Retail Store Clustering: Optimize Your Store Strategy
Retail store clustering is a powerful data analysis technique that groups similar stores based on various characteristics. This allows retailers to better understand local market trends and tailor their product offerings and marketing strategies to local demand. Key benefits include:
- Better understanding of market needs
- Improved store planning and operations
- More efficient allocation of resources
- Enhanced customer experience
- Competitive advantage
Our clustering solution lets you customize inputs, assign weights to each feature, analyze the results, and iterate until you're satisfied. In this article, we’ll guide you through how to leverage your data using this solution.
Getting Started with the Clustering Solution
Before you begin, make sure to download the project and sample data:
Step-by-Step Guide
Step 1: Input Setup
Start by setting the location of the data file, specifying the number of clusters, and assigning weights to each feature in the model.
Step 2: Data Loading and Merging
Next, load your data into KNIME and merge the relevant sheets. Ensure that your dataset uses the same sheet names as the sample data, as the sheet name parameter is passed to the sample.
Step 3: Exclude Outliers
You can optionally exclude certain stores that may be outliers based on their features. This helps refine the model’s accuracy.
Step 4: Filter by Store Capacity
This step allows you to filter and group stores by capacity. For example, you can focus only on department stores, excluding others. This is useful when store size is a key factor in your distribution strategy.
Step 5: Apply User Weights
User weights are applied to the features, and some transformation steps are executed before sending the resulting table to the next step. To assign weights to each product category column, use the Column List Loop Node. This node enables you to change the names of each category column to anonymous columns and apply user-defined weights. It accommodates any number of product category columns.
Note: If your dataset contains more features than the sample dataset, some nodes may require adjustment. If so, please leave a comment or contact us.
You can use both K-means and hierarchical models by feeding your data into each model. After this, run the model to create groups for use in the results panel.
- Cluster Store Count Distribution
- Store Count by Cluster and Store Capacity Group
- Cluster - Store Details
- Cluster Averages
Additionally, you can visualize the cluster store distribution on the map using the OSM Map View Node. Customize the map tooltip within the model using the column filter node at the top of the metanode.
You can compare the performances of two models by checking their silhouette scores.
Exporting Results
Finally, export the results to Excel using the Excel Writer Node for further analysis.
Conclusion
In conclusion, retail store clustering is an essential tool for retailers to gain a competitive advantage and improve their overall performance. Our solution allows you to easily cluster your stores in just a few clicks.
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