Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages

Digital marketing has been extensively researched and developed remarkably rapidly over the last decade. Within this field, hundreds of scientific publications and patents have been produced, but the accuracy of prediction technologies leaves much to be desired. Conversion prediction remains a probl...

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Autores principales:	Ruslan Korniichuk, Mariusz Boryczka
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	classification conversion rate prediction landing pages machine learning marketing communications readability indices Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/59e8ac552d4c42a5b78348babc4a44fb
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spelling	oai:doaj.org-article:59e8ac552d4c42a5b78348babc4a44fb2021-11-25T17:29:13ZConversion Rate Prediction Based on Text Readability Analysis of Landing Pages10.3390/e231113881099-4300https://doaj.org/article/59e8ac552d4c42a5b78348babc4a44fb2021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1388https://doaj.org/toc/1099-4300Digital marketing has been extensively researched and developed remarkably rapidly over the last decade. Within this field, hundreds of scientific publications and patents have been produced, but the accuracy of prediction technologies leaves much to be desired. Conversion prediction remains a problem for most marketing professionals. In this article, the authors, using a dataset containing landing pages content and their conversions, show that a detailed analysis of text readability is capable of predicting conversion rates. They identify specific features that directly affect conversion and show how marketing professionals can use the results of this work. In their experiments, the authors show that the applied machine learning approach can predict landing page conversion. They built five machine learning models. The accuracy of the built machine learning model using the SVM algorithm is promising for its implementation. Additionally, the interpretation of the results of this model was conducted using the SHAP package. Approximately 60% of purchases are made by nonmembers, and this paper may be suitable for the cold-start problem.Ruslan KorniichukMariusz BoryczkaMDPI AGarticleclassificationconversion rate predictionlanding pagesmachine learningmarketing communicationsreadability indicesScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1388, p 1388 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification conversion rate prediction landing pages machine learning marketing communications readability indices Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	classification conversion rate prediction landing pages machine learning marketing communications readability indices Science Q Astrophysics QB460-466 Physics QC1-999 Ruslan Korniichuk Mariusz Boryczka Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
description	Digital marketing has been extensively researched and developed remarkably rapidly over the last decade. Within this field, hundreds of scientific publications and patents have been produced, but the accuracy of prediction technologies leaves much to be desired. Conversion prediction remains a problem for most marketing professionals. In this article, the authors, using a dataset containing landing pages content and their conversions, show that a detailed analysis of text readability is capable of predicting conversion rates. They identify specific features that directly affect conversion and show how marketing professionals can use the results of this work. In their experiments, the authors show that the applied machine learning approach can predict landing page conversion. They built five machine learning models. The accuracy of the built machine learning model using the SVM algorithm is promising for its implementation. Additionally, the interpretation of the results of this model was conducted using the SHAP package. Approximately 60% of purchases are made by nonmembers, and this paper may be suitable for the cold-start problem.
format	article
author	Ruslan Korniichuk Mariusz Boryczka
author_facet	Ruslan Korniichuk Mariusz Boryczka
author_sort	Ruslan Korniichuk
title	Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
title_short	Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
title_full	Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
title_fullStr	Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
title_full_unstemmed	Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
title_sort	conversion rate prediction based on text readability analysis of landing pages
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/59e8ac552d4c42a5b78348babc4a44fb
work_keys_str_mv	AT ruslankorniichuk conversionratepredictionbasedontextreadabilityanalysisoflandingpages AT mariuszboryczka conversionratepredictionbasedontextreadabilityanalysisoflandingpages
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Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages

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