Kolanut Distribution · FMCG distribution

Sales Performance Analysis

Find out which products, regions and customer channels drive a distributor's sales, and what its discounts really cost.

ExcelPower BIPythonSQLBeginner

Overview

The business

Kolanut Distribution sells drinks, snacks, household and personal care products by the carton to wholesalers, supermarkets and kiosks across six regions of Nigeria.

Each sales rep looks after their own customers, and discounts of 5% or 10% are common to win bigger orders. Management can see total sales each month but not what's behind them.

Your brief

The sales director wants to understand where revenue comes from and whether discounts are paying for themselves, before setting next year's targets.

Questions to answer

1What are monthly sales, and how do they trend?
2Which product categories and regions sell the most?
3How do wholesale, supermarket and kiosk customers differ?
4How much revenue is given away in discounts?

What to deliver

A short report or slide deck answering the four questions, with one chart each.
A recommendation on discounts, backed by your numbers.
Your workbook, dashboard or notebook.

Suggested approach

Revenue for a line is quantity × unit_price × (1 − discount_pct / 100).
Bring in category from products and channel and region from customers.
The discount given away on a line is quantity × unit_price × discount_pct / 100.
Compare like with like: the data runs from January 2025 to June 2026, so 2026 is only half a year.

Data

Kolanut Distribution (sales). An FMCG distributor's order lines from January 2025 to June 2026, with its 90 customers (shops, supermarkets and wholesalers across six regions) and 16 products. Prices rose in January 2026.

3 files · 4,372 rows in total · 144 kB. Download everything as a ZIP.

customers.csv

90 rows · 8 columns · 7 kB

Download

The shops, supermarkets and wholesalers Kolanut sells to.

Column	Type	Description	Values	Missing
customer_id	Number	Unique customer number.	1 to 90	None
customer_name	Text	Business name.	90 distinct	None
channel	Text	Wholesale, Supermarket or Kiosk.	3 distinct	None
region	Text	One of six sales regions, such as Lagos or North West.	6 distinct	None
city	Text	City or town.	28 distinct	None
sales_rep	Text	Kolanut sales rep who looks after the customer.	6 distinct	None
joined_date	Date	Date they became a customer.	2022-01-13 to 2026-04-25	None
credit_limit	Number	Maximum they can owe at any time, in naira.	150,000 to 4,950,000	None

orders.csv

4,266 rows · 7 columns · 137 kB

Download

Every order line from January 2025 to June 2026. One order line is one product on one order.

Column	Type	Description	Values	Missing
order_id	Number	Unique order line number.	10,001 to 14,266	None
order_date	Date	Date of the order.	2025-01-01 to 2026-06-30	None
customer_id	Number	Customer who ordered. Links to customers.customer_id.	1 to 90	None
product_id	Number	Product ordered. Links to products.product_id.	1 to 16	None
quantity	Number	Cartons ordered.	1 to 30	None
unit_price	Number	Price per carton in naira, before discount. Prices rose in January 2026.	3,600 to 24,600	None
discount_pct	Number	Discount given on the line: 0, 5 or 10 (per cent).	0 to 10	None

products.csv

16 rows · 4 columns · 678 B

Download

Kolanut's 16 products. Each is sold by the carton.

Column	Type	Description	Values	Missing
product_id	Number	Unique product number.	1 to 16	None
product_name	Text	Product and pack size, for example Malt drink 330ml (24).	16 distinct	None
category	Text	Beverages, Snacks, Household or Personal care.	4 distinct	None
list_price	Number	Current list price per carton, in naira.	4,000 to 24,600	None

Get started

Starter code to get you going. The Python runs as it is in Google Colab: it loads the data straight from this site. For SQL, import each CSV as a table with the same name as the file.

PythonMonthly revenue

import pandas as pd

orders = pd.read_csv("https://academy.cloudtechanalytics.com/datasets/sales/orders.csv", parse_dates=["order_date"])
orders["revenue"] = orders["quantity"] * orders["unit_price"] * (1 - orders["discount_pct"] / 100)

monthly = orders.groupby(orders["order_date"].dt.to_period("M"))["revenue"].sum()
monthly.plot(title="Monthly revenue (₦)")

SQLRevenue by category

SELECT p.category,
       ROUND(SUM(o.quantity * o.unit_price * (1 - o.discount_pct / 100.0))) AS revenue
FROM orders o
JOIN products p ON p.product_id = o.product_id
GROUP BY p.category
ORDER BY revenue DESC;

Power BIIn Power BI

Get data → Text/CSV and load all three files.
In Model view, relate orders to products (product_id) and customers (customer_id).
Create a measure: Revenue = SUMX(orders, orders[quantity] * orders[unit_price] * (1 - orders[discount_pct] / 100)).
Build a line chart of Revenue by month and a bar chart by category.

