you purchase for your Pay-Per-Click campaigns (PPCs).
Statistical needs vary depending on site size. Therefore it is up to statistics programmes to present the statistics in a way that is useful for webmasters of different sized sites.
Large sites, for example, are more interested in trends. Larger sites generate higher volumes of data, in which clickstreams may not be very interesting, unless usability is being improved. As there are too many clickstreams (e.g. sites which receive several thousand visitors a day), for large sites, often aggregates are more helpful, while smaller sites are interested in discreet data.
Trends are aggregate statistics. For example, a site’s bounce rate is an aggregate statistic. Bounce rate are stats designed for the purpose of identifying patterns which are hidden within the stats.
Discreet stats such as clickstreams, will tell you what individual people are doing on your site. Discreet stats are not aggregates, as you are actually seeing what the data is “built” of.
This type of information (clickstream analysis) is very useful for development purposes and understanding user reaction (aka usability). If you are designing a new site, knowing how first-time visitors navigate will help to determine how successful the site is, and what changes need to be made.
Website traffic data presented in Google Analytics is sampled.
To illustrate some of the difficulties associated with counting and measuring, consider a statistic that tells you how many people voted in an election. Counting votes is a difficult process and re-counts are often undertaken and it's not unusual to reach different totals every time.
When polls are released, the number presented is an extrapolation, based on a percentage of people contacted by phone, or asked at the door for whom they voted.
Opentracker presents trends derived from actual clicks. This is how we narrow the margin of error. We use optimization techniques based on cookies and visitors to improve accuracy.
When the trends presented are derived from actual clicks, the margin of error is narrowed. Traffic measurement techniques based on cookies improve accuracy.
Our point is that data, (i.e. statistics) are numbers created by people. Therefore it is important to understand how these numbers are defined and generated.
The data collected with cookies gives insight into site visitors over time, the traffic is deduced from unique visitors and there is minimal 'double-counting' of visitors. We often asked why Opentracker's traffic numbers are often lower than those recorded by log files and this is why.
We believe Opentracker to be at least 30% (and probably much higher) more accurate than standard web tracking and statistics solutions currently available.