NORMA eResearch @NCI Library

Identifying Factors Contributing to Lead Conversion Using Machine Learning to Gain Business Insights

Sharma, Mansi (2023) Identifying Factors Contributing to Lead Conversion Using Machine Learning to Gain Business Insights. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (1MB) | Preview


Digital marketing has become a major factor contributing to a company’s revenue. Since, the people invest most of their time online, it is important do promotions online in order to run a business. Companies promote their products online in a lot of ways such as showing advertisements, sending newsletters and so on. This makes people get interested in their products and are called leads. Since not every lead will turn into customer, it is beneficial to identify those leads who have the potential and nurture them in becoming customers. There are a number of factors which contribute in converting a lead to a customer. The goal of this research is to predict the lead conversion with the help of machine learning and determine the most contributing factors in this process. The study uses 5 different machine learning algorithms, namely, Logistic Regression, Decision Tree, Random Forest, CatBoost and XGBoost in predicting the lead conversion. It also aims to visualise the results so that it is easily understandable to the non-technical stakeholders and thus, to help sales and marketing teams in planning to target leads. Among the best performing models, importance of all the variables are calculated from Logistic Regression, Random Forest and CatBoost and the most contributing ones are selected. These variables are then used to create visualizations in Tableau that will help businesses in making marketing and sales strategies.

Item Type: Thesis (Masters)
Ul Ain, Qurrat
Uncontrolled Keywords: Lead Conversion; Binary Classification; Feature Importance; Tableau
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
H Social Sciences > HF Commerce > Marketing > e Marketing
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 25 May 2023 16:52
Last Modified: 25 May 2023 16:52

Actions (login required)

View Item View Item