# Data Labeling

### **Introduction**

Data labeling is the process of tagging data with meaningful and informative labels to train machine learning models. In predictive analytics, labeled data is crucial as it provides the model with examples of correct behavior. This document will guide you through the process of preparing and labeling data for three predictive models: 

Lead Scoring, 

Churn Prediction, 

and MQL to SQL Conversion.

### **1. Lead Scoring Model (Converted: Yes/No)**

**Objective:** Predict if a lead will convert into a customer.

**Dataset Example:**

| Lead\_ID | Industry | Company\_Size | Interaction\_Count | Converted |
| -------- | -------- | ------------- | ------------------ | --------- |
| 001      | Tech     | 50-100        | 5                  | Yes       |
| 002      | Finance  | 100-500       | 2                  | No        |

**Steps:**

1. **Data Collection:** Gather data on leads, including their industry, company size, and interactions with your platform.
2. **Labeling:** For each lead, label them as 'Yes' if they converted into a customer and 'No' if they didn't.
3. **Reasoning:** Labeling helps the model understand patterns of conversion based on the features provided.

### **2. Churn Prediction Model (Churned: Yes/No)**

**Objective:** Predict if a customer will churn or leave your service.

**Dataset Example:**

| Customer\_ID | Monthly\_Usage | Support\_Tickets | Feedback\_Score | Churned |
| ------------ | -------------- | ---------------- | --------------- | ------- |
| A1           | 50 hrs         | 2                | 4.5             | No      |
| B2           | 10 hrs         | 5                | 2.8             | Yes     |

**Steps:**

1. **Data Collection:** Gather data on customer usage patterns, support interactions, and feedback scores.
2. **Labeling:** For each customer, label them as 'Yes' if they churned and 'No' if they continued using your service.
3. **Reasoning:** Labeling helps the model identify signs of customer dissatisfaction or reduced engagement, which might lead to churn.

### **3. MQL to SQL Conversion Model (Converted: Yes/No)**

**Objective:** Predict if a Marketing Qualified Lead (MQL) will become a Sales Qualified Lead (SQL).

**Dataset Example:**

| MQL\_ID | Webinar\_Attendance | Downloaded\_Content | Email\_Click\_Rate | Converted |
| ------- | ------------------- | ------------------- | ------------------ | --------- |
| M1      | 2                   | Yes                 | 15%                | Yes       |
| M2      | 0                   | No                  | 5%                 | No        |

**Steps:**

1. **Data Collection:** Gather data on MQLs, including their engagement with webinars, content downloads, and email interactions.
2. **Labeling:** For each MQL, label them as 'Yes' if they became an SQL and 'No' if they didn't.
3. **Reasoning:** Labeling helps the model recognize patterns of engagement that indicate a lead's readiness to move to the sales stage.

### **Conclusion**

Data labeling is a foundational step in predictive analytics. By providing clear, accurate labels, you enable your predictive models to learn from past data and make accurate future predictions. Ensure your labels are consistent and based on well-defined criteria to achieve the best results with Graphite Note's no-code predictive analytics platform.


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