Predicting the success rate of Marketing Campaign using linear regression algorithm

Author

Chikowero, Sithabile.

Title

Predicting the success rate of Marketing Campaign using linear regression algorithm

Abstract

The goal of this research is to predict the success of marketing campaigns using a Linear Regression algorithm. By leveraging historical data from previous campaigns, the study develops a predictive model to aid businesses in strategic decision-making. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Key factors influencing campaign success, including market trends, marketing mix, campaign execution, product characteristics, and customer demographics, are identified and analyzed. The findings aim to provide businesses with a robust tool for optimizing marketing strategies and improving the likelihood of successful product launches. This research addresses the gap in utilizing predictive analytics for marketing campaign evaluation and offers practical insights for enhancing marketing effectiveness in a competitive market environment.

Date

June 2024

Publisher

BUSE

Keywords

Linear Regression algorithm,

Mean Absolute Error (MAE),

Mean Squared Error (MSE),

Root Mean Squared Error (RMSE),

R-squared (R²).

Supervisor

Mr. Zano,