Sunlight Ski and Bike Product Data Analysis: Optimizing Inventory and Sales

Project Overview

Sunlight Ski and Bike, a beloved local store in Glenwood Springs, faces challenges in inventory management and sales analysis due to inconsistent product category data. This project aims to leverage data-driven insights to improve product categorization, identify top-selling items, understand sales patterns, and inform future business decisions.

Problem Statement

Inaccurate and incomplete product categories hinder reporting, leading to:

• Inefficient inventory management: Stockouts and overstocking result in lost revenue and customer dissatisfaction.
• Limited sales analysis: Difficulty identifying top-selling products, understanding demand patterns, and making informed purchasing decisions.
• Missed opportunities for personalization: Inability to offer personalized recommendations based on customer preferences.

Objectives

1. Clean and standardize product categories: Establish a clear and consistent classification scheme for accurate reporting and analysis.
2. Identify top-selling products and patterns: Uncover popular items, brands, sizes, and pricing dynamics to inform purchasing and promotion strategies.
3. Explore seasonal and promotional influences: Understand how seasons, promotions, and external events impact sales.
4. Lay the groundwork for personalization: Analyze customer purchase history to make tailored product recommendations.

Data Description

• Dataset: Sunlight Ski and Bike's historical product data, including product details, sales figures, and customer information.
• Key features: Product name, category (inconsistent), brand, size, color, price, sale date, quantity sold.

Methodology

Data Wrangling

• Clean and format the data: Address missing values, inconsistencies, and incorrect data types.
• Create a new "ParentCategory" column to establish a hierarchical classification scheme.
• Document all cleaning and transformation steps for reproducibility.

Exploratory Data Analysis (EDA)

• Visualize distributions of products across categories.
• Analyze correlations between sales and product attributes.
• Identify trends and patterns in customer purchase behavior.
• Utilize statistical methods to summarize and understand the data.

Analysis and Modeling (Future Phases)

• Develop predictive models for demand forecasting and product recommendations.
• Segment customers based on purchase history and preferences.
• Optimize pricing strategies based on demand and sales trends.

Ethical Considerations

• Mitigate biases by employing unbiased data sampling and considering diverse perspectives during analysis.
• Ensure transparency in methodology and data manipulation.

Deliverables

• Clean and well-structured product dataset with standardized categories.
• Comprehensive EDA report with insights and visualizations.
• Actionable recommendations for improved inventory management, targeted promotions, and personalized customer experiences.

Project Timeline

• Phase 1: Data Wrangling and EDA (Completed)
• Phase 2: Predictive Modeling and Customer Segmentation (Upcoming)
• Phase 3: Implementation and Evaluation (Upcoming)

This project demonstrates the power of data analysis in optimizing business operations. By cleaning, analyzing, and interpreting Sunlight Ski and Bike's product data, we aim to provide actionable insights that will lead to increased efficiency, stronger sales, and a more personalized customer experience.