This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

There are many potential insights one can draw from this dataset. The ecommerce data provided contains information about sales orders, including the order ID, order date, shipping date, customer names, location (country, city, state), product categories, product names, sales amount, and profit amount. The dataset covers a range of orders over several years, with information on different product categories and their associated sales. It also provides insights into the distribution of orders across cities and states. After importing the data into Tableau, one can sort to see which states have the most total sales (CA, WA, AZ), which product categories have the highest profit (chairs, phones, machines), and various other intersections of data. The analysis from this data can be used to make decisions about what products to increase or reduce stock of, which states to focus on to push sales, and how to maximize profits by looking at which product categories have the highest profit margins.

If you’re interested, please take a look!

Dataset originally from https://www.kaggle.com/datasets/imgowthamg/walmart-sales-dataset

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

The reference for the dataset and the dashboard was Youtube Channel codebasics. I have used a fictitious company called Atlix where the Sales Director want the sales data to be in a proper format which can help in decision making.

We have a total of 5 tables namely customers, products, markets, date & transactions. The data is exported from Mysql to Tableau.

In tableau , inner joins were used.

In the transactions table, we notice that sum sales amount figures are either negative or zero while the sales qty is either 1 or more. This cannot be right. Therefore, we filter the sales amount table in Tableau by having the least sales amount as minimum 1.

When currency column from transactions table was grouped in MySql, we could see ‘USD’ and ‘INR’ showing up. We cannot have a sales data showing two currencies. This was rectified by converting the USD sales amount into INR by taking the latest exchange rate at Rs.81.

We make the above change in tableau by creating a new calculated field called ‘Normalised Sales Amount’. If [Sales Amount] == ‘USD’ then [Sales Amount] * 81 else [Sales Amount] End.

Conclusion: The dashboard prepared is an interactive dashboard with filters. For eg. By Clicking on Mumbai under “Sales by Markets” we will see the results change in the other charts as well as they Will now show the results pertaining only to Mumbai. This can be done by year , month, customers , products etc. Parameter with filter has also been created for top customers and top products. This produces a slider which can be used to view the top 10 customers and products and slide it accordingly.

Following information can be passed on to the sales team or director.

Total Sales: from Jun’17 to Feb’20 has been INR 12.83 million. There is a drop of 57% in the sales revenue from 2018 to 2019. The year 2020 has not been considered as it only account for 2 months data. Markets: Mumbai which is the top most performing market and accounts for 51% of the total sales market has seen a drop in sales of almost 64% from 2018 to 2019. Top Customers: Path was on 2nd position in terms of sales in the year 2018. It accounted for 19% of the total sales after Electricalslytical which accounted for 21% of the total sales. But in year 2019, both Electricalslytical and Path were the 2nd and 4th highest customers by sales. By targeting the specific markets and customers through new ideas such as promotions, discounts etc we can look to reverse the trend of decreasing sales.

Inspiration

What are the countries where the basic subscription is cheaper? And more expensive? What are the countries where the standard subscription is cheaper? And more expensive? What are the countries where the premium subscription is cheaper? And more expensive? Which manager earned the most? and with which product? Which manager earned the least?

This is a self-guided project.

PROBLEM STATEMENT: What underlying trends could the company be missing out on in our Pizza Sales data that can aid in gap analysis of its business sales.

OBJECTIVES: 1. Generate Key Performance Indicators (KPIs) of the Pizza Sales data for insight gain into underlying business performance. 2. Visualize important aspects of the Pizza Sales data to gain insight and understand key trends\

I dived into the csv dataset to uncover patterns within the Pizza Sales data which spanned across a calendar.

Used Microsoft SQL SMSS to perform EDA (Exploratory Data Analysis); ergo, identifying trends and sales patterns.

Having completed that, I used the Microsoft Power BI to create a visualization as a means to visually represent of my analytical findings to technical and non-technical viewers.

STEPS COMPLETED: Data Importation SQL Data analysis query writing Data Cleaning Data Processing Data Visualization Report/Dashboard Development

This dataset is based on the Superstore Sales data from Kaggle, containing global order records from 2015 to 2018. It includes detailed information such as order dates, sales revenue, profit, shipping modes, product categories, customer segments, and regional distribution.

The data serves as the foundation for a Power BI dashboard designed to extract actionable business insights. It is ideal for exploring trends in sales performance, market opportunities, and operational efficiency.

Dataset: The dataset used for this project is a Coffee Shop Sales Dataset sourced from Kaggle. It contains detailed sales transaction data from a coffee shop, including product categories (e.g., coffee, tea, bakery items), store locations, transaction dates, and revenue generated. This dataset is ideal for analyzing various business metrics, such as product performance, sales patterns, and store efficiency.

