Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages

Area of Science:

• Digital Marketing
• Machine Learning
• Natural Language Processing

Background:

• Digital marketing has seen rapid advancements, yet accurate conversion prediction remains a challenge.
• Existing prediction technologies in digital marketing often lack sufficient accuracy.
• Conversion prediction is a persistent problem for marketing professionals seeking to optimize campaigns.

Purpose of the Study:

• To investigate the efficacy of text readability analysis in predicting landing page conversion rates.
• To identify specific textual features that significantly influence conversion outcomes.
• To demonstrate a practical machine learning application for marketing professionals.

Main Methods:

• Utilized a dataset of landing page content and associated conversion data.
• Applied machine learning techniques, including building five predictive models.
• Employed the Support Vector Machine (SVM) algorithm for model development and the SHAP package for interpretation.

Main Results:

• Text readability analysis proved capable of predicting conversion rates.
• The Support Vector Machine (SVM) model demonstrated promising accuracy for practical implementation.
• Identified key features within landing page text that directly impact conversion.
• The study addresses the cold-start problem, with relevance to non-member purchases.

Conclusions:

• Detailed analysis of text readability is a viable method for predicting landing page conversion rates.
• Machine learning models, particularly SVM, can effectively predict conversions based on content analysis.
• The findings provide actionable insights for marketing professionals to enhance landing page effectiveness.