Marketing department would like to increase email campaign engagement by segmenting the customer-base using their buying habbits.
The data science team has identified 4 customer segments. The 4 customer segments were given descriptions based on the customer’s top product purchases.
Segment 1 Preferences: Road Bikes, Above $3000 (Premium Models)
Segment 2 Preferences: Mountain Bikes, Above $3000 (Premium Models)
Segment 3 Preferences: Road Bikes, Below $3000 (Economical Models)
Segment 4 Preferences: Both Road and Mountain, Below $3000 (Economical Models)
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 category_2.
This is a 2D Projection based on customer similarity that exposes 4 clusters, which are key segments in the customer base.
The 4 customer segments were given descriptions based on the customer’s top product purchases.
Segment 1 Preferences: Road Bikes, Above $3000 (Premium Models)
Segment 2 Preferences: Mountain Bikes, Above $3000 (Premium Models)
Segment 3 Preferences: Road Bikes, Below $3000 (Economical Models)
Segment 4 Preferences: Both Road and Mountain, Below $3000 (Economical Models)