根据点击和印象数据估算谷歌有机搜索结果中与设备相关的点击率

IF 2.4 3区 管理学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Aslib Journal of Information Management Pub Date : 2024-01-10 DOI:10.1108/ajim-04-2023-0107
Artur Strzelecki, Andrej Miklosik
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引用次数: 0

摘要

目的自上次使用已知数据计算点击率(CTR)值以来,搜索引擎的使用情况发生了变化。本研究的目的是提供一种可复制的方法,利用程序化访问从谷歌搜索引擎获取数据,并从检索到的数据中计算点击率值,以显示自上一次研究发表以来点击率发生了哪些变化。设计/方法/途径在本研究中,作者根据实际点击和印象数据提出了有机搜索结果中的估计点击率值,并建立了利用谷歌程序化访问收集该数据的协议。在这项研究中,作者收集了 416,386 次点击、31,648,226 次印象和 8,861,416 次每日查询的数据。研究结果研究结果表明,点击率与之前学术研究和行业基准中报告的数值相比都有所下降。估计结果表明,谷歌有机搜索结果中排名第一的结果的点击率为 9.28%,排名第二和第三的结果的点击率分别为 5.82% 和 3.11%。作者还展示了不同类型设备的点击率差异。在台式机设备上,点击率随着排名位置的降低而稳步下降。在智能手机上,点击率一开始很高,但随后迅速下降,从第 13 位开始出现前所未有的增长。实践启示理论启示包括生成一个搜索引擎结果和用户行为的最新数据集,供研究界使用,创建一个生成新数据集的独特方法,并展示有关点击率趋势的最新信息。对管理的影响包括:企业需要重视优化谷歌搜索结果(除有机文本结果外)的其他形式,以及应用本研究方法确定其网站点击率的可能性。原创性/价值本研究提供了一种获取真实点击率数据的新方法,并按设备分类估算了谷歌顶级有机搜索结果的当前点击率。
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Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data

Purpose

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

Design/methodology/approach

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

Findings

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

Practical implications

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

Originality/value

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

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来源期刊
Aslib Journal of Information Management
Aslib Journal of Information Management COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.30
自引率
19.20%
发文量
79
期刊介绍: Aslib Journal of Information Management covers a broad range of issues in the field, including economic, behavioural, social, ethical, technological, international, business-related, political and management-orientated factors. Contributors are encouraged to spell out the practical implications of their work. Aslib Journal of Information Management Areas of interest include topics such as social media, data protection, search engines, information retrieval, digital libraries, information behaviour, intellectual property and copyright, information industry, digital repositories and information policy and governance.
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