The dataset has the following key attributes:

Product Categories: Coffee, tea, bakery, branded items, etc. Store Locations: Hell’s Kitchen, Astoria, Lower Manhattan. Sales Transactions: Includes revenue, product type, and date/time details. Link to dataset: https://www.kaggle.com/datasets/ahmedmohamedibrahim1/coffee-shop-sales-dataset

Methodology Wrap-Up: In this project, the goal was to create an Interactive Sales Dashboard using Excel to derive actionable insights from the coffee shop's sales data. The process began with data collection and preparation, ensuring the dataset was ready for analysis. Various data analysis techniques were applied using Excel Pivot Tables, and multiple charts were created to visualize the data clearly and effectively.

The visualizations, including bar charts, pie charts, line charts, and treemaps, were enhanced by interactive slicers, enabling users to explore specific data segments, such as product categories, store locations, and time patterns. The analysis focused on identifying the best-performing products and stores, revenue trends, and sales distribution, providing business insights that could help the coffee shop optimize its operations.

This dashboard demonstrates the power of data visualization and business intelligence in understanding customer behavior and improving decision-making processes within a retail context.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19501062%2Fecf38457fd8d1cc58196670eb79cfb1e%2FScreenshot%202024-06-10%20232658.png?generation=1718042573089666&alt=media" alt=""> This dataset provides a detailed analysis of sales and profit data for corporate customers across various regions and provinces. It has been meticulously cleaned, formatted, and enriched with visualizations to uncover valuable insights for business decision-making.

Key Features: Customer Segmentation: The data is specifically focused on corporate customers, allowing for targeted analysis of this segment's purchasing behavior.

Regional Breakdown: Sales and profits are analyzed across different regions and provinces, enabling the identification of regional trends and opportunities.

Profitability Analysis: The dataset highlights the most profitable and least profitable product sub-categories within each region, aiding in strategic inventory and pricing decisions.

Conditional Formatting: Visual cues highlight top-performing orders, profit margins, and regional demarcations, making the data easier to interpret.

Pivot Tables: Included pivot tables summarize key findings, such as the top 3 profitable sub-categories per region.

Potential Use Cases: Sales Strategy: Identify high-performing regions and product categories to focus marketing and sales efforts.

Inventory Management: Optimize inventory levels by understanding which products are most profitable in each region.

Pricing Optimization: Adjust pricing strategies based on regional profit margins and product performance.

Customer Insights: Gain a deeper understanding of corporate customer preferences and buying patterns.

Business Reporting: Utilize the formatted data and visualizations to create compelling reports for stakeholders.

Project Introduction and Goals

This project is focused on analyzing a sales dataset using Google Sheets for data cleaning and Tableau for visualizations. The main objective is to uncover actionable insights such as top performing countries, best selling products, and monthly sales trends. I aim to present these findings through an interactive dashboard that can be used by business stakeholders for decision making.

Process Overview

1. Data Cleaning (Google Sheets) • Removed blank rows and filtered out missing values. • Standardized product and region names for consistency. • Split combined columns (e.g., date & time) for easier analysis. • Replaced missing or incorrect values with relevant estimates (e.g., average or “unknown”).

2. Exploratory Analysis • Calculated total sales by country. • Identified the best-selling products and frequent buyers. • Tracked monthly sales trends.

3. Visualization (Tableau)

   • Created a dynamic sales dashboard including: • Line chart showing sales over time • Pie chart of product categories • Bar chart of top 10 customers by revenue • Country-wise sales comparison

4. Conclusion

The analysis reveals key patterns in sales distribution, highlights top contributors to revenue, and suggests areas needing attention (e.g., low-performing countries). The dashboard enables real-time filtering and deeper insight for users.

https://public.tableau.com/views/Salesperformanceanalysis_17478415969510/Salesperformanceanalysis?:language=en-GB&:sid=&:display_count=n&:origin=viz_share_link

****Attribute information:****

Row ID: A unique identifier for each row in the table Order ID: The identifier for each sales order Order Date: The date the order was placed Ship Date: The date the order was shipped Delivery Duration: The amount of time it took to deliver the order Ship Mode: The shipping method used for the order Customer ID: The identifier for the customer who placed the order Customer Name: The name of the customer who placed the order Country: The customer's country City: The customer's city State: The customer's state Postal Code: The customer's postal code Region: The customer's region Product ID: The identifier for the product that was ordered Category: The category of the product that was ordered (e.g., furniture, office supplies, technology) Sub-Category - This attribute likely refers to a subcategory within a larger product category (e.g., Tables within Furniture). (Bookcases - Chairs - Labels - Tables - Storage - Furnishings - Art - Phones - Binders - Appliances - Paper - Others). Product Name - This attribute specifies the name of the product sold. (Bush Somerset Collection Bookcase - Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back - Self-Adhesive Address Labels for Typewriters by Universal - Bretford CP4500 Series Slim Rectangular Table - Others).