When you've finished

Put your work on GitHub with a short README, add it to your portfolio, and share what you found. The Git & GitHub and Student Portfolio courses show you how.

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Overview

The business

Kolanut Distribution sells drinks, snacks, household and personal care products by the carton to wholesalers, supermarkets and kiosks across six regions of Nigeria.

Each sales rep looks after their own customers, and discounts of 5% or 10% are common to win bigger orders. Management can see total sales each month but not what's behind them.

Your brief

The sales director wants to understand where revenue comes from and whether discounts are paying for themselves, before setting next year's targets.

Questions to answer

1What are monthly sales, and how do they trend?
2Which product categories and regions sell the most?
3How do wholesale, supermarket and kiosk customers differ?
4How much revenue is given away in discounts?

What to deliver

A short report or slide deck answering the four questions, with one chart each.
A recommendation on discounts, backed by your numbers.
Your workbook, dashboard or notebook.

Suggested approach

Revenue for a line is quantity × unit_price × (1 − discount_pct / 100).
Bring in category from products and channel and region from customers.
The discount given away on a line is quantity × unit_price × discount_pct / 100.
Compare like with like: the data runs from January 2025 to June 2026, so 2026 is only half a year.

Data

3 files · 4,372 rows in total · 144 kB. Download everything as a ZIP.

customers.csv

90 rows · 8 columns · 7 kB

Download

The shops, supermarkets and wholesalers Kolanut sells to.

Column	Type	Description	Values	Missing
customer_id	Number	Unique customer number.	1 to 90	None
customer_name	Text	Business name.	90 distinct	None
channel	Text	Wholesale, Supermarket or Kiosk.	3 distinct	None
region	Text	One of six sales regions, such as Lagos or North West.	6 distinct	None
city	Text	City or town.	28 distinct	None
sales_rep	Text	Kolanut sales rep who looks after the customer.	6 distinct	None
joined_date	Date	Date they became a customer.	2022-01-13 to 2026-04-25	None
credit_limit	Number	Maximum they can owe at any time, in naira.	150,000 to 4,950,000	None

orders.csv

4,266 rows · 7 columns · 137 kB

Download

Every order line from January 2025 to June 2026. One order line is one product on one order.

Column	Type	Description	Values	Missing
order_id	Number	Unique order line number.	10,001 to 14,266	None
order_date	Date	Date of the order.	2025-01-01 to 2026-06-30	None
customer_id	Number	Customer who ordered. Links to customers.customer_id.	1 to 90	None
product_id	Number	Product ordered. Links to products.product_id.	1 to 16	None
quantity	Number	Cartons ordered.	1 to 30	None
unit_price	Number	Price per carton in naira, before discount. Prices rose in January 2026.	3,600 to 24,600	None
discount_pct	Number	Discount given on the line: 0, 5 or 10 (per cent).	0 to 10	None

products.csv

16 rows · 4 columns · 678 B

Download

Kolanut's 16 products. Each is sold by the carton.

Column	Type	Description	Values	Missing
product_id	Number	Unique product number.	1 to 16	None
product_name	Text	Product and pack size, for example Malt drink 330ml (24).	16 distinct	None
category	Text	Beverages, Snacks, Household or Personal care.	4 distinct	None
list_price	Number	Current list price per carton, in naira.	4,000 to 24,600	None

Get started

Starter code to get you going. The Python runs as it is in Google Colab: it loads the data straight from this site. For SQL, import each CSV as a table with the same name as the file.

PythonMonthly revenue

import pandas as pd

orders = pd.read_csv("https://academy.cloudtechanalytics.com/datasets/sales/orders.csv", parse_dates=["order_date"])
orders["revenue"] = orders["quantity"] * orders["unit_price"] * (1 - orders["discount_pct"] / 100)

monthly = orders.groupby(orders["order_date"].dt.to_period("M"))["revenue"].sum()
monthly.plot(title="Monthly revenue (₦)")

SQLRevenue by category

SELECT p.category,
       ROUND(SUM(o.quantity * o.unit_price * (1 - o.discount_pct / 100.0))) AS revenue
FROM orders o
JOIN products p ON p.product_id = o.product_id
GROUP BY p.category
ORDER BY revenue DESC;

Power BIIn Power BI

Get data → Text/CSV and load all three files.
In Model view, relate orders to products (product_id) and customers (customer_id).
Create a measure: Revenue = SUMX(orders, orders[quantity] * orders[unit_price] * (1 - orders[discount_pct] / 100)).
Build a line chart of Revenue by month and a bar chart by category.

When you've finished

Put your work on GitHub with a short README, add it to your portfolio, and share what you found. The Git & GitHub and Student Portfolio courses show you how.