Predictive Lead Scoring using ML

Overview:

Predictive Lead Scoring is a method used to analyze lead behavior in historical customer data to find patterns resulting in a positive business outcome, such as a closed deal with a client. In this study, I developed a lead scoring model using the Bank Marketing dataset, which contains the outcome of clients subscribing to a term deposit or not based on a direct marketing campaign performed by a Portuguese bank.

Model Design:

Since the evaluation of a classification model is tricky, I assumed that Losing a potential customer cost > Sales Resource Cost as a business objective, which statistically translates to developing a model that gives Low False Negatives and High True Positives, with balancing False Positives.

Model Outcome:

To mimic a real-time model evaluation, I separated ~10,000 observation points from the dataset and trained on ~30,000 observation points. The following is the result of my trained LightGBM model on the hold-out dataset.

75.04% of Leads predicted by the model have resulted in conversion. And, a 29.56% False Positive rate, 24.95% False Negative Rate is observed.

Segmenting Leads based on Model Predictions:

Data Source

UCI Machine Learning Repository - Bank Marketing dataset.

Final Report & Package Walk-Through

To reproduce this study, use modules in ‘src’ directory of this repo. (setup instructions below) and walk-through of the package is presented in the final report

Setup instructions

Creating Python environment

This repository has been tested on Python 3.7.6.

1. Cloning the repository:

git clone https://github.com/abhijitpai000/predictive_lead_scoring.git

1. Navigate to the git clone repository.

cd predictive_lead_scoring

1. Download raw data from the data source link and place in “datasets” directory

2. Install virtualenv

pip install virtualenv

virtualenv lead_scoring

1. Activate it by running:

lead_scoring/Scripts/activate

1. Install project requirements by using:

pip install -r requirements.txt

Note

• For make_dataset(), please place the raw data (bank-additional -> bank-additional-full.csv from data source) in the ‘datasets’ directory.