Sales - This attribute shows the total sales amount for each product. Values are listed in currency format Quantity - This attribute specifies the number of units sold for each product. Integer values. Discount - This attribute indicates the discount offered on the product. Discount Value - This attribute shows the total discount amount applied to the product. Profit - This attribute shows the profit earned on the sale of each product. COGS - This attribute likely refers to each product's Cost of Goods Sold. COGS = Sales - Profit

This dataset provides detailed sales data from FlipMart, including transaction details, product categories, regional sales performance, and time-based trends.

Key Features :

Columns : Product names, categories, sales regions, sales amounts, and timestamps. Applications : Useful for sales forecasting, e-commerce analytics, and customer behavior modeling. Data Source : Inspired by retail analytics for educational and project-based use.

Perfect for learners, researchers, and professionals exploring trends in retail sales and developing predictive models for e-commerce.

Context

This data is random generated in Excel to practice forecasting and visualizations.

Content

The two branches utilize data of thousands of generated product data with nearly 200 different employees. Product ID numbers are randomly generated for each file

Acknowledgements

This project was for my practice

Inspiration

This is a dashboard creation which represents a region-wise sales of limited products:

• Data representation region-wise.
• To enlist profitable sales of products.
• It shows data visualization based on revenue and sales.

End results:

This dashboard visualized sales of categorical products on the basis of region. This may help in understanding the profitable product with regionwise sales.

This Power BI dashboard provides an in-depth analysis of Nike's US sales performance for 2020-21. It includes key insights on revenue trends, top-selling products, regional performance, customer segmentation, and seasonal variations. The interactive visualizations help identify growth opportunities and areas for improvement. Ideal for business analysts, marketers, and data enthusiasts looking to explore Nike’s sales data effectively.

These datasets provide a comprehensive and detailed view of the sales and financial performance of the grocery store, including information about sales by city, region, and customer, as well as overall sales, profits, and trends over time. This information can be used to make data-driven decisions about inventory management, crew staffing, and marketing strategy, in order to improve sales and profits. Credit to the original owner of this dataset

A data analyst collects and stores data on sales numbers, market research, logistics, linguistics, or other behaviors. They bring technical expertise to ensure the quality and accuracy of that data, then process, design, and present it in ways to help people, businesses, and organizations make better decisions. To contribute to the success of business by utilizing data analysis techniques, like sales forecasting.

Download data CSV files: https://drive.google.com/drive/folders/1HDkNHNslI3rgCv9LZzGtxag8JvYzss-b

This interactive Tableau dashboard provides a detailed analysis of car sales trends from 2022 to 2023. It explores key metrics such as total sales, average car prices, and sales distribution by car type, color, and region.

Key Features: 📊 Sales Overview: Total sales, quantity, and price analysis. 📈 Monthly Trends: A time-series visualization of sales growth. 🎨 Car Color Preferences: Pie chart showing distribution by color. 🌍 Regional Sales Breakdown: Geospatial analysis of sales across the U.S. 🏆 Model-wise Performance: Sales comparison across different car brands. ⚙️ Engine & Transmission Impact: Filtering options to analyze impact by car type. This dashboard is ideal for automotive industry analysts, data enthusiasts, and business decision-makers interested in sales performance insights.

📌 Tools Used: Tableau, Data Cleaning & Preparation.

About Dataset

This dataset contains 3,547 coffee sales transactions recorded across different times of the day, days of the week, and months of the year. It captures not only what type of coffee was purchased but also when and how much was spent. The dataset can be used to analyze customer buying behavior, peak sales periods, and product preferences.

Content

Hour_of_day Hour of purchase (6 AM – 10 PM).

Cash_type Payment method (all transactions via card).

Money Transaction amount (18.12 – 38.7 currency units).

Coffee_name Type of coffee purchased (Latte, Americano, Cappuccino, etc.).

Time_of_Day Morning, Afternoon, or Evening.

Weekday / Weekdaysort → Day of the week (Mon–Sun).

Month_name / Monthsort → Month of the year (Jan–Dec).

Date Transaction date.

Time Exact purchase timestamp.

Covers 12 months of sales data (2024).

Most popular coffee: Americano with Milk.

Highest sales period: Afternoons and Tuesdays.

Spending per transaction averages around 31.6 units.

Diwali sales analysis involves examining sales data during the Diwali festival period to identify trends, assess product performance, analyze regional variations, evaluate marketing effectiveness, monitor competitors, gain customer insights, and forecast future sales. This analysis helps businesses optimize strategies, drive sales growth, and maximize revenue during one of the most significant festivals in India.