Custom Variables allow website owners to extend Google Analytics’ measurement capabilities and track information that is meaningful to them. In this series of three articles, Barbara Pezzi, Director of Analytics and Search Optimisation for Fairmont Raffles Hotels International, shares how she makes use of custom variables to better understand how to better engage with her customers. You can familiarise yourself with custom variables in Part 1 and Part 2 of this series. – Ed.

Our hotels are located all over the world. As a result, our website visitors are just as geographically diverse. We are planning to expand our language offerings this year and want to understand how each language we currently offer is performing and which languages we should prioritise for.

On the Swissotel site we offer content in English, German, Russian, Spanish, French, Arabic and Chinese. We don’t have separate websites for each language and, in some cases, only part of the content is translated. For example, we only provide Chinese translations for content related to our hotels in China. Furthermore, the checkout pages are only available in German and English and located on a separate subdomain. As a result, it is very difficult to assess how our content is being consumed in different languages and the impact of language on our sales.


Content Languages – Page level custom variable
To help understand how our foreign-language content is consumed, I decided to use page-level custom variables in Google Analytics. Page-level custom variables apply to a single pageview and allow you to track attributes related to that page such as category, section, author, or, in our case, language. They are very useful for grouping together related pages in our reports.

The custom variables code used is fairly straightforward. On each page, we set a page-level custom variable called “content_language” and set its value to the language code for the language that the page content is written in.

For example, on all our English pages we have:

_gaq.push(['_setCustomVar', 1, 'content_language', 'EN', 3]);

On each of our German pages we have:

_gaq.push(['_setCustomVar', 1, 'content_language', 'DE', 3]);

With these page-level custom variables in place, I can see how popular each language is:

By creating an advanced segment based on this custom variable, I can now view which keywords are generating traffic for each language and make any necessary tweaks to our SEO and paid search campaigns.

Additionally, by applying the same advanced segment to our product report, I segment which hotels are being booked in a given language. We use this insight to coordinate our marketing strategies so that we are promoting the right properties in the right languages to the right markets.

We would not be able to gain such valuable insights without custom variables and I am looking forward to the day Google increases the maximum number of custom variables, since I have already used up all my slots.

That brings our series on custom variables to an end – for now. I hope that these posts have inspired you to take a closer look at your site, identify dimensions that are truly unique and important to your business, and adopt custom variables to measure them.

Posted by Barbara Pezzi, Director of Analytics and Search Optimisation, Fairmont Raffles Hotels International

Custom Variables allow website owners to extend Google Analytics’ measurement capabilities and track information that is meaningful to them. In this series of three articles, Barbara Pezzi, Director of Analytics and Search Optimisation for Fairmont Raffles Hotels International, shares how she makes use of custom variables to better understand how to better engage with her customers. You can familiarise yourself with custom variables in part one of this series. – Ed.

We recently introduced the option of booking more than one room within a single reservation on our Fairmont sites. In the past, if a visitor wanted to book three rooms, she would have had to go through the checkout process three times.

This new option greatly improved the booking experience for our customers. We knew this both intuitively and anecdotally, but we wanted to understand the impact of this change with data. We wanted to understand the popularity of this new feature, which group of hotels benefited the most (e.g. city hotels or resorts), and what the impact has been on our booking rates.

Booking types - Session-level custom variables
We used a session-level custom variable in Google Analytics to help us answer these questions. Session variables are applied to a single session and help you understand specific behaviours that happen during a particular session or visit.

As with all custom variables, the code to add to your Google Analytics snippet is very simple: a single function placed in the confirmation page above the _trackPageview() call. We set a custom variable called “booker” each time a booking was made, and its value would be “single” or “multiple” depending on the number of rooms booked.

_gaq.push(['_setCustomVar', 1, 'booker', 'single', 2]);


Analysing the data
Using this custom variable, we could conveniently assess the proportion of sales through our sites for single or multiple rooms.



