🎯 Cigarette POS ML Platform

Production-ready machine learning models for retail optimization

✓ All Models Active
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Customer Segmentation

Identify 5 distinct customer segments using K-Means clustering for targeted marketing campaigns.

  • Premium Regulars
  • Budget Shoppers
  • Occasional Buyers
Try Segmentation →
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Purchase Prediction

Forecast customer purchase value with XGBoost to optimize upselling and inventory management.

  • Real-time predictions
  • Confidence intervals
  • Upselling recommendations
Predict Purchase →
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Churn Risk Scoring

Detect at-risk customers using Gradient Boosting and deploy retention campaigns with ROI tracking.

  • Risk tier classification
  • Retention recommendations
  • Expected ROI calculation
Assess Churn Risk →
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Sentiment Analysis

Analyze customer feedback using TextBlob NLP to detect satisfaction and trigger appropriate responses.

  • Positive/Negative detection
  • Priority classification
  • Action recommendations
Analyze Sentiment →
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POS Location Ranking

Rank and evaluate POS locations based on revenue, efficiency, and customer satisfaction metrics.

  • Composite scoring
  • Performance benchmarking
  • Optimization insights
Rank Locations →

Model Performance Metrics

Training Samples
10,000
Purchase Prediction MAE
$6.07
Churn Model AUC
1.000
Customer Segments
5

Business Impact

📈 Revenue Optimization

Increase sales by 15-25% through targeted upselling and personalized offers

🎯 Customer Retention

Reduce churn by 30-40% with proactive intervention campaigns

⚡ Real-time Insights

Make data-driven decisions instantly at the point of sale

💡 Smart Marketing

Deploy segment-specific campaigns with measurable ROI