Datacost Fusion: Big Data-Driven Efficiency for Production and Advertising

Published: 12 June 2024| Version 1 | DOI: 10.17632/tyy7jrth6y.1
sangita biswas


This dataset used in this study encompasses a comprehensive collection of records detailing production and advertising. Key Attributes: Clustering data Date: Timestamp of each record, allowing for temporal analysis. Product_ID: Unique identifier for each product. Production_Quantity: Number of units produced on each day. Advertising_Channel: The medium used for advertising (e.g., social media, television, print). Ad_Spend: Amount of money spent on advertising for each channel. Sales: Number of units sold per day. Customer_Demographics: Information on customer segments, including age, gender, and location. Page_Views: Number of page views generated by the advertisement. Clicks: Number of clicks on the advertisements. Data Preprocessing: To prepare the dataset for analysis, several preprocessing steps were undertaken: Data Cleaning: Removal of duplicate entries and handling of missing values through imputation. Normalization: Scaling of numerical features to ensure uniformity. Categorical Encoding: Conversion of categorical variables (e.g., Advertising_Channel) into numerical format using one-hot encoding. Usage in Study: This dataset facilitated the development and validation of our budget allocation algorithms and predictive models. By analyzing historical performance data, we were able to derive insights into optimal budget distribution and forecast future advertising impacts on sales and production efficiency.



Rajalakshmi Engineering College


Supply Chain Management, Data Fusion, Automated Segmentation, Communication Campaign, Advertising Strategy, Cost Management, Sales Forecasting, Productivity, Retail Customer Strategy, Revenue Management, Return on Investment, Budget System