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| Web traffic: 2009 starts slowly… |
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| After a growth slowdown of web traffic for 2 years (for an equivalent number of websites), 2008 wasn’t an exception. However, we can observe some stabilization of this slowdown with a 5.9% evolution rate (vs. +6.7% in 2007). Moreover, after a mixed 2008 year-end, the beginning of 2009 is not more buoyant… |
| Cross section: |
- Study carried out from January 2001 to January 2009
- Cross-section of 268,517 websites
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| After a good performance during the third quarter of 2008, the growth is not as strong during the fourth quarter |
The table below presents the evolution of web traffic, for an equivalent number of websites, for each fourth quarter (October - December) from 2002 to 2008.
For an equivalent number of websites, we can observe a growth slowdown in web traffic during the fourth quarter of 2008, for 3 consecutive years.
The web traffic evolution rate fluctuated between +17.1% and +27.1% from October to December 2003, 2004 and 2005. This evolution was only +7.7% during the fourth quarter of 2006, +6.3% in the fourth quarter of 2007 and +4.3% in 2008:
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We can observe a growth slowdown of web visits measured during the fourth quarter of 2008 compared with the fourth quarter of 2007 over November and December:
- Evolution in October 2008: +8.2% (vs. +3.9% in October 2007). The evolution rate is similar to October 2006.
- Evolution in November 2008: +2.4% (vs. +5.1% in November 2007).
- Evolution in December 2008: +2.6% (vs. +10.1% in December 2007).
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| For an equivalent number of websites, the annual evolution rate was about +5.9% in 2008 |
Let us analyze the 2008 seasonality as a whole.
The website traffic analysis showed seasonality in terms of volume (key periods over the year being the summer lows or the January highs). Using non-parametric methods (to deconstruct a time series for seasonal adjustment), we were able to extract these monthly seasonal fluctuations.
The chart below shows the monthly seasonal fluctuations recorded in 2008 compared to those between 2002 and 2007.
Thus, we observe some specificity in 2008, and a good start of the year:
- January 2008 : peak of +12% vs. +9% for the average seasonal fluctuations recorded from January 2002 to 2007,
- February 2008 : peak of +6% vs. +1% for the average seasonal fluctuations recorded from February 2002 to 2007.
While 2008 year-end showed unusual off-peak values:
- November 2008 : off-peak value of -5% vs. +1% for the average seasonal fluctuations recorded from November 2002 to 2007,
- December 2008: off-peak value of -6% vs. -1% for the average seasonal fluctuations recorded from December 2002 to 2007.
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The graph below shows the annual evolution rate of web traffic, for an equivalent number of websites, between 2002 and 2008.
After having risen for several years, hitting +25.2% in 2004 and 2005, the annual evolution rate dropped to +10.1% in 2006 and +6.7% in 2007.
This slowdown of annual growth observed for the past two years hadn’t stop in 2008 but was less important. Indeed, the web traffic evolution rate, for an equivalent number of websites, enjoyed a 5.9% increase. Just as a reference, we forecasted in October 2008 a growth rate of 6.4% (refer to our study: «Web traffic: forecasts for the end of 2008»).
Finally, with a long term view, web traffic is still rising, for an equivalent number of websites. Using 100 as a baseline for visits recorded in 2001, visits recorded in 2008 are at a baseline of 251: 2.5 times as many in 7 years.
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| Underachievement for January 2009 |
The chart below shows the seasonal fluctuations recorded from January 2002 to January 2009.
The average seasonal fluctuation recorded from January 2002 to January 2008 peaked at +9.3%. We can clearly observe a seasonal fluctuation of only +3.1% in January 2009.
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For the first time in 7 years, the web traffic evolution rate recorded in January 2009 is nil:
- Evolution between January 2008 and January 2009: -0.1%.
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Web traffic growth continued to slow down in 2008, for an equivalent number of websites. However, we observed a stabilization with a 5.9% evolution rate in 2008, vs. +6.7% in 2007 and +10.1% in 2006. On a long term basis, web traffic jumps about +151% in 7 years for an equivalent number of websites.
The beginning of 2009 (just as 2008 year-end) is less promising: January usually shows a distinct improvement of web traffic. This year, we can observe some stabilization compared to January 2008 (-0.1%). Will February get better results?
Stay tuned as AT Internet Institute (ex XiTi Monitor) will provide you the evolution of web traffic.
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Methodology
The indicator used for this study, measuring the number of visits recorded based on a constant cross section of sites, reveals the evolution of website activity.
In the absence of official data regarding the number and composition of existing websites, it is not actually possible to present an indicator that reflects the evolution of Web users ’total Web consumption.
In this study, we looked at website traffic. This covers the evolution in audiences generated by an equivalent number of websites. The series of data successively integrates daily rates of evolution, calculated on a like-for-like perimeter on all sites audited by AT Internet. Thus, the evolution of this indicator does not reflect the evolution of the AT Internet cross section, but website arrivals and departures within this cross section are integrated in the calculation of our indicator.
The audience registered on sites within the AT Internet cross section revealed the existence of seasonal fluctuations that processing methods make it possible to extract.
Tools adapted* to processing chronological series enabled us to break down this monthly series. The components are:
- The trend of the series which represents long-term evolution of the series.
- The seasonal component representing yearly or, in our case, monthly fluctuations that are repeated more or less regularly from year to year. It reveals phases of growth and recession.
A moving average provides an adjustment of the monthly audience; it corresponds to an estimation of the global trend. The moving average necessitates, at time t, measurements of the series at time t, as well as measures around t.
In our study, a moving average of 13 terms, symmetric centered on t time, is adapted in order to measure the annual trend of a series of monthly data. To measure the moving average at month m, measurements from month m, month m-1 to m-6, and month m+1 to m+6 are required. This explains the necessity to estimate this moving average at the end of the period. Thus, for example, concerning the month of January 2009, when the months from February to July 2009 will be over, the moving average calculated replacing the current estimate, can bring about a slight correction of the seasonal fluctuation.
The annual trend when the data required for the calculation of the moving average is incomplete is an estimation by successive linear regressions, under the hypothesis of quasi-stability of the evolution of the annual trend for the short-term.
*Non-parametric method to deconstruct a time series for seasonal adjustment.
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