Using Clustering Technique in Retail

Using clustering in retail buying and planning can significantly enhance decision-making by uncovering hidden patterns in customer behaviour, product performance, and store dynamics. Here's a breakdown of how clustering is applied and its benefits:

🔍 What is Clustering?

Clustering is an unsupervised machine learning technique that groups similar data points together based on shared characteristics—without predefined labels.

In retail, it helps in segmenting:

• Customers

• Products

• Stores

• Time periods (seasonal trends)

🛒 Applications in Retail Buying & Planning

1. Customer Segmentation

Group customers based on:

• Purchase behaviour

• Frequency and monetary value (RFM analysis)

• Demographics

• Product preferences

🔹 Why?

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  Helps tailor product assortments, promotions, and marketing strategies for each customer segment.

2. Product Clustering

Group products by:

• Sales performance

• Seasonality

• Price sensitivity

• Customer demographics that buy them

🔹 Why?

• Enables better assortment planning, markdown strategies, and demand forecasting.

3. Store Clustering

Cluster stores by:

• Location

• Sales volume

• Customer demographics

• Product preferences

🔹 Why?

• Improves localization of product mix, inventory allocation, and regional marketing strategies.

4. Time-Based Clustering

Identify time periods with similar sales patterns.

🔹 Why?

• Improves seasonal buying and helps identify emerging trends or slow periods for promotions.

📊 Example: Store Clustering in Fashion Retail

Goal: Optimize regional assortmentsFeatures: Sales per category, store size, customer profile, location Result:

• Cluster A: Urban, trend-driven stores → more fast fashion

• Cluster B: Suburban, family-oriented → more basics & larger sizes

Planners can then tailor buys per cluster.