Sales Forecasting with Timeseries Analysis
Predict sales trends based on historical data, enabling eCommerce businesses to manage inventory, plan promotions, and set realistic revenue targets.
ABC Segmentation for Product Management
Categorize products into 'A', 'B', and 'C' segments based on their sales volume and profitability. This helps in prioritizing high-value products, optimizing inventory, and crafting targeted marketing strategies for each segment.
Cart Abandonment Predictions
Identify potential reasons for cart abandonment and predict which customers are likely to leave without making a purchase, allowing for timely interventions and personalized offers.
Product Recommendation Engine
Leverage binary and multi-class classification to suggest products that customers are likely to purchase based on their browsing history and past purchases.
Customer Lifetime Value (CLV) Modeling
Determine the total value a customer brings over their shopping lifetime, guiding marketing spend and customer retention strategies.
RFM Analysis for Customer Segmentation
Segment customers based on their Recency, Frequency, and Monetary interactions with the store, enabling targeted marketing campaigns and personalized shopping experiences.
Pricing Optimization using Regression Models
Determine the optimal price point for products to maximize sales and revenue, ensuring competitiveness and profitability.
Churn Prediction for Repeat Customers
Identify customers who are less likely to make repeat purchases, allowing for targeted re-engagement campaigns.
Stock and Inventory Predictions
Forecast inventory needs based on sales trends and seasonal fluctuations, ensuring optimal stock levels and minimizing holding costs.
Ad Campaign Performance Predictions
Predict the ROI of advertising campaigns, guiding ad spend and optimizing marketing strategies for maximum impact.