{"title":"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","authors":"Artur Strzelecki, Andrej Miklosik","doi":"10.1108/ajim-04-2023-0107","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.</p><!--/ Abstract__block -->","PeriodicalId":53152,"journal":{"name":"Aslib Journal of Information Management","volume":"45 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data\",\"authors\":\"Artur Strzelecki, Andrej Miklosik\",\"doi\":\"10.1108/ajim-04-2023-0107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Practical implications</h3>\\n<p>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.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.</p><!--/ Abstract__block -->\",\"PeriodicalId\":53152,\"journal\":{\"name\":\"Aslib Journal of Information Management\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aslib Journal of Information Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/ajim-04-2023-0107\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aslib Journal of Information Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/ajim-04-2023-0107","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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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