Overview
-
Our Cat-Group solution groups stores using a bottom-up approach (user nominated sales mix).
-
It uses the kmeans method to determine which stores are grouped together.
-
The solution allows for the creation of several clusters using user nominated product attribute drivers.
-
It can identify the most accurate cluster based on how close stores are to the centre of the cluster.
-
Users can name clusters, exclude or manually make changes.
-
We import data covering store attributes, product attributes and performance.
Examples
Store Results
​
-
Product drivers and 4 Cluster Scheme.
Choose Product Drivers
Stores in Cluster
Store List
Number of Iterations and Scheme Accuracy
Kmeans Iterations
Cluster Accuracy
Output reports
Stores Importance by Attribute
Product Importance by type of Attribute
Demographic Importance by Type
Output Reports (Report Writer)
​
-
Users can create their own reports for stores or products.
-
An interactive Microsoft Excel workbook is delivered.
Project requirements​
​
-
We complete a discovery process to find out your key objectives and goals for the Clustering project.
-
To achieve the expected results of an accurate projection, data is key. All data is reviewed to ensure we have clean data to run the project.
-
Any project delivery is designed around the available data that can be used.
-
Please contact us and we will be happy to discuss your requirements further.