A New Approach to the Time Spent on Site Metric

This summer, Neilen/NetRatings declared the “Total Minutes” metric as the best measure of website engagement. This is certainly debatable, but the purpose of this post is to illustrate a new method of understanding the Time Spent on Site metric by applying a technique used by people in the Actuarial Sciences and Demography: the Life Table.

Some Background on Life Tables

The Life Table is a tool typically used to analyze and predict patterns of mortality among populations. The most familiar measure that gets generated by a Life Table is Life Expectancy. Life Expectancy is always great fodder for news headlines; at the time of this article’s publication, the Centers for Disease Control and Prevention just announced that Average Life Expectancy for people in the U.S. is nearly 78 years. This statistic was calculated using a Life Table.

A Life Table works by taking an imaginary population cohort (typically 100,000) and “aging” them through their lives by using currently estimated Age-Specific Death Rates. Why do this to calculate life expectancy rather than looking at vital statistics (for example, averaging the age of death of everyone that dies in a given year)? Because our population is influenced by generational patterns of growth and is not uniform. There are a lot more baby boomers and children of baby boomers than people born during the depression or during the “baby bust” (like me) that followed the baby boom. These age-specific population differences would skew Life Expectancy if calculated using the average age of death low now (since the boomers are entering their retirement years) and would skew it high in twenty years, when the likelihood of their mortality most increases.

Life Tables and Time Spent on Site

Using the Life Table technique to examine Time Spent on Site is fairly straightforward. Take a look at this table and/or follow the link to the Google Spreadsheet below it.

Time on Site Calculation Chart
View/Download this Spreadsheet

Working left to right, fill in the first two columns with your period bounds. In my (completely made up) example, I use minute-by-minute data to “age” the site visitors unitl there are none. Pay special attention to the first row, which are reserved for bounces. Next, fill in the period-specific site exit rate in the fourth column for each period. Again, the first row is reserved for your bounce rate.

From here, simple calculations take you home. First, calculate the visitors left on site (starting with 100,000 in the first row and subtracting visitors that left during the previous period) and visitors exiting site (calculated by multiplying visitors remaining by the current period-specific exit rate) for each period, working your way down the table until there are zero visitors left. Next, calculate the total time on site for that period by multiplying the length of the given period by the visitors remaining on site, then subtracting half of the product of the length of the given period and the number of visitors exiting the site. Accounting for people exiting the site in this manner assumes that people in each period leave the site at a uniform rate and leads to a calculation error. The scale of this error is in proportion to the size of the periods being measured.

The final two columns are where the great value in this method come to light. The first of the two, total time on site, is calculated by starting at the bottom and creating a cumulative total for all minutes spent on site in the current period and all subsequent periods. The final column, average time spent on site for everyone in this period and beyond, is calculated by dividing the total time on site by the number of visitors remaining for the current period.

Interpreting the Results

Looking at the final column, representing the total average time spent on site for this period and beyond, will give great insight into your time spent on site metrics. As you can see from my example (and has a similar pattern in real life tables with infant mortality rates), bounces have a big effect on time spent on site. In my example, the average time spent on site is about .50 minutes (which will exactly match the metric reported from my analytics software). However, factoring out bounces, you can see that the average time spent on site for a non-bounce user is 1.24 minutes. Visitors that come to my site and stay for 3 minutes will, on average, stay an additional .60 minutes.

With the growth of web video, AJAX-based site structures, and other new technologies, Time Spent on Site is growing in importance. Perhaps this method of interpreting visitor behavior can help some people understand this metric a bit better.