Paste from Excel → pick features → choose K → cluster & visualize
How to Use the Store Clustering Tool
This tool helps you group your stores into clusters based on their characteristics like sales, size, and location
type. Perfect for understanding store performance patterns and making data-driven decisions.
📊 Step-by-Step Instructions
Prepare Your Data
Create a spreadsheet with your store data. The first column must be store names,
followed by metrics like Sales, Square Footage, Climate, etc. Each row represents one store.
Load Your Data
Copy your data from Excel/Google Sheets and paste it into the text area, or click "Load
Demo" to see an example. The tool automatically detects columns and parses your data.
Select Features
Choose which columns to use for clustering by checking the boxes. Good features include sales metrics, store
size, customer demographics, or location characteristics. Tip: Start with 2-4 features.
Choose Number of Clusters
Set how many groups you want (K value). Start with 3-4 clusters. You can experiment with different numbers to
find the most meaningful groupings for your business.
Select Chart Axes
Pick which metrics to display on the X and Y axes of your visualization. This doesn't affect clustering but
helps you see the results clearly.
Cluster Stores
Click the big blue "Cluster Stores" button. The algorithm will group your
stores and show results in seconds.
Analyze Results
• Chart: See your stores plotted with different colors/shapes for each cluster
• Data Table: View your original data with cluster assignments
• Cluster Centers: Understand what makes each cluster unique
• Download: Export results with cluster assignments
💡 Pro Tips
Feature Selection: Include both quantitative (sales, size) and qualitative (climate, location
type) data
Data Quality: Clean data works best - remove stores with missing key metrics
Interpretation: Look at cluster centers to understand what drives each group
Iteration: Try different feature combinations and K values to find insights
❓ Common Use Cases
Store Performance: Group high, medium, and low performers
Market Segmentation: Identify similar market conditions
Resource Allocation: Tailor strategies by cluster characteristics
Expansion Planning: Find locations similar to your best stores
Need help? This tool uses the K-Means algorithm to automatically find patterns in your store
data. No statistics background required!
Configuration
1) Paste Data
Works with Excel paste (tab-separated) or CSV. First row must be headers.
One row per store. Include a store name column.
