From Words to Conversions: Leveraging Large Language Models for Dynamic Landing Page Generation

In modern business, the demand for effective online marketing is undeniable. Nevertheless, creating landing pages for marketing campaigns poses a fundamental question: Should the implementation be the domain of a web developer or a marketing specialist? The interdisciplinary nature of this task, requiring a fusion of coding and marketing skills, underscores a significant gap in current research. Addressing this gap, this paper introduces a specialized AI landing page generator that operates through natural language input, eliminating the need for coding expertise. This empowers individuals without coding knowledge or marketing expertise to create impactful landing pages independently.Usability tests were conducted to evaluate the system, followed by interviews with marketing and web development professionals. The results demonstrated that business specialists perceived the quality of AI-generated content positively. The AI provided a basic structure and introduced new ideas, while human refinement is needed to ensure the uniqueness of the content. In addition, the technical audits revealed high performance, accessibility, and search engine optimization of the landing pages generated. Furthermore, comparisons between GPT-3.5 and GPT-4 revealed that GPT-4 generally produced better quality content and was preferred by the participants for its creativity and code quality. These findings highlight the potential of AI-driven tools to streamline the creation of landing pages, enhancing efficiency in digital marketing and web development.

Authors

Institutions

Publication Details

Published
2025-08-07
DOI
https://doi.org/10.1109/acdsa65407.2025.11166477
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Words to Conversions: Leveraging Large Language Models for Dynamic Landing Page Generation

Andreas Stöckl, Rimbert Rudisch-sommer, Verena Schickmair
Natural Language Processing Techniques
article

From Words to Conversions: Leveraging Large Language Models for Dynamic Landing Page Generation

Andreas Stöckl, Rimbert Rudisch-sommer, Verena Schickmair
article en

Abstract

In modern business, the demand for effective online marketing is undeniable. Nevertheless, creating landing pages for marketing campaigns poses a fundamental question: Should the implementation be the domain of a web developer or a marketing specialist? The interdisciplinary nature of this task, requiring a fusion of coding and marketing skills, underscores a significant gap in current research. Addressing this gap, this paper introduces a specialized AI landing page generator that operates through natural language input, eliminating the need for coding expertise. This empowers individuals without coding knowledge or marketing expertise to create impactful landing pages independently.Usability tests were conducted to evaluate the system, followed by interviews with marketing and web development professionals. The results demonstrated that business specialists perceived the quality of AI-generated content positively. The AI provided a basic structure and introduced new ideas, while human refinement is needed to ensure the uniqueness of the content. In addition, the technical audits revealed high performance, accessibility, and search engine optimization of the landing pages generated. Furthermore, comparisons between GPT-3.5 and GPT-4 revealed that GPT-4 generally produced better quality content and was preferred by the participants for its creativity and code quality. These findings highlight the potential of AI-driven tools to streamline the creation of landing pages, enhancing efficiency in digital marketing and web development.

University of Applied Sciences Upper Austria (AT)
Openalex Percentile: Top 86%
Natural Language Processing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

From Words to Conversions: Leveraging Large Language Models for Dynamic Landing Page Generation — Andreas Stöckl, Rimbert Rudisch-sommer, et al. · (2025) | TGRS Research Map | TGRS