Retail store clustering is a data analysis technique that groups similar stores based on various characteristics. It allows retailers to identify regional market trends and preferences, enabling them to tailor their product offerings and marketing strategies to local demand.
- Better Understanding of Market Demands
- Improved Store Planning and Operations
- Better Resource Allocation
- Improved Customer Experience
- Competitive Advantage
With our clustering solution, you can customize the inputs and assign weight to each input and analyze the results and iterate as many times to get a satisfactory result.
In this post, we'll go over our Clustering Solution and what users can do to use their own data.
Click here to Download the Project from Official Knime Page
Click here to download the Sample Data
First, a user should run the first step and fill the input sections with
data file location, cluster count and weight to each feature used in the model.
In the second step, data will be loaded to Knime and different data sheets will be joined. In your dataset, it is important to keep the same sheet names with sample data since they are passed a sheet name parameter.
Then in the third step, you might want to exclude certain stores from the model because they might be outliers in terms of their features.
In the fourth step, you might want to cluster the stores within their respective capacity group. For example, filtering only big stores will cluster only those stores and exclude others from the model. This might be useful if your allocation plans highly dependent on the store size.
In the fifth step, user weights will be applied to features and several transformation steps will be applied and the final table will be sent to 1. and 2. model.
To assign weight to each unknown number of product category column, we use the Column List Loop Node.
This node allows us to rename each category column to anonymous column and apply the user weight to each column. With this node, no matter how many columns of product category you have, you will be able to assign weight to each of them.
Note: There might be some adjustments needed in some nodes if your dataset has more features than sample dataset. Please leave a comment or reach us if that's the case.
After we passed the data to each model, both K-means and Hierarchical model will be ready to use.
In the sixth step, models will run and some groupings will be made to be used in results dashboards.
The result dashboards, contains :
- Cluster Store Count Distribution
- Store Count by Cluster and Store Capacity Group
- Cluster - Store Details
- Cluster Averages
Also, you can see the cluster store distribution on the map with the help of OSM Map View Node.
You can customize the map tooltip inside the 1. or 2. model by using the column filter node at top of the metanode.
You can also compare the performances of two model by checking their silhoutte scores .
You can also export the results to Excel with Excel Writer Node for further analysis.
In conclusion, retail store clustering is an essential tool for retailers to gain a competitive advantage and improve their overall performance. With our solution, you can easily cluster your stores with just a few clicks.
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