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PRODUCT PRICING VS SALES VOLUME ANALYSIS
The Product Pricing vs Sales Volume Analysis is a data-driven analytics system designed to examine the relationship between product prices and sales volume. Pricing is an important factor that can influence customer purchasing behavior, revenue generation, and overall business performance. Organizations often maintain large amounts of sales and pricing information, but manually analyzing the relationship between these variables can be difficult. The proposed system provides a centralized platform for analyzing pricing and sales patterns. The system collects relevant information such as product name, product category, selling price, quantity sold, revenue, discount, sales date, and geographical or customer segments where available. The collected data is cleaned, processed, and organized to calculate useful business metrics. Price ranges can be compared with corresponding sales volumes to understand how sales change at different price levels. The dashboard presents the analysis through interactive charts, graphs, tables, KPI cards, and comparison visualizations. A price-versus-sales-volume chart can help users observe whether higher or lower prices are associated with changes in sales quantity. Additional visualizations can show product-wise revenue, average selling price, sales volume trends, discount impact, and category-level comparisons. The proposed system can support business managers, sales teams, and analysts in evaluating pricing performance. By comparing historical pricing and sales data, users can identify products with strong sales at particular price ranges and products whose sales volume changes significantly after price adjustments. The system provides analytical information rather than automatically determining the ideal selling price. Overall, the Product Pricing vs Sales Volume Analysis provides a centralized and visual approach to understanding pricing and sales relationships. It reduces manual data analysis and helps users identify important patterns in product performance. The system can be further enhanced with price elasticity estimation, demand forecasting, scenario analysis, and machine-learning-based pricing recommendations.
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