Customer Clusters
Clusters helps with personalization and allow to reduce promotions without sacrificing revenue.
PERSONALIZED SHOPPING INSIGHTS
What is clustering
Why clustering
What it brings me
Clustering of shopping missions
Identify key shopping missions
Using cluster analysis of shopping history
Customer segmentation
Assign customers to shopping missions
Update regularly for every customer
Product matching
Match promo products to shopping missions
Each shopping mission has linked products
Communicate personalized offers
Communicate personalized offers
Leaflet newsletter
Push notifications
Optimize offers
Utilize findings from shopping missions
Optimize promoted products and discounts
Personalized discounts
Introduce personalized discounts
Discounts only for selected customers
FROM NUMBERS TO CLUSTERS
Detailed purchase numbers enable us to identify patterns in customers behaviour and cluster them accordingly.
Algorithm can generate any number of clusters, with 6-8 clusters often considered optimal from business perspective.
Input
- Beauty
- Beers
- Bread
- Cheese
- Cooking
- Dairy
- Deli
- Durables
- Frozen
- Fruits
- Ham
- Healthy
- Kids
- Meat
- Pet
- Snacks
- Soft Drinks
- Spirits
- Sweets
- Tobacco
- Tea & Coffee
- Veggies
- Wine
- Yogurt
Clustering algorithm
Output
Predefined number of most significant clusters is defined
Clusters are described as personas
Every customer belongs to 1 cluster
Affinity to all other clusters is calculated
Algorithm provides a way how to reassign customers to clusters on regular basis
OUTCOMES TO EXPECT
After the several iterations the resulting clusters may look for example like this
Cooking cluster
- Customers loving fresh products (fruit & veg)
- Buys products for cooking & baking
- Not buys alcohol and meat
- Smallest segment
Wine cluster
- Customers heavily buying wine
- Average buyers of other alcohol
- Not buying Yogurt, bread or dairy
- Average sized segment
