Retail transaction sales analysis to uncover revenue trends, top-performing products, and regional/customer patterns to inform business decisions.
- Dashboard / Data Visualization
- Data Cleaning / Wrangling
- End-to-End
- Project Overview
- Objectives
- Project Scope & Tools
- Data Workflow
- Data Model & Schema
- Analysis & Metrics
- Key Insights
- Recommendations
- Assumptions & Limitations
- Future Enhancements
- Deliverables
- Author
Context: A retail company needed to understand sales performance across products, customers, and regions for 2023–2024.
Problem Statement: Sales trends were unclear, and the company wanted to identify top-performing products, underperforming products, and revenue drivers by month, category, region, and customer segment.
Approach: Cleaned multi-sheet data using Power Query, created a date table, and established relationships in Power Pivot. Built pivot tables for multiple dimensions and designed a dashboard with interactive slicers.
Outcome: The final dashboard revealed strong seasonal patterns, revenue concentration among top-performing products, and disproportionate contribution from specific customer segments — insights that were not visible in the raw data alone.
- Primary Objective: Build a sales dashboard to highlight top-performing products, underperforming products, and revenue trends by month, region, and customer segment.
- Secondary Objective 1: Quantify revenue contributions by gender, age group, and customer to inform marketing and inventory strategies.
- Secondary Objective 2: Compare 2023 vs 2024 revenue to determine year-over-year growth patterns.
| Dimension | Details |
|---|---|
| In Scope | Sales, Products, and Customers tables; analysis of revenue, top/bottom products, monthly trends, region, customer, and demographic contributions |
| Out of Scope | Analysis at individual Product ID, Customer ID, and Employee ID level was excluded. The project focused on aggregated performance (category, region, demographic segments) rather than entity-level tracking, as the objective was to identify overall revenue patterns and concentration — not individual performance metrics |
| Time Period | January 2023 – December 2024 |
| Granularity | Transaction-level data aggregated by month, product, region, and customer demographics |
| Category | Tool(s) Used |
|---|---|
| Data Storage | Excel |
| Data Processing | Power Query, Power Pivot |
| Analysis | Excel pivot tables, Excel formulas |
| Visualization | Excel dashboard (pivot charts, slicers |
| Version Control | GitHub |
| Documentation | Markdown |
Sales.xlsx, Products.xlsx, Customers.xlsx
↓
Power Query: cleaned duplicates, standardized dates,created date table
↓
Power Pivot: established relationships between Sales, Products, Customers, and Date table
↓
Pivot Tables: monthly revenue, YoY revenue, top 10 products, revenue by region, age, gender, customer
↓
Dashboard: interactive charts and slicers for category, region, and year
- Source: Three-sheet transactional dataset containing Sales, Products, and Customers.
- Ingestion: Loaded into Power Query for cleaning.
- Cleaning: Removed duplicates, standardized date formats, handled missing product/customer IDs.
- Transformation: Created date table with Month, Year, Weekday/Weekend. Created calculated columns for revenue, YoY change.
- Analysis: Built pivot tables by month, year, region, product, customer, gender, and age. Applied number formatting (K/M) to the revenues.
- Output: Interactive dashboard with total units sold, revenue, revenue by day type, top/underperforming products, revenue by customer, category, and region.
| Field Name | Data Type | Description | Example Value |
|---|---|---|---|
order_id |
int | Unique transaction ID | 1 |
order_date |
date | Date of sale | 2023-03-15 |
product_id |
int | Product identifier | 20 |
ocustomer_id |
int | Customer identifier | 45 |
employee_id |
int | Employee who handled the sales | 12 |
quantity |
int | Units sold | 155 |
unit_price |
float | Price per unit | 634.0 |
Row count (approx.): 5001 Date range: 2023-01-01 – 2024-12-31
| Field Name | Data Type | Description | Example Value |
|---|---|---|---|
product_id |
int | Product identifier | 20 |
product_name |
string | Name of the product | Spaghetti |
category |
string | Product category | Grocery |
Row count (approx.): 51
| Field Name | Data Type | Description | Example Value |
|---|---|---|---|
customer_id |
int | Cusomer identifier | 1 |
customer_name |
string | Name of customer | Jacob Jones |
region |
string | Customer region | South |
gender |
string | Customer gender | Male |
age |
int | Customer age | 55 |
Row count (approx.): 301 Key join / relationship:
Sales.product_id→Products.product_id;Sales.customer_id→Customers.customer_id;Sales.order_date→DateTable.date
Explored patterns in revenue by month, year, region, product, and customer. Used pivot tables and calculated fields in Power Pivot for YoY analysis. Focused on identifying top/underperforming products and revenue drivers.
| Metric | Plain-Language Definition | Why It Matters |
|---|---|---|
Total Revenue |
Sum of quantity by unit price | Measures total business income per period, category, and segment |
Total Units Sold |
Total number of products sold | Evaluates sales volume contribution per product |
YoY change |
Percentage difference between 2024 vs 2023 revenue | Tracks growth trends and seasonal performance |
Revenue by Customer and Region |
Sum of revenue by customer and region | Determines high-value customers and high-performing regions |
- Descriptive statistics: revenue, units sold, YoY change
- Trend analysis across months and years
- Segmentation by product category, region, customer, age, and gender
- Top 10 and underperforming product identification
- Interactive visualization via Excel dashboard and slicer
Insight 1: Monthly Revenue Trends January and November are peak months, while February and June are low. Suggests seasonal demand patterns influencing revenue.
Insight 2: Revenue by Category Female customers contribute 65% of revenue. Age group 19–37 generates the highest revenue. Marketing and sales strategies should reflect these segments.
Insight 3: Product Performance Top 10 products contribute disproportionately to revenue, while underperforming products generate minimal revenue. Focus inventory and promotions on high performers.
Insight 4: Regional Performance West and East regions outperform North and South. Regional strategies may need adjustment to boost lagging regions.
| Priority | Recommendation | Based On | Suggested Owner |
|---|---|---|---|
| High | Increase marketing focus on top 10 products | Insight 3: Product Performance | Sales/Marketing Team |
| Medium | Optimize inventory allocation for peak months | Insight 1: Monthly Trends | Operations Team |
| Medium | Target campaigns for high-value demographics (female, age 19–37) | Insight 2: Customer Segments | Marketing Team |
| Low | Investigate low-performing regions to identify barriers | Insight 4: Regional Performance | Regional Managers |
- Transaction-level sales records were assumed to be complete and accurately recorded for the entire January 2023 – December 2024 period, as no independent validation against a source accounting system was performed.
- The analysis assumed that aggregating data to monthly, regional, and category levels was sufficient to identify revenue patterns, given that the project objective focused on performance concentration rather than individual-level behavior.
- The dataset does not include cost data, profit margins, or expense information. As a result, the analysis focuses solely on revenue performance and cannot assess profitability or margin efficiency.
- The analysis is descriptive rather than predictive. It identifies patterns and concentration but does not model future revenue performance.
- The dataset does not include inventory levels or stock availability. Revenue fluctuations may therefore reflect supply constraints rather than demand changes.
- No marketing, promotional, or campaign data was available. Therefore, changes in revenue over time cannot be attributed to external business activities.
- Add profit and margin analysis to prioritize high-margin products.
- Automate monthly data updates using Power Query or Python scripts
- Include time-of-day analysis to optimize staffing and promotions.
| Deliverable | Description | Location |
|---|---|---|
| Sales Analysis Dashboard | Interactive Excel dashboard with KPIs, trends, and slicers | https://1drv.ms/x/c/4a9770274467d72d/IQDkJ6khsww_TqsnxuVaXLp8ARb4NA27IxvzLuhWvVLcX_E?e=z7r0Kz |
Okikiola Oyeniyi Data Analyst
- 🔗 https://www.linkedin.com/in/okikiola-oyeniyi-?
- 💼 https://oyeniyiokikiola.github.io/
- 📧 oyeniyiokikiola@gmail.com
Last updated: Feb 2026