Furniture Sales Analysis

Michael Olaniyi Jeremiah
Aug 15
3 min read

By Michael Olaniyi Jeremiah

Project Overview

The Furniture Sales Analysis project was designed to transform raw sales transaction records into actionable business insights. This analysis not only uncovered performance patterns but also highlighted opportunities for growth and operational improvements. By leveraging Excel’s data processing and visualization capabilities, the project demonstrates the full journey from data cleaning to dashboard creation.

Data Collection & Overview

The dataset for this project was sourced from historical transaction records of a furniture sales business, capturing detailed operational, customer, and product-level information across multiple regions and time periods. It comprised 2,122 rows and 20 fields, providing a comprehensive foundation for in-depth analysis.

Fields in the Dataset:

Order ID: Unique identifier for each transaction.
Order Date: Date the order was placed.
Ship Date : Date the order was shipped.
Ship Mode: Method of shipping used for delivery.
Customer ID: Unique identifier for each customer.
Customer Name: Full name of the customer.
Segment: Market segment classification (e.g., Consumer, Corporate).
Country, City, State, Region – Geographical sales information.
Product ID: Unique identifier for each product.
Category & Sub-Category: Product classification hierarchy.
Product Name: Name/description of the product sold.
Sales: Revenue generated per transaction.
Quantity: Units sold in each transaction.
Profit: Profit or loss from each transaction.
Duration: Enriched field calculated as the number of days between Order Date and Ship Date, used for delivery performance analysis.
Month: Enriched field extracted from Order Date to enable monthly trend analysis.

Data Cleaning & Preparation Steps:

Removed duplicate records to ensure data integrity.
Checked for and addressed missing values in critical columns (Order Date, Sales, Profit).
Standardized date formats and ensured proper chronological order.
Validated numeric values to remove anomalies such as negative or illogical sales figures.

Data Preparation Tools & Techniques:

Excel for initial data cleaning, enrichment, and pivot table creation.
Power Query for structured transformation and automation of cleaning steps.
Pivot Tables & Charts to summarize KPIs and build an interactive dashboard.

This refined dataset provided the analytical backbone for generating insights into sales performance, operational efficiency, and profitability trends, ultimately guiding actionable business recommendations.

Key Performance Indicators (KPIs)

KPI	Total	CY	PY	YoY %
Sales	$163.8K	$44.5K	$50.8K	-12%
Quantity	1.8K	501	556	-10%
Profit	-$2,871	-$1,281	-$254	405%

Insights:

Sales and quantity dropped slightly compared to the previous year, but profit improved significantly despite being negative overall.
This indicates improved cost control and operational efficiency in certain areas.

Data Analysis

Using Pivot Tables and Excel formulas, multiple analytical perspectives were explored:

Year-over-Year (YoY) performance to compare current and previous year figures.
Regional and Category breakdowns to identify top-performing markets and products.
Profitability insights to highlight profit drains (e.g., high-sales but low-profit items).
Order fulfillment patterns using the Duration metric to detect bottlenecks.

Data Visualization

The analysis was brought to life with interactive Excel dashboards:

KPI cards for quick performance monitoring.
Trend charts showing monthly sales and profit movements.
Category and Region breakdowns in visually appealing bar and pie charts.
Conditional formatting to flag negative profit margins.

Dashboard Creation

The final dashboard integrates:

Slicers for Month, Region, and Category filters.
Real-time updates via dynamic pivot tables.
Intuitive color coding for positive vs. negative trends.
A clean, professional layout for quick decision-making.

Key Findings and Recommendations

Key Findings	Recommendations
Sales dropped by 12% YoY (from $50.8K to $44.5K), indicating reduced demand or market challenges.	Investigate market conditions, customer feedback, and competitive actions; launch targeted sales campaigns to recover lost revenue.
Quantity sold decreased by 10% YoY (from 556 to 501 units).	Revise product mix, improve promotions, and bundle offers to increase units sold.
Profit improved by 405% YoY but still remains negative (-$2,871), suggesting cost or pricing issues.	Conduct cost optimization, renegotiate supplier terms, and review pricing strategy to achieve profitability.
Certain product categories have high sales but low or negative profit margins.	Reassess category pricing and discount structure; focus on higher-margin products.
Performance varies across regions, with some significantly underperforming.	Strengthen marketing and distribution in low-performing regions using localized strategies.
Shipping duration varies widely, potentially affecting customer satisfaction.	Streamline fulfillment processes, optimize warehouse operations, and work with faster logistics partners.
Seasonal sales trends show peak months with sharp dips in off-peak periods.	Plan seasonal promotions, targeted ads, and stock adjustments to maximize peak season performance and mitigate off-peak slumps.