Pizza-Sales-Analysis

Analysis of a year's worth of sales from a fictitious pizza place.

I used 3 main libraries in python;

pandas

numpy

matplotlib

After analysis, I arrived at the following insights:

Key Business Insights & Strategic Recommendations

Peak Sales Time: 12pm–1pm (Lunch Rush)

Insight:

Sales peak during lunchtime (12pm–1pm).

Implications:

1. This is the highest-demand operational window.

2. Efficiency and speed are critical during this hour.

Recommendations:

1.Increase staff during peak hours.

2.Pre-prepare high-demand pizzas before noon.

3.Reduce waiting time to improve customer satisfaction.

4.Consider slight price optimization if demand is very strong.

Classic Category = Highest Sales & Revenue

Insight:

1.The Classic category generates the highest volume and revenue.

2.It is the core revenue driver.

Implications:

1.This is the backbone of the business.

2.Strong and stable customer demand.

Recommendations:

1.Feature Classics as “Signature” or “Best Seller.”

2.Bundle with drinks/sides to increase average order value.

3.Maintain quality consistency.

Chicken Pizza = Highest Average Price & Still Popular

Insight:

1.Chicken pizzas have the highest average price.

2.They still perform well in demand(The thai chicken pizza,the barbecue chicken pizza, the carlifonia chicken pizza)

Implications:

1.Customers are willing to pay more for it.

Recommendations:

1.Use limited-time promotions (not constant discounts).

2.Position as a premium “Chef’s Special.”

3.Use it strategically to drive traffic on slow days.

Fridays & Saturdays = Highest Sales

Sundays & Mondays = Lowest Sales

Insight:

1.Weekend demand is strong.

2.Early-week demand is weak.

Recommendations:

1.Ensure enough staff presence during fridays and saturdays

2.Consider themed promotions(e.g Barbecue Monday) to boost sales for monday and sunday and midweek