Lead conversion and scoring with machine learning

Abstract: A lead is described by an individual or organization with an interest in the products or services that an entity sells. Machine Learning appears to be a very effective way of improving the performance of the corporate leads. Predicting the suc-cess of new leads is a critical aspect to enhance the effectiveness and efficiency of mar-keting, and to tackle this problem. It has been proposed in this paper a solution sup-ported in the Machine Learning approach. This solution is implemented to increase the winning probability of leads. The Logistics Regression has been selected as a final with Accuracy above 80% with the value of area under the ROC curve (AUC) is high (0.89). “Lead Score” >=85 are hot leads and should be targeted first. Around 80% of total conversions could be attained by targeting 50% of the total client base. There are 13 important features from the final model. The top 7 features have a positive impact on lead conversion. The bottom 6 features have impacted lead conversion negatively. Deep Learning models like Deep Neural Network (DNN) and Sequence Model (LSTM) can be tested on both original data and additional data for future scope.


Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Classification Models, Hot Leads, Business Impact


Conference Published in: 7th International Joint Conference on Advances in Computational Intelligence IJCACI 2023


Sunil Kumar Singh

Rashmi Agarwal

Shinu Abhi

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