This project started with a simple but important question:
What is actually driving revenue in retail , who the customers are, what they buy, or when they buy it?
I wasn’t trying to predict the future or reinvent retail strategy. I wanted to understand patterns in customer behavior using the data that was available to me, and then see what those patterns could realistically tell us.
The dataset focused mainly on sales and revenue related information. I did not have access to cost, profit margins, customer lifetime value, or repeat purchase data.
Because of that, this analysis is revenue and behavior-driven, not profitability-driven.
That matters, and I’m intentional about saying it upfront because ,conclusions are only as strong as the data behind them.
One thing became clear very early on:
even though not everyone was buying, the people who did buy weren’t spending small.
Most customers were making medium to bulk purchases, often at relatively high unit prices. That immediately told me something important , the issue wasn’t that the products lacked value. Instead, it suggested that engagement and reach might be the real bottleneck.