Optimizing Sales Funnel Efficiency: Deep Learning Techniques for Lead Scoring

Abstract

Segmenting new commercial leads is a critical endeavor for contemporary businesses operating in highly competitive markets, aiming to unearth lucrative opportunities and bolster their Return On Investment (ROI). Business lead scoring entails attributing a score, representing the likelihood of a lead to make a purchase, to each potential lead generated for the business. These leads' interactions across various marketing channels on the internet yield valuable attributes, including pertinent information such as contact details, lead source, and channel, alongside behavioral cues like response speed and movement tracking. This process aids in evaluating the quality of opportunities and their stage in the purchasing journey. Moreover, an accurate lead scoring mechanism empowers marketing and sales teams to prioritize leads effectively and respond promptly, thereby enhancing the likelihood of conversion. Leveraging machine learning algorithms can streamline this process.
 In this study, the authors conducted a comparative analysis of the performance of various machine learning (ML) algorithms in predicting lead scores. The Random Forest and Decision Tree models emerged with the highest accuracy scores, reaching 93.02% and 91.47%, respectively. Notably, the Decision Tree and Logistic Regression models exhibited shorter training times, which can prove pivotal when handling extensive datasets.