Nykaa E-Commerce Sales & Customer Insights Dashboard
Developed an interactive analytics dashboard to analyze Nykaa’s e-commerce sales, customer behavior, product performance, and discount impact, enabling data-driven decision-making through Power BI visualizations.
Project Overview
Designed an end-to-end analytics and decision-support dashboard for Nykaa using sales, customer, product, pricing, and discount data to drive retail performance insights. Built a structured data cleaning and preprocessing pipeline using Python (Pandas) to handle missing values, correct data types, and resolve data inconsistencies. Performed feature engineering to derive key retail KPIs including total revenue, order count, average order value (AOV), customer purchase frequency, discount contribution, and category-level sales. Modeled cleaned datasets for efficient analysis and developed a multi-page Power BI dashboard for executive and operational stakeholders. Created an executive overview layer presenting sales trends, revenue growth, order volume, customer distribution, and AOV for rapid performance monitoring. Conducted product and category-level analysis to identify top-performing brands, high-revenue categories, and underperforming segments. Analyzed pricing and discount impact on sales volume and revenue to evaluate promotional effectiveness. Evaluated customer behavior patterns including repeat purchases, order value distribution, and customer contribution to revenue. Implemented interactive filters, slicers, and drill-down capabilities for dynamic exploration across time, products, categories, and customer segments. Translated analytical insights into actionable recommendations focused on product prioritization, pricing optimization, promotion planning, and customer retention.
Project Gallery
Key Features & Implementation
- Cleaned and preprocessed Nykaa e-commerce sales and customer data using Python, handling missing values, correcting data types, and resolving inconsistencies.
- Engineered key retail KPIs including total revenue, order count, average order value (AOV), customer purchase frequency, and discount impact metrics.
- Built a multi-page Power BI dashboard featuring executive KPIs, sales trends, customer insights, and product/category performance analysis.
- Analyzed product and category-level performance to identify top revenue contributors and underperforming segments.
- Evaluated customer behavior patterns including repeat purchases, order value distribution, and discount sensitivity.
- Implemented interactive slicers, filters, and drill-down functionality for dynamic exploration of sales data.
- Translated analytical findings into actionable recommendations for pricing optimization, product prioritization, and customer retention strategies.