Misconceptions in Web Analytics

1) The Hotel problem

The hotel problem is generally the first problem encountered by a user of web analytics. The term was first coined by Rufus Evison explaining the problem at one of the Emetrics Summits and has now gained popularity as a simple expression of the problem and its resolution.

The problem is that the unique visitors for each day in a month do not add up to the same total as the unique visitors for that month. This appears to an inexperienced user to be a problem in whatever analytics software they are using. In fact it is a simple property of the metric definitions.

The way to picture the situation is by imagining a hotel. The hotel has two rooms (Room A and Room B).

Day 1 Day 2 Day 3 Total
Room A John John Jane 2 Unique Users
Room B Mark Jane Mark 2 Unique Users
Total 2 2 2 ?

As the table shows, the hotel has two unique users each day over three days. The sum of the totals with respect to the days is therefore six.
During the period each room has had two unique users. The sum of the totals with respect to the rooms is therefore four.

Actually only three visitors have been in the hotel over this period. The problem is that a person who stays in a room for two nights will get counted twice if you count them once on each day, but is only counted once if you are looking at the total for the period. Any software for web analytics will sum these correctly for whatever time period, thus leading to the problem when a user tries to compare the totals.

2) New visitors + Repeat visitors unequal to total visitors

Another common misconception in web analytics is that the sum of the new visitors and the repeat visitors ought to be the total number of visitors. Again this becomes clear if the visitors are viewed as individuals on a small scale, but still causes a large number of complaints that analytics software cannot be working because of a failure to understand the metrics.
Here the culprit is the metric of a new visitor.

There is really no such thing as a new visitor when you are considering a web site from an ongoing perspective. If a visitor makes their first visit on a given day and then returns to the web site on the same day they are both a new visitor and a repeat visitor for that day. So if we look at them as an individual which are they? The answer has to be both, so the definition of the metric is at fault.

A new visitor is not an individual; it is a fact of the web measurement. For this reason it is easiest to conceptualize the same facet as a first visit (or first session). This resolves the conflict and so removes the confusion. Nobody expects the number of first visits to add to the number of repeat visitors to give the total number of visitors. The metric will have the same number as the new visitors, but it is clearer that it will not add in this fashion.

On the day in question there was a first visit made by our chosen individual. There was also a repeat visit made by the same individual. The number of first visits and the number of repeat visits will add up to the total number of visits for that day.

What is Web Click Analytics

Web Click analytics is a special type of web intelligence that gives special attention to clicks (Point-and-click).

Generally in internet marketing, click analytics focuses on on-site analytics. An editor of a web site uses click analytics to determine the performance of his or her particular site, with regards to where the users of the site are clicking ie what are the various pattern of clicking.
Also, Click analytics may happen real-time or "unreal"-time, depending on the type of information sought.

Typically, front-page editors on high-traffic news media sites will want to monitor their pages in real-time, to optimize the content. Editors, designers or other types of stakeholders may analyze clicks on a wider time frame to aid them assess performance of writers, design elements or advertisements etc.

Different data about clicks may be gathered in at least two ways. Ideally, a click is "logged" when it occurs, and this method requires some functionality that picks up relevant information when the event occurs.

Alternatively, one may institute the assumption that a page view is a result of a click, and therefore log a simulated click that lead to that page view.