A Dynamic Predictive Lead Scoring System for Inside Sales

Lead scoring is essential for lead management. The result of lead scoring is a list consists of leads with scores assigned indicating how likely each lead can be converted into the next stage of sales process. The Lamb or Spam and the Rule-Based are the two lead scoring methods that have been discussed in the literature. As various machine learning algorithms and artificial intelligence started to reemerge, predictive lead scoring models seem to be the next promising solution for lead scoring activity. This research project aims to develop a dynamic predictive lead scoring system that leverages on predictive analytics to automate lead scoring process based on historical customer data for a more accurate and reliable result. The outcome of this research project will demonstrate the value of application of data-driven predictive analytics in inside sales by offering business practitioners a model that can help optimize resource allocation and ultimately improve company success.

Intern: 
Migao Wu
Faculty Supervisor: 
Morad Benyoucef;Pavel Andreev
Province: 
Ontario
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