ROAS

Title: ROAS Optimization Problem for Maximizing Advertising Return

Abstract:
This dataset has been created to analyze and optimize the Return on Advertising Spend (ROAS) in digital marketing campaigns. The data has been collected from an e-commerce platform's advertising activities based on various targeted keywords. It includes essential metrics such as advertising budgets, impressions, click-through rates, sales volumes, and revenue. The primary objective is to identify the most efficient advertising spending strategies using optimization techniques to maximize returns within a given budget.

Dataset Contents:
The dataset is divided into six groups, each containing the following variables:

Keywords: The targeted search terms used in the advertising campaign.
Advertising Spend: The budget allocated to a specific keyword (USD).
Num. of Impressions: The number of times the ad was displayed.
Num. of Clicks: The number of times the ad was clicked.
Hit Rate (%): The conversion rate of the advertisement.
Sales Volume: The total number of sales generated from the ad.
Revenue: The total revenue generated from the advertisement (USD).
ROAS (Return on Advertising Spend): Revenue divided by advertising spend.
Proposed CPM (Proposed Cost Per Mille): The cost per thousand impressions suggested by optimization algorithms.
Actual CPM (Actual Cost Per Mille): The realized cost per thousand impressions.
Selected CPM (Final Cost Per Mille): The chosen cost per thousand impressions after optimization.
Potential Use Cases:
This dataset can be utilized for:

Digital Marketing Optimization: Developing budget management strategies to maximize ROAS.
Machine Learning & Optimization: Applying metaheuristic algorithms to determine the most effective ad spending strategy.
E-Commerce Performance Analysis: Evaluating the effectiveness of advertising campaigns in driving sales.
Financial Planning: Measuring the return on investment for digital marketing expenditures.
Target Users:
This dataset is beneficial for academic researchers, digital marketers, data analysts, and engineers working on optimization problems.