Business Data Problem : Bike shop Company
Part III : Customer Segmentation
Problem Statement
Marketing department would like to increase email engagement by segmenting the customer-base using their buying habits.
Solution Summary
The data science team has identified 4 customer segments. The 4 customer segments were given descriptions
based on the customer’s top product purchases.
1. Segment 1 Preferences: Road Bikes, Above $3000 (Premium Models)
2. Segment 2 Preferences: Mountain Bikes, Above $3000 (Premium Models)
3. Segment 3 Preferences: Road Bikes, Below $3000 (Economical Models)
4. Segment 4 Preferences: Both Road and Mountain, Below $3000 (Economical Models)
Customer Preferences
Heat map
Our Customer-base consists of 30 bike shops. Several customers have purchasing preferences for Road or Mountain bikes based on the proportion of bikes purchased by category_1 and categroy_2.
Customer Segmentation
This is a 2D Projection based on customer similarity that exposes 4 clusters, which are key segments in ourcustomer base.
Customer Preferences By Segment
The 4 customer segments were given descriptions based on the customer’s top product purchases.
1. Segment 1 Preferences: Road Bikes, Above $3000 (Premium Models)
2. Segment 2 Preferences: Mountain Bikes, Above $3000 (Premium Models)
3. Segment 3 Preferences: Road Bikes, Below $3000 (Economical Models)
4. Segment 4 Preferences: Both Road and Mountain, Below $3000 (Economical Models)
Conclusion / References
The solution I provided to my boss is a cutting-edge report totally made with R language programming.
The repository of this project is on my github channel, where you can find the .pdf report (link below).
→ https://github.com/dimsu/customer_segmentation
Also if you hurry, I made an interactive report that you can find on my rpubs website here (link below).
