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Purpose -- Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics. Design/methodology/approach -- The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform? Findings -- The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic. Practical implications -- This analysis may help webmasters to design successful online advertising strategies. Originality/value -- This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website. [ABSTRACT FROM AUTHOR]

Purpose - The aim of this paper is to develop a new user-friendly in-house tracking methodology for academics to analyse the effectiveness of visits (return visit behaviour and length of sessions) depending on their traffic source: direct visits, referring site entries and search engine visits. In other words, how deep do visitors navigate into the web site? Which is their internal performance depending on their traffic source? Design/methodology/approach - This paper addresses these questions by time series analysis of Google Analytics data. Some statistical matters with regard to the use of Google Analytics data in combination with time series methodology are fine-tuned. Findings - Return visits are the main engine for nurturing session length, but which type of traffic source nurtures these return visits? In order to answer this question, an important distinction must be made between "total return visits" and "marginal return visits". Site entries stay longer to the extent their "marginal return effectiveness" is higher. For our particular web site direct visits are the most effective ones, followed by search engine visits and only thirdly link-entries. Research limitations/implications - This methodology is critical for an effective web site traffic source monitoring and benchmarking that may lead to better web site strategies. Originality/value - The importance of this paper is not the particular web site but the new methodology tested to arrive at these results, an experiment that could be repeated with different web sites. [ABSTRACT FROM AUTHOR]