01 / Project · Excel

Misuo Sales Dashboard

An interactive Excel dashboard analyzing 126 real furniture orders to surface best-selling products, top sales channels, and delivery performance for an Egyptian furniture retailer.

Context: Misuo — Egyptian furniture retailer
Role: Data analyst, end to end
Data window: 126 orders · Jul–Dec 2024
Deliverable: Single-page interactive Excel dashboard

Misuo Sales Dashboard — KPI cards, customer distribution, delivery status, top cities, sales by platform, best-selling item, and best color sold in a single view — Exhibit 01 — The finished dashboard: one page, nine KPIs, three interactive slicers

What the business learned

Total Sales

EGP 1,281,986

Total Orders

126

Dashboard Average Price

EGP 10,256 (exported view)

Customer Distribution

72% Individuals / 28% Companies

Delivery Status

89% Delivered / 8% Cancelled / 2% Failed / 1% Returned

Best-Selling Category

Beds — EGP 456,905

Best-Selling Color

White — 53%

Top City by Sales

New Cairo — EGP 338,694

Top Sales Platform

Homzmart — EGP 868,668

Sales by channel

Two-thirds of revenue rides on one platform — a concentration risk, not just a success story.

What happened to the 126 orders

Roughly 1 in 9 orders doesn't end in a kept sale — and cancellations, not couriers, are the biggest reason.

Business Insights

Concentration looks like success until you chart it

Homzmart alone generated EGP 868,668 — roughly two-thirds of platform sales, more than every other channel combined. On a leaderboard that reads as a win; on a chart it reads as dependency. Misuo should set a measurable channel-diversification target while negotiating with Homzmart from strength.

High-ticket categories get first claim on resources

Beds generated EGP 456,905 — more than sofas, tables, corner sets, and armchairs combined in this dataset. The concentration makes stock availability, margins, and supplier terms useful areas for management to review.

Not every lost order is a delivery problem

Roughly 1 in 9 orders did not end in a delivered sale, while cancellations (8%) exceeded failed deliveries (2%). Treating those outcomes separately creates clearer follow-up questions; the dataset alone does not identify their causes.

Geography tells you where operations should live

New Cairo (EGP 338,694) and 6th of October (EGP 208,601) lead the next three cities by a wide margin — core markets for delivery-hub and marketing planning. An audit of delivery lead times elsewhere could separate controllable gaps from genuine demand differences.

Core colors are inventory policy, not taste

White (53%) and black (40%) account for 93% of color share in the recorded orders. That pattern supports reviewing core-stock levels, while a larger and more recent sample should inform any stocking rule.

Project Evidence

EGP 1,281,986 in tracked sales across 126 orders, fully broken down by product, platform, and city.
Uncovered a revenue concentration risk: one platform (Homzmart) drives roughly two-thirds of measured sales.
Separated the 89% delivered share from cancellations, failed deliveries, and returns so each outcome can be investigated independently.

The Business Problem

Misuo is an Egyptian furniture retailer selling beds, sofas, tables, corner sets, and armchairs through a mix of online marketplaces, reseller platforms, and direct channels. This project turns 126 raw order records into a single-page Excel dashboard that lets Misuo's management see, at a glance, which products sell, where sales come from, and how reliably orders get delivered.

The dashboard was built entirely in Excel using a structured Table, calculated columns, PivotTables, PivotCharts, and Slicers — no external BI tool was used.

Every order was being logged as a flat row in a spreadsheet — order status, dates, customer, address, payment method, price, quantity, shipping cost, product details, platform, and color. This is a complete and usable dataset, but in its raw form it cannot answer a business question without someone manually filtering, sorting, and summing rows every time.

Without a summarized, visual view of this data, Misuo's management had no fast way to answer questions that directly affect commercial decisions: which products are best- and worst-sellers, which sales platform is generating the most revenue, which cities or areas matter most, and how much revenue is lost to cancellations, failed deliveries, or returns.

For a small-to-mid-size retailer like Misuo, revenue is concentrated in a handful of products, platforms, and cities — a delay in spotting a shift has a direct, compounding effect on revenue.

Objectives

Identify the best- and worst-selling products
Track Total Sales, Total Orders, and Average Selling Price
Break down sales by platform
Identify the best-selling product and best-selling color
Review delivery status and delivery-efficiency fields
Identify the top-selling cities (orders by area)
Compare individual vs. company customers

The Approach

Dataset

126 real order records, 24 fields each, stored in an Excel structured Table (Table1, A1:X127)
Order dates span 27 Jul 2024 – 14 Dec 2024
Fields cover order status, delivery responsibility, order/delivery dates, customer name and address, payment method, unit price, quantity, shipping cost, calculated sales value, product description, sales platform, metal/fabric color, and product category
A second sheet holds the original Arabic-language project brief and required KPI list
Bilingual (Arabic/English) field names and values throughout

Data Cleaning

Inconsistent category spelling and whitespace — e.g. a product category appeared both with and without a trailing space, which would undercount that category in any pivot built without normalizing the text first
Inconsistent sales-platform spelling with the same whitespace issue
A handful of rows had a numeric price value mistakenly entered into the payment-method field
8 of 126 rows had missing or placeholder address data, slightly understating city-level totals
The original dataset was never edited to fix these issues — any cleaning is done on a separate, clearly documented copy, per a fixed data-cleaning policy

Analysis Process

Structured Table (Table1) with autofilter spanning 126 rows × 24 columns
Calculated column: Sales = Unit Price × Quantity, using structured table references
Calculated column: an IF() formula classifying each order's delivery as handled by the shipping company or a named sales rep
PivotTables feeding every KPI card and chart on the dashboard
PivotCharts: bar charts (Top 5 Cities, Sales by Platform, Best-Selling Item, Best Color Sold) and donut charts (Customer Distribution, Delivery Status Breakdown)
Slicers for interactive filtering by Customer Type, Delivery Efficiency, and Payment Method

Microsoft ExcelExcel Structured TablesPivotTablesPivotChartsSlicersExcel Formulas

Field Notes

Lessons from this project you can use in your own work:

Before you build a single pivot, scan your category columns for trailing spaces and spelling variants. In this 126-row dataset the same category appeared with and without a trailing space — a pivot built on the raw text would have silently split one real category into two smaller ones, and no error message would ever tell you.
Never clean your only copy. The original sheet here was left untouched and all cleaning happened on a separate, documented copy — so every number on the dashboard can be traced back to the raw data it came from. That's what makes your analysis defensible when someone challenges it.
Use structured-table references for calculated columns. They extend to new rows automatically — which quietly removes one of the most common ways dashboards break after handoff: someone appends data and the formulas don't follow.
Write the KPI list into the workbook before you build anything. "Did I cover everything?" becomes a checklist you can point at, instead of a feeling — and the brief becomes part of the artifact you hand over.

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