Managing the customer information about behavioral characteristics collected online is a challenge in digital marketing. Low performing websites not only minimize hotels’ return on investment, but also may damage the brand. It is therefore, of the utmost importance for the hotels and other businesses operating online, to observe and measure the website’s ability to convert a visitor. For this purpose the web analytics is used (Kvikne, 2013).
The web analytics is an approach that involves collecting, measuring, monitoring, analyzing and reporting web usage data in order to understand visitor’s experiences. Such tool can assist to optimize websites, therefore accomplish business goals and/or improve customer satisfaction and loyalty (Hasan, Morris, & Probets, 2009).
As Waisberg and Kaushik (2009) define the “Web Analytics is an act of increasing a website’s persuasion and relevance to achieve higher conversion rates.” Moreover, the authors call it the science and the art of improving websites in order to increase their profitability through
improving the customer’s website experience (Waisberg & Kaushik, 2009, p. 5). Web Analytics Association (2008) suggests that the Web Analytics is “the measurement, collection, analysis and reporting of Internet data for the purpose of understanding and optimizing Web usage”.
Therefore, Web Analytics is not a technology to produce reports. It is a process that proposes a virtuous cycle for website optimization (Waisberg & Kaushik, 2009, p. 5). Avinash Kaushik, in his book Web Analytics: An Hour a Day states that the web analytics is the analysis of qualitative and quantitative data of the website, and the competition, to drive a continual improvement of the customers and the potential customers online experience (Cutroni, 2010).
This definition encapsulates three main tasks every business must tackle when doing web analytics:
Measuring quantitative and qualitative date.
Continuously improving the website
Aligning the measurement strategy with the business strategy (Cutroni, 2010)
As Clifton (2012) states the term Web Analytics covers many areas that require different data-collection techniques. For instance, there are offsite tools which measure the size of the company’s potential audience (opportunity), the company’s share of voice (visibility), and the buzz (comments and sentiments) that is happening on the Internet as a whole. On the other hand there are onsite tools used to measure the visitor’s onsite journey, its drivers and the company’s website performance (Clifton, 2012).
Content and transactional sites rely heavily on traffic and audience measurement, and relevant measures are defined by Roberts and Zahay (2012) as:
Traffic data that describes activity on the site. This includes metrics such as number of visitors, sessions, and page views.
Audience data that describes the behavior of people on the site, where they come from, what paths they take through the site, and whether they take desired actions (Kvikne, 2013).
Google Analytics, the most sophisticated web analytics tool (Fang, 2007), was launched on November 11, 2005 (Clifton, 2012). This is a straightforward tool, is easy to set up and the most importantly- is free (Kvikne, 2013).
The information that Google Analytics generate is quite big. The data generated by the Google Analytics can be illustrated as a cycle which consist the following elements: Acquisition;
Behavior and Conversion. Acquisition shows where the website acquired the visitor, in other
words where the visitors found the website. This part includes the reports showing the number of visitors grouped in different channels, such as Organic search, Social Media or Paid search.
Moreover, in here could be found the Referral Traffic, which is the list of websites from which the visitor moved to the company’s website. Behavior illustrates visitor’s activities on the website. This part includes the reports about how many page views occurred on the website during the specific time period, what was the average time spent on the page and what was the bounce rate (Kvikne, 2013). As Clifton (2012) puts it this rate illustrates the number of visitors entering and then leaving the site after having viewed only one page without any other action or event triggered (Clifton, 2012). The last element of the cycle- Conversion includes the reports about what could be learned from the previous two elements of the cycle or what are the outcomes. In order for this report to make sense the company should create the specific goals while setting up its Google Analytics. There are different types of goals which are grouped in four major categories. Destination goal tracks if the visitor reaches the page or the spot on the page wanted to be reached by the company. Duration goals show how many visitors spent the time desired to be spent by the site owner. Number of pages goal illustrate if the visitor visited as many pages on the website as the site owner wanted him or her to visit. And the last category of the goals is the Event goals that measure if the visitor took the action desired by the website owner. This action can be watching the video, downloading the application, downloading the questioner and so on (Kvikne, 2013).
One of the most important features of any analytics tool is performing segmentation.
Segmentation involves going deeper into the data in order to understand how the small segments
perform and how their performance influence the overall performance of the website (Cutroni, 2010).
A simple example of the segmentation is viewing the website traffic according to the physical location of the visitor. Moreover, Google Analytics can group the visitors according to their Gender, Age and Interests.
Tonkin, Whitmore and Cutroni (2010) state that the Google Analytics assists businesses doing the following:
Make better decisions about online strategy and tactics: The tool gives the general understanding of what is happening with the businesses’ online presence. This
information can assist to raise the overall quality of the business decisions made by the marketers and the managers.
Be more goal-driven: By setting the measurable goals, which correspond with real business value, Google analytics can assist taking specific actions and measuring the success of those actions.
Eliminate waste: Using the tool business can see if the business initiatives fail to impact objectives, so the attention and the budges can be shifted elsewhere.
Reward success: Once the success is defined and measured, the business can also take actions to reward the people and campaigns that have the most positive impact.
Plan for the future: Once the base of the analytics date is built, the past performance can be used to predict future trends and estimate the success of the future campaigns (Tonkin, Whitmore, & Cutroni, 2011).
3.1. Introduction
This study has the correlational research design using the survey method to determine the correlation between the set of independent and dependent variables. Independent variables are the following factors: Information, Navigation, Usability, Customization, Download Speed, Security and Available; while dependent variables are hotel website REACH and hotel website LOYALTY.
This chapter starts with providing the information about website sample used in the research.
Further, it presents the measures employed and discusses the procedures of the research, as well as the procedures of approaching the data collected.