The working life of Museum of London

There are a number of ways in which I gathered information and analysed the data to evaluate the effectiveness of social software in increasing visits to Museum of London and the Museum’s main website. I used a combination of quantitative and qualitative data, including drawing upon my own experience as Web Content Manager working on these websites.

In today’s post, I will explain some of the methodology I used to answer the set of questions I identified at the start of my research to help me measure the effectiveness of the Museum of London blog (this site referred to as MyMOL) on the Museum of London website. These questions can be seen in my first blog post on this subject.

Primary research method for analysing effectiveness of Museum blog (MyMOL)

First method: use of web analytics

I used the website logs to analyse and compare the patterns in visit and visitor numbers of the Museum of London website with that of MyMOL. The web statistics gathered, analysed and compared were for the duration of one year (1 April 2008 to 31 March 2009). This period of time allowed for patterns to emerge, and correlations between the sites to be discerned, if any.

The web statistics gathered for each site were the:

• number of unique visits,
• number of unique page views,
• number of unique visitors,
• number of repeat visitors,
• number of new visitors,
• average duration of visits, and
• number of referrals made between sites

(Terminologies above will be described in greater details in the research findings.)

To ensure website statistics gathered were comparative for the Museum of London website and MyMOL, I used statistics from Google Analytics for both.

The percentage of new and repeat visitors and the duration of visits were used to demonstrate how engaged visitors were with each site and its content, which also provided an insight into how valuable visitors found the information (though these values have limitations - see ‘Recognised benefits and limitations of web analytics’ below for more on this).

To help answer the question of whether MyMOL was attracting new audience and encouraging more visits and engagement with the Museum of London website, or whether it was only taking visitors away from it, I looked at any referrals that resulted in visits made from the Museum of London website to MyMOL, and from MyMOL to the Museum of London website, for the same period of time.

Second method: Museum of London website emails and MyMOL blog comments

The second research method I used for finding out how engaged visitors were with the Museum of London website and MyMOL, was to compare the number of enquiries that come through the Museum of London website with the number of comments made on MyMOL blog entries for the duration of the year.

As it was difficult to track every single enquiry that came via the website due to the fact that email addresses are explicitly published on all Museum of London websites and offline publications, I only counted enquiries submitted to the info@museumoflondon.org.uk email, which is the generic email address for all web enquiries, including emails via the contact forms on Museum of London website.

I calculated the number of enquiries received with number of visits made to Museum of London website to arrive at an average number of enquiries per visit, per month.

To compare this with MyMOL, I looked at the number of comments made to blog entries and calculated the average number of comments per visit, per month.

This indicated how engaged visitors were with the sites, both individually, as well as in comparison with each other.

Recognised benefits and limitations of web analytics

Web Analytics is the statistical measure of a visitor’s journey through websites and is described as “the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage” (Web Analytics Association 2009).

Canadian Heritage Information Network claims that success of a website is “determined by its usage”. Web analytics helps measure this usage by enabling site managers to gather information about where visitors come from, what pages they view and how long they stay on the site. Web analytics also enable site managers to find out how many visitors are visiting the site over a period of time and if those visitors make repeat visits.

However, whilst this is true, there are problems with how the information is gathered in the logs for analysis. For example, visits and visitor numbers are not accurate. “Lots of tools use IP address to assign visitor status, but when a customer is using an ISP such as AOL they are on a dynamic IP. This means that if they come to your site today on IP ‘A’, tomorrow they might come on IP ‘B’. This would be tracked as 2 visitors each with 1 visit, even though it was really 1 visitor with 2 visits” (Kyrnin 2009). On the other hand, “some visitors may use more than one computer on a given day” and “more than one visitor may use the same computer” but it all appears “as one unique visitor” (Dash 2006). In addition, if cookies are used to track visits and visitors, the result is compromised as many people delete cookies or refuse to accept them (Kyrnin 2009).

Page views can also be misleading as people may see information on other sites instead of the original website, and so pages will not be counted. Also, the duration of visits could show up as longer then is true if visitors look at one site but keep open the browser for another site, to which they come back to after a length of time. On the other hand, if visitors view only one page, duration of time will not be logged at all.

There is also an assumption that processes for measurements “relate to an action by a human visitor”(Web Analytics Association’s Web Analytics Definitions). However, sometimes website crawlers visit websites to index pages for search engines. Some web analytic software includes these visits, whereas Google Analytics excludes them, which gives a more accurate result. However, if JavaScript is turned off on a person’s browser, Google Analytics will not be able to gather any data for any visits from that browser, thus decreasing the visitor and visit numbers, although switching off JavaScript is not a common occurrence.

Even with the recognised limitations of web analytics, other then carrying out intensive web surveys (which has limitations of its own), using the only means currently available to measure the success of a site, is still very strong. As the limitations apply to both the Museum of London website and MyMOL web statistics, and as both analysis are done using Google Analytics, I thought the results will balance against each other and as long as I am flexible and take into account these limitations, the information will still prove to be valuable.

References

Canadian Heritage Information Network. “Web Analytics ? Measuring for Success.” Canadian Heritage Information Network. 15 January 2009. Web. Accessed 21 August 2009. <http://www.chin.gc.ca/English/Digital_Content/Web_Analytics/index.html>

Dash, R.K. “What Is Web Analytics? Web Metrics?” Weblog post. Chameleon TechnoBabble. 26 April 2006. Web. Accessed 21 August 2009. <http://www.chameleonintegration.com/2006/04/26/what-is-web-analytics-web-metrics/>

Kyrnin, J. “What Web Analytics Can be Tracked: An Overview of Metrics That Can Be Tracked.” About.com. Web. Accessed 21 August 2009. <http://webdesign.about.com/od/analytics/a/what_can_track.htm>

Web Analytics Association. (a) “About Web Analytics Association.” Web Analytics Association. Web. Accessed 21 August 2009. <http://www.webanalyticsassociation.org/aboutus/>

Web Analytics Association. (b) “Web Analytics Definitions.” Web Analytics Association. 22 September. 2008. Web. Accessed 21 August 2009. <http://www.webanalyticsassociation.org/attachments/committees/5/WAA_Web_Analytics_Definitions_20080922_For_Public_Comment.pdf> p.7