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Retail Analytics with R: Analyze Product Sales, Customer Trends, and Inventory Performance
Paperback
Series: Real-World Data Science with R, Book 26
General ComputersGeneral SociologyProbability & Statistics
Publisher: Independently Published
Published: Mar 16 2026
Pages: 110
Weight: 0.35
Height: 0.23 Width: 6.00 Depth: 9.00
Language: English
Retail Analytics with R: Analyze Product Sales, Customer Trends, and Inventory Performance
Retail businesses generate massive amounts of data every day from product sales and customer purchases to inventory movement and demand trends. However, many retailers struggle to transform this raw data into meaningful insights that improve decision-making and drive growth. Without the right analytical approach, businesses may miss opportunities to increase sales, understand customer behavior, and manage inventory efficiently.
Retail Analytics with R is a practical guide that shows how to analyze retail data using the powerful R programming language. This book introduces readers to the essential tools and techniques needed to examine product sales performance, understand customer shopping patterns, forecast demand, and optimize inventory management using data-driven methods.
The book begins with the foundations of retail analytics, explaining how retail data is structured and how key performance indicators are used to measure business performance. Readers will learn how to prepare and clean retail datasets, ensuring that data is organized and ready for analysis. The guide then moves into exploratory data analysis, helping readers uncover patterns in product sales and identify top-performing or underperforming products.
Customer analytics is another important focus of the book. Readers will learn how to analyze purchasing behavior, segment customers based on spending habits, and identify high-value customers who contribute most to revenue. The book also introduces demand forecasting techniques that allow retailers to predict future sales trends and prepare for seasonal changes in customer demand.
In addition, the book covers inventory analytics and stock optimization strategies. Readers will learn how to monitor stock levels, detect overstock and stockout risks, and use data insights to maintain balanced inventory systems that reduce costs and improve product availability.
Throughout the book, practical examples, charts, and analytical tables are used to explain concepts clearly and demonstrate how retail data can be transformed into visual insights. These examples make complex analytical techniques easier to understand and apply in real retail environments.
This book is ideal for data analysts, business analysts, retail professionals, students, and anyone interested in applying data science techniques to retail operations. By the end of the book, readers will understand how to build a complete retail analytics workflow from data preparation and visualization to forecasting and business reporting.
Retail Analytics with R provides the practical knowledge needed to turn retail data into actionable insights that support smarter decisions, improved customer understanding, and stronger business performance.
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