Bernard Joseph Oyakhilome

Sales & Inventory Performance Analysis

This project is a real-world Business Intelligence (BI) analysis focused on retail sales and inventory performance across different locations and customer demographics. Built using Power BI, SQL Server, and Excel Power Query, the dashboard reveals insights that drive actionable business decisions.

📑 Table of Contents

1. Background and Overview
2. Data Structure Overview
3. Executive Summary
4. Insight Deep Dive: 2023 Performance Drop
5. Recommendation & Professional Storytelling

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Project Structure

• Dataset: Download Dataset
• Power BI File: Download PBIX File
• Portfolio Overview: Download Full Doc Report

• Assets:

  

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1. Background and Overview

In this project, I developed a Sales Performance Dashboard to provide real-time insights into revenue growth, profit margins, and demographic-driven sales behavior across multiple locations.

Key Objectives:

• Track sales trends over multiple years
• Evaluate profit efficiency against cost
• Identify age group segments driving revenue
• Compare sales & profitability by city/location
• Support executive decisions on growth strategy and market targeting

Key Insights:

• Strong YoY Revenue Growth of 32.6%, reaching £4.25M
• High profitability with 74% profit margin, totaling £3.15M
• Middle-aged adults account for 68.02% of sales
• Equal revenue and profit distribution across Paris, London, and Accra
• 2023 shows sharp decline in sales

Strategic Recommendations:

• Focus on middle-aged adult campaigns
• Investigate 2023 performance drop
• Expand in consistently performing cities
• Align sales with seasonal demand

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2. Data Structure Overview

Dataset Composition:

• Fields: TransactionDate, Product, AgeGroup, Location
• Metrics: Revenue, Profit, TotalCost
• Time dimensions: Weekday, Month, Year, Quarter

ETL Workflow:

• Excel Power Query: Cleaned nulls, standardized dates, added age groups
  
• SQL Server:

-- Calculate Sales and Cost
SELECT
    SbS.*,
    CAST(SbS.quantity_sold AS FLOAT) * CAST(SbS.unit_price AS FLOAT) AS Sales,
    CAST(SbS.quantity_sold AS FLOAT) * CAST(PR.current_cost AS FLOAT) AS Cost
FROM JendolSuperStore.dbo.[Sales by Store] AS SbS
INNER JOIN JendolSuperStore.dbo.Product AS PR
    ON SbS.product_id = PR.product_id;

  
 -- Calculate Age and Age Group
SELECT
    *,
    DATEDIFF(YEAR, birthdate, GETDATE()) AS Age,
    CASE
        WHEN DATEDIFF(YEAR, birthdate, GETDATE()) <= 39 THEN 'Young Adult'
        WHEN DATEDIFF(YEAR, birthdate, GETDATE()) <= 59 THEN 'Middle-Age Adult'
        ELSE 'Old Adult'
    END AS Age_Group
FROM JendolSuperStore.dbo.Customer;

• Power BI: Star schema model, built with DAX for YoY %, Profit Margin, Total Revenue, Sales Growth
  
  

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3. Executive Summary

This dashboard offers a clear, high-level overview of business performance, helping decision-makers quickly grasp key insights:

• Overall financial performance including revenue, cost, and profit
• Year-over-year growth trends from 2020 to 2023
• Customer purchasing patterns across different age groups
• Performance consistency across three international locations

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4. Insight Deep Dive: 2023 Performance Drop

Upon examining the Sales Growth Trend Over Time, the line chart clearly shows:

• A steady incline from 2020 (£1.05M) through 2022 (£1.04M)
• A drastic fall to £0.0M in 2023, despite consistent cost baselines

Possible Causes:

• Incomplete or missing data
• Operational interruptions or market changes
• Faulty source file import

Next Steps:

• Validate 2023 data integrity
• Check SQL extract logs and Excel imports

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5. Recommendation

This dashboard presents a compelling narrative:

• Consistent revenue growth (except 2023)
• Dominant demographic: Middle Age Adults
• Balanced city-wide profitability

Next Strategic Steps:

• Re-audit 2023 data entries
• Strengthen outreach to core demographics
• Improve location-specific planning
• Automate anomaly detection in dashboards

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Author

Bernard Joseph
Data Analyst
🔗 LinkedIn | 🌐 Portfolio | 📧 Email