Retail Sales Analysis (2024)

6 min readMay 16, 2024

Quick Links:

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1. Introduction

With over five years of immersion in the retail industry, I have developed a deep appreciation for the complexities and dynamics that drive retail sales. This experience has profoundly shaped my understanding of market trends, consumer behavior, and operational efficiencies. In this portfolio project, I delve into a detailed dataset from the Fast-Moving Consumer Goods (FMCG) retail sector. I aim to peel back the layers of retail operations, offering a meticulous analysis of sales trends, store performance evaluations, and insights into consumer purchasing patterns. By exploring this rich dataset, I seek to illuminate the nuances of the retail world, providing a comprehensive perspective that combines industry experience with data-driven analysis.

2. Overview of Data

This project taps into a comprehensive dataset from the FMCG retail industry, capturing data from over 10,000 customers across both e-commerce and physical store channels. It includes detailed sales records — what was bought, where, and buyer demographics — providing a rich foundation for analyzing trends, pinpointing top-performing outlets, and uncovering deep insights into consumer behavior. This dataset not only enhances our understanding of current market dynamics but also assists in forecasting emerging retail trends.

Product Order Table:

Store Information Table:

City Sales Data Table:

dataset: retail_sales_data.xlsx

3. Sales Analysis Project

User Persona: Emily Robertson, HR Manager

Background: Emily Robertson is an HR Manager at a mid-sized retail company, with a strong background in Human Resources Management. She is 35 years old and holds a bachelor’s degree in her field.

Professional Skills: Emily excels in managing employee relations and implementing HR policies effectively. She is proactive and highly detail-oriented.

Experience with Sales Data: While Emily is skilled in HR, she has limited experience in interpreting complex sales data. She seeks to better understand how sales figures impact workforce management and employee performance.

Goals:

Understand the impact of sales trends on staffing requirements.
Identify training opportunities that correlate with sales outcomes.
Enhance employee retention strategies by linking performance with sales metrics.

Challenges:

Emily finds it challenging to interpret detailed sales reports and analytics.
She strives to align HR strategies with fluctuating sales trends effectively.

Needs:

Emily needs tools that translate sales data into actionable HR insights.
She requires training to enhance her ability to analyze sales data relevant to HR functions.

Professional Environment: Emily operates in a dynamic corporate setting, where she interacts with various department heads and is often involved in strategic meetings focused on sales and market trends.

Aspirations: Emily aims to integrate HR more closely with other departments, particularly sales, to enhance strategic planning and employee satisfaction across the company.

Empathy Map

To align our analysis with the HR Manager’s perspective, we consider their goals, challenges, and the insights they seek from the sales data.

Objective

The objective of this project is to enhance sales performance and employee satisfaction by developing a data-driven strategy that aligns workforce attributes with sales outcomes. By analyzing sales data and correlating it with workforce performance, we aim to identify areas for targeted training and development. Additionally, the project seeks to integrate various data sources to provide a holistic understanding of how workforce management impacts sales efficiency. This approach will enable the creation of actionable insights to improve overall organizational effectiveness and drive business success.

Key Performance Indicators(KPIs)

Key Performance Indicators for this project include:

Key Questions to Answer:

Q1. What is the count of managers at the region, state and city level?
Q2. How are Regional sales managers performing in sales and profit?
Q3. Who are top performing state and city sales managers under Whitney Martinez?
Q4. What are the sales under Whitney Martinez by store type?
Q5. Who are the top performing sales reps in sales and profit?

4. Process

To ensure the data is clean, accurate, and ready for analysis, I utilized a combination of Python, SQL, and Excel. Each tool played a vital role in different stages of the data preparation process:

Python: Leveraged for data cleaning and preprocessing tasks. Using libraries such as Pandas and NumPy, I handled missing values, standardized formats, and performed initial exploratory data analysis (EDA) to understand the data distribution and detect any anomalies.
SQL: Employed to query the database and extract relevant subsets of data. SQL scripts were used to join multiple tables, filter records based on specific conditions, and aggregate data to create summary tables. These operations ensured that only pertinent data was pulled for analysis, optimizing performance and accuracy.
Excel: Used for preliminary data exploration and visualization. Excel’s pivot tables and charts provided a quick way to spot trends and patterns, guiding the subsequent in-depth analysis. Additionally, Excel was instrumental in manually inspecting and correcting data issues that automated processes might miss.

Visualization in Tableau

With the cleaned and prepared dataset, I moved on to creating interactive visualizations in Tableau:

Dashboard Creation: Designed comprehensive dashboards to visualize key metrics and trends. These dashboards included bar charts, line graphs, and scatter plots to represent sales data across different dimensions, such as time, product categories, and geographical regions.
Interactivity: Added filters and parameters to make the dashboards interactive, allowing users to drill down into specific areas of interest. This functionality enabled stakeholders to explore data dynamically and gain insights tailored to their needs.
Storytelling: Structured the visualizations in a narrative format to tell a compelling story. By linking related charts and adding annotations, I highlighted significant findings and provided context to the data, making it easier for non-technical users to understand the insights.

This meticulous approach to data preparation and visualization not only showcased my technical skills but also ensured that the analysis was thorough, accurate, and actionable.

5. Explore full Retail Sales Analysis Data Story Here👇

View my dashboard in Tableau Public.

6. What I Learned

This project significantly enhanced my skills in several key areas:

Data Visualization: I deepened my expertise in using Tableau to create interactive and insightful visualizations. By transforming complex datasets into accessible and engaging dashboards, I learned how to effectively communicate data-driven insights.
Data Preparation: Leveraging Python and SQL for data cleaning and manipulation improved my proficiency in preparing large datasets for analysis. I mastered techniques to handle missing values, normalize data formats, and perform intricate data queries.
Business Intelligence Alignment: The project reinforced the importance of aligning data analysis with business objectives. Understanding how to translate sales data into actionable HR insights highlighted the critical role of data in strategic decision-making.
Cross-functional Insights: By integrating various data sources, I learned how to provide a holistic view of the business, enabling comprehensive analysis across different dimensions such as sales, workforce performance, and customer behavior.

7. Conclusion

This sales analysis project provided deep insights into the FMCG retail industry and demonstrated the potential of data-driven decision-making in enhancing business outcomes. The detailed analysis of sales trends, store performance, and customer purchasing patterns offered valuable perspectives for optimizing retail operations.

Moving forward, the insights gained from this project will inform more targeted strategies for sales optimization and workforce management. By continuing to leverage data analytics, I aim to contribute to more effective decision-making processes that drive business success and employee satisfaction. This project underscored the transformative power of data and analytics in the retail sector, highlighting the endless possibilities for innovation and improvement.