This project focuses on analyzing sales data for the month of November and December 2024 to uncover insights, trends, and patterns that can drive better business decisions. The analysis leverages the power of Excel and Power BI for actionable insights through dashboards, reports, visualization and storytelling. The goal is to empower stakeholders with data-driven decision-making that leads to increased revenue and market.
🏆 Identify top-selling products and profitable segments: Determine which products are driving the most revenue and profit.
🔎 Analyze sales trends: (Uncover patterns in sales over time monthly and seasonal to predict future performance of products.
📈 Evaluate regional performance:* Compare sales performance across different geographic regions to identify area of strength and weekness.
📌 Understand customer behaviour: Analyze customer demographics and purchasing patterns to optimize marketing and sales effort.
⚡ Develop actionable recommedations: Provide clear and concise recommendations based on data analysis, enabling informed decision-making.
🖥️ Build interactive dashboards for decision-making.
|Purpose | Tools/Tech Used
|--------------------------------------------------------|----------------------------------|
| Data Cleaning, Excel formulas, Pivot tables and Chart | Excel (Data Handling) |
| capabilities | |
| Dashboard & Visualization, Provide | Power BI (Interactive Dashboard) |
| dynamic and insightful view, Power BI's DAX for data | |
| modeling. | |
- Source: Kaggle.com
- Rows x Columns: 1,000 rows x 10 columns
- Key Columns:Total Revenue, Order, Avg Order Value, Total Quantity.
- Data Cleaning & Preparation (Excel):
- Removing duplicates, handling missing values, and correcting inconsistencies like triming (manager colum)
- Transforming data into a suitable format for analysis (date conversions, data type adjustments).
- Creating calculated columns (weekday, month, weekday).
- Data Modeling (Power BI):
- Creating calculated measures using DAX (total revenue, order, average order value,total quantity).
- Data Analysis & Visualization (Power BI):
- Creating interactive dashboards to visualize key performance indicators (KPIs) and trends.
- Developing visualizations to compare sales performance across different dimensions such as (product category, region, customers behaviour).
- Conducting in-depth analysis to identify key drivers of sales performance.
- Insights & Recommendations:
- Documenting key findings and insights derived from the analysis.
- Formulating actionable recommendations to improve sales performance based on the data-driven insights.
- Top Performing Products: Burgers, fries and chicken sandwiches are the top selling products with a contribution to over 80% of overall revenue.
- Sales Trends: The month of November recorded to be the highest revenue generating month as it withness it's peak period of over 277k in revenue.
- Regional Performance: Regions like Lisbon, London, Madrid are the best performing regions and regions like Berlin and Paris are underperforming regions with reason tied to customer demographics and purchasing patterns as reasons for the differences.
- Profitability Analysis: Products like Burgers 48.99%, fries 16.33% and chicken sandwiches 14.9% are top revenue generating products while regions like Lisbon 31.41%, London 27.45%, and Madrid 17.7% are the top regions with high sales where these revenue were generated from.
The Power BI dashboard provides an interactive view of the sales data, allowing users to:
- Track Key Performance Indicators (KPIs): View real-time metrics such as total revenue, order, average order value,total quantity.
- Drill Down into Data: Explore sales performance at different levels of granularity ( by product, region, customer,).
- Filter and Segment Data: Analyze sales data based on specific criteria (date range, region, product, customer segment).
- Identify Trends and Patterns: Visually identify patterns and trends in the data through interactive charts and graphs.
Based on the analysis, the following recommendations are proposed:
- Focus on Top-selling Products: Top selling products should be made available at all times to meet customers demand, as more demands equals more sales also discount should be given to reoccurring customers.
- Address Underperforming Regions: Region with low performance can engage more on social media for more publicity for potential customers. Also managers should go for coaching opportunities which enables them to leverage their skills to grow the business.
- Target Specific Customer Segments: Customer behavioral should be considered (frequent buyers, high-value customers & dormant customers) must be put into checks and also sales campaigns such as (loyalty programs, exclusive early access to products, personalized offers, VIP discounts, premium bundles, Win-back campaigns (special discounts, reminder emails).
- Optimize Pricing Strategy: Product should be made pricey and affordable to customers at all times based on profitability analysis made.
- Improve Customer Retention: Recommend strategies such as (enhance customer service, personalization, loyalty programs, regular communication, feedback and consistent quality to improve customer retention and increase customer lifetime value].
Pull requests are welcome! If you’d like to contribute: Fork the repo 🍴 Create a new branch 🌿 Commit your changes 💾 Submit a PR 🚀
This project is released under the [MIT License] (see LICENSE file). Data usage should respect the original dataset’s license.
👤 Author: Opara Franklin 🌐 GitHub: https://github.com/oparafranklin0070-stack 📩 Email: oparafranklin0070@gmail.com
