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

In this project, I conducted a comprehensive analysis of retail and warehouse sales data to derive actionable insights. The primary objective was to understand sales trends, evaluate performance across channels, and identify key contributors to overall business success.

To achieve this, I transformed raw data into interactive Excel dashboards that highlight sales performance and channel contributions, providing a clear and concise representation of business metrics.

Key Highlights of the Project:

Created two dashboards: Sales Dashboard and Contribution Dashboard. Answered critical business questions, such as monthly trends, channel performance, and top contributors. Presented actionable insights with professional visuals, making it easy for stakeholders to make data-driven decisions.

https://www.kaggle.com/code/mithilesh9/amazon-sales-data-analysis-using-python

Dataset Description This dataset contains a 100 rows of sales data for Amazon, including the region, country, item type, sales channel, order priority, order date, order ID, ship date, units sold, unit price, unit cost, total revenue, total cost, and total profit.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19501062%2F5d10a624d07eefb2240c474ca00114b6%2FScreenshot%202024-06-25%20135139.png?generation=1719303822906805&alt=media" alt="">

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

Project Objective: analyzing sales data to identify sales trends, peak periods, customer preferences, and customers segment based on purchasing behavior. The goal is to derive actionable insights for better targeting and strategy formulation.

The project includes 5 files: 1. E-commerce Data Analysis Project.csv: The database is composed of (8 rows X 18,590 columns). 2. Final_E_Code.py: The python script used for data cleaning, EDA, and data analysis and visualization. 3. Presentation.pdf: The deck of slides which uses the analysis and visualization produced by the python script to derive insights and recommendations. 4. LICENSE: The dataset is licensed under the GNU General Public License v3.0 (GPL-3.0). 5. README.md: It includes the project objective and attribution.

🔍 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

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

About Datasets:

Domain : Sales Project: McDonalds Sales Analysis Project Dataset: START-Dashboard Dataset Type: Excel Data Dataset Size: 100 records

KPI's: 1. Customer Satisfaction 2. Sales by Country 2022 3. 2021-2022 Sales Trend 4. Sales 5. Profit 6. Customers

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains dashboard, hyperlink, shapes, icons, map, radar chart, line chart, doughnut chart, KPIs, formatting.

Description: Explore a comprehensive dataset of e-commerce sales, encompassing a variety of product categories, pricing, customer reviews, and sales trends over the past year. This dataset is ideal for analyzing market trends, customer behavior, and sales performance. Explore into the data to uncover insights that can optimize product listings, pricing strategies, and marketing campaigns.

Columns:

product_id: Unique identifier for each product. product_name: Name of the product. category: Product category. price: Price of the product. review_score: Average customer review score (1 to 5). review_count: Total number of reviews. sales_month_1 to sales_month_12: Monthly sales data for each product over the past year. Potential Analyses:

Identify top-performing product categories. Analyze the impact of pricing on sales and customer reviews. Discover seasonal sales trends and patterns. Evaluate customer satisfaction based on review scores and counts.

Problem Statement: Sales management has gained importance to meet increasing competition and the need for improved methods of distribution to reduce cost and to increase profits. Sales management today is the most important function in a commercial and business enterprise. We need to extract all the Amazon sales datasets, transform them using data cleaning and data preprocessing and then finally loading it for analysis. We need to visualize sales trend month-wise, year-wise and yearly-month wise. Moreover, we need to find key metrics and factors and show meaningful relationships between attributes.

Approach The main goal of the project is to find key metrics and factors and then show meaningful relationships between them based on different features available in the dataset.

Data Collection : Imported data from various datasets available in the project using Pandas library.

Data Cleaning : Removed missing values and created new features as per insights.

Data Preprocessing : Modified the structure of data in order to make it more understandable and suitable and convenient for statistical analysis.

Data Analysis : I started analyzing dataset using Pandas,Numpy,Matplotlib and Seaborn.

Data Visualization : Plotted graphs to get insights about dependent and independent variables. Also used Tableau and PowerBI for data visulization.

The link for the Excel project to download can be found on GitHub here. It includes the raw data, Pivot Tables, and an interactive dashboard with Pivot Charts and Slicers. The project also includes business questions and the formulas I used to answer. The image below is included for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2F61e460b5f6a1fa73cfaaa33aa8107bd5%2FBusinessQuestions.png?generation=1686190703261971&alt=media" alt=""> The link for the Tableau adjusted dashboard can be found here.

A screenshot of the interactive Excel dashboard is also included below for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fe581f1fce8afc732f7823904da9e4cce%2FScooter%20Dashboard%20Image.png?generation=1686190815608343&alt=media" alt="">

Dataset

This dataset was created by Sonal Anand

Released under Apache 2.0

Contents

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

Explore the dynamic world of Adidas US Sales with this comprehensive dataset. The dataset encapsulates detailed information on sales transactions, retailer details, product categories, and more. Each entry includes critical metrics such as total sales, operating profit, units sold, and various operational aspects.

Key Points: - Rich sales data spanning from 2020 to 2021. - Granular details on product types, retailers, and sales methods. - Insights into regional performance, pricing strategies, and operating margins. - Ideal for exploratory data analysis, predictive modeling, and business strategy formulation.

Dataset Columns Which I am Using For Analysis: - Retailer - Retailer ID - Invoice Date - Region - State - City - Product - Price per Unit - Units Sold - Total Sales - Operating Profit - Operating Margin - Sales Method - Year This dataset to derive actionable insights, refine business strategies, and elevate your data analysis skills. Dive into the world of Adidas US Sales and uncover the stories hidden in the numbers.

12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by order Number and it's date, product type and it's quantity, cost and purchase address

Column descriptors : Each dataset has:

Order Id: The id of the order.

Product: The type of the product bought.

Quantity Ordered: How many of the product was ordered.

Price Each: The price of a single item.

Order Date: When the product was ordered including the year,month, day, hours and minutes

Purchase Adrress: Where to deliver the order.

Questions: Question 1: What was the best month for sales? How much was earned that month?

Question 2 : Which city had the highest number of sales?

Question 3: What time should we display advertisements to maximize the likelihood of purchasses and Sales?

Question 4: What products are most often sold together?

Question 5: Which product was sold the most?

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

Tool: Microsoft Excel

Dataset: Coffee Sales

Process: 1. Data Cleaning: • Remove duplicates and blanks. • Standardize date and currency formats.

1. Data Manipulation: • Sorting and filtering function to work
   with interest subsets of data. • Use XLOOKUP, INDEX-MATCH and IF
   formula for efficient data manipulation, such as retrieving, matching and organising information in spreadsheets

2. Data Analysis: • Create Pivot Tables and Pivot Charts with the formatting to visualize trends.

3. Dashboard Development: • Insert Slicers with the formatting for easy filtering and dynamic updates.

Highlights: This project aims to understand coffee sales trends by country, roast type, and year, which could help identify marketing opportunities and customer segments.

This dataset contains video game sales data prepared for an Excel data analysis and dashboard project.

It includes detailed information on:

Game titles

Platforms

Genres

Publishers

Regional and global sales

The dataset was cleaned, structured, and analyzed in Microsoft Excel to explore patterns in the global video game market. It can be used to:

Practice data cleaning and pivot tables

Build interactive dashboards

Perform sales comparisons across regions and genres

Develop business insights from entertainment data

🧩 File Information

Format: .xlsx (Excel Workbook)

Columns: Name, Platform, Year, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales

💡 Use Cases

Excel dashboard and chart creation

Data visualization and storytelling

Business and market analysis practice

Portfolio or learning projects

👤 Prepared by

Adewale Lateef W — for data analysis and Excel dashboard learning purposes.

Blinkit Sales Dataset: Analyzing Online Grocery Trends

This dataset contains sales transaction data from Blinkit, an online grocery delivery platform. It provides valuable insights into customer purchasing behavior, product demand, revenue trends, and sales performance over time.

Dataset Overview:

• Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps.
• Useful for demand forecasting, price optimization, trend analysis, and business insights.
• Helps in understanding customer behavior and seasonal variations in online grocery shopping.

Potential Use Cases:

• Time Series Analysis: Analyze sales trends over different periods.
• Demand Forecasting: Predict future product demand based on historical data.
• Price Optimization: Identify the impact of pricing on sales and revenue.
• Customer Behavior Analysis: Understand buying patterns and preferences.
• Market Trends: Explore how different factors affect grocery sales performance.

This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects.

This dataset is part of a Sales Analytics Power BI Project based on over 10,000 rows of transactional data. It was designed to help users practice and demonstrate skills relevant to Microsoft’s PL-300 Power BI Data Analyst Associate certification.

The dataset includes data related to:

📅 Sales Transactions (with dates and revenue)

🧾 Orders (order count, customer ID)

💰 Profit & Cost

📦 Products and Categories

🧍 Customers

All visuals and metrics were built in Power BI Desktop, including KPIs, time intelligence, and dynamic filters

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23140460%2F98f5cef786f667ff8aebf7f8cce7867d%2FScreenshot%202025-04-15%20193755.png?generation=1744735089929006&alt=media" alt="">