Campaign ROI Analyzer with AI-powered predictive Analysis

Abstract

This project is dedicated to exploring the contribution of digital marketing to startup growth and will focus on tackling the problem of uncertainty associated with the prediction of performance metrics, potentially resulting in ineffective spending of budget and unstable ROI rates. This project provides a concept for designing and implementing an AI-based Campaign ROI Analyzer for optimizing campaign outcomes. To begin with, a primary survey of startup owners was conducted with the purpose of determining the existing state of marketing in the area and the problems encountered when calculating ROI. On the basis of the results collected from the survey, a synthetic dataset with information about potential campaign situations was created. Then, a supervised learning algorithm using linear regression was developed for the calculation of expected ROI, depending on various parameters, such as marketing budget, choice of platform, targeting efficiency, content quality, and marketing experience. Data analysis was carried out in the form of visual representation of data, evaluation of model performance with the help of statistical metrics of error, budget optimization simulation, and analysis of the influence of certain campaign parameters. Moreover, a prototype of a user-friendly dashboard was created, where users could input their campaign details to receive estimated ROI values. Thus, this project highlights the possibility of applying predictive analytics to the process of startup marketing management and points to further areas for improvement.