Would it be handy if you could predict the volume of visitors to your destination or business in advance?
Visitor data tends to get viewed through a rear view mirror. Afterall, you don’t know how many people visited next week, only how many came last week. Sometimes, visitor statistics don’t get reviewed at all until the end of the season.
But… if you have both physical visitor data and website visitor data, you may just be able to predict the future. And knowing where potential trouble lies means you can concentrate limited marketing resources where they’re required.
How can this future forecasting work? Well, the rhythm of the tourist as website visitor and the tourist as physical visitor are offset. The website research visit occurs prior to the physical visit.
That offset could be months apart, with a drop in research activity in February - April, indicating fewer physical visits in July and August. Or the offset could be just days or even hours apart.
Understand that offset - the relationship between the website visits and the physical visit - and you can build yourself an early warning system to steer your marketing.
Comparing by eye (well, by numbers actually)
While I will come on to statistical wizardry, you can break the rear view mirror mindset by doing nothing more than paying close attention to your web analytics data and graphing it in Excel alongside your physical visitor data.
Here is a real life example from a tourist destination website. In this instance goal conversions have been used in lieu of physical visitor numbers, but given that the goals relate to conversions that tie to a physical visit - short notice accommodation bookings for example - it works for a demonstration.
As we see in this graph, the top line shows website visitors of this tourism destination website got off to a slower start in the 2008 season compared to 2007.
This suggests that slightly fewer people were on the website planning trips early in 2008, compared to 2007. By April, May and June 2008, website visitors were distinctly down, suggesting trouble ahead for the main season.
And more worryingly, in the lower line the 2008 goal (conversion) data shows an even more pronounced drop against 2007.
Just as the website visitor data suggested earlier in the season, come the peak of July, conversions were well down (and so, therefore was revenue).
Using a spreadsheet to highlight the proportional changes in visitors and conversions compared to a previous period, the website data gives a clue of the trouble ahead. Fewer tourists researching the destination during the typical planning period warns of fewer tourists in the main season ahead.
This is enough to raise a warning flag and trigger tactical marketing action.
However, the website data becomes far more powerfully predictive when you accurately crack the web research to physical visit “lead time” for your business.
That requires a little dash of statistics. (Don’t worry if there’s cobwebs on your school days maths - agencies like us can do this for you, or you can get an Excel whizz to help).
Correlating website visits and physical visits
With certain types of destinations, attractions and other tourism businesses it is possible to work out an approximate “research lead time” - in effect the delay between a peak of activity on key pages of the website (or overall website visits) and a corresponding peak in physical visits.
For the real life visitor attraction shown in the graph below, that research lead time is 4 to 5 days (with some variance according to day of week).
You can see in the graph that visits at the attraction (the lower line) track closely with visits to the website that occur 5 days earlier (the higher line). Five days is enough of a window for that attraction to now test tactical marketing actions - for example when website peaks do not materialise.
How did we get to the 5 day mark?
It involved building a matrix of physical visits data with visits to the website and page views of research specific pages, then off-setting those website vists back through specific units of time. Correlation analysis was run (you can do this in Excel) to see where strengths of potential relationships in the data lie.
While pages like maps and directions showed correlation at just a day or so lead time, classic research pages and overall site visits showed the strongest correlations at five days. (Again, this is a simple view - day of week and time of year is also a factor).
By joining up the relationships between website visits and physical tourist visits, this tourism company now has a short, specific window of opportunity that it didn’t have before. By monitoring its website barometer, it can react in order to “save” lost visits before they occur. More than that, it can time specific messages and promotions far more effectively than before.
So, if it would be handy if you could predict the volume of visitors to your destination or business in advance, think about joining up your online web analytics data and your offline visits.
This entry was posted on Monday, August 11th, 2008 at 10:53 pm and is filed under Future trends, Marketing strategy, Online customer behaviour, Visitor attraction research, Web analytics and web measurement. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.