Icon

Retail Store Clustering

This workflow uses a fictional retail company dataset to cluster stores for grouping them into Pre-Season Planning Markets.

Fictional dataset includes :

Stores : 1000 stores from all around the world with Country-City-Coordinates information.
Category Sales % : Contains 5 different product category and their relative sales pcs % per store.
Store Capacity: Divides stores into 3 capacity groups.. Big-Medium-Small
Customer Profile: Divides stores into 3 customer group : Type 1 - 2 - 3

Based on this dataset, this workflow enables users to assign weight to each of these parameters and run 2 different clustering methods and analyze the results on this platform with insight sparking visuals.


This workflow is created by BI-FI Blogs. Check our blog for detailed guide of this workflow.

INPUT STEP MODEL STEP RESULTS STEP RETAIL STORE CLUSTERING FRAMEWORKThis workflow uses a fictional retail company dataset to cluster stores for grouping them into Pre-Season Planning Markets. Fictional dataset includes :Stores : 1000 stores from all around the world with Country-City-Coordinates information.Category Sales % : Contains 5 different product category and their relative sales pcs % per store.Store Capacity: Divides stores into 3 capacity groups.. Big-Medium-SmallCustomer Profile: Includes Trendy Customer % for each store.Based on this dataset, this workflow enables users to assign weight to each of these parameters and run 2 different clustering methods and analyze the results on this platform with insightsparking visuals.This workflow is created by BI-FI Blogs. Check our blog for detailed guide of this workflow. ( All Rights Reserved) 6.2 STEP8.2 STEPExport Result to Excel5. STEP1. STEP7.1 STEP6.1 STEP7.2 STEP3. STEP(optional)Cluster Filter forMap ViewCluster Filter forMap View8.1 STEP2. STEP4. STEP(optional)1.Model Map View2. Model Map View2. MODEL HierarchicalClustering Excel Writer ClusteringData Prep INPUT STEP 1.MODEL RESULTS 1. MODEL ( K-Means) 2. MODEL RESULTS FILTER OUTUNWANTED STORES Nominal RowFilter Widget Nominal RowFilter Widget MODEL PERFORMANCES Load & Join STORE CAPACITYGROUP FILTER OSM Map View OSM Map View INPUT STEP MODEL STEP RESULTS STEP RETAIL STORE CLUSTERING FRAMEWORKThis workflow uses a fictional retail company dataset to cluster stores for grouping them into Pre-Season Planning Markets. Fictional dataset includes :Stores : 1000 stores from all around the world with Country-City-Coordinates information.Category Sales % : Contains 5 different product category and their relative sales pcs % per store.Store Capacity: Divides stores into 3 capacity groups.. Big-Medium-SmallCustomer Profile: Includes Trendy Customer % for each store.Based on this dataset, this workflow enables users to assign weight to each of these parameters and run 2 different clustering methods and analyze the results on this platform with insightsparking visuals.This workflow is created by BI-FI Blogs. Check our blog for detailed guide of this workflow. ( All Rights Reserved) 6.2 STEP8.2 STEPExport Result to Excel5. STEP1. STEP7.1 STEP6.1 STEP7.2 STEP3. STEP(optional)Cluster Filter forMap ViewCluster Filter forMap View8.1 STEP2. STEP4. STEP(optional)1.Model Map View2. Model Map View2. MODEL HierarchicalClustering Excel Writer ClusteringData Prep INPUT STEP 1.MODEL RESULTS 1. MODEL ( K-Means) 2. MODEL RESULTS FILTER OUTUNWANTED STORES Nominal RowFilter Widget Nominal RowFilter Widget MODEL PERFORMANCES Load & Join STORE CAPACITYGROUP FILTER OSM Map View OSM Map View

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: Burak Yilmaz
Created at: 2023-01-15
On NodePit since: 2023-01-29
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: K-meansHierarchical ClusteringClusteringRetail Store ClusteringRetail StoreComponentsMap Visual

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!