Department of Computer Science and Information Systems

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    Click-Through Rate: An Overview of Scientific Research in Management
    (CRC Press, 2023) Sharma, Yashvardhan; Chanda, Udayan
    Since the introduction of online advertising, an increasing number of advertisers and firms are relying on this ad type to acquire consumers, making it an important revenue source for search engines and other internet giants like Google, Amazon, etc. Clicks are used as a metric in two ways: (i) for advertisers, it is used to measure the effectiveness of an advertisement, and (ii) for internet companies, it is used as a quality metric for their search engine or website. The click-through rate is the ratio of the number of impressions to the number of clicks of an advertisement. Managers are using this metric to allocate the budget for their advertisement campaigns based on their effectiveness. In the last decade, due to the growing industry demand, it has attracted scholars from industry and academia. This literature review article examines click-through rate evolution from an empirical standpoint using the bibliometric methods, reviewing 596 articles from the Scopus index. This review article (i) identifies the most influential articles, authors, and journals, serving as a baseline for future research, and (ii) charts the evolution of the topic over the last decade, assisting managers and future researchers in gaining a performance-based comprehensive view of click-through rate.
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    CTR Prediction: A Bibliometric Review of Scientific Literature
    (CRC Press, 2023) Sharma, Yashvardhan; Chanda, Udayan
    Internet giants like Google, Facebook, and Amazon have relied heavily on revenue from online advertising sales in recent years. The Click-through rate of an advertisement is the percentage of people who clicked it out of those who viewed it. The CTR as a metric represents the performance of their online advertisements. For over two decades now, researchers in academia and industry have paid great attention to getting good CTR prediction accuracy because of the demand it has generated in the digital world. This paper reviews the scholarly literature on CTR prediction during the previous decade by a bibliometric analysis of 1051 publications from journals indexed by Scopus. The goals of this research are to (1) conduct a structured quantitative analysis of the bibliometric data, (2) chart the development of CTR Prediction research, and (3) identify the most recent and relevant research literature and viewpoints in the field. A handful of studies have been conducted on this subject, and they have presented an in-depth analysis of specific methods and learning models being applied for CTR prediction. In addition to the previously submitted studies, this literature review aims to provide an overall bibliometric analysis showing the evolution of techniques employed for CTR prediction in the articles published over the last ten years, using a combination of bibliographic coupling, citation, and co-citation. The outcome of this literature evaluation will aid future researchers in gaining a deeper comprehension of the scientific studies around CTR prediction.
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    Marketing Metrics and Advertisement Campaign Budget: A VECM Approach
    (Taylor & Francis, 2023) Chanda, Udayan
    The use of internet advertising as a primary customer acquisition strategy is becoming increasingly common among businesses. Internet companies like Google, Facebook, and Amazon have become platforms for these online advertisements. Standard metrics like impressions, clicks, conversions, click-through rate (CTR), and cost per acquisition (CPA) are used by marketing managers to evaluate the efficiency of advertisements. Managers mainly utilize these indicators to allocate funds to their advertising campaigns, which are then used for bidding on other advertising opportunities. Online advertising is dynamic, and advertising campaigns are susceptible to multiple shocks in demand. Using the data collected from an advertising company that places search ads on e-commerce websites on behalf of consumerpackaged goods companies, we developed a multivariate time series model to investigate the effect of impulse shocks on a specific keyword and its performance. According to the model, we observe the impact of these sudden, impulsive shocks on impressions, clicks, and conversions that define the efficiency of an advertising campaign, a short-run equilibrium among these metrics, and the evolving nature of the keyword in paid search advertisements, and forecast the metrics using the vector error correction estimates. This model can aid managers in their campaign budget allocation decision-making to ensure they can withstand these fluctuations in demand while avoiding either overspending or underspending and longevity of the performance of a keyword