Introduction

Graphite Note offers a suite of powerful machine learning and advanced analytics models designed to empower businesses to make data-driven decisions efficiently. Each model is tailored to address specific business needs, from forecasting future trends to segmenting customer bases. With these models, users can transform raw data into actionable insights quickly and without the need for complex coding.

Here’s a quick introduction to each type of model:

1. Timeseries Forecast: Ideal for predicting future values in timeseries data, such as sales or demand, based on historical patterns and seasonality.

2. Binary Classification: Used to classify data into two distinct groups (e.g., yes/no or true/false) based on historical data patterns.

3. Multi-Class Classification: Expands classification to multiple categories, allowing predictions across several possible outcomes.

4. Regression: A model that predicts a continuous numeric value (e.g., sales amount or customer age) based on other input features.

5. General Segmentation: Unsupervised learning that groups similar entities together, helpful in creating customer or product segments based on numeric similarities.

6. RFM Customer Segmentation: A specialized segmentation technique that segments customers based on Recency, Frequency, and Monetary value, aiding in targeted marketing.

7. Customer Lifetime Value: Predicts the future value of customers, estimating metrics like repeat purchase date and overall customer value for better retention strategies.

8. New vs Returning Customers: Provides insights into customer behavior by segmenting new and returning customers over various time frames (daily, weekly, monthly, etc.).

9. Customer Cohort Analysis: Groups customers based on their first purchase date, allowing businesses to analyze behavior patterns over time.

10. ABC Analysis: Categorizes items into A, B, and C categories based on their impact on a chosen metric, helping prioritize resources on high-impact items.