EVALUATING WEB REAL ESTATE VIA PIXEL EFFICIENCY ANALYSIS

EVALUATING WEB REAL ESTATE VIA PIXEL EFFICIENCY ANALYSIS

ABSTRACT

 

This research presents a quantitative web analytics approach tailored for academic libraries. Specifically, we introduce pixel efficiency analysis, with the associated measures of pixel efficiency value and conversion efficiency value, as a web analytic approach for evaluating potential website changes. Pixel efficiency analysis is the practice of relating screen real estate measured in pixels to the achievement of organizational goals and key performance indicators as indicated by quantifiable user behavioral interactions on a webpage. We employ both the concept and measures through a case study focusing on high traffic webpages of an academic library website for a major research university. An overarching web analytics investigation in combination with pixel efficiency analysis of four of the library’s major webpages identifies the key areas of improvement in regards to real estate usage and provides quantifying numbers to support the improvement. Based on these results, we investigate changes to each of the four pages utilizing A/B testing of tens of thousands of library patrons and the measurements of pixel efficiency value and conversion efficiency value to examine the effect on user behaviour, demonstrating the value of pixel efficiency analysis. Our research findings show the capability of pixel efficiency analysis to provide insight not delivered by existing web analytics approaches for academic libraries. Namely, we emphasize the importance of page real estate by showing that components of a webpage can be optimized and that users overall prefer the optimized web layouts. Real estate usage is expected to be increasingly important given the trend towards mobile, and it is an increasingly important consideration within web analytics and design. While specifically tailored to academic libraries, pixel efficiency analysis has applications to all websites and has significant potential for future research.

 

CHAPTER 1: INTRODUCTION

 

Advances in technology are continually shifting the online landscape for academic libraries, as well as other organizations, especially in regards to offerings of online capabilities. Academic library sites offer resources, such as e-journals, e-books, enhanced search features, and virtual reference services (Aharony, 2012). Yet, these increased online services come at a cost. Increased electronic services have pushed expenditures of academic libraries at a growth rate 12% above the inflation rate, while decreasing the amount of physical assets offered (Regazzi, 2012). Libraries must, however, invest in these services to offer the high quality online services today’s consumers (e.g. professors, students, and staff) have come to expect.

 

The increasing importance and investment in web services offered by academic libraries is apparent, but this increase in online offerings comes with challenges, namely high quality of service expectations by library patrons. These expectations formed by consumers have largely been a result of commercial non-library offerings, in particular services like Google Scholar (Kesselman & Watstein, 2005). These levels of service concerns by libraries are not unwarranted.

 

For example, Brophy and Bawden (2005) show that Google Scholar, in comparison to most library services, ranks superior in accessibility and coverage when conducting academic-related queries. While library services rank superior in quality of search results, the researchers (Brophy & Bawden, 2005) point out that the emerging generation of scholars is likely to prefer accessibility over, perhaps, a marginal quality increase.

 

It is therefore imperative that academic libraries engage in a process of continual improvement of their offered online services and site features to better support their customer base and bring their services in line with commercial expectations. Libraries must achieve this, while also attempting to better understand the specific information needs of academic consumers, which we propose differs from typical online information seeking.

 

To better understand the information seeking behaviors of academic users, some academic libraries have turned to the practice of web analytics. The reported results, though largely exploratory and descriptive in nature, are mixed (Betty, 2009; Black, 2009; Deschenes, 2014; Fang, 2007; Fang & Crawford, 2008; Ghaphery, 2005; Loftus, 2012; Memmott & deVries, 2010; Turner, 2010; Whang, 2007; Young, 2014). By the very definition of web analytics, some sort of optimization and/or enhancement is to be expected, as the definition of web analytics offered is “…the objective tracking, collection, measurement, reporting, and analysis of quantitative Internet data to optimize websites and web marketing initiatives” (Kaushik, 2007c). So, while the descriptive findings of past research have resulted in website improvements, the descriptive nature fails to advance a field of unique information seeking behavior: academic libraries. This notion should not necessarily be taken in surprise, as Kaushik (2007c) notes that web analytics is still in its “infancy” (p. 7).

 

While we initially mention that ‘web analytics’ has been used to optimize and enhance academic library websites, we find it important to note that future research in this area may incorporate phrases such as ‘digital analytics’ and ‘web analytics 2.0’ (Google, n.d.; Kaushik, 2007a, 2007c). To emphasize the importance of these differences, Kaushik (2007a) defines web analytics 2.0 as

“the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline)” (para. 5). This definition differs from the original definition in that it is more complex and demands the need for a holistic approach to data analysis that is synchronized with organizational goals. This holistic approach has been lacking in most web analytic studies previously conducted for academic libraries. By adopting best practices of digital analytics, academic libraries stand to increase their competiveness relative to commercial offerings (e.g. Google Scholar), while also seeing an increase in return on investment (ROI). Adopting more comprehensive data-driven approaches will also inherently allow libraries to increase their expertise within the field of data science, which is particularly important as academic libraries seek to expand research data services to accommodate students within the shifting learning environment (Tenopir, Sandusky, Allard, & Birch, 2014).  Increasing competiveness with commercial offerings and also increasing ROI is vital to the future success of academic library websites; but, these cannot be achieved without adopting strategic approaches that embody utilization of multi-methodological approaches that are accompanied by tactical web analytic measures.

 

Thus, the goal of this research is to propose a strategic framework for academic libraries that can increase the effectiveness and efficiency of academic library website presence. This strategic framework is supported by empirical data from a case study, an academic research library that is a part of a major U.S. university. Research findings highlight the impact of leveraging a holistic web analytics approach (i.e. web analytics 2.0 or digital analytics) for academic libraries by utilizing pixel efficiency analysis, an approach we developed specifically for academic libraries and similar organizations. Pixel efficiency analysis inherently directs an analyst to employ best practices of web analytics through a strategic analysis of combined real estate usage and user behavior. Within our research, we (1) employ an overarching web analytics investigation on four major webpages of the library, while making special note of real estate inefficiencies; (2) make iterative changes via A/B testing based upon identified weaknesses and record results; and (3) report the findings through tactical measures that tie back to organizational objectives and key performance indicators (KPIs).

 

In short, this research seeks to maximize the efficiency of screen real estate utilization, while achieving maximum effectiveness. Screen real estate is defined as, “the amount of space available on a display for an application to provide output” (Usability First, n.d.) and, in our research, is measured in pixels. Pixels can be thought of as a measurement similar to that of square feet or square meters for a room measurement, just in this case the measurement is screen real estate.

 

Our research findings show that pixel efficiency analysis proves useful beyond that of current measures associated with web analytics typical within academic libraries, while having applicability to websites and organizations in general.

 

EVALUATING WEB REAL ESTATE VIA PIXEL EFFICIENCY ANALYSIS

 

 

 

 

Sharing is caring!