Marketing struggles with diverse customer needs. Customer Segmentation addresses this by: - Grouping similar customers. - Enabling targeted, relevant campaigns. Discover how K-Means Clustering can enhance segmentation! A THREAD!
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1. Prepare Your Data Collect key customer data: - Demographics: Age, income, location - Behavior: Purchases, spending, products - Preferences: Shopping habits, product choices
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2. Clean & Normalize Data - Handle missing values to avoid bias. - Normalize features (e.g., income, purchases) for balanced clustering.
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3. Find Optimal Clusters Use the Elbow Method: - Plot clusters (K) vs. SSE. - Find the "elbow point" where the curve flattens for actionable groupings.
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4. Apply K-Means Group customers by similarity, assigning each to the nearest cluster center. 5. Analyze Results - Label clusters in your dataset. - Identify patterns and unique traits using visualizations like scatter plots.
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Targeted Marketing Strategies - High-Spender, Low-Frequency: Retention via loyalty programs. - Moderate-Spender, High-Frequency: Upsell with bundles and recommendations. - Low-Spender, Low-Frequency: Re-engage with discounts and promotions.
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Segmenting customers with K-Means enables personalized marketing, boosting engagement and sales. Master customer segmentation with our K-Means Clustering training at Algoritma Data Science School. Sign up now:
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