Online Advertisement Using Web Analytics Software: A Comparison Using AHP Method

Online Advertisement Using Web Analytics Software: A Comparison Using AHP Method

Manu Sharma, Sudhanshu Joshi
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJBAN.2020040102
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This article describes a analytic-hierarchy-process (AHP) application to identify and evaluate the best online advertising analytics software. This technique is multi-criteria and used in this study by comparing the top four web advertising analytics software. AHP uses pair-wise comparison of matrices. There are six criteria identified for evaluation: Ad scheduling, ad targeting, creative banner rotation, features, performance, cost and for each criterion, a matrix of pair-wise comparison with web-analytics software i.e. Google analytics, Accenture Analytics, Funnel and, Moat Analytics were evaluated. AHP is an effective method for multi-objective decision-making, and optimization. Thus, it helps web advertisers to evaluate the existing web advertising analytics software for posting their web advertisements.
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1. Introduction

Recent years has witnessed phenomenal growth of internet users and similar trends are expected in near future as well, although 3.9 billion people are still not adoptive towards online usage for personal and commercial activities (WEF, 2018). Therefore, ‘Internet inclusion’ becomes a significant challenge and its cornerstones include- ICT Infrastructure, affordable user charges, IT awareness, Cultural acceptability, and user-oriented content (Nisha, 2016).

With fast emergence towards cyber-driven society, the users become the center of attraction for commercial advertisers, directly or indirectly through Internet websites. Big Commercial websites include, Google, Yahoo, Hotmail, engage into huge portfolio of free Internet services to its users while generate revenue from advertising (Cvijikj & Michahelles, 2013). Conceptually, Web Advertising uses web to deliver promotional/advertising messages to existing and potential consumers and it becomes the focal strategy for online websites (Kumar & Sethi, 2009; Roel & Fridgeirsdottir, 2009).In recent times, Web advertising has become a multi-domain entity (banners, popups, pay-per, sky scrappers, interstitial, etc.) with varieties of underlined activities (viz. inter and intra Communication, content-oriented Communication and Customer Initiated Communication) with the primary aim to generate Sales Revenue (De Haans, Wiesel, & Pauwels, 2016).Web advertising has addressed an exponential growth from $72.5 billion in FY 2016 to $88.0 billion in FY 2017 (Interactive Advertising Bureau, IAB 2018). The business models are more agile and adaptive towards technological changes and recent advancements to enhance their Advertisement revenues (Kumar, Jacob, & Sriskandarajah, 2006; Javan et al., 2018; Kaul et al., 2018). The adaption includes customization and dynamic optimization of online advertisement (Amiri & Menon, 2003; Gilbert & Powell-Perry, 2001). Firms are still engaged in optimizing the strategies for enhancing the efficiency of web advertising (Hanssens, 2009; Sethuraman, Tellis, & Briesch, 2011; De Haans, Wiesel, & Pauwels, 2016).

The industry has witnessed the transformation in the pattern of Revenue models, portfolio management. ‘Brand-keywords’ paid search is gradually become passive with the changing business, while ‘Non-Brand keywords’ influences new and casual users (Blake, Nosko, & Tadelis, 2015). Frequent, retargeting based on customer product preferences and purchasing history becomes also important (Braun & Moe, 2013; Lambrecht et. al., 2014). Also, online advertising coverage becoming more advanced and has replaced offline marketing (Li & Kannan, 2014). Besides, Budget allocation decisions across various forms of advertisements, have also become an area of concern for marketers (Dekimpe & Hanssens, 2007; Karuga et al., 2001; O'keefe 1998; Raghu et al., 2001).

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