For anyone who owns a website, a top-priority is usually driving traffic up. Whether it’s a content-based site like a blog or forum, or a sales-driven site like an online store, we all want as many people as possible to visit our sites. Most hosting solutions offer some kind of access to information about visitors and companies like WebTrends have made a fortune building advanced analysis mechanisms to help answer questions like “What websites are referring to my site” or “what are the most popular pages on my site” and so on.
As site owners do various things to try to draw more visitors, like doing SEO (Search Engine Optimization) or buying ads on other websites, we typically examine our traffic patterns over time, to see if these efforts are working, and this is also a good way to find out if there might be some problem with a site (for example, some application error that’s preventing visitors from using the site fully or partially). As we look at such trend reports, we sometimes find that traffic has decreased despite our efforts. This can be very frustrating, of course…but it’s important to know that sometimes, this trend is nothing more than seasonal fluctuations. Seasonal fluctuations are changes to traffic trends that are caused by normal seasonal human behavior, and have nothing to do with your content. For example, during Christmas, many people go on vacations and spend less time on the internet. If you measure your traffic over the period of October-January, you’re bound to see a decrease in traffic, no matter what you do to the site. Understanding these trends is very important, because it allows you to adjust your expectations, and see how you’re really doing despite these trends.
Trends are a game of numbers, and so I gathered some statistics that can help you understand these trends. Below you can find a trend-graph of visitors and changes over the year. I should note that these numbers represent the trends in the technical-content world, and not commercial site trends (which would be different, because during Thanks Giving, for example, people spend less time reading articles, but more time shopping online). The data below is for the year 2013, and based on data from several high-volume content websites with a total visitor count in the vicinity of 16 million visitors a month. I don’t claim that this represents the entire world, but I feel this is a pretty good and accurate sample, statistically. Use at your own risk…
Visitors trending
Below is the graph representing visitors from January (1) to December (12) of 2013:
The Y axis intentionally doesn’t contain the “real” numbers, because they don’t really matter…every site would have a different count, so treat these as a factor, rather than a hard number. If you plot your own visitor count on a graph like this, you should expect to have a similar graphic pattern. However, what really matters to us is not the bottom numbers, but the CHANGE over time. The change patterns I calculated are below, both in table form and graph:
| Month | Change from prev. month |
| January | 36.0 % |
| February | -18.1 % |
| March | 21.3 % |
| April | 19.5 % |
| May | -3.3 % |
| June | -7.3 % |
| July | 4.8 % |
| August | -5.8 % |
| September | 5.5 % |
| October | 5.2 % |
| November | -20.4 % |
| December | -20.8 % |
| January | 36.0 % |
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Conclusion
I should say again that the numbers above are not absolute, and can be affected by many factors. For example, if your content focusses solely on Operating Systems, then something like a release of a new version of Windows could throw off the curve quite a bit. However, generally, the above values mean that it’s perfectly normal for your visitor count to drop by about 20% from October to November (where Thanks Giving happens) and then go back up by about 36% in January, as people finish their holiday vacations and go back to work.
To apply this to your own data, calculate your month-over-month change, and reduce the global change from it to see your “real” change. For example:
| Month | Visitors | % change | Global Change % | Adjusted % |
| January | 6211 | N/A | 36.0 | N/A |
| February | 5182 | -16.6 | -18.1 | 1.5 |
| March | 6455 | 24.6 | 21.3 | 3.3 |
| April | 7883 | 22.1 | 19.5 | 2.6 |
| May | 6934 | -12.0 | -3.3 | -8.8 |
| June | 6732 | -2.9 | -7.3 | 4.4 |
| July | 7118 | 5.7 | 4.8 | 0.9 |
| August | 6619 | -7.0 | -5.8 | -1.2 |
| September | 7111 | 7.4 | 5.5 | 1.9 |
| October | 7894 | 11.0 | 5.2 | 5.8 |
| November | 6411 | -18.8 | -20.4 | 1.7 |
| December | 5224 | -18.5 | -20.8 | 2.3 |
In the above example, when the change is adjusted, you can see that for most months, you have an average growth rate of about 2.7%. You can also see that there was a major dip in May and a major spike in October. This could be good news, if the October spike matches some action you took to improve your traffic. Another good news in the example above is that the major dip in visitors in August is not really 7%, but only 1.2%, as 5.8% are due to seasonal trends. The not so great news above is that something unusual happened in May. It might have been something going wrong with the site itself, or something else, like a global disaster. Either way, this gives you the data to know what’s going on.