We are also able to assess corresponding traffic and conversion data and even see the increased value of “multiple” booking visits. By creating an advanced segment, I could identify which traffic channels sent me these customers and adjust my marketing activities accordingly.




Identifying relevant keywords
Since organic search traffic from Google is sending us almost 45% of all multiple “bookers”, we decided to drill down deeper and look at the keywords that sent us the traffic. We do a number of things with these keywords: add them to our paid search campaigns, optimise for them in our SEO strategy, and use them as seeds for identifying new keywords. Our multiple room bookers, for example, seem to favour our more traditional hotels and resorts. With this information, we can now look at adding content to our websites that would appeal to this group.



I can also easily identify which hotels seem to be more popular with multiple room bookers and advise the property accordingly. They can then use this information to create new offers that might appeal to small groups or families who are likely to book multiple rooms.


Identifying key markets
We were interested in identifying the countries of residence of our customers, and in particular, which locations were more likely to provide us with multiple room bookings.



Based on the data, we can now run adjust our marketing campaigns in the best performing markets to include multi-room offers.


Applying our learnings
Session-level custom variables allowed us to effectively analyse a scenario that was unique to our websites and wasn’t immediately available in Google Analytics standard e-commerce reports. We are now in the process of introducing the multi-booking functionality to the Swissotel websites and feel that we have a head start in ensuring its success thanks to the learnings we gained from our Fairmont sites.

Stay tuned for part 3 in this series, in which I will discuss page-level custom variables.

Posted by Barbara Pezzi, Director of Analytics and Search Optimisation, Fairmont Raffles Hotels International

Custom Variables allow website owners to extend Google Analytics’ measurement capabilities and track information that is meaningful to them. In this series of three articles, Barbara Pezzi, Director of Analytics and Search Optimisation for Fairmont Raffles Hotels International, shares how she makes use of custom variables to understand how to engage more effectively with her customers. – Ed.

One of the latest features of Google Analytics that I’m excited about is Custom Variables. Google Analytics has a very extensive list of default dimensions and metrics, such as time on site and traffic sources, that it tracks for your website. While these dimensions and metrics cover most websites’ needs, every website and business is unique, with its own set of objectives and goals. You might want to track certain visitor segments or user behaviour that is not reflected in the default set of metrics.

Custom variables allow you to extend Google Analytics’ default metrics and dimensions to track information that is meaningful to you by labelling interactions with your site at three levels: visitor, session and page. You can then segments and run custom reports based on these variables. You can learn all about the technical implementation of custom variables in the Google Analytics Code Site.

The possibilities are endless with custom variables. You could for example:

• identify segments of visitors based on a specific landing page that they visited
• identify staff-visits vs non-staff visits
• identify and analyse sessions during which a visitor posted a comment on your blog or subscribed to your newsletter

We use custom variables for a number of purposes on our Fairmont and Swissotel websites. Over the next few weeks, I will walk you through three examples, one for each variable type (visitor, session, page).


Loyalty Members – Visitor-level custom variables
A segment of our customers that we focus on because of their value to us are our loyalty program members. When a customer signs up to our Club Swiss Gold program, they start as a ‘Classic’ member, and then progress to become an ‘Elite’ member.

We want to understand the difference in behaviour and purchase patterns between our Classic and Elite members. Google Analytics can’t easily provide us with that insight by default, but with visit-level custom variables, we can answer this question.

Visitor-level custom variables allows us to distinguish categories of visitors across multiple sessions. We are essentially bucketing our users into our own custom categories. Visitor-level custom variables are best used for attributes of a visitor, such as their membership level or product preference, that you wish to track over multiple visits.

On our Swissotel site, we set the visitor-level custom variable whenever a member logs in by inserting a line of code into our Google Analytics tracking code. The code involved is very simple: a single function placed above the _trackPageview() call on the same page:

_gaq.push(['_setCustomVar', 1, 'Membership', 'logged_in_classic', 1]);

We are now able to distinguish between members and non-members as well as membership levels. Within the custom variable report, I can now see at a glance information like site usage, goal conversion data, and ecommerce data, which is broken down by membership levels.

