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Sales Data Analytics Project Series: Project on Department Store

Jul 12, 2024

Sales Data Analytics Project Series: Project on Department Store

Session 1: Introduction

  • Instructor: Madula, a Data Analyst with 1.5 years of experience
  • Tools: Excel, SQL, PowerBI, Tableau
  • Project Overview:
    • Analytical problem involving the sales of a department store
    • Focus on sales analytics using Tableau

Session 2: Tools & Requirements

  • Tools: Tableau, Excel
  • Requirements:
    • Reliable system with Tableau and Excel installed
    • Basic understanding of the sales domain
    • Familiarity with Tableau's UI

Session 3: Project Overview

  • Domains Covered:
    • Sales Data Analytics
    • Business benefits from sales data analytics
    • Data sources for sales data
  • Project Phases:
    1. Problem Statement
    2. Data Understanding
    3. Data Loading into Tableau
    4. Visualizations
    5. Dashboarding
    6. Analysis and Conclusion
  • Key Concepts:
    • Business Understanding
    • Data Collection (Primary & Secondary Sources)
    • Data Processing & Cleaning
    • Data Analysis & Dashboarding

Session 4: Data Analysis Flowchart

  • Steps:
    1. Business Understanding
    2. Problem Statement Understanding
    3. Data Collection (Primary vs. Secondary Sources)
    4. Data Processing & Cleaning
    5. Data Analysis & Dashboarding
    6. Report Generation
  • Data Collection:
    • Primary Sources: Surveys, Questionnaires
    • Secondary Sources: Repositories
  • Data Cleaning:
    • Remove duplicates
    • Fix structural errors
    • Handle missing data
    • Check for correct data types

Session 5: Sales Data Analytics Benefits

  • Definition: Analyzing sales data to gain insights and make data-driven decisions
  • Data Sources:
    • CRM systems
    • Sales reports
    • Customer feedback
  • Business Benefits:
    • Understanding customer behaviors
    • Evaluating sales strategies
    • Optimizing sales processes
    • Improving revenue

Session 6: Data Sources

  • Sources Overview:
    • CRM: Collects customer interactions
    • Sales Reports: Detailed sales performance
    • Website Analytics: Tracks customer behaviors online
    • Social Media: Campaign data
    • Customer Feedback: Direct insights

Session 7: Problem Statement

  • Project Data: Department store sales data across the USA
  • Owner’s Requirements:
    • Dashboard for tracking sales, profit, and quantity of items sold
    • Analysis of product categories by region
    • Validation that customers buy more than two products per order

Session 8: Data Overview

  • Dataset: 19 columns, 10,000 rows
  • Key Variables:
    • Order ID, Order Date, Ship Date
    • Customer ID, Customer Name
    • Sales Agent ID
    • Country, City, State, Postal Code, Region
    • Product ID, Product Name, Category, Subcategory
    • Sales, Quantity
  • Data Types:
    • Correct data types: date, text, number, geographical

Session 9: Data Loading

  • File Type: CSV
  • Tool: Tableau
  • Process:
    • Verify data types
    • Load data into Tableau
    • Use extract connection for data visualization

Session 10-13: Visualizations

  • Visualization 1: Yearly Sales
    • Line chart showing sales per month with average line
  • Visualization 2: Sales by Category
    • Bar chart showing sales per category
  • Visualization 3: Sales by Quantity
    • Histogram showing sales by quantity
  • Visualization 4: Sales by Region
    • Map showing sales by state

Session 14: Cards Creation

  • Types of Cards:
    • Sales Card
    • Quantity Card
    • Profit Card (calculated field: 30% of sales)
  • Format: Centered text with customized colors and fonts

Session 15-16: Dashboarding

  • Setup:
    • Fixed size: 1500x850 pixels
    • Use of containers for organization
    • Placement of visualizations and cards
  • Styling:
    • Background color: Black
    • Title: Department Store Sales Analytics
    • Uniform fonts and colors
  • Interactions: Making the dashboard dynamic using filters and slicers

Session 17: Conclusion

  • Findings:
    • Sales trends over time
    • Significant regions and categories
    • Customer purchases and behaviors
  • Business Insights:
    • Concentrate on technology products
    • Focus marketing on low-sales regions
    • Potential increase in revenue by leveraging insights

Assignments

  • Practice Tasks:
    • Identify top 5 sales agents
    • Determine top 5 products in terms of sales for each category
    • Analyze top products based on region
    • Identify top 5 customers in terms of sales and items purchased

Final Thoughts

  • Summary: Covered sales data analytics, project execution, and dashboard creation
  • Thank You: Expressed gratitude to viewers and summarized the learning journey

Full transcript