Design of a Machine Learning Model in Customer Relationship Management to Identify Leads in an IT Company

In today’s era, organizations are increasingly prioritizing process automation to optimize efficiency and drive sales. One area where Machine Learning (ML) techniques can be particularly valuable is in automating tasks such as lead classification for sales. In this project, we have designed and developed a C# program that effectively extracts contacts’ marketing interactions from a Customer Relationship Management (CRM) and obtains their key attributes and counters. To enhance the lead classification process and ensure optimal allocation of resources, we have employed Natural Language Processing (NLP) techniques to categorize job titles. Additionally, we have utilized a logistic regression model to accurately predict whether a lead will convert into a client or not. By leveraging these ML techniques, we can strategically focus our firm’s resources for maximum effectiveness. Overall, our work involves leveraging the power of ML, NLP, and logistic regression within a C# program to automate contact extraction, feature extraction, and lead classification in CRM marketing interactions. This approach enables us to drive efficiency, enhance sales outcomes, and allocate resources more effectively.