I can use this custom variable to create an advanced segment for additional insights, such as countries of our classic members and conversion rates across countries. We use these insights to identify any potential deficiencies in language coverage or regional product preference.

Or, we can look at which property classic members book the most and use that insight to create more offers for popular hotels and increase bookings for less popular properties.

We are now in the process of updating our loyalty program, and these insights are invaluable in helping us make improvements based on our customers’ preferences.

Stay tuned for part two of this series, in which I’ll cover how we use session-level custom variables on Fairmont’s sites.

Posted by Barbara Pezzi, Director of Analytics and Search Optimisation, Fairmont Raffles Hotels International

Barbara Pezzi joined Swissotel Hotels and Resorts in 2001. As Director of Web Marketing, Barbara is responsible for developing and managing Swissotel’s search engine optimization, search engine marketing and web analytics and for managing their pay-per-click campaigns in Google AdWords. Barbara has been active in the hospitality industry for over 15 years.

With so much data available in tools like Google Analytics, it’s easy to get sidetracked and overwhelmed by all the options for optimising my digital marketing spend. When in doubt, I tend to stick to the “follow the money” principle, and ask:

• Where am I spending my money and is it working? 
• Who is giving my business money and how can I get more? 

I can start to find answers to these questions with advanced segments in Google Analytics. Most of you probably know that a segment is a subset of your data. Usually, it refers to a subset of visitors whose behaviour you’d like to see and analyse. For example, you may want to analyse only “Paid Traffic” or “Organic Traffic” and compare these segments side-by-side in reports. I use advanced segments to understand the behaviour of my paid visitors and to answer questions like “What happened after paid visitors clicked on an ad” and “How does the behaviour of paid visitors differ from the behaviour of organic visitors from the same countries or markets?”

Recently, we used advanced segments to improve our advertising campaigns for paid visitors from different countries. We run Google AdWords campaigns in Australia, the US and the UK for one of our Singapore properties. The goal of the campaign is to drive sales by getting prospective customers to click on our AdWords ads and then make a purchase on our site. I let the campaign run for a few weeks and then I begin to optimise it. To do this, I first create advanced segments for paid visitors from Australia, the US and the UK. Each campaign has a unique name, as illustrated in the screenshot below.

This segment enables me to compare the e-commerce conversion rate of paid visitors - or the percent of paid visitors that make a purchase on my site - with the e-commerce conversion rate of organic visitors. 

Digging deeper, I can compare additional metrics such as “average order value” to analyse how much paid visitors from each country typically order.

Based on the above, I can quickly establish that paid visitors from the UK spend twice as much as their Australian and American counterparts in a single transaction. But our site gets fewer visits from the UK compared to the US and Australia, and I see room for improvement in our conversion rates from paid UK visitors.

From there, it’s a natural progression to segment further and analyse what our AdWords visitors from the UK were doing on our site and what content they’re consuming. I create a new segment specifically for paid visitors from the UK.

This segment gives me more insight into how I can make my site more attractive to paid visitors from the UK. I know, for instance, that UK visitors spend much more time viewing our rooms and restaurants compared with Australians, who favour the promotions and packages section of the site. With this information, I can tailor my ad texts and landing pages and place greater emphasis on “beautiful rooms and suites” for UK visitors and “great deals” for Australians visitors.

After a few months of optimisation, we have more than doubled our number of visits and transactions from the UK campaign and maintained the initial high average order value. The post-optimisation campaign results speak for themselves. By segmenting and refining our campaign, we were able to almost double our e-commerce conversion rate and, more importantly, to improve our per-visit value metric significantly.

As you can see, advanced segments have been a huge asset to me in understanding who my visitors are and what they enjoy about our business offering. My parting words of advice are “keep on segmenting!”


Watch out for more posts from Barbara in the near future. If you want to hear more about how Barbara takes full advantage of Google Analytics, read her first post here, and watch her videos here and here.

Barbara Pezzi, Director Webmarketing, Fairmont Raffles Hotels International