Lead Scoring using Logistic Regression

A company called X Education, which sells online courses, generates a large number of leads for its sales team with a significant marketing investment.

The goals of the analytics project are to

• build a model to assign a lead score to each of the leads (where a higher lead score indicates a more promising lead),
• identify the most promising leads, called 'Hot Leads', for the sales team, and
• develop solutions to some problems presented by the company.

The model will be used to provide more qualified leads to the sales team and improve the lead conversion rate.

Analysis is done as per the following steps:

• Data Understanding, Cleaning & Visualisation
• Data Preparation
• Model Building
• Model Evaluation
• Model Interpretation

The best logistic regression model resulting from the modelling process has 17 predictor variables and the following performance metrics

Training Dataset

• Accuracy = 0.82
• Sensitivity = 0.78
• Specificity = 0.84
• Precision = 0.76
• Recall = 0.78

Test Dataset

• Accuracy = 0.82
• Sensitivity = 0.77
• Specificity = 0.85
• Precision = 0.77
• Recall = 0.77

Assigning lead scores to all of the 9240 leads (data points) results in 3603 hot leads.