1. Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages
- Author
-
Ruslan Korniichuk and Mariusz Boryczka
- Subjects
Computer science ,Science ,QC1-999 ,General Physics and Astronomy ,Marketing communication ,Machine learning ,computer.software_genre ,Astrophysics ,Field (computer science) ,Article ,Landing page ,marketing communications ,Digital marketing ,landing pages ,business.industry ,Physics ,Readability ,Support vector machine ,QB460-466 ,machine learning ,classification ,readability indices ,conversion rate prediction ,Artificial intelligence ,business ,computer - Abstract
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.
- Published
- 2021
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