Web analytics

Techniques used to assess and improve the contribution of e-marketing to a business, including reviewing traffic volume, referrals, clickstreams, online reach data, customer satisfaction surveys, leads and sales.

Companies that have a successful approach to online marketing often seem to share a common characteristic. They attach great importance and devote resources to monitoring the success of their online marketing and putting in place the processes to continuously improve the performance of their digital channels. This culture of measurement is visible in the UK bank Alliance and Leicester, which in 2004 reported that they spent over 20% of their £80 million marketing communications budget on online marketing. Stephen Leonard, head of e-commerce, described their process as 'Test, Learn, Refine' (Revolution, 2004). Graeme Findlay, senior manager, customer acquision of ecommerce at A&L explains further:

Our online approach is integrated with our offline brand and creative strategy, with a focus on direct, straightforward presentation of strong value-led messages. Everything we do online, including creative, is driven by an extensive and dynamic testing process.

Seth Romanow, Director of Customer Knowledge at Hewlett-Packard, speaking at the 2004 E-metrics summit, described their process as 'Measure, Report, Analyse, Optimize'. Amazon refers to their approach as 'The Culture of Metrics' (see Case Study 9). Jim Sterne, who convenes an annual event devoted to improving online performance (www.emetrics.ora). has summarised his view on the required approach in his book Web Metrics (Sterne, 2002) as 'TIMITI', which stands for Try It! Measure It! Tweak It!, i.e. online content should be reviewed and improved continuously rather than as a periodic or ad-hoc process. The importance of defining an appropriate approach to measurement and improvement is such that the term 'web analytics' has developed to describe this key Internet marketing activity. A web analytics association (www.waa.ora) has been developed by vendors, consultants and researchers in this area. Eric Petersen, an analyst specialising in web analytics, defines it as follows:

Web analytics is the assessment of a variety of data, including web traffic, web-based transactions, web server performance, usability studies, user submitted information [i.e. surveys], and related sources to help create a generalised understanding of the visitor experience online.

Petersen (2004)

You can see that in addition to what are commonly referred to as 'site statistics' about web traffic, sales transactions, usability and researching customers' views through surveys are also included. We believe, though, that the definition can be improved further - it suggests analysis for the sake of it - whereas the business purpose of analytics should be emphasised. The definition could also refer to comparison of site visitor volumes and demographics relative to competitors using panels and ISP collected data. Our definition is:

Web analytics is the customer-centred evaluation of the effectiveness of Internet-based marketing in order to improve the business contribution of online channels to an organisation.

A more recent definition from the Web Analytics Association (WAA, www.webanalvticsassociation.ora) in 2005 is:

Web Analytics is the objective tracking, collection, measurement, reporting and analysis of quantitative Internet data to optimize websites and web marketing initiatives.

To succeed in a measured approach to improving results from Internet marketing we suggest that there are four main prerequisites, which are broken down as shown in Figure 9.1 into the quality of the web analytics processes including defining the right improvement measures and purchasing the right tools and the management processes such as putting in place a process where staff review results and then modify their marketing activities accordingly. In this chapter, we will review both approaches.


Have we selected the right diagnostic metrics to improve performance?


Can our analytics software collect, aggregate and visualise metrics?


Are the measures reviewed and interpreted at the right time?

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