Year: 2024
Type: article
Abstract: The intended objective is to develop a robust supervised machine learning model to predict the conversion rate of marketing campaigns including advertisement, promotions and offers. This entails leveraging a diverse set of features that encapsulate various aspects of the campaign. By employing historical data which has customer transactions and preferences, the model aims to discern patterns and relationships, ultimately providing a predictive score that can assess the likelihood of successful conversions for different marketing initiatives. The end goal is to enhance strategic decision-making by offering insights into the factors that significantly influence campaign success or failure, thereby optimizing future marketing efforts for improved conversion outcomes. Overall, this paper aims to serve as a reference point for both academia and industry professionals to implement and evaluate marketing strategy campaigns, particularly from the technical point of view. We will build a machine learning model that predicts the conversion rate/score of marketing campaigns based on various features. Leveraging a diverse set of features like campaign type, duration, budget, audience, audience, and age group, the model aimed to offer insights into the factors which can influence the campaign success score.
Source: Computer Science and Engineering
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404548663