# Retail-sales-case-study

This case study uses a simulated dataset I created for practice (4,800 orders, about $599K revenue). The method is real, and I'll apply the same steps to real business data.

## The Question

Revenue grew steadily in the first half of the year, then dropped in Q3. The business question: what caused the dip, and where should the team focus next?

## The Data

One year of order-level data with these columns: date, category, region, customer (new or returning) and revenue.

## **How I Cleaned the Data**

• Removed duplicates: repeated order IDs

• Checked missing values: filled or dropped depending on the column

• Fixed formats: standardised dates and category names

• Flagged outliers: used the IQR method on order values

import pandas as pd

df = pd.read\_csv("retail\_sales\_sample.csv", parse\_dates=\["date"\])

df = df.drop\_duplicates()

df\["category"\] = df\["category"\].str.strip().str.title()

df\["month"\] = df\["date"\].dt.to\_period("M")

monthly = df.groupby("month")\["revenue"\].sum()

## Finding 1: Revenue Fell in July to September

Monthly revenue held at roughly $52-56K from January to June. It then dropped to about $44K in July, $41K in August and $39K in September, before recovering in Q4.

![](https://cdn.hashnode.com/uploads/covers/6acc76e07c09648311fcf497/b966bad2-ef75-47fb-aa7e-865589e8ba50.png align="center")

## Finding 2: Electronics Drove Most of the Drop

Comparing Q3's monthly average to the rest of the year, Electronics fell about 31%, the biggest drop. Beauty fell 20%, Fashion 14% and Home 12%. Grocery stayed flat (+3%), so everyday demand held up.

![](https://cdn.hashnode.com/uploads/covers/6acc76e07c09648311fcf497/7cfbb837-28dd-4d12-88b4-33223c0ab437.png align="center")

## Finding 3: The South Region Was Hit Hardest

Every region declined in Q3, but the South fell about 33%, compared with 15-22% elsewhere. That points to a regional problem on top of the category issue.

![](https://cdn.hashnode.com/uploads/covers/6acc76e07c09648311fcf497/0e1bacb0-1a0e-47f1-b54a-aa25da4bd387.png align="center")

## One More Insight

Returning customers spent about $140 per order versus $113 for new customers, roughly 24% more. Keeping repeat buyers matters.

## Recommendations

Investigate Electronics in Q3: check pricing, stock and competitor promotions.

Review the South region: look at delivery times, local campaigns and stock levels.

Invest in retention: loyalty rewards and win-back emails for returning customers.

Track weekly KPIs: build a Power BI or Tableau dashboard so dips are caught early.

Tools used: Python (pandas, matplotlib), Excel, SQL

[Contact with me on LinkedIn](https://www.linkedin.com/in/alaefule-richard)

[Please click](https://www.linkedin.com/in/alaefule-richard)

[Click to view the dataset](https://docs.google.com/spreadsheets/d/1XKc3VlFO_6aM0sGASaml1eMilZbkZdpfBa1pnTR6OyE/edit?usp=drivesdk)