Brand Choice, Revenue and Descriptive Statistics by Customer Segments
- Python 3.7.3
- Libraries: pandas, NumPy, Matplotlib, Seaborn, scikit-learn
Dataset and its legend can be found in data folder.
pickle folder contains the results of customer segmentation (StandardScaler, PCA, KMeans).
The analysis can be found as Jupyter Notebook here:
In this project, I analyzed purchase behavior of 500 customers that bought 5 different brands of chocolate candy bars in a physical FMCG store during 2 years. In total, they made 58,693 transactions, captured through the loyalty cards they used at checkout.
Based on the results of customer segmentation, I explored the segments sizes and answered the following business questions:
- How often do people from different segments visit the store?
- What brand do customer segments prefer on average?
- How much revenue each customer segment brings?
There are 4 different customer segments:
- Fewer-Opportunities (38%)
- Career-Focused (22%)
- Standard (20%)
- Well-Off (20%).
Customer segments don't differ much in shopping frequency - with about 120 store visits on average during 2 years. The least homogenous group in this respect is Career-Opportunities segment with customers visiting the store as least as about 80 times as much as about 160 times.
Fewer-Opportunities is the biggest customer segment (38%). However, it brings less money in terms of revenue because customers from this group prefer brands that are inexpensive (almost 70% buys Brand 2).
We should focus our marketing efforts more on Career-Opportunities segment (22%) which brings the most revenue and seems to be loyal to the most expensive brand (63% prefer Brand 5). This group also buys chocolate candy bars most frequently among all segments. Hence, it is a good idea to calculate price elasticity to see if we can increase prices for this segment even further without loosing customers.
This project is part of "Customer Analytics" course by 365DataScience
However, I modified and streamlined some code (e.g. through using matrix multiplication when calculating Revenue or by using agg function when aggregating multiple